Experiment: dtufc_hyperprior-featurecoding_sd35_individual Log file: output-fixed/sd35/lambda0.004/hyperprior-featurecoding-8bit-individual/sd35cond/dtufc_hyperprior-featurecoding_sd35_individual.log DTUFCCodecConfig: arch: hyperprior-featurecoding handler: sd35 checkpoint: codec_weights/hyperprior_hybrid/bmshj2018-hyperprior_lambda0.004_epochs600_lr0.0001_bs180_patch256-256_checkpoint_best.pth.tar transform_type: kmeans transform_mapping:featurecoding_utils/transform_mapping/kmeans10samples-8bits/mapping.json bit_depth: 8 device: cuda:0 Loading checkpoint: codec_weights/hyperprior_hybrid/bmshj2018-hyperprior_lambda0.004_epochs600_lr0.0001_bs180_patch256-256_checkpoint_best.pth.tar Checkpoint epoch: 559 Loaded hyperprior-featurecoding (1-channel) on cuda:0 Loaded quantization points from featurecoding_utils/transform_mapping/kmeans10samples-8bits/sd35cond/coco_fewshot-8bit_text_encoder-item0_clip_pooled_prompt_embeds.json: torch.Size([256]) Loaded per-key quantization points for key 'text_encoder-item0.clip_pooled_prompt_embeds' from featurecoding_utils/transform_mapping/kmeans10samples-8bits/sd35cond/coco_fewshot-8bit_text_encoder-item0_clip_pooled_prompt_embeds.json Loaded quantization points from featurecoding_utils/transform_mapping/kmeans10samples-8bits/sd35cond/coco_fewshot-8bit_text_encoder-item0_clip_prompt_embeds.json: torch.Size([256]) Loaded per-key quantization points for key 'text_encoder-item0.clip_prompt_embeds' from featurecoding_utils/transform_mapping/kmeans10samples-8bits/sd35cond/coco_fewshot-8bit_text_encoder-item0_clip_prompt_embeds.json Loaded quantization points from featurecoding_utils/transform_mapping/kmeans10samples-8bits/sd35cond/coco_fewshot-8bit_text_encoder-item3_clip_pooled_prompt_embeds.json: torch.Size([256]) Loaded per-key quantization points for key 'text_encoder-item3.clip_pooled_prompt_embeds' from featurecoding_utils/transform_mapping/kmeans10samples-8bits/sd35cond/coco_fewshot-8bit_text_encoder-item3_clip_pooled_prompt_embeds.json Loaded quantization points from featurecoding_utils/transform_mapping/kmeans10samples-8bits/sd35cond/coco_fewshot-8bit_text_encoder-item3_clip_prompt_embeds.json: torch.Size([256]) Loaded per-key quantization points for key 'text_encoder-item3.clip_prompt_embeds' from featurecoding_utils/transform_mapping/kmeans10samples-8bits/sd35cond/coco_fewshot-8bit_text_encoder-item3_clip_prompt_embeds.json Loaded quantization points from featurecoding_utils/transform_mapping/kmeans10samples-8bits/sd35cond/coco_fewshot-8bit_text_encoder_2-item1_clip_pooled_prompt_embeds.json: torch.Size([256]) Loaded per-key quantization points for key 'text_encoder_2-item1.clip_pooled_prompt_embeds' from featurecoding_utils/transform_mapping/kmeans10samples-8bits/sd35cond/coco_fewshot-8bit_text_encoder_2-item1_clip_pooled_prompt_embeds.json Loaded quantization points from featurecoding_utils/transform_mapping/kmeans10samples-8bits/sd35cond/coco_fewshot-8bit_text_encoder_2-item1_clip_prompt_embeds.json: torch.Size([256]) Loaded per-key quantization points for key 'text_encoder_2-item1.clip_prompt_embeds' from featurecoding_utils/transform_mapping/kmeans10samples-8bits/sd35cond/coco_fewshot-8bit_text_encoder_2-item1_clip_prompt_embeds.json Loaded quantization points from featurecoding_utils/transform_mapping/kmeans10samples-8bits/sd35cond/coco_fewshot-8bit_text_encoder_2-item4_clip_pooled_prompt_embeds.json: torch.Size([256]) Loaded per-key quantization points for key 'text_encoder_2-item4.clip_pooled' from featurecoding_utils/transform_mapping/kmeans10samples-8bits/sd35cond/coco_fewshot-8bit_text_encoder_2-item4_clip_pooled_prompt_embeds.json Loaded quantization points from featurecoding_utils/transform_mapping/kmeans10samples-8bits/sd35cond/coco_fewshot-8bit_text_encoder_2-item4_clip_prompt_embeds.json: torch.Size([256]) Loaded per-key quantization points for key 'text_encoder_2-item4.clip_prompt' from featurecoding_utils/transform_mapping/kmeans10samples-8bits/sd35cond/coco_fewshot-8bit_text_encoder_2-item4_clip_prompt_embeds.json Loaded quantization points from featurecoding_utils/transform_mapping/kmeans10samples-8bits/sd35cond/coco_fewshot-8bit_text_encoder_3-item2_t5_prompt_embeds.json: torch.Size([256]) Loaded per-key quantization points for key 'text_encoder_3-item2.t5_prompt_embeds' from featurecoding_utils/transform_mapping/kmeans10samples-8bits/sd35cond/coco_fewshot-8bit_text_encoder_3-item2_t5_prompt_embeds.json Loaded quantization points from featurecoding_utils/transform_mapping/kmeans10samples-8bits/sd35cond/coco_fewshot-8bit_text_encoder_3-item5_t5_prompt_embeds.json: torch.Size([256]) Loaded per-key quantization points for key 'text_encoder_3-item5.t5_prompt_embeds' from featurecoding_utils/transform_mapping/kmeans10samples-8bits/sd35cond/coco_fewshot-8bit_text_encoder_3-item5_t5_prompt_embeds.json Loaded quantization points from featurecoding_utils/transform_mapping/kmeans10samples-8bits/sd35cond/coco_fewshot-8bit_vae_encoder_vae_encoder_f0.json: torch.Size([256]) Loaded per-key quantization points for key 'vae.encoder_f0' from featurecoding_utils/transform_mapping/kmeans10samples-8bits/sd35cond/coco_fewshot-8bit_vae_encoder_vae_encoder_f0.json Loaded quantization points from featurecoding_utils/transform_mapping/kmeans10samples-8bits/sd35cond/coco_fewshot-8bit_vae_encoder_vae_encoder_f1.json: torch.Size([256]) Loaded per-key quantization points for key 'vae.encoder_f1' from featurecoding_utils/transform_mapping/kmeans10samples-8bits/sd35cond/coco_fewshot-8bit_vae_encoder_vae_encoder_f1.json Loaded quantization points from featurecoding_utils/transform_mapping/kmeans10samples-8bits/sd35cond/coco_fewshot-8bit_vae_decoder.json: torch.Size([256]) Loaded per-key quantization points for key 'vae.decoder' from featurecoding_utils/transform_mapping/kmeans10samples-8bits/sd35cond/coco_fewshot-8bit_vae_decoder.json Loaded per-key mappings: model=sd35 Keys: ['text_encoder-item0.clip_pooled_prompt_embeds', 'text_encoder-item0.clip_prompt_embeds', 'text_encoder-item3.clip_pooled_prompt_embeds', 'text_encoder-item3.clip_prompt_embeds', 'text_encoder_2-item1.clip_pooled_prompt_embeds', 'text_encoder_2-item1.clip_prompt_embeds', 'text_encoder_2-item4.clip_pooled', 'text_encoder_2-item4.clip_prompt', 'text_encoder_3-item2.t5_prompt_embeds', 'text_encoder_3-item5.t5_prompt_embeds', 'vae.encoder_f0', 'vae.encoder_f1', 'vae.decoder'] ---------------- ------------------------------------------------------------------------------------------------------------------------------ Handler sd35 Strategy individual Architecture hyperprior-featurecoding Checkpoint codec_weights/hyperprior_hybrid/bmshj2018-hyperprior_lambda0.004_epochs600_lr0.0001_bs180_patch256-256_checkpoint_best.pth.tar Transform type kmeans Transform config featurecoding_utils/transform_mapping/kmeans10samples-8bits/mapping.json Input ../datasets/FeatureCoding-StableDiffusion3.5/100samples_features Output output-fixed/sd35/lambda0.004/hyperprior-featurecoding-8bit-individual/sd35cond ---------------- ------------------------------------------------------------------------------------------------------------------------------ Files found: 100 ---------------------------------------------------------------------- 💪 Processing: ../datasets/FeatureCoding-StableDiffusion3.5/100samples_features/000000002153.zst (1/100) [1/4] FRONTEND: Loading features from ../datasets/FeatureCoding-StableDiffusion3.5/100samples_features/000000002153.zst... Original data structure: root: [Dict] with 8 keys key['text_encoder-item0']: [Dict] with 2 keys key['clip_pooled_prompt_embeds']: [Tensor] shape=torch.Size([768]), dtype=torch.float16, device=cpu key['clip_prompt_embeds']: [Tensor] shape=torch.Size([77, 768]), dtype=torch.float16, device=cpu key['text_encoder_2-item1']: [Dict] with 2 keys key['clip_pooled_prompt_embeds']: [Tensor] shape=torch.Size([1280]), dtype=torch.float16, device=cpu key['clip_prompt_embeds']: [Tensor] shape=torch.Size([77, 1280]), dtype=torch.float16, device=cpu key['text_encoder_3-item2']: [Dict] with 1 keys key['t5_prompt_embeds']: [Tensor] shape=torch.Size([77, 4096]), dtype=torch.float16, device=cpu key['text_encoder-item3']: [Dict] with 2 keys key['clip_pooled_prompt_embeds']: [Tensor] shape=torch.Size([768]), dtype=torch.float16, device=cpu key['clip_prompt_embeds']: [Tensor] shape=torch.Size([77, 768]), dtype=torch.float16, device=cpu key['text_encoder_2-item4']: [Dict] with 2 keys key['clip_pooled_prompt_embeds']: [Tensor] shape=torch.Size([1280]), dtype=torch.float16, device=cpu key['clip_prompt_embeds']: [Tensor] shape=torch.Size([77, 1280]), dtype=torch.float16, device=cpu key['text_encoder_3-item5']: [Dict] with 1 keys key['t5_prompt_embeds']: [Tensor] shape=torch.Size([77, 4096]), dtype=torch.float16, device=cpu key['vae.encoder-item6']: [Dict] with 2 keys key['vae.encoder_f0']: [Tensor] shape=torch.Size([32, 128, 128]), dtype=torch.float16, device=cpu key['vae.encoder_f1']: [Tensor] shape=torch.Size([32, 128, 128]), dtype=torch.float16, device=cpu key['vae.decoder']: [Tensor] shape=torch.Size([16, 128, 128]), dtype=torch.float16, device=cpu ⌛️ [1/4] FRONTEND: Load time: 0.008s ------------------------------------------------------------ SD3.5 Features Summary ------------------------------------------------------------ Number of text encoders: 3 Encoder names: ['text_encoder', 'text_encoder_2', 'text_encoder_3'] Has VAE latents: True Has VAE encoder features: True Data type: torch.float16 text_encoder-item0: clip_pooled_prompt_embeds: torch.Size([768]), torch.float16 clip_prompt_embeds: torch.Size([77, 768]), torch.float16 text_encoder_2-item1: clip_pooled_prompt_embeds: torch.Size([1280]), torch.float16 clip_prompt_embeds: torch.Size([77, 1280]), torch.float16 text_encoder_3-item2: t5_prompt_embeds: torch.Size([77, 4096]), torch.float16 text_encoder-item3: clip_pooled_prompt_embeds: torch.Size([768]), torch.float16 clip_prompt_embeds: torch.Size([77, 768]), torch.float16 text_encoder_2-item4: clip_pooled_prompt_embeds: torch.Size([1280]), torch.float16 clip_prompt_embeds: torch.Size([77, 1280]), torch.float16 text_encoder_3-item5: t5_prompt_embeds: torch.Size([77, 4096]), torch.float16 vae.decoder latents: torch.Size([16, 128, 128]) vae.encoder features: vae.encoder_f0: torch.Size([32, 128, 128]), torch.float16 vae.encoder_f1: torch.Size([32, 128, 128]), torch.float16 ------------------------------------------------------------ [2/4] FRONTEND: Pack + Encode (strategy: individual)... IndividualPacker: text_encoder-item0.clip_pooled_prompt_embeds: torch.Size([768])->torch.Size([1, 1, 1, 768]) text_encoder-item0.clip_prompt_embeds: torch.Size([77, 768])->torch.Size([1, 1, 77, 768]) text_encoder_2-item1.clip_pooled_prompt_embeds: torch.Size([1280])->torch.Size([1, 1, 1, 1280]) text_encoder_2-item1.clip_prompt_embeds: torch.Size([77, 1280])->torch.Size([1, 1, 77, 1280]) text_encoder_3-item2.t5_prompt_embeds: torch.Size([77, 4096])->torch.Size([1, 1, 77, 4096]) text_encoder-item3.clip_pooled_prompt_embeds: torch.Size([768])->torch.Size([1, 1, 1, 768]) text_encoder-item3.clip_prompt_embeds: torch.Size([77, 768])->torch.Size([1, 1, 77, 768]) text_encoder_2-item4.clip_pooled_prompt_embeds: torch.Size([1280])->torch.Size([1, 1, 1, 1280]) text_encoder_2-item4.clip_prompt_embeds: torch.Size([77, 1280])->torch.Size([1, 1, 77, 1280]) text_encoder_3-item5.t5_prompt_embeds: torch.Size([77, 4096])->torch.Size([1, 1, 77, 4096]) vae.encoder_f0: torch.Size([32, 128, 128])->torch.Size([1, 1, 512, 1024]) vae.encoder_f1: torch.Size([32, 128, 128])->torch.Size([1, 1, 512, 1024]) vae.decoder: torch.Size([16, 128, 128])->torch.Size([1, 1, 512, 512]) Using per-key quantization points (text_encoder-item0.clip_pooled_prompt_embeds: torch.Size([256])) for text_encoder-item0.clip_pooled_prompt_embeds text_encoder-item0.clip_pooled_prompt_embeds: 736B, BPFP=7.6667 Using per-key quantization points (text_encoder-item0.clip_prompt_embeds: torch.Size([256])) for text_encoder-item0.clip_prompt_embeds text_encoder-item0.clip_prompt_embeds: 6,772B, BPFP=0.9161 Using per-key quantization points (text_encoder_2-item1.clip_pooled_prompt_embeds: torch.Size([256])) for text_encoder_2-item1.clip_pooled_prompt_embeds text_encoder_2-item1.clip_pooled_prompt_embeds: 1,208B, BPFP=7.5500 Using per-key quantization points (text_encoder_2-item1.clip_prompt_embeds: torch.Size([256])) for text_encoder_2-item1.clip_prompt_embeds text_encoder_2-item1.clip_prompt_embeds: 10,768B, BPFP=0.8740 Using per-key quantization points (text_encoder_3-item2.t5_prompt_embeds: torch.Size([256])) for text_encoder_3-item2.t5_prompt_embeds text_encoder_3-item2.t5_prompt_embeds: 31,568B, BPFP=0.8007 Using per-key quantization points (text_encoder-item3.clip_pooled_prompt_embeds: torch.Size([256])) for text_encoder-item3.clip_pooled_prompt_embeds text_encoder-item3.clip_pooled_prompt_embeds: 760B, BPFP=7.9167 Using per-key quantization points (text_encoder-item3.clip_prompt_embeds: torch.Size([256])) for text_encoder-item3.clip_prompt_embeds text_encoder-item3.clip_prompt_embeds: 4,024B, BPFP=0.5444 Using per-key quantization points (text_encoder_2-item4.clip_pooled: torch.Size([256])) for text_encoder_2-item4.clip_pooled_prompt_embeds text_encoder_2-item4.clip_pooled_prompt_embeds: 1,268B, BPFP=7.9250 Using per-key quantization points (text_encoder_2-item4.clip_prompt: torch.Size([256])) for text_encoder_2-item4.clip_prompt_embeds text_encoder_2-item4.clip_prompt_embeds: 6,996B, BPFP=0.5679 Using per-key quantization points (text_encoder_3-item5.t5_prompt_embeds: torch.Size([256])) for text_encoder_3-item5.t5_prompt_embeds text_encoder_3-item5.t5_prompt_embeds: 18,416B, BPFP=0.4671 Using per-key quantization points (vae.encoder_f0: torch.Size([256])) for vae.encoder_f0 vae.encoder_f0: 20,704B, BPFP=0.3159 Using per-key quantization points (vae.encoder_f1: torch.Size([256])) for vae.encoder_f1 vae.encoder_f1: 20,704B, BPFP=0.3159 Using per-key quantization points (vae.decoder: torch.Size([256])) for vae.decoder vae.decoder: 8,464B, BPFP=0.2583 ⌛️ [2/4] FRONTEND: Frontend time: 0.692s (Pack+Encode) [3/4] BACKEND: Decode + Unpack... Using per-key quantization points (text_encoder-item0.clip_pooled_prompt_embeds: torch.Size([256])) for text_encoder-item0.clip_pooled_prompt_embeds Using per-key quantization points (text_encoder-item0.clip_prompt_embeds: torch.Size([256])) for text_encoder-item0.clip_prompt_embeds Using per-key quantization points (text_encoder_2-item1.clip_pooled_prompt_embeds: torch.Size([256])) for text_encoder_2-item1.clip_pooled_prompt_embeds Using per-key quantization points (text_encoder_2-item1.clip_prompt_embeds: torch.Size([256])) for text_encoder_2-item1.clip_prompt_embeds Using per-key quantization points (text_encoder_3-item2.t5_prompt_embeds: torch.Size([256])) for text_encoder_3-item2.t5_prompt_embeds Using per-key quantization points (text_encoder-item3.clip_pooled_prompt_embeds: torch.Size([256])) for text_encoder-item3.clip_pooled_prompt_embeds Using per-key quantization points (text_encoder-item3.clip_prompt_embeds: torch.Size([256])) for text_encoder-item3.clip_prompt_embeds Using per-key quantization points (text_encoder_2-item4.clip_pooled: torch.Size([256])) for text_encoder_2-item4.clip_pooled_prompt_embeds Using per-key quantization points (text_encoder_2-item4.clip_prompt: torch.Size([256])) for text_encoder_2-item4.clip_prompt_embeds Using per-key quantization points (text_encoder_3-item5.t5_prompt_embeds: torch.Size([256])) for text_encoder_3-item5.t5_prompt_embeds Using per-key quantization points (vae.encoder_f0: torch.Size([256])) for vae.encoder_f0 Using per-key quantization points (vae.encoder_f1: torch.Size([256])) for vae.encoder_f1 Using per-key quantization points (vae.decoder: torch.Size([256])) for vae.decoder IndividualUnPacker: text_encoder-item0.clip_pooled_prompt_embeds: torch.Size([768]) text_encoder-item0.clip_prompt_embeds: torch.Size([77, 768]) text_encoder_2-item1.clip_pooled_prompt_embeds: torch.Size([1280]) text_encoder_2-item1.clip_prompt_embeds: torch.Size([77, 1280]) text_encoder_3-item2.t5_prompt_embeds: torch.Size([77, 4096]) text_encoder-item3.clip_pooled_prompt_embeds: torch.Size([768]) text_encoder-item3.clip_prompt_embeds: torch.Size([77, 768]) text_encoder_2-item4.clip_pooled_prompt_embeds: torch.Size([1280]) text_encoder_2-item4.clip_prompt_embeds: torch.Size([77, 1280]) text_encoder_3-item5.t5_prompt_embeds: torch.Size([77, 4096]) vae.decoder: torch.Size([16, 128, 128]) vae.encoder_f0: torch.Size([32, 128, 128]) vae.encoder_f1: torch.Size([32, 128, 128]) ⌛️ [3/4] BACKEND: Backend time: 0.510s [4/4] METRICS: Computing MSE Breakdown... Using per-key quantization points (text_encoder-item0.clip_pooled_prompt_embeds: torch.Size([256])) for text_encoder-item0.clip_pooled_prompt_embeds Using per-key quantization points (text_encoder-item0.clip_prompt_embeds: torch.Size([256])) for text_encoder-item0.clip_prompt_embeds Using per-key quantization points (text_encoder_2-item1.clip_pooled_prompt_embeds: torch.Size([256])) for text_encoder_2-item1.clip_pooled_prompt_embeds Using per-key quantization points (text_encoder_2-item1.clip_prompt_embeds: torch.Size([256])) for text_encoder_2-item1.clip_prompt_embeds Using per-key quantization points (text_encoder_3-item2.t5_prompt_embeds: torch.Size([256])) for text_encoder_3-item2.t5_prompt_embeds Using per-key quantization points (text_encoder-item3.clip_pooled_prompt_embeds: torch.Size([256])) for text_encoder-item3.clip_pooled_prompt_embeds Using per-key quantization points (text_encoder-item3.clip_prompt_embeds: torch.Size([256])) for text_encoder-item3.clip_prompt_embeds Using per-key quantization points (text_encoder_2-item4.clip_pooled: torch.Size([256])) for text_encoder_2-item4.clip_pooled_prompt_embeds Using per-key quantization points (text_encoder_2-item4.clip_prompt: torch.Size([256])) for text_encoder_2-item4.clip_prompt_embeds Using per-key quantization points (text_encoder_3-item5.t5_prompt_embeds: torch.Size([256])) for text_encoder_3-item5.t5_prompt_embeds Using per-key quantization points (vae.encoder_f0: torch.Size([256])) for vae.encoder_f0 Using per-key quantization points (vae.encoder_f1: torch.Size([256])) for vae.encoder_f1 Using per-key quantization points (vae.decoder: torch.Size([256])) for vae.decoder IndividualUnPacker: text_encoder-item0.clip_pooled_prompt_embeds: torch.Size([768]) text_encoder-item0.clip_prompt_embeds: torch.Size([77, 768]) text_encoder_2-item1.clip_pooled_prompt_embeds: torch.Size([1280]) text_encoder_2-item1.clip_prompt_embeds: torch.Size([77, 1280]) text_encoder_3-item2.t5_prompt_embeds: torch.Size([77, 4096]) text_encoder-item3.clip_pooled_prompt_embeds: torch.Size([768]) text_encoder-item3.clip_prompt_embeds: torch.Size([77, 768]) text_encoder_2-item4.clip_pooled_prompt_embeds: torch.Size([1280]) text_encoder_2-item4.clip_prompt_embeds: torch.Size([77, 1280]) text_encoder_3-item5.t5_prompt_embeds: torch.Size([77, 4096]) vae.decoder: torch.Size([16, 128, 128]) vae.encoder_f0: torch.Size([32, 128, 128]) vae.encoder_f1: torch.Size([32, 128, 128]) Per-key MSE Breakdown (Quant=Truncation+Quantization, Codec=Compression): Key Quant-MSE Total-MSE ------------------------------------------------------------------------------------- text_encoder-item0.clip_pooled_prompt_embeds 0.00017200 0.95494636 text_encoder-item0.clip_prompt_embeds 0.00025464 35.16017739 text_encoder_2-item1.clip_pooled_prompt_embeds 0.00020464 0.85673828 text_encoder_2-item1.clip_prompt_embeds 0.00016240 0.13009319 text_encoder_3-item2.t5_prompt_embeds 0.00000839 0.00365759 text_encoder-item3.clip_pooled_prompt_embeds 0.00841799 1.01420029 text_encoder-item3.clip_prompt_embeds 0.00023247 23.90570887 text_encoder_2-item4.clip_pooled_prompt_embeds 0.00016751 0.67051430 text_encoder_2-item4.clip_prompt_embeds 0.00009830 0.47049568 text_encoder_3-item5.t5_prompt_embeds 0.00001263 0.00227117 vae.encoder_f0 0.00635250 2.62802196 vae.encoder_f1 0.00635834 2.62814617 vae.decoder 0.00019940 0.05525560 ------------------------------------------------------------------------------------- TOTAL 0.00300073 2.79863094 (elements=2,260,992) ---------------------- -------------------------------------------------------- SAMPLE-WISE STATISTICS ---------------------- -------------------------------------------------------- Handler sd35 Strategy individual Architecture hyperprior-featurecoding ---------------------- -------------------------------------------------------- Total Elements 2260992 Total Bytes 132388 BPFP 0.4684 bits/point EBPFP 0.9368 equivalent bits/point MSE 2.798631 ---------------------- -------------------------------------------------------- Time: 1.210s Load: 0.008s, Pack+Encode: 0.692s, Decode+Unpack: 0.510s ---------------------- -------------------------------------------------------- Restored Feature Format: [Dict] with 8 keys key['text_encoder-item0']: [Dict] with 2 keys key['clip_pooled_prompt_embeds']: [Tensor] shape=torch.Size([768]), dtype=torch.float16, device=cpu key['clip_prompt_embeds']: [Tensor] shape=torch.Size([77, 768]), dtype=torch.float16, device=cpu key['text_encoder_2-item1']: [Dict] with 2 keys key['clip_pooled_prompt_embeds']: [Tensor] shape=torch.Size([1280]), dtype=torch.float16, device=cpu key['clip_prompt_embeds']: [Tensor] shape=torch.Size([77, 1280]), dtype=torch.float16, device=cpu key['text_encoder_3-item2']: [Dict] with 1 keys key['t5_prompt_embeds']: [Tensor] shape=torch.Size([77, 4096]), dtype=torch.float16, device=cpu key['text_encoder-item3']: [Dict] with 2 keys key['clip_pooled_prompt_embeds']: [Tensor] shape=torch.Size([768]), dtype=torch.float16, device=cpu key['clip_prompt_embeds']: [Tensor] shape=torch.Size([77, 768]), dtype=torch.float16, device=cpu key['text_encoder_2-item4']: [Dict] with 2 keys key['clip_pooled_prompt_embeds']: [Tensor] shape=torch.Size([1280]), dtype=torch.float16, device=cpu key['clip_prompt_embeds']: [Tensor] shape=torch.Size([77, 1280]), dtype=torch.float16, device=cpu key['text_encoder_3-item5']: [Dict] with 1 keys key['t5_prompt_embeds']: [Tensor] shape=torch.Size([77, 4096]), dtype=torch.float16, device=cpu key['vae.decoder']: [Tensor] shape=torch.Size([16, 128, 128]), dtype=torch.float16, device=cpu key['vae.encoder']: [Dict] with 2 keys key['vae.encoder_f0']: [Tensor] shape=torch.Size([32, 128, 128]), dtype=torch.float16, device=cpu key['vae.encoder_f1']: [Tensor] shape=torch.Size([32, 128, 128]), dtype=torch.float16, device=cpu 💾 Converting with 2.7986 MSE: from ../datasets/FeatureCoding-StableDiffusion3.5/100samples_features/000000002153.zst to output-fixed/sd35/lambda0.004/hyperprior-featurecoding-8bit-individual/sd35cond/000000002153.zst 💪 Processing: ../datasets/FeatureCoding-StableDiffusion3.5/100samples_features/000000002431.zst (2/100) [1/4] FRONTEND: Loading features from ../datasets/FeatureCoding-StableDiffusion3.5/100samples_features/000000002431.zst... Original data structure: root: [Dict] with 8 keys key['text_encoder-item0']: [Dict] with 2 keys key['clip_pooled_prompt_embeds']: [Tensor] shape=torch.Size([768]), dtype=torch.float16, device=cpu key['clip_prompt_embeds']: [Tensor] shape=torch.Size([77, 768]), dtype=torch.float16, device=cpu key['text_encoder_2-item1']: [Dict] with 2 keys key['clip_pooled_prompt_embeds']: [Tensor] shape=torch.Size([1280]), dtype=torch.float16, device=cpu key['clip_prompt_embeds']: [Tensor] shape=torch.Size([77, 1280]), dtype=torch.float16, device=cpu key['text_encoder_3-item2']: [Dict] with 1 keys key['t5_prompt_embeds']: [Tensor] shape=torch.Size([77, 4096]), dtype=torch.float16, device=cpu key['text_encoder-item3']: [Dict] with 2 keys key['clip_pooled_prompt_embeds']: [Tensor] shape=torch.Size([768]), dtype=torch.float16, device=cpu key['clip_prompt_embeds']: [Tensor] shape=torch.Size([77, 768]), dtype=torch.float16, device=cpu key['text_encoder_2-item4']: [Dict] with 2 keys key['clip_pooled_prompt_embeds']: [Tensor] shape=torch.Size([1280]), dtype=torch.float16, device=cpu key['clip_prompt_embeds']: [Tensor] shape=torch.Size([77, 1280]), dtype=torch.float16, device=cpu key['text_encoder_3-item5']: [Dict] with 1 keys key['t5_prompt_embeds']: [Tensor] shape=torch.Size([77, 4096]), dtype=torch.float16, device=cpu key['vae.encoder-item6']: [Dict] with 2 keys key['vae.encoder_f0']: [Tensor] shape=torch.Size([32, 128, 128]), dtype=torch.float16, device=cpu key['vae.encoder_f1']: [Tensor] shape=torch.Size([32, 128, 128]), dtype=torch.float16, device=cpu key['vae.decoder']: [Tensor] shape=torch.Size([16, 128, 128]), dtype=torch.float16, device=cpu ⌛️ [1/4] FRONTEND: Load time: 0.009s ------------------------------------------------------------ SD3.5 Features Summary ------------------------------------------------------------ Number of text encoders: 3 Encoder names: ['text_encoder', 'text_encoder_2', 'text_encoder_3'] Has VAE latents: True Has VAE encoder features: True Data type: torch.float16 text_encoder-item0: clip_pooled_prompt_embeds: torch.Size([768]), torch.float16 clip_prompt_embeds: torch.Size([77, 768]), torch.float16 text_encoder_2-item1: clip_pooled_prompt_embeds: torch.Size([1280]), torch.float16 clip_prompt_embeds: torch.Size([77, 1280]), torch.float16 text_encoder_3-item2: t5_prompt_embeds: torch.Size([77, 4096]), torch.float16 text_encoder-item3: clip_pooled_prompt_embeds: torch.Size([768]), torch.float16 clip_prompt_embeds: torch.Size([77, 768]), torch.float16 text_encoder_2-item4: clip_pooled_prompt_embeds: torch.Size([1280]), torch.float16 clip_prompt_embeds: torch.Size([77, 1280]), torch.float16 text_encoder_3-item5: t5_prompt_embeds: torch.Size([77, 4096]), torch.float16 vae.decoder latents: torch.Size([16, 128, 128]) vae.encoder features: vae.encoder_f0: torch.Size([32, 128, 128]), torch.float16 vae.encoder_f1: torch.Size([32, 128, 128]), torch.float16 ------------------------------------------------------------ [2/4] FRONTEND: Pack + Encode (strategy: individual)... IndividualPacker: text_encoder-item0.clip_pooled_prompt_embeds: torch.Size([768])->torch.Size([1, 1, 1, 768]) text_encoder-item0.clip_prompt_embeds: torch.Size([77, 768])->torch.Size([1, 1, 77, 768]) text_encoder_2-item1.clip_pooled_prompt_embeds: torch.Size([1280])->torch.Size([1, 1, 1, 1280]) text_encoder_2-item1.clip_prompt_embeds: torch.Size([77, 1280])->torch.Size([1, 1, 77, 1280]) text_encoder_3-item2.t5_prompt_embeds: torch.Size([77, 4096])->torch.Size([1, 1, 77, 4096]) text_encoder-item3.clip_pooled_prompt_embeds: torch.Size([768])->torch.Size([1, 1, 1, 768]) text_encoder-item3.clip_prompt_embeds: torch.Size([77, 768])->torch.Size([1, 1, 77, 768]) text_encoder_2-item4.clip_pooled_prompt_embeds: torch.Size([1280])->torch.Size([1, 1, 1, 1280]) text_encoder_2-item4.clip_prompt_embeds: torch.Size([77, 1280])->torch.Size([1, 1, 77, 1280]) text_encoder_3-item5.t5_prompt_embeds: torch.Size([77, 4096])->torch.Size([1, 1, 77, 4096]) vae.encoder_f0: torch.Size([32, 128, 128])->torch.Size([1, 1, 512, 1024]) vae.encoder_f1: torch.Size([32, 128, 128])->torch.Size([1, 1, 512, 1024]) vae.decoder: torch.Size([16, 128, 128])->torch.Size([1, 1, 512, 512]) Using per-key quantization points (text_encoder-item0.clip_pooled_prompt_embeds: torch.Size([256])) for text_encoder-item0.clip_pooled_prompt_embeds text_encoder-item0.clip_pooled_prompt_embeds: 740B, BPFP=7.7083 Using per-key quantization points (text_encoder-item0.clip_prompt_embeds: torch.Size([256])) for text_encoder-item0.clip_prompt_embeds text_encoder-item0.clip_prompt_embeds: 6,864B, BPFP=0.9286 Using per-key quantization points (text_encoder_2-item1.clip_pooled_prompt_embeds: torch.Size([256])) for text_encoder_2-item1.clip_pooled_prompt_embeds text_encoder_2-item1.clip_pooled_prompt_embeds: 1,192B, BPFP=7.4500 Using per-key quantization points (text_encoder_2-item1.clip_prompt_embeds: torch.Size([256])) for text_encoder_2-item1.clip_prompt_embeds text_encoder_2-item1.clip_prompt_embeds: 11,164B, BPFP=0.9062 Using per-key quantization points (text_encoder_3-item2.t5_prompt_embeds: torch.Size([256])) for text_encoder_3-item2.t5_prompt_embeds text_encoder_3-item2.t5_prompt_embeds: 29,624B, BPFP=0.7514 Using per-key quantization points (text_encoder-item3.clip_pooled_prompt_embeds: torch.Size([256])) for text_encoder-item3.clip_pooled_prompt_embeds text_encoder-item3.clip_pooled_prompt_embeds: 760B, BPFP=7.9167 Using per-key quantization points (text_encoder-item3.clip_prompt_embeds: torch.Size([256])) for text_encoder-item3.clip_prompt_embeds text_encoder-item3.clip_prompt_embeds: 4,024B, BPFP=0.5444 Using per-key quantization points (text_encoder_2-item4.clip_pooled: torch.Size([256])) for text_encoder_2-item4.clip_pooled_prompt_embeds text_encoder_2-item4.clip_pooled_prompt_embeds: 1,268B, BPFP=7.9250 Using per-key quantization points (text_encoder_2-item4.clip_prompt: torch.Size([256])) for text_encoder_2-item4.clip_prompt_embeds text_encoder_2-item4.clip_prompt_embeds: 6,996B, BPFP=0.5679 Using per-key quantization points (text_encoder_3-item5.t5_prompt_embeds: torch.Size([256])) for text_encoder_3-item5.t5_prompt_embeds text_encoder_3-item5.t5_prompt_embeds: 18,416B, BPFP=0.4671 Using per-key quantization points (vae.encoder_f0: torch.Size([256])) for vae.encoder_f0 vae.encoder_f0: 15,936B, BPFP=0.2432 Using per-key quantization points (vae.encoder_f1: torch.Size([256])) for vae.encoder_f1 vae.encoder_f1: 15,940B, BPFP=0.2432 Using per-key quantization points (vae.decoder: torch.Size([256])) for vae.decoder vae.decoder: 6,660B, BPFP=0.2032 ⌛️ [2/4] FRONTEND: Frontend time: 0.293s (Pack+Encode) [3/4] BACKEND: Decode + Unpack... Using per-key quantization points (text_encoder-item0.clip_pooled_prompt_embeds: torch.Size([256])) for text_encoder-item0.clip_pooled_prompt_embeds Using per-key quantization points (text_encoder-item0.clip_prompt_embeds: torch.Size([256])) for text_encoder-item0.clip_prompt_embeds Using per-key quantization points (text_encoder_2-item1.clip_pooled_prompt_embeds: torch.Size([256])) for text_encoder_2-item1.clip_pooled_prompt_embeds Using per-key quantization points (text_encoder_2-item1.clip_prompt_embeds: torch.Size([256])) for text_encoder_2-item1.clip_prompt_embeds Using per-key quantization points (text_encoder_3-item2.t5_prompt_embeds: torch.Size([256])) for text_encoder_3-item2.t5_prompt_embeds Using per-key quantization points (text_encoder-item3.clip_pooled_prompt_embeds: torch.Size([256])) for text_encoder-item3.clip_pooled_prompt_embeds Using per-key quantization points (text_encoder-item3.clip_prompt_embeds: torch.Size([256])) for text_encoder-item3.clip_prompt_embeds Using per-key quantization points (text_encoder_2-item4.clip_pooled: torch.Size([256])) for text_encoder_2-item4.clip_pooled_prompt_embeds Using per-key quantization points (text_encoder_2-item4.clip_prompt: torch.Size([256])) for text_encoder_2-item4.clip_prompt_embeds Using per-key quantization points (text_encoder_3-item5.t5_prompt_embeds: torch.Size([256])) for text_encoder_3-item5.t5_prompt_embeds Using per-key quantization points (vae.encoder_f0: torch.Size([256])) for vae.encoder_f0 Using per-key quantization points (vae.encoder_f1: torch.Size([256])) for vae.encoder_f1 Using per-key quantization points (vae.decoder: torch.Size([256])) for vae.decoder IndividualUnPacker: text_encoder-item0.clip_pooled_prompt_embeds: torch.Size([768]) text_encoder-item0.clip_prompt_embeds: torch.Size([77, 768]) text_encoder_2-item1.clip_pooled_prompt_embeds: torch.Size([1280]) text_encoder_2-item1.clip_prompt_embeds: torch.Size([77, 1280]) text_encoder_3-item2.t5_prompt_embeds: torch.Size([77, 4096]) text_encoder-item3.clip_pooled_prompt_embeds: torch.Size([768]) text_encoder-item3.clip_prompt_embeds: torch.Size([77, 768]) text_encoder_2-item4.clip_pooled_prompt_embeds: torch.Size([1280]) text_encoder_2-item4.clip_prompt_embeds: torch.Size([77, 1280]) text_encoder_3-item5.t5_prompt_embeds: torch.Size([77, 4096]) vae.decoder: torch.Size([16, 128, 128]) vae.encoder_f0: torch.Size([32, 128, 128]) vae.encoder_f1: torch.Size([32, 128, 128]) ⌛️ [3/4] BACKEND: Backend time: 0.449s [4/4] METRICS: Computing MSE Breakdown... Using per-key quantization points (text_encoder-item0.clip_pooled_prompt_embeds: torch.Size([256])) for text_encoder-item0.clip_pooled_prompt_embeds Using per-key quantization points (text_encoder-item0.clip_prompt_embeds: torch.Size([256])) for text_encoder-item0.clip_prompt_embeds Using per-key quantization points (text_encoder_2-item1.clip_pooled_prompt_embeds: torch.Size([256])) for text_encoder_2-item1.clip_pooled_prompt_embeds Using per-key quantization points (text_encoder_2-item1.clip_prompt_embeds: torch.Size([256])) for text_encoder_2-item1.clip_prompt_embeds Using per-key quantization points (text_encoder_3-item2.t5_prompt_embeds: torch.Size([256])) for text_encoder_3-item2.t5_prompt_embeds Using per-key quantization points (text_encoder-item3.clip_pooled_prompt_embeds: torch.Size([256])) for text_encoder-item3.clip_pooled_prompt_embeds Using per-key quantization points (text_encoder-item3.clip_prompt_embeds: torch.Size([256])) for text_encoder-item3.clip_prompt_embeds Using per-key quantization points (text_encoder_2-item4.clip_pooled: torch.Size([256])) for text_encoder_2-item4.clip_pooled_prompt_embeds Using per-key quantization points (text_encoder_2-item4.clip_prompt: torch.Size([256])) for text_encoder_2-item4.clip_prompt_embeds Using per-key quantization points (text_encoder_3-item5.t5_prompt_embeds: torch.Size([256])) for text_encoder_3-item5.t5_prompt_embeds Using per-key quantization points (vae.encoder_f0: torch.Size([256])) for vae.encoder_f0 Using per-key quantization points (vae.encoder_f1: torch.Size([256])) for vae.encoder_f1 Using per-key quantization points (vae.decoder: torch.Size([256])) for vae.decoder IndividualUnPacker: text_encoder-item0.clip_pooled_prompt_embeds: torch.Size([768]) text_encoder-item0.clip_prompt_embeds: torch.Size([77, 768]) text_encoder_2-item1.clip_pooled_prompt_embeds: torch.Size([1280]) text_encoder_2-item1.clip_prompt_embeds: torch.Size([77, 1280]) text_encoder_3-item2.t5_prompt_embeds: torch.Size([77, 4096]) text_encoder-item3.clip_pooled_prompt_embeds: torch.Size([768]) text_encoder-item3.clip_prompt_embeds: torch.Size([77, 768]) text_encoder_2-item4.clip_pooled_prompt_embeds: torch.Size([1280]) text_encoder_2-item4.clip_prompt_embeds: torch.Size([77, 1280]) text_encoder_3-item5.t5_prompt_embeds: torch.Size([77, 4096]) vae.decoder: torch.Size([16, 128, 128]) vae.encoder_f0: torch.Size([32, 128, 128]) vae.encoder_f1: torch.Size([32, 128, 128]) Per-key MSE Breakdown (Quant=Truncation+Quantization, Codec=Compression): Key Quant-MSE Total-MSE ------------------------------------------------------------------------------------- text_encoder-item0.clip_pooled_prompt_embeds 0.00020777 0.92112398 text_encoder-item0.clip_prompt_embeds 0.00022609 108.26190476 text_encoder_2-item1.clip_pooled_prompt_embeds 0.00019887 0.91517315 text_encoder_2-item1.clip_prompt_embeds 0.00019493 0.18779333 text_encoder_3-item2.t5_prompt_embeds 0.00000845 0.00359077 text_encoder-item3.clip_pooled_prompt_embeds 0.00841799 1.01420029 text_encoder-item3.clip_prompt_embeds 0.00023247 23.90570887 text_encoder_2-item4.clip_pooled_prompt_embeds 0.00016751 0.67051430 text_encoder_2-item4.clip_prompt_embeds 0.00009830 0.47049568 text_encoder_3-item5.t5_prompt_embeds 0.00001263 0.00227117 vae.encoder_f0 0.01130640 3.32165742 vae.encoder_f1 0.01130902 3.32108355 vae.decoder 0.00020860 0.05133892 ------------------------------------------------------------------------------------- TOTAL 0.00529919 5.03419621 (elements=2,260,992) ---------------------- -------------------------------------------------------- SAMPLE-WISE STATISTICS ---------------------- -------------------------------------------------------- Handler sd35 Strategy individual Architecture hyperprior-featurecoding ---------------------- -------------------------------------------------------- Total Elements 2260992 Total Bytes 119584 BPFP 0.4231 bits/point EBPFP 0.8462 equivalent bits/point MSE 5.034196 ---------------------- -------------------------------------------------------- Time: 0.751s Load: 0.009s, Pack+Encode: 0.293s, Decode+Unpack: 0.449s ---------------------- -------------------------------------------------------- Restored Feature Format: [Dict] with 8 keys key['text_encoder-item0']: [Dict] with 2 keys key['clip_pooled_prompt_embeds']: [Tensor] shape=torch.Size([768]), dtype=torch.float16, device=cpu key['clip_prompt_embeds']: [Tensor] shape=torch.Size([77, 768]), dtype=torch.float16, device=cpu key['text_encoder_2-item1']: [Dict] with 2 keys key['clip_pooled_prompt_embeds']: [Tensor] shape=torch.Size([1280]), dtype=torch.float16, device=cpu key['clip_prompt_embeds']: [Tensor] shape=torch.Size([77, 1280]), dtype=torch.float16, device=cpu key['text_encoder_3-item2']: [Dict] with 1 keys key['t5_prompt_embeds']: [Tensor] shape=torch.Size([77, 4096]), dtype=torch.float16, device=cpu key['text_encoder-item3']: [Dict] with 2 keys key['clip_pooled_prompt_embeds']: [Tensor] shape=torch.Size([768]), dtype=torch.float16, device=cpu key['clip_prompt_embeds']: [Tensor] shape=torch.Size([77, 768]), dtype=torch.float16, device=cpu key['text_encoder_2-item4']: [Dict] with 2 keys key['clip_pooled_prompt_embeds']: [Tensor] shape=torch.Size([1280]), dtype=torch.float16, device=cpu key['clip_prompt_embeds']: [Tensor] shape=torch.Size([77, 1280]), dtype=torch.float16, device=cpu key['text_encoder_3-item5']: [Dict] with 1 keys key['t5_prompt_embeds']: [Tensor] shape=torch.Size([77, 4096]), dtype=torch.float16, device=cpu key['vae.decoder']: [Tensor] shape=torch.Size([16, 128, 128]), dtype=torch.float16, device=cpu key['vae.encoder']: [Dict] with 2 keys key['vae.encoder_f0']: [Tensor] shape=torch.Size([32, 128, 128]), dtype=torch.float16, device=cpu key['vae.encoder_f1']: [Tensor] shape=torch.Size([32, 128, 128]), dtype=torch.float16, device=cpu 💾 Converting with 5.0342 MSE: from ../datasets/FeatureCoding-StableDiffusion3.5/100samples_features/000000002431.zst to output-fixed/sd35/lambda0.004/hyperprior-featurecoding-8bit-individual/sd35cond/000000002431.zst 💪 Processing: ../datasets/FeatureCoding-StableDiffusion3.5/100samples_features/000000003661.zst (3/100) [1/4] FRONTEND: Loading features from ../datasets/FeatureCoding-StableDiffusion3.5/100samples_features/000000003661.zst... Original data structure: root: [Dict] with 8 keys key['text_encoder-item0']: [Dict] with 2 keys key['clip_pooled_prompt_embeds']: [Tensor] shape=torch.Size([768]), dtype=torch.float16, device=cpu key['clip_prompt_embeds']: [Tensor] shape=torch.Size([77, 768]), dtype=torch.float16, device=cpu key['text_encoder_2-item1']: [Dict] with 2 keys key['clip_pooled_prompt_embeds']: [Tensor] shape=torch.Size([1280]), dtype=torch.float16, device=cpu key['clip_prompt_embeds']: [Tensor] shape=torch.Size([77, 1280]), dtype=torch.float16, device=cpu key['text_encoder_3-item2']: [Dict] with 1 keys key['t5_prompt_embeds']: [Tensor] shape=torch.Size([77, 4096]), dtype=torch.float16, device=cpu key['text_encoder-item3']: [Dict] with 2 keys key['clip_pooled_prompt_embeds']: [Tensor] shape=torch.Size([768]), dtype=torch.float16, device=cpu key['clip_prompt_embeds']: [Tensor] shape=torch.Size([77, 768]), dtype=torch.float16, device=cpu key['text_encoder_2-item4']: [Dict] with 2 keys key['clip_pooled_prompt_embeds']: [Tensor] shape=torch.Size([1280]), dtype=torch.float16, device=cpu key['clip_prompt_embeds']: [Tensor] shape=torch.Size([77, 1280]), dtype=torch.float16, device=cpu key['text_encoder_3-item5']: [Dict] with 1 keys key['t5_prompt_embeds']: [Tensor] shape=torch.Size([77, 4096]), dtype=torch.float16, device=cpu key['vae.encoder-item6']: [Dict] with 2 keys key['vae.encoder_f0']: [Tensor] shape=torch.Size([32, 128, 128]), dtype=torch.float16, device=cpu key['vae.encoder_f1']: [Tensor] shape=torch.Size([32, 128, 128]), dtype=torch.float16, device=cpu key['vae.decoder']: [Tensor] shape=torch.Size([16, 128, 128]), dtype=torch.float16, device=cpu ⌛️ [1/4] FRONTEND: Load time: 0.007s ------------------------------------------------------------ SD3.5 Features Summary ------------------------------------------------------------ Number of text encoders: 3 Encoder names: ['text_encoder', 'text_encoder_2', 'text_encoder_3'] Has VAE latents: True Has VAE encoder features: True Data type: torch.float16 text_encoder-item0: clip_pooled_prompt_embeds: torch.Size([768]), torch.float16 clip_prompt_embeds: torch.Size([77, 768]), torch.float16 text_encoder_2-item1: clip_pooled_prompt_embeds: torch.Size([1280]), torch.float16 clip_prompt_embeds: torch.Size([77, 1280]), torch.float16 text_encoder_3-item2: t5_prompt_embeds: torch.Size([77, 4096]), torch.float16 text_encoder-item3: clip_pooled_prompt_embeds: torch.Size([768]), torch.float16 clip_prompt_embeds: torch.Size([77, 768]), torch.float16 text_encoder_2-item4: clip_pooled_prompt_embeds: torch.Size([1280]), torch.float16 clip_prompt_embeds: torch.Size([77, 1280]), torch.float16 text_encoder_3-item5: t5_prompt_embeds: torch.Size([77, 4096]), torch.float16 vae.decoder latents: torch.Size([16, 128, 128]) vae.encoder features: vae.encoder_f0: torch.Size([32, 128, 128]), torch.float16 vae.encoder_f1: torch.Size([32, 128, 128]), torch.float16 ------------------------------------------------------------ [2/4] FRONTEND: Pack + Encode (strategy: individual)... IndividualPacker: text_encoder-item0.clip_pooled_prompt_embeds: torch.Size([768])->torch.Size([1, 1, 1, 768]) text_encoder-item0.clip_prompt_embeds: torch.Size([77, 768])->torch.Size([1, 1, 77, 768]) text_encoder_2-item1.clip_pooled_prompt_embeds: torch.Size([1280])->torch.Size([1, 1, 1, 1280]) text_encoder_2-item1.clip_prompt_embeds: torch.Size([77, 1280])->torch.Size([1, 1, 77, 1280]) text_encoder_3-item2.t5_prompt_embeds: torch.Size([77, 4096])->torch.Size([1, 1, 77, 4096]) text_encoder-item3.clip_pooled_prompt_embeds: torch.Size([768])->torch.Size([1, 1, 1, 768]) text_encoder-item3.clip_prompt_embeds: torch.Size([77, 768])->torch.Size([1, 1, 77, 768]) text_encoder_2-item4.clip_pooled_prompt_embeds: torch.Size([1280])->torch.Size([1, 1, 1, 1280]) text_encoder_2-item4.clip_prompt_embeds: torch.Size([77, 1280])->torch.Size([1, 1, 77, 1280]) text_encoder_3-item5.t5_prompt_embeds: torch.Size([77, 4096])->torch.Size([1, 1, 77, 4096]) vae.encoder_f0: torch.Size([32, 128, 128])->torch.Size([1, 1, 512, 1024]) vae.encoder_f1: torch.Size([32, 128, 128])->torch.Size([1, 1, 512, 1024]) vae.decoder: torch.Size([16, 128, 128])->torch.Size([1, 1, 512, 512]) Using per-key quantization points (text_encoder-item0.clip_pooled_prompt_embeds: torch.Size([256])) for text_encoder-item0.clip_pooled_prompt_embeds text_encoder-item0.clip_pooled_prompt_embeds: 732B, BPFP=7.6250 Using per-key quantization points (text_encoder-item0.clip_prompt_embeds: torch.Size([256])) for text_encoder-item0.clip_prompt_embeds text_encoder-item0.clip_prompt_embeds: 6,420B, BPFP=0.8685 Using per-key quantization points (text_encoder_2-item1.clip_pooled_prompt_embeds: torch.Size([256])) for text_encoder_2-item1.clip_pooled_prompt_embeds text_encoder_2-item1.clip_pooled_prompt_embeds: 1,216B, BPFP=7.6000 Using per-key quantization points (text_encoder_2-item1.clip_prompt_embeds: torch.Size([256])) for text_encoder_2-item1.clip_prompt_embeds text_encoder_2-item1.clip_prompt_embeds: 10,920B, BPFP=0.8864 Using per-key quantization points (text_encoder_3-item2.t5_prompt_embeds: torch.Size([256])) for text_encoder_3-item2.t5_prompt_embeds text_encoder_3-item2.t5_prompt_embeds: 26,776B, BPFP=0.6792 Using per-key quantization points (text_encoder-item3.clip_pooled_prompt_embeds: torch.Size([256])) for text_encoder-item3.clip_pooled_prompt_embeds text_encoder-item3.clip_pooled_prompt_embeds: 760B, BPFP=7.9167 Using per-key quantization points (text_encoder-item3.clip_prompt_embeds: torch.Size([256])) for text_encoder-item3.clip_prompt_embeds text_encoder-item3.clip_prompt_embeds: 4,024B, BPFP=0.5444 Using per-key quantization points (text_encoder_2-item4.clip_pooled: torch.Size([256])) for text_encoder_2-item4.clip_pooled_prompt_embeds text_encoder_2-item4.clip_pooled_prompt_embeds: 1,268B, BPFP=7.9250 Using per-key quantization points (text_encoder_2-item4.clip_prompt: torch.Size([256])) for text_encoder_2-item4.clip_prompt_embeds text_encoder_2-item4.clip_prompt_embeds: 6,996B, BPFP=0.5679 Using per-key quantization points (text_encoder_3-item5.t5_prompt_embeds: torch.Size([256])) for text_encoder_3-item5.t5_prompt_embeds text_encoder_3-item5.t5_prompt_embeds: 18,416B, BPFP=0.4671 Using per-key quantization points (vae.encoder_f0: torch.Size([256])) for vae.encoder_f0 vae.encoder_f0: 8,856B, BPFP=0.1351 Using per-key quantization points (vae.encoder_f1: torch.Size([256])) for vae.encoder_f1 vae.encoder_f1: 8,856B, BPFP=0.1351 Using per-key quantization points (vae.decoder: torch.Size([256])) for vae.decoder vae.decoder: 5,060B, BPFP=0.1544 ⌛️ [2/4] FRONTEND: Frontend time: 0.292s (Pack+Encode) [3/4] BACKEND: Decode + Unpack... Using per-key quantization points (text_encoder-item0.clip_pooled_prompt_embeds: torch.Size([256])) for text_encoder-item0.clip_pooled_prompt_embeds Using per-key quantization points (text_encoder-item0.clip_prompt_embeds: torch.Size([256])) for text_encoder-item0.clip_prompt_embeds Using per-key quantization points (text_encoder_2-item1.clip_pooled_prompt_embeds: torch.Size([256])) for text_encoder_2-item1.clip_pooled_prompt_embeds Using per-key quantization points (text_encoder_2-item1.clip_prompt_embeds: torch.Size([256])) for text_encoder_2-item1.clip_prompt_embeds Using per-key quantization points (text_encoder_3-item2.t5_prompt_embeds: torch.Size([256])) for text_encoder_3-item2.t5_prompt_embeds Using per-key quantization points (text_encoder-item3.clip_pooled_prompt_embeds: torch.Size([256])) for text_encoder-item3.clip_pooled_prompt_embeds Using per-key quantization points (text_encoder-item3.clip_prompt_embeds: torch.Size([256])) for text_encoder-item3.clip_prompt_embeds Using per-key quantization points (text_encoder_2-item4.clip_pooled: torch.Size([256])) for text_encoder_2-item4.clip_pooled_prompt_embeds Using per-key quantization points (text_encoder_2-item4.clip_prompt: torch.Size([256])) for text_encoder_2-item4.clip_prompt_embeds Using per-key quantization points (text_encoder_3-item5.t5_prompt_embeds: torch.Size([256])) for text_encoder_3-item5.t5_prompt_embeds Using per-key quantization points (vae.encoder_f0: torch.Size([256])) for vae.encoder_f0 Using per-key quantization points (vae.encoder_f1: torch.Size([256])) for vae.encoder_f1 Using per-key quantization points (vae.decoder: torch.Size([256])) for vae.decoder IndividualUnPacker: text_encoder-item0.clip_pooled_prompt_embeds: torch.Size([768]) text_encoder-item0.clip_prompt_embeds: torch.Size([77, 768]) text_encoder_2-item1.clip_pooled_prompt_embeds: torch.Size([1280]) text_encoder_2-item1.clip_prompt_embeds: torch.Size([77, 1280]) text_encoder_3-item2.t5_prompt_embeds: torch.Size([77, 4096]) text_encoder-item3.clip_pooled_prompt_embeds: torch.Size([768]) text_encoder-item3.clip_prompt_embeds: torch.Size([77, 768]) text_encoder_2-item4.clip_pooled_prompt_embeds: torch.Size([1280]) text_encoder_2-item4.clip_prompt_embeds: torch.Size([77, 1280]) text_encoder_3-item5.t5_prompt_embeds: torch.Size([77, 4096]) vae.decoder: torch.Size([16, 128, 128]) vae.encoder_f0: torch.Size([32, 128, 128]) vae.encoder_f1: torch.Size([32, 128, 128]) ⌛️ [3/4] BACKEND: Backend time: 0.446s [4/4] METRICS: Computing MSE Breakdown... Using per-key quantization points (text_encoder-item0.clip_pooled_prompt_embeds: torch.Size([256])) for text_encoder-item0.clip_pooled_prompt_embeds Using per-key quantization points (text_encoder-item0.clip_prompt_embeds: torch.Size([256])) for text_encoder-item0.clip_prompt_embeds Using per-key quantization points (text_encoder_2-item1.clip_pooled_prompt_embeds: torch.Size([256])) for text_encoder_2-item1.clip_pooled_prompt_embeds Using per-key quantization points (text_encoder_2-item1.clip_prompt_embeds: torch.Size([256])) for text_encoder_2-item1.clip_prompt_embeds Using per-key quantization points (text_encoder_3-item2.t5_prompt_embeds: torch.Size([256])) for text_encoder_3-item2.t5_prompt_embeds Using per-key quantization points (text_encoder-item3.clip_pooled_prompt_embeds: torch.Size([256])) for text_encoder-item3.clip_pooled_prompt_embeds Using per-key quantization points (text_encoder-item3.clip_prompt_embeds: torch.Size([256])) for text_encoder-item3.clip_prompt_embeds Using per-key quantization points (text_encoder_2-item4.clip_pooled: torch.Size([256])) for text_encoder_2-item4.clip_pooled_prompt_embeds Using per-key quantization points (text_encoder_2-item4.clip_prompt: torch.Size([256])) for text_encoder_2-item4.clip_prompt_embeds Using per-key quantization points (text_encoder_3-item5.t5_prompt_embeds: torch.Size([256])) for text_encoder_3-item5.t5_prompt_embeds Using per-key quantization points (vae.encoder_f0: torch.Size([256])) for vae.encoder_f0 Using per-key quantization points (vae.encoder_f1: torch.Size([256])) for vae.encoder_f1 Using per-key quantization points (vae.decoder: torch.Size([256])) for vae.decoder IndividualUnPacker: text_encoder-item0.clip_pooled_prompt_embeds: torch.Size([768]) text_encoder-item0.clip_prompt_embeds: torch.Size([77, 768]) text_encoder_2-item1.clip_pooled_prompt_embeds: torch.Size([1280]) text_encoder_2-item1.clip_prompt_embeds: torch.Size([77, 1280]) text_encoder_3-item2.t5_prompt_embeds: torch.Size([77, 4096]) text_encoder-item3.clip_pooled_prompt_embeds: torch.Size([768]) text_encoder-item3.clip_prompt_embeds: torch.Size([77, 768]) text_encoder_2-item4.clip_pooled_prompt_embeds: torch.Size([1280]) text_encoder_2-item4.clip_prompt_embeds: torch.Size([77, 1280]) text_encoder_3-item5.t5_prompt_embeds: torch.Size([77, 4096]) vae.decoder: torch.Size([16, 128, 128]) vae.encoder_f0: torch.Size([32, 128, 128]) vae.encoder_f1: torch.Size([32, 128, 128]) Per-key MSE Breakdown (Quant=Truncation+Quantization, Codec=Compression): Key Quant-MSE Total-MSE ------------------------------------------------------------------------------------- text_encoder-item0.clip_pooled_prompt_embeds 0.00020323 0.95281442 text_encoder-item0.clip_prompt_embeds 0.00022402 47.98036729 text_encoder_2-item1.clip_pooled_prompt_embeds 0.00024964 1.11377983 text_encoder_2-item1.clip_prompt_embeds 0.00015987 0.12499061 text_encoder_3-item2.t5_prompt_embeds 0.00000778 0.00295826 text_encoder-item3.clip_pooled_prompt_embeds 0.00841799 1.01420029 text_encoder-item3.clip_prompt_embeds 0.00023247 23.90570887 text_encoder_2-item4.clip_pooled_prompt_embeds 0.00016751 0.67051430 text_encoder_2-item4.clip_prompt_embeds 0.00009830 0.47049568 text_encoder_3-item5.t5_prompt_embeds 0.00001263 0.00227117 vae.encoder_f0 1.19630027 6.77417183 vae.encoder_f1 1.19630098 6.77337694 vae.decoder 0.00023596 0.04179461 ------------------------------------------------------------------------------------- TOTAL 0.55486265 5.05484472 (elements=2,260,992) ---------------------- -------------------------------------------------------- SAMPLE-WISE STATISTICS ---------------------- -------------------------------------------------------- Handler sd35 Strategy individual Architecture hyperprior-featurecoding ---------------------- -------------------------------------------------------- Total Elements 2260992 Total Bytes 100300 BPFP 0.3549 bits/point EBPFP 0.7098 equivalent bits/point MSE 5.054845 ---------------------- -------------------------------------------------------- Time: 0.746s Load: 0.007s, Pack+Encode: 0.292s, Decode+Unpack: 0.446s ---------------------- -------------------------------------------------------- Restored Feature Format: [Dict] with 8 keys key['text_encoder-item0']: [Dict] with 2 keys key['clip_pooled_prompt_embeds']: [Tensor] shape=torch.Size([768]), dtype=torch.float16, device=cpu key['clip_prompt_embeds']: [Tensor] shape=torch.Size([77, 768]), dtype=torch.float16, device=cpu key['text_encoder_2-item1']: [Dict] with 2 keys key['clip_pooled_prompt_embeds']: [Tensor] shape=torch.Size([1280]), dtype=torch.float16, device=cpu key['clip_prompt_embeds']: [Tensor] shape=torch.Size([77, 1280]), dtype=torch.float16, device=cpu key['text_encoder_3-item2']: [Dict] with 1 keys key['t5_prompt_embeds']: [Tensor] shape=torch.Size([77, 4096]), dtype=torch.float16, device=cpu key['text_encoder-item3']: [Dict] with 2 keys key['clip_pooled_prompt_embeds']: [Tensor] shape=torch.Size([768]), dtype=torch.float16, device=cpu key['clip_prompt_embeds']: [Tensor] shape=torch.Size([77, 768]), dtype=torch.float16, device=cpu key['text_encoder_2-item4']: [Dict] with 2 keys key['clip_pooled_prompt_embeds']: [Tensor] shape=torch.Size([1280]), dtype=torch.float16, device=cpu key['clip_prompt_embeds']: [Tensor] shape=torch.Size([77, 1280]), dtype=torch.float16, device=cpu key['text_encoder_3-item5']: [Dict] with 1 keys key['t5_prompt_embeds']: [Tensor] shape=torch.Size([77, 4096]), dtype=torch.float16, device=cpu key['vae.decoder']: [Tensor] shape=torch.Size([16, 128, 128]), dtype=torch.float16, device=cpu key['vae.encoder']: [Dict] with 2 keys key['vae.encoder_f0']: [Tensor] shape=torch.Size([32, 128, 128]), dtype=torch.float16, device=cpu key['vae.encoder_f1']: [Tensor] shape=torch.Size([32, 128, 128]), dtype=torch.float16, device=cpu 💾 Converting with 5.0548 MSE: from ../datasets/FeatureCoding-StableDiffusion3.5/100samples_features/000000003661.zst to output-fixed/sd35/lambda0.004/hyperprior-featurecoding-8bit-individual/sd35cond/000000003661.zst 💪 Processing: ../datasets/FeatureCoding-StableDiffusion3.5/100samples_features/000000011149.zst (4/100) [1/4] FRONTEND: Loading features from ../datasets/FeatureCoding-StableDiffusion3.5/100samples_features/000000011149.zst... Original data structure: root: [Dict] with 8 keys key['text_encoder-item0']: [Dict] with 2 keys key['clip_pooled_prompt_embeds']: [Tensor] shape=torch.Size([768]), dtype=torch.float16, device=cpu key['clip_prompt_embeds']: [Tensor] shape=torch.Size([77, 768]), dtype=torch.float16, device=cpu key['text_encoder_2-item1']: [Dict] with 2 keys key['clip_pooled_prompt_embeds']: [Tensor] shape=torch.Size([1280]), dtype=torch.float16, device=cpu key['clip_prompt_embeds']: [Tensor] shape=torch.Size([77, 1280]), dtype=torch.float16, device=cpu key['text_encoder_3-item2']: [Dict] with 1 keys key['t5_prompt_embeds']: [Tensor] shape=torch.Size([77, 4096]), dtype=torch.float16, device=cpu key['text_encoder-item3']: [Dict] with 2 keys key['clip_pooled_prompt_embeds']: [Tensor] shape=torch.Size([768]), dtype=torch.float16, device=cpu key['clip_prompt_embeds']: [Tensor] shape=torch.Size([77, 768]), dtype=torch.float16, device=cpu key['text_encoder_2-item4']: [Dict] with 2 keys key['clip_pooled_prompt_embeds']: [Tensor] shape=torch.Size([1280]), dtype=torch.float16, device=cpu key['clip_prompt_embeds']: [Tensor] shape=torch.Size([77, 1280]), dtype=torch.float16, device=cpu key['text_encoder_3-item5']: [Dict] with 1 keys key['t5_prompt_embeds']: [Tensor] shape=torch.Size([77, 4096]), dtype=torch.float16, device=cpu key['vae.encoder-item6']: [Dict] with 2 keys key['vae.encoder_f0']: [Tensor] shape=torch.Size([32, 128, 128]), dtype=torch.float16, device=cpu key['vae.encoder_f1']: [Tensor] shape=torch.Size([32, 128, 128]), dtype=torch.float16, device=cpu key['vae.decoder']: [Tensor] shape=torch.Size([16, 128, 128]), dtype=torch.float16, device=cpu ⌛️ [1/4] FRONTEND: Load time: 0.009s ------------------------------------------------------------ SD3.5 Features Summary ------------------------------------------------------------ Number of text encoders: 3 Encoder names: ['text_encoder', 'text_encoder_2', 'text_encoder_3'] Has VAE latents: True Has VAE encoder features: True Data type: torch.float16 text_encoder-item0: clip_pooled_prompt_embeds: torch.Size([768]), torch.float16 clip_prompt_embeds: torch.Size([77, 768]), torch.float16 text_encoder_2-item1: clip_pooled_prompt_embeds: torch.Size([1280]), torch.float16 clip_prompt_embeds: torch.Size([77, 1280]), torch.float16 text_encoder_3-item2: t5_prompt_embeds: torch.Size([77, 4096]), torch.float16 text_encoder-item3: clip_pooled_prompt_embeds: torch.Size([768]), torch.float16 clip_prompt_embeds: torch.Size([77, 768]), torch.float16 text_encoder_2-item4: clip_pooled_prompt_embeds: torch.Size([1280]), torch.float16 clip_prompt_embeds: torch.Size([77, 1280]), torch.float16 text_encoder_3-item5: t5_prompt_embeds: torch.Size([77, 4096]), torch.float16 vae.decoder latents: torch.Size([16, 128, 128]) vae.encoder features: vae.encoder_f0: torch.Size([32, 128, 128]), torch.float16 vae.encoder_f1: torch.Size([32, 128, 128]), torch.float16 ------------------------------------------------------------ [2/4] FRONTEND: Pack + Encode (strategy: individual)... IndividualPacker: text_encoder-item0.clip_pooled_prompt_embeds: torch.Size([768])->torch.Size([1, 1, 1, 768]) text_encoder-item0.clip_prompt_embeds: torch.Size([77, 768])->torch.Size([1, 1, 77, 768]) text_encoder_2-item1.clip_pooled_prompt_embeds: torch.Size([1280])->torch.Size([1, 1, 1, 1280]) text_encoder_2-item1.clip_prompt_embeds: torch.Size([77, 1280])->torch.Size([1, 1, 77, 1280]) text_encoder_3-item2.t5_prompt_embeds: torch.Size([77, 4096])->torch.Size([1, 1, 77, 4096]) text_encoder-item3.clip_pooled_prompt_embeds: torch.Size([768])->torch.Size([1, 1, 1, 768]) text_encoder-item3.clip_prompt_embeds: torch.Size([77, 768])->torch.Size([1, 1, 77, 768]) text_encoder_2-item4.clip_pooled_prompt_embeds: torch.Size([1280])->torch.Size([1, 1, 1, 1280]) text_encoder_2-item4.clip_prompt_embeds: torch.Size([77, 1280])->torch.Size([1, 1, 77, 1280]) text_encoder_3-item5.t5_prompt_embeds: torch.Size([77, 4096])->torch.Size([1, 1, 77, 4096]) vae.encoder_f0: torch.Size([32, 128, 128])->torch.Size([1, 1, 512, 1024]) vae.encoder_f1: torch.Size([32, 128, 128])->torch.Size([1, 1, 512, 1024]) vae.decoder: torch.Size([16, 128, 128])->torch.Size([1, 1, 512, 512]) Using per-key quantization points (text_encoder-item0.clip_pooled_prompt_embeds: torch.Size([256])) for text_encoder-item0.clip_pooled_prompt_embeds text_encoder-item0.clip_pooled_prompt_embeds: 728B, BPFP=7.5833 Using per-key quantization points (text_encoder-item0.clip_prompt_embeds: torch.Size([256])) for text_encoder-item0.clip_prompt_embeds text_encoder-item0.clip_prompt_embeds: 6,756B, BPFP=0.9140 Using per-key quantization points (text_encoder_2-item1.clip_pooled_prompt_embeds: torch.Size([256])) for text_encoder_2-item1.clip_pooled_prompt_embeds text_encoder_2-item1.clip_pooled_prompt_embeds: 1,216B, BPFP=7.6000 Using per-key quantization points (text_encoder_2-item1.clip_prompt_embeds: torch.Size([256])) for text_encoder_2-item1.clip_prompt_embeds text_encoder_2-item1.clip_prompt_embeds: 11,572B, BPFP=0.9393 Using per-key quantization points (text_encoder_3-item2.t5_prompt_embeds: torch.Size([256])) for text_encoder_3-item2.t5_prompt_embeds text_encoder_3-item2.t5_prompt_embeds: 30,700B, BPFP=0.7787 Using per-key quantization points (text_encoder-item3.clip_pooled_prompt_embeds: torch.Size([256])) for text_encoder-item3.clip_pooled_prompt_embeds text_encoder-item3.clip_pooled_prompt_embeds: 760B, BPFP=7.9167 Using per-key quantization points (text_encoder-item3.clip_prompt_embeds: torch.Size([256])) for text_encoder-item3.clip_prompt_embeds text_encoder-item3.clip_prompt_embeds: 4,024B, BPFP=0.5444 Using per-key quantization points (text_encoder_2-item4.clip_pooled: torch.Size([256])) for text_encoder_2-item4.clip_pooled_prompt_embeds text_encoder_2-item4.clip_pooled_prompt_embeds: 1,268B, BPFP=7.9250 Using per-key quantization points (text_encoder_2-item4.clip_prompt: torch.Size([256])) for text_encoder_2-item4.clip_prompt_embeds text_encoder_2-item4.clip_prompt_embeds: 6,996B, BPFP=0.5679 Using per-key quantization points (text_encoder_3-item5.t5_prompt_embeds: torch.Size([256])) for text_encoder_3-item5.t5_prompt_embeds text_encoder_3-item5.t5_prompt_embeds: 18,416B, BPFP=0.4671 Using per-key quantization points (vae.encoder_f0: torch.Size([256])) for vae.encoder_f0 vae.encoder_f0: 16,276B, BPFP=0.2484 Using per-key quantization points (vae.encoder_f1: torch.Size([256])) for vae.encoder_f1 vae.encoder_f1: 16,276B, BPFP=0.2484 Using per-key quantization points (vae.decoder: torch.Size([256])) for vae.decoder vae.decoder: 9,496B, BPFP=0.2898 ⌛️ [2/4] FRONTEND: Frontend time: 0.294s (Pack+Encode) [3/4] BACKEND: Decode + Unpack... Using per-key quantization points (text_encoder-item0.clip_pooled_prompt_embeds: torch.Size([256])) for text_encoder-item0.clip_pooled_prompt_embeds Using per-key quantization points (text_encoder-item0.clip_prompt_embeds: torch.Size([256])) for text_encoder-item0.clip_prompt_embeds Using per-key quantization points (text_encoder_2-item1.clip_pooled_prompt_embeds: torch.Size([256])) for text_encoder_2-item1.clip_pooled_prompt_embeds Using per-key quantization points (text_encoder_2-item1.clip_prompt_embeds: torch.Size([256])) for text_encoder_2-item1.clip_prompt_embeds Using per-key quantization points (text_encoder_3-item2.t5_prompt_embeds: torch.Size([256])) for text_encoder_3-item2.t5_prompt_embeds Using per-key quantization points (text_encoder-item3.clip_pooled_prompt_embeds: torch.Size([256])) for text_encoder-item3.clip_pooled_prompt_embeds Using per-key quantization points (text_encoder-item3.clip_prompt_embeds: torch.Size([256])) for text_encoder-item3.clip_prompt_embeds Using per-key quantization points (text_encoder_2-item4.clip_pooled: torch.Size([256])) for text_encoder_2-item4.clip_pooled_prompt_embeds Using per-key quantization points (text_encoder_2-item4.clip_prompt: torch.Size([256])) for text_encoder_2-item4.clip_prompt_embeds Using per-key quantization points (text_encoder_3-item5.t5_prompt_embeds: torch.Size([256])) for text_encoder_3-item5.t5_prompt_embeds Using per-key quantization points (vae.encoder_f0: torch.Size([256])) for vae.encoder_f0 Using per-key quantization points (vae.encoder_f1: torch.Size([256])) for vae.encoder_f1 Using per-key quantization points (vae.decoder: torch.Size([256])) for vae.decoder IndividualUnPacker: text_encoder-item0.clip_pooled_prompt_embeds: torch.Size([768]) text_encoder-item0.clip_prompt_embeds: torch.Size([77, 768]) text_encoder_2-item1.clip_pooled_prompt_embeds: torch.Size([1280]) text_encoder_2-item1.clip_prompt_embeds: torch.Size([77, 1280]) text_encoder_3-item2.t5_prompt_embeds: torch.Size([77, 4096]) text_encoder-item3.clip_pooled_prompt_embeds: torch.Size([768]) text_encoder-item3.clip_prompt_embeds: torch.Size([77, 768]) text_encoder_2-item4.clip_pooled_prompt_embeds: torch.Size([1280]) text_encoder_2-item4.clip_prompt_embeds: torch.Size([77, 1280]) text_encoder_3-item5.t5_prompt_embeds: torch.Size([77, 4096]) vae.decoder: torch.Size([16, 128, 128]) vae.encoder_f0: torch.Size([32, 128, 128]) vae.encoder_f1: torch.Size([32, 128, 128]) ⌛️ [3/4] BACKEND: Backend time: 0.446s [4/4] METRICS: Computing MSE Breakdown... Using per-key quantization points (text_encoder-item0.clip_pooled_prompt_embeds: torch.Size([256])) for text_encoder-item0.clip_pooled_prompt_embeds Using per-key quantization points (text_encoder-item0.clip_prompt_embeds: torch.Size([256])) for text_encoder-item0.clip_prompt_embeds Using per-key quantization points (text_encoder_2-item1.clip_pooled_prompt_embeds: torch.Size([256])) for text_encoder_2-item1.clip_pooled_prompt_embeds Using per-key quantization points (text_encoder_2-item1.clip_prompt_embeds: torch.Size([256])) for text_encoder_2-item1.clip_prompt_embeds Using per-key quantization points (text_encoder_3-item2.t5_prompt_embeds: torch.Size([256])) for text_encoder_3-item2.t5_prompt_embeds Using per-key quantization points (text_encoder-item3.clip_pooled_prompt_embeds: torch.Size([256])) for text_encoder-item3.clip_pooled_prompt_embeds Using per-key quantization points (text_encoder-item3.clip_prompt_embeds: torch.Size([256])) for text_encoder-item3.clip_prompt_embeds Using per-key quantization points (text_encoder_2-item4.clip_pooled: torch.Size([256])) for text_encoder_2-item4.clip_pooled_prompt_embeds Using per-key quantization points (text_encoder_2-item4.clip_prompt: torch.Size([256])) for text_encoder_2-item4.clip_prompt_embeds Using per-key quantization points (text_encoder_3-item5.t5_prompt_embeds: torch.Size([256])) for text_encoder_3-item5.t5_prompt_embeds Using per-key quantization points (vae.encoder_f0: torch.Size([256])) for vae.encoder_f0 Using per-key quantization points (vae.encoder_f1: torch.Size([256])) for vae.encoder_f1 Using per-key quantization points (vae.decoder: torch.Size([256])) for vae.decoder IndividualUnPacker: text_encoder-item0.clip_pooled_prompt_embeds: torch.Size([768]) text_encoder-item0.clip_prompt_embeds: torch.Size([77, 768]) text_encoder_2-item1.clip_pooled_prompt_embeds: torch.Size([1280]) text_encoder_2-item1.clip_prompt_embeds: torch.Size([77, 1280]) text_encoder_3-item2.t5_prompt_embeds: torch.Size([77, 4096]) text_encoder-item3.clip_pooled_prompt_embeds: torch.Size([768]) text_encoder-item3.clip_prompt_embeds: torch.Size([77, 768]) text_encoder_2-item4.clip_pooled_prompt_embeds: torch.Size([1280]) text_encoder_2-item4.clip_prompt_embeds: torch.Size([77, 1280]) text_encoder_3-item5.t5_prompt_embeds: torch.Size([77, 4096]) vae.decoder: torch.Size([16, 128, 128]) vae.encoder_f0: torch.Size([32, 128, 128]) vae.encoder_f1: torch.Size([32, 128, 128]) Per-key MSE Breakdown (Quant=Truncation+Quantization, Codec=Compression): Key Quant-MSE Total-MSE ------------------------------------------------------------------------------------- text_encoder-item0.clip_pooled_prompt_embeds 0.00018694 0.95865774 text_encoder-item0.clip_prompt_embeds 0.00030342 23.95113171 text_encoder_2-item1.clip_pooled_prompt_embeds 0.00066702 0.87567692 text_encoder_2-item1.clip_prompt_embeds 0.00020355 0.12840412 text_encoder_3-item2.t5_prompt_embeds 0.00000815 0.00373103 text_encoder-item3.clip_pooled_prompt_embeds 0.00841799 1.01420029 text_encoder-item3.clip_prompt_embeds 0.00023247 23.90570887 text_encoder_2-item4.clip_pooled_prompt_embeds 0.00016751 0.67051430 text_encoder_2-item4.clip_prompt_embeds 0.00009830 0.47049568 text_encoder_3-item5.t5_prompt_embeds 0.00001263 0.00227117 vae.encoder_f0 0.00586287 1.98412931 vae.encoder_f1 0.00587438 1.98380661 vae.decoder 0.00017677 0.08426298 ------------------------------------------------------------------------------------- TOTAL 0.00277565 2.21005083 (elements=2,260,992) ---------------------- -------------------------------------------------------- SAMPLE-WISE STATISTICS ---------------------- -------------------------------------------------------- Handler sd35 Strategy individual Architecture hyperprior-featurecoding ---------------------- -------------------------------------------------------- Total Elements 2260992 Total Bytes 124484 BPFP 0.4405 bits/point EBPFP 0.8809 equivalent bits/point MSE 2.210051 ---------------------- -------------------------------------------------------- Time: 0.748s Load: 0.009s, Pack+Encode: 0.294s, Decode+Unpack: 0.446s ---------------------- -------------------------------------------------------- Restored Feature Format: [Dict] with 8 keys key['text_encoder-item0']: [Dict] with 2 keys key['clip_pooled_prompt_embeds']: [Tensor] shape=torch.Size([768]), dtype=torch.float16, device=cpu key['clip_prompt_embeds']: [Tensor] shape=torch.Size([77, 768]), dtype=torch.float16, device=cpu key['text_encoder_2-item1']: [Dict] with 2 keys key['clip_pooled_prompt_embeds']: [Tensor] shape=torch.Size([1280]), dtype=torch.float16, device=cpu key['clip_prompt_embeds']: [Tensor] shape=torch.Size([77, 1280]), dtype=torch.float16, device=cpu key['text_encoder_3-item2']: [Dict] with 1 keys key['t5_prompt_embeds']: [Tensor] shape=torch.Size([77, 4096]), dtype=torch.float16, device=cpu key['text_encoder-item3']: [Dict] with 2 keys key['clip_pooled_prompt_embeds']: [Tensor] shape=torch.Size([768]), dtype=torch.float16, device=cpu key['clip_prompt_embeds']: [Tensor] shape=torch.Size([77, 768]), dtype=torch.float16, device=cpu key['text_encoder_2-item4']: [Dict] with 2 keys key['clip_pooled_prompt_embeds']: [Tensor] shape=torch.Size([1280]), dtype=torch.float16, device=cpu key['clip_prompt_embeds']: [Tensor] shape=torch.Size([77, 1280]), dtype=torch.float16, device=cpu key['text_encoder_3-item5']: [Dict] with 1 keys key['t5_prompt_embeds']: [Tensor] shape=torch.Size([77, 4096]), dtype=torch.float16, device=cpu key['vae.decoder']: [Tensor] shape=torch.Size([16, 128, 128]), dtype=torch.float16, device=cpu key['vae.encoder']: [Dict] with 2 keys key['vae.encoder_f0']: [Tensor] shape=torch.Size([32, 128, 128]), dtype=torch.float16, device=cpu key['vae.encoder_f1']: [Tensor] shape=torch.Size([32, 128, 128]), dtype=torch.float16, device=cpu 💾 Converting with 2.2101 MSE: from ../datasets/FeatureCoding-StableDiffusion3.5/100samples_features/000000011149.zst to output-fixed/sd35/lambda0.004/hyperprior-featurecoding-8bit-individual/sd35cond/000000011149.zst 💪 Processing: ../datasets/FeatureCoding-StableDiffusion3.5/100samples_features/000000023937.zst (5/100) [1/4] FRONTEND: Loading features from ../datasets/FeatureCoding-StableDiffusion3.5/100samples_features/000000023937.zst... Original data structure: root: [Dict] with 8 keys key['text_encoder-item0']: [Dict] with 2 keys key['clip_pooled_prompt_embeds']: [Tensor] shape=torch.Size([768]), dtype=torch.float16, device=cpu key['clip_prompt_embeds']: [Tensor] shape=torch.Size([77, 768]), dtype=torch.float16, device=cpu key['text_encoder_2-item1']: [Dict] with 2 keys key['clip_pooled_prompt_embeds']: [Tensor] shape=torch.Size([1280]), dtype=torch.float16, device=cpu key['clip_prompt_embeds']: [Tensor] shape=torch.Size([77, 1280]), dtype=torch.float16, device=cpu key['text_encoder_3-item2']: [Dict] with 1 keys key['t5_prompt_embeds']: [Tensor] shape=torch.Size([77, 4096]), dtype=torch.float16, device=cpu key['text_encoder-item3']: [Dict] with 2 keys key['clip_pooled_prompt_embeds']: [Tensor] shape=torch.Size([768]), dtype=torch.float16, device=cpu key['clip_prompt_embeds']: [Tensor] shape=torch.Size([77, 768]), dtype=torch.float16, device=cpu key['text_encoder_2-item4']: [Dict] with 2 keys key['clip_pooled_prompt_embeds']: [Tensor] shape=torch.Size([1280]), dtype=torch.float16, device=cpu key['clip_prompt_embeds']: [Tensor] shape=torch.Size([77, 1280]), dtype=torch.float16, device=cpu key['text_encoder_3-item5']: [Dict] with 1 keys key['t5_prompt_embeds']: [Tensor] shape=torch.Size([77, 4096]), dtype=torch.float16, device=cpu key['vae.encoder-item6']: [Dict] with 2 keys key['vae.encoder_f0']: [Tensor] shape=torch.Size([32, 128, 128]), dtype=torch.float16, device=cpu key['vae.encoder_f1']: [Tensor] shape=torch.Size([32, 128, 128]), dtype=torch.float16, device=cpu key['vae.decoder']: [Tensor] shape=torch.Size([16, 128, 128]), dtype=torch.float16, device=cpu ⌛️ [1/4] FRONTEND: Load time: 0.008s ------------------------------------------------------------ SD3.5 Features Summary ------------------------------------------------------------ Number of text encoders: 3 Encoder names: ['text_encoder', 'text_encoder_2', 'text_encoder_3'] Has VAE latents: True Has VAE encoder features: True Data type: torch.float16 text_encoder-item0: clip_pooled_prompt_embeds: torch.Size([768]), torch.float16 clip_prompt_embeds: torch.Size([77, 768]), torch.float16 text_encoder_2-item1: clip_pooled_prompt_embeds: torch.Size([1280]), torch.float16 clip_prompt_embeds: torch.Size([77, 1280]), torch.float16 text_encoder_3-item2: t5_prompt_embeds: torch.Size([77, 4096]), torch.float16 text_encoder-item3: clip_pooled_prompt_embeds: torch.Size([768]), torch.float16 clip_prompt_embeds: torch.Size([77, 768]), torch.float16 text_encoder_2-item4: clip_pooled_prompt_embeds: torch.Size([1280]), torch.float16 clip_prompt_embeds: torch.Size([77, 1280]), torch.float16 text_encoder_3-item5: t5_prompt_embeds: torch.Size([77, 4096]), torch.float16 vae.decoder latents: torch.Size([16, 128, 128]) vae.encoder features: vae.encoder_f0: torch.Size([32, 128, 128]), torch.float16 vae.encoder_f1: torch.Size([32, 128, 128]), torch.float16 ------------------------------------------------------------ [2/4] FRONTEND: Pack + Encode (strategy: individual)... IndividualPacker: text_encoder-item0.clip_pooled_prompt_embeds: torch.Size([768])->torch.Size([1, 1, 1, 768]) text_encoder-item0.clip_prompt_embeds: torch.Size([77, 768])->torch.Size([1, 1, 77, 768]) text_encoder_2-item1.clip_pooled_prompt_embeds: torch.Size([1280])->torch.Size([1, 1, 1, 1280]) text_encoder_2-item1.clip_prompt_embeds: torch.Size([77, 1280])->torch.Size([1, 1, 77, 1280]) text_encoder_3-item2.t5_prompt_embeds: torch.Size([77, 4096])->torch.Size([1, 1, 77, 4096]) text_encoder-item3.clip_pooled_prompt_embeds: torch.Size([768])->torch.Size([1, 1, 1, 768]) text_encoder-item3.clip_prompt_embeds: torch.Size([77, 768])->torch.Size([1, 1, 77, 768]) text_encoder_2-item4.clip_pooled_prompt_embeds: torch.Size([1280])->torch.Size([1, 1, 1, 1280]) text_encoder_2-item4.clip_prompt_embeds: torch.Size([77, 1280])->torch.Size([1, 1, 77, 1280]) text_encoder_3-item5.t5_prompt_embeds: torch.Size([77, 4096])->torch.Size([1, 1, 77, 4096]) vae.encoder_f0: torch.Size([32, 128, 128])->torch.Size([1, 1, 512, 1024]) vae.encoder_f1: torch.Size([32, 128, 128])->torch.Size([1, 1, 512, 1024]) vae.decoder: torch.Size([16, 128, 128])->torch.Size([1, 1, 512, 512]) Using per-key quantization points (text_encoder-item0.clip_pooled_prompt_embeds: torch.Size([256])) for text_encoder-item0.clip_pooled_prompt_embeds text_encoder-item0.clip_pooled_prompt_embeds: 740B, BPFP=7.7083 Using per-key quantization points (text_encoder-item0.clip_prompt_embeds: torch.Size([256])) for text_encoder-item0.clip_prompt_embeds text_encoder-item0.clip_prompt_embeds: 5,888B, BPFP=0.7965 Using per-key quantization points (text_encoder_2-item1.clip_pooled_prompt_embeds: torch.Size([256])) for text_encoder_2-item1.clip_pooled_prompt_embeds text_encoder_2-item1.clip_pooled_prompt_embeds: 1,204B, BPFP=7.5250 Using per-key quantization points (text_encoder_2-item1.clip_prompt_embeds: torch.Size([256])) for text_encoder_2-item1.clip_prompt_embeds text_encoder_2-item1.clip_prompt_embeds: 11,472B, BPFP=0.9312 Using per-key quantization points (text_encoder_3-item2.t5_prompt_embeds: torch.Size([256])) for text_encoder_3-item2.t5_prompt_embeds text_encoder_3-item2.t5_prompt_embeds: 27,820B, BPFP=0.7057 Using per-key quantization points (text_encoder-item3.clip_pooled_prompt_embeds: torch.Size([256])) for text_encoder-item3.clip_pooled_prompt_embeds text_encoder-item3.clip_pooled_prompt_embeds: 760B, BPFP=7.9167 Using per-key quantization points (text_encoder-item3.clip_prompt_embeds: torch.Size([256])) for text_encoder-item3.clip_prompt_embeds text_encoder-item3.clip_prompt_embeds: 4,024B, BPFP=0.5444 Using per-key quantization points (text_encoder_2-item4.clip_pooled: torch.Size([256])) for text_encoder_2-item4.clip_pooled_prompt_embeds text_encoder_2-item4.clip_pooled_prompt_embeds: 1,268B, BPFP=7.9250 Using per-key quantization points (text_encoder_2-item4.clip_prompt: torch.Size([256])) for text_encoder_2-item4.clip_prompt_embeds text_encoder_2-item4.clip_prompt_embeds: 6,996B, BPFP=0.5679 Using per-key quantization points (text_encoder_3-item5.t5_prompt_embeds: torch.Size([256])) for text_encoder_3-item5.t5_prompt_embeds text_encoder_3-item5.t5_prompt_embeds: 18,416B, BPFP=0.4671 Using per-key quantization points (vae.encoder_f0: torch.Size([256])) for vae.encoder_f0 vae.encoder_f0: 14,292B, BPFP=0.2181 Using per-key quantization points (vae.encoder_f1: torch.Size([256])) for vae.encoder_f1 vae.encoder_f1: 14,292B, BPFP=0.2181 Using per-key quantization points (vae.decoder: torch.Size([256])) for vae.decoder vae.decoder: 6,272B, BPFP=0.1914 ⌛️ [2/4] FRONTEND: Frontend time: 0.292s (Pack+Encode) [3/4] BACKEND: Decode + Unpack... Using per-key quantization points (text_encoder-item0.clip_pooled_prompt_embeds: torch.Size([256])) for text_encoder-item0.clip_pooled_prompt_embeds Using per-key quantization points (text_encoder-item0.clip_prompt_embeds: torch.Size([256])) for text_encoder-item0.clip_prompt_embeds Using per-key quantization points (text_encoder_2-item1.clip_pooled_prompt_embeds: torch.Size([256])) for text_encoder_2-item1.clip_pooled_prompt_embeds Using per-key quantization points (text_encoder_2-item1.clip_prompt_embeds: torch.Size([256])) for text_encoder_2-item1.clip_prompt_embeds Using per-key quantization points (text_encoder_3-item2.t5_prompt_embeds: torch.Size([256])) for text_encoder_3-item2.t5_prompt_embeds Using per-key quantization points (text_encoder-item3.clip_pooled_prompt_embeds: torch.Size([256])) for text_encoder-item3.clip_pooled_prompt_embeds Using per-key quantization points (text_encoder-item3.clip_prompt_embeds: torch.Size([256])) for text_encoder-item3.clip_prompt_embeds Using per-key quantization points (text_encoder_2-item4.clip_pooled: torch.Size([256])) for text_encoder_2-item4.clip_pooled_prompt_embeds Using per-key quantization points (text_encoder_2-item4.clip_prompt: torch.Size([256])) for text_encoder_2-item4.clip_prompt_embeds Using per-key quantization points (text_encoder_3-item5.t5_prompt_embeds: torch.Size([256])) for text_encoder_3-item5.t5_prompt_embeds Using per-key quantization points (vae.encoder_f0: torch.Size([256])) for vae.encoder_f0 Using per-key quantization points (vae.encoder_f1: torch.Size([256])) for vae.encoder_f1 Using per-key quantization points (vae.decoder: torch.Size([256])) for vae.decoder IndividualUnPacker: text_encoder-item0.clip_pooled_prompt_embeds: torch.Size([768]) text_encoder-item0.clip_prompt_embeds: torch.Size([77, 768]) text_encoder_2-item1.clip_pooled_prompt_embeds: torch.Size([1280]) text_encoder_2-item1.clip_prompt_embeds: torch.Size([77, 1280]) text_encoder_3-item2.t5_prompt_embeds: torch.Size([77, 4096]) text_encoder-item3.clip_pooled_prompt_embeds: torch.Size([768]) text_encoder-item3.clip_prompt_embeds: torch.Size([77, 768]) text_encoder_2-item4.clip_pooled_prompt_embeds: torch.Size([1280]) text_encoder_2-item4.clip_prompt_embeds: torch.Size([77, 1280]) text_encoder_3-item5.t5_prompt_embeds: torch.Size([77, 4096]) vae.decoder: torch.Size([16, 128, 128]) vae.encoder_f0: torch.Size([32, 128, 128]) vae.encoder_f1: torch.Size([32, 128, 128]) ⌛️ [3/4] BACKEND: Backend time: 0.445s [4/4] METRICS: Computing MSE Breakdown... Using per-key quantization points (text_encoder-item0.clip_pooled_prompt_embeds: torch.Size([256])) for text_encoder-item0.clip_pooled_prompt_embeds Using per-key quantization points (text_encoder-item0.clip_prompt_embeds: torch.Size([256])) for text_encoder-item0.clip_prompt_embeds Using per-key quantization points (text_encoder_2-item1.clip_pooled_prompt_embeds: torch.Size([256])) for text_encoder_2-item1.clip_pooled_prompt_embeds Using per-key quantization points (text_encoder_2-item1.clip_prompt_embeds: torch.Size([256])) for text_encoder_2-item1.clip_prompt_embeds Using per-key quantization points (text_encoder_3-item2.t5_prompt_embeds: torch.Size([256])) for text_encoder_3-item2.t5_prompt_embeds Using per-key quantization points (text_encoder-item3.clip_pooled_prompt_embeds: torch.Size([256])) for text_encoder-item3.clip_pooled_prompt_embeds Using per-key quantization points (text_encoder-item3.clip_prompt_embeds: torch.Size([256])) for text_encoder-item3.clip_prompt_embeds Using per-key quantization points (text_encoder_2-item4.clip_pooled: torch.Size([256])) for text_encoder_2-item4.clip_pooled_prompt_embeds Using per-key quantization points (text_encoder_2-item4.clip_prompt: torch.Size([256])) for text_encoder_2-item4.clip_prompt_embeds Using per-key quantization points (text_encoder_3-item5.t5_prompt_embeds: torch.Size([256])) for text_encoder_3-item5.t5_prompt_embeds Using per-key quantization points (vae.encoder_f0: torch.Size([256])) for vae.encoder_f0 Using per-key quantization points (vae.encoder_f1: torch.Size([256])) for vae.encoder_f1 Using per-key quantization points (vae.decoder: torch.Size([256])) for vae.decoder IndividualUnPacker: text_encoder-item0.clip_pooled_prompt_embeds: torch.Size([768]) text_encoder-item0.clip_prompt_embeds: torch.Size([77, 768]) text_encoder_2-item1.clip_pooled_prompt_embeds: torch.Size([1280]) text_encoder_2-item1.clip_prompt_embeds: torch.Size([77, 1280]) text_encoder_3-item2.t5_prompt_embeds: torch.Size([77, 4096]) text_encoder-item3.clip_pooled_prompt_embeds: torch.Size([768]) text_encoder-item3.clip_prompt_embeds: torch.Size([77, 768]) text_encoder_2-item4.clip_pooled_prompt_embeds: torch.Size([1280]) text_encoder_2-item4.clip_prompt_embeds: torch.Size([77, 1280]) text_encoder_3-item5.t5_prompt_embeds: torch.Size([77, 4096]) vae.decoder: torch.Size([16, 128, 128]) vae.encoder_f0: torch.Size([32, 128, 128]) vae.encoder_f1: torch.Size([32, 128, 128]) Per-key MSE Breakdown (Quant=Truncation+Quantization, Codec=Compression): Key Quant-MSE Total-MSE ------------------------------------------------------------------------------------- text_encoder-item0.clip_pooled_prompt_embeds 0.00027243 0.90704974 text_encoder-item0.clip_prompt_embeds 0.00024120 24.47940764 text_encoder_2-item1.clip_pooled_prompt_embeds 0.00025189 0.97753305 text_encoder_2-item1.clip_prompt_embeds 0.00017312 0.11967713 text_encoder_3-item2.t5_prompt_embeds 0.00000806 0.00291543 text_encoder-item3.clip_pooled_prompt_embeds 0.00841799 1.01420029 text_encoder-item3.clip_prompt_embeds 0.00023247 23.90570887 text_encoder_2-item4.clip_pooled_prompt_embeds 0.00016751 0.67051430 text_encoder_2-item4.clip_prompt_embeds 0.00009830 0.47049568 text_encoder_3-item5.t5_prompt_embeds 0.00001263 0.00227117 vae.encoder_f0 0.00779453 2.41266918 vae.encoder_f1 0.00779802 2.41255021 vae.decoder 0.00023829 0.04979095 ------------------------------------------------------------------------------------- TOTAL 0.00367359 2.41820738 (elements=2,260,992) ---------------------- -------------------------------------------------------- SAMPLE-WISE STATISTICS ---------------------- -------------------------------------------------------- Handler sd35 Strategy individual Architecture hyperprior-featurecoding ---------------------- -------------------------------------------------------- Total Elements 2260992 Total Bytes 113444 BPFP 0.4014 bits/point EBPFP 0.8028 equivalent bits/point MSE 2.418207 ---------------------- -------------------------------------------------------- Time: 0.744s Load: 0.008s, Pack+Encode: 0.292s, Decode+Unpack: 0.445s ---------------------- -------------------------------------------------------- Restored Feature Format: [Dict] with 8 keys key['text_encoder-item0']: [Dict] with 2 keys key['clip_pooled_prompt_embeds']: [Tensor] shape=torch.Size([768]), dtype=torch.float16, device=cpu key['clip_prompt_embeds']: [Tensor] shape=torch.Size([77, 768]), dtype=torch.float16, device=cpu key['text_encoder_2-item1']: [Dict] with 2 keys key['clip_pooled_prompt_embeds']: [Tensor] shape=torch.Size([1280]), dtype=torch.float16, device=cpu key['clip_prompt_embeds']: [Tensor] shape=torch.Size([77, 1280]), dtype=torch.float16, device=cpu key['text_encoder_3-item2']: [Dict] with 1 keys key['t5_prompt_embeds']: [Tensor] shape=torch.Size([77, 4096]), dtype=torch.float16, device=cpu key['text_encoder-item3']: [Dict] with 2 keys key['clip_pooled_prompt_embeds']: [Tensor] shape=torch.Size([768]), dtype=torch.float16, device=cpu key['clip_prompt_embeds']: [Tensor] shape=torch.Size([77, 768]), dtype=torch.float16, device=cpu key['text_encoder_2-item4']: [Dict] with 2 keys key['clip_pooled_prompt_embeds']: [Tensor] shape=torch.Size([1280]), dtype=torch.float16, device=cpu key['clip_prompt_embeds']: [Tensor] shape=torch.Size([77, 1280]), dtype=torch.float16, device=cpu key['text_encoder_3-item5']: [Dict] with 1 keys key['t5_prompt_embeds']: [Tensor] shape=torch.Size([77, 4096]), dtype=torch.float16, device=cpu key['vae.decoder']: [Tensor] shape=torch.Size([16, 128, 128]), dtype=torch.float16, device=cpu key['vae.encoder']: [Dict] with 2 keys key['vae.encoder_f0']: [Tensor] shape=torch.Size([32, 128, 128]), dtype=torch.float16, device=cpu key['vae.encoder_f1']: [Tensor] shape=torch.Size([32, 128, 128]), dtype=torch.float16, device=cpu 💾 Converting with 2.4182 MSE: from ../datasets/FeatureCoding-StableDiffusion3.5/100samples_features/000000023937.zst to output-fixed/sd35/lambda0.004/hyperprior-featurecoding-8bit-individual/sd35cond/000000023937.zst 💪 Processing: ../datasets/FeatureCoding-StableDiffusion3.5/100samples_features/000000027620.zst (6/100) [1/4] FRONTEND: Loading features from ../datasets/FeatureCoding-StableDiffusion3.5/100samples_features/000000027620.zst... Original data structure: root: [Dict] with 8 keys key['text_encoder-item0']: [Dict] with 2 keys key['clip_pooled_prompt_embeds']: [Tensor] shape=torch.Size([768]), dtype=torch.float16, device=cpu key['clip_prompt_embeds']: [Tensor] shape=torch.Size([77, 768]), dtype=torch.float16, device=cpu key['text_encoder_2-item1']: [Dict] with 2 keys key['clip_pooled_prompt_embeds']: [Tensor] shape=torch.Size([1280]), dtype=torch.float16, device=cpu key['clip_prompt_embeds']: [Tensor] shape=torch.Size([77, 1280]), dtype=torch.float16, device=cpu key['text_encoder_3-item2']: [Dict] with 1 keys key['t5_prompt_embeds']: [Tensor] shape=torch.Size([77, 4096]), dtype=torch.float16, device=cpu key['text_encoder-item3']: [Dict] with 2 keys key['clip_pooled_prompt_embeds']: [Tensor] shape=torch.Size([768]), dtype=torch.float16, device=cpu key['clip_prompt_embeds']: [Tensor] shape=torch.Size([77, 768]), dtype=torch.float16, device=cpu key['text_encoder_2-item4']: [Dict] with 2 keys key['clip_pooled_prompt_embeds']: [Tensor] shape=torch.Size([1280]), dtype=torch.float16, device=cpu key['clip_prompt_embeds']: [Tensor] shape=torch.Size([77, 1280]), dtype=torch.float16, device=cpu key['text_encoder_3-item5']: [Dict] with 1 keys key['t5_prompt_embeds']: [Tensor] shape=torch.Size([77, 4096]), dtype=torch.float16, device=cpu key['vae.encoder-item6']: [Dict] with 2 keys key['vae.encoder_f0']: [Tensor] shape=torch.Size([32, 128, 128]), dtype=torch.float16, device=cpu key['vae.encoder_f1']: [Tensor] shape=torch.Size([32, 128, 128]), dtype=torch.float16, device=cpu key['vae.decoder']: [Tensor] shape=torch.Size([16, 128, 128]), dtype=torch.float16, device=cpu ⌛️ [1/4] FRONTEND: Load time: 0.008s ------------------------------------------------------------ SD3.5 Features Summary ------------------------------------------------------------ Number of text encoders: 3 Encoder names: ['text_encoder', 'text_encoder_2', 'text_encoder_3'] Has VAE latents: True Has VAE encoder features: True Data type: torch.float16 text_encoder-item0: clip_pooled_prompt_embeds: torch.Size([768]), torch.float16 clip_prompt_embeds: torch.Size([77, 768]), torch.float16 text_encoder_2-item1: clip_pooled_prompt_embeds: torch.Size([1280]), torch.float16 clip_prompt_embeds: torch.Size([77, 1280]), torch.float16 text_encoder_3-item2: t5_prompt_embeds: torch.Size([77, 4096]), torch.float16 text_encoder-item3: clip_pooled_prompt_embeds: torch.Size([768]), torch.float16 clip_prompt_embeds: torch.Size([77, 768]), torch.float16 text_encoder_2-item4: clip_pooled_prompt_embeds: torch.Size([1280]), torch.float16 clip_prompt_embeds: torch.Size([77, 1280]), torch.float16 text_encoder_3-item5: t5_prompt_embeds: torch.Size([77, 4096]), torch.float16 vae.decoder latents: torch.Size([16, 128, 128]) vae.encoder features: vae.encoder_f0: torch.Size([32, 128, 128]), torch.float16 vae.encoder_f1: torch.Size([32, 128, 128]), torch.float16 ------------------------------------------------------------ [2/4] FRONTEND: Pack + Encode (strategy: individual)... IndividualPacker: text_encoder-item0.clip_pooled_prompt_embeds: torch.Size([768])->torch.Size([1, 1, 1, 768]) text_encoder-item0.clip_prompt_embeds: torch.Size([77, 768])->torch.Size([1, 1, 77, 768]) text_encoder_2-item1.clip_pooled_prompt_embeds: torch.Size([1280])->torch.Size([1, 1, 1, 1280]) text_encoder_2-item1.clip_prompt_embeds: torch.Size([77, 1280])->torch.Size([1, 1, 77, 1280]) text_encoder_3-item2.t5_prompt_embeds: torch.Size([77, 4096])->torch.Size([1, 1, 77, 4096]) text_encoder-item3.clip_pooled_prompt_embeds: torch.Size([768])->torch.Size([1, 1, 1, 768]) text_encoder-item3.clip_prompt_embeds: torch.Size([77, 768])->torch.Size([1, 1, 77, 768]) text_encoder_2-item4.clip_pooled_prompt_embeds: torch.Size([1280])->torch.Size([1, 1, 1, 1280]) text_encoder_2-item4.clip_prompt_embeds: torch.Size([77, 1280])->torch.Size([1, 1, 77, 1280]) text_encoder_3-item5.t5_prompt_embeds: torch.Size([77, 4096])->torch.Size([1, 1, 77, 4096]) vae.encoder_f0: torch.Size([32, 128, 128])->torch.Size([1, 1, 512, 1024]) vae.encoder_f1: torch.Size([32, 128, 128])->torch.Size([1, 1, 512, 1024]) vae.decoder: torch.Size([16, 128, 128])->torch.Size([1, 1, 512, 512]) Using per-key quantization points (text_encoder-item0.clip_pooled_prompt_embeds: torch.Size([256])) for text_encoder-item0.clip_pooled_prompt_embeds text_encoder-item0.clip_pooled_prompt_embeds: 732B, BPFP=7.6250 Using per-key quantization points (text_encoder-item0.clip_prompt_embeds: torch.Size([256])) for text_encoder-item0.clip_prompt_embeds text_encoder-item0.clip_prompt_embeds: 6,164B, BPFP=0.8339 Using per-key quantization points (text_encoder_2-item1.clip_pooled_prompt_embeds: torch.Size([256])) for text_encoder_2-item1.clip_pooled_prompt_embeds text_encoder_2-item1.clip_pooled_prompt_embeds: 1,200B, BPFP=7.5000 Using per-key quantization points (text_encoder_2-item1.clip_prompt_embeds: torch.Size([256])) for text_encoder_2-item1.clip_prompt_embeds text_encoder_2-item1.clip_prompt_embeds: 11,480B, BPFP=0.9318 Using per-key quantization points (text_encoder_3-item2.t5_prompt_embeds: torch.Size([256])) for text_encoder_3-item2.t5_prompt_embeds text_encoder_3-item2.t5_prompt_embeds: 28,436B, BPFP=0.7213 Using per-key quantization points (text_encoder-item3.clip_pooled_prompt_embeds: torch.Size([256])) for text_encoder-item3.clip_pooled_prompt_embeds text_encoder-item3.clip_pooled_prompt_embeds: 760B, BPFP=7.9167 Using per-key quantization points (text_encoder-item3.clip_prompt_embeds: torch.Size([256])) for text_encoder-item3.clip_prompt_embeds text_encoder-item3.clip_prompt_embeds: 4,024B, BPFP=0.5444 Using per-key quantization points (text_encoder_2-item4.clip_pooled: torch.Size([256])) for text_encoder_2-item4.clip_pooled_prompt_embeds text_encoder_2-item4.clip_pooled_prompt_embeds: 1,268B, BPFP=7.9250 Using per-key quantization points (text_encoder_2-item4.clip_prompt: torch.Size([256])) for text_encoder_2-item4.clip_prompt_embeds text_encoder_2-item4.clip_prompt_embeds: 6,996B, BPFP=0.5679 Using per-key quantization points (text_encoder_3-item5.t5_prompt_embeds: torch.Size([256])) for text_encoder_3-item5.t5_prompt_embeds text_encoder_3-item5.t5_prompt_embeds: 18,416B, BPFP=0.4671 Using per-key quantization points (vae.encoder_f0: torch.Size([256])) for vae.encoder_f0 vae.encoder_f0: 21,532B, BPFP=0.3286 Using per-key quantization points (vae.encoder_f1: torch.Size([256])) for vae.encoder_f1 vae.encoder_f1: 21,536B, BPFP=0.3286 Using per-key quantization points (vae.decoder: torch.Size([256])) for vae.decoder vae.decoder: 6,976B, BPFP=0.2129 ⌛️ [2/4] FRONTEND: Frontend time: 0.293s (Pack+Encode) [3/4] BACKEND: Decode + Unpack... Using per-key quantization points (text_encoder-item0.clip_pooled_prompt_embeds: torch.Size([256])) for text_encoder-item0.clip_pooled_prompt_embeds Using per-key quantization points (text_encoder-item0.clip_prompt_embeds: torch.Size([256])) for text_encoder-item0.clip_prompt_embeds Using per-key quantization points (text_encoder_2-item1.clip_pooled_prompt_embeds: torch.Size([256])) for text_encoder_2-item1.clip_pooled_prompt_embeds Using per-key quantization points (text_encoder_2-item1.clip_prompt_embeds: torch.Size([256])) for text_encoder_2-item1.clip_prompt_embeds Using per-key quantization points (text_encoder_3-item2.t5_prompt_embeds: torch.Size([256])) for text_encoder_3-item2.t5_prompt_embeds Using per-key quantization points (text_encoder-item3.clip_pooled_prompt_embeds: torch.Size([256])) for text_encoder-item3.clip_pooled_prompt_embeds Using per-key quantization points (text_encoder-item3.clip_prompt_embeds: torch.Size([256])) for text_encoder-item3.clip_prompt_embeds Using per-key quantization points (text_encoder_2-item4.clip_pooled: torch.Size([256])) for text_encoder_2-item4.clip_pooled_prompt_embeds Using per-key quantization points (text_encoder_2-item4.clip_prompt: torch.Size([256])) for text_encoder_2-item4.clip_prompt_embeds Using per-key quantization points (text_encoder_3-item5.t5_prompt_embeds: torch.Size([256])) for text_encoder_3-item5.t5_prompt_embeds Using per-key quantization points (vae.encoder_f0: torch.Size([256])) for vae.encoder_f0 Using per-key quantization points (vae.encoder_f1: torch.Size([256])) for vae.encoder_f1 Using per-key quantization points (vae.decoder: torch.Size([256])) for vae.decoder IndividualUnPacker: text_encoder-item0.clip_pooled_prompt_embeds: torch.Size([768]) text_encoder-item0.clip_prompt_embeds: torch.Size([77, 768]) text_encoder_2-item1.clip_pooled_prompt_embeds: torch.Size([1280]) text_encoder_2-item1.clip_prompt_embeds: torch.Size([77, 1280]) text_encoder_3-item2.t5_prompt_embeds: torch.Size([77, 4096]) text_encoder-item3.clip_pooled_prompt_embeds: torch.Size([768]) text_encoder-item3.clip_prompt_embeds: torch.Size([77, 768]) text_encoder_2-item4.clip_pooled_prompt_embeds: torch.Size([1280]) text_encoder_2-item4.clip_prompt_embeds: torch.Size([77, 1280]) text_encoder_3-item5.t5_prompt_embeds: torch.Size([77, 4096]) vae.decoder: torch.Size([16, 128, 128]) vae.encoder_f0: torch.Size([32, 128, 128]) vae.encoder_f1: torch.Size([32, 128, 128]) ⌛️ [3/4] BACKEND: Backend time: 0.451s [4/4] METRICS: Computing MSE Breakdown... Using per-key quantization points (text_encoder-item0.clip_pooled_prompt_embeds: torch.Size([256])) for text_encoder-item0.clip_pooled_prompt_embeds Using per-key quantization points (text_encoder-item0.clip_prompt_embeds: torch.Size([256])) for text_encoder-item0.clip_prompt_embeds Using per-key quantization points (text_encoder_2-item1.clip_pooled_prompt_embeds: torch.Size([256])) for text_encoder_2-item1.clip_pooled_prompt_embeds Using per-key quantization points (text_encoder_2-item1.clip_prompt_embeds: torch.Size([256])) for text_encoder_2-item1.clip_prompt_embeds Using per-key quantization points (text_encoder_3-item2.t5_prompt_embeds: torch.Size([256])) for text_encoder_3-item2.t5_prompt_embeds Using per-key quantization points (text_encoder-item3.clip_pooled_prompt_embeds: torch.Size([256])) for text_encoder-item3.clip_pooled_prompt_embeds Using per-key quantization points (text_encoder-item3.clip_prompt_embeds: torch.Size([256])) for text_encoder-item3.clip_prompt_embeds Using per-key quantization points (text_encoder_2-item4.clip_pooled: torch.Size([256])) for text_encoder_2-item4.clip_pooled_prompt_embeds Using per-key quantization points (text_encoder_2-item4.clip_prompt: torch.Size([256])) for text_encoder_2-item4.clip_prompt_embeds Using per-key quantization points (text_encoder_3-item5.t5_prompt_embeds: torch.Size([256])) for text_encoder_3-item5.t5_prompt_embeds Using per-key quantization points (vae.encoder_f0: torch.Size([256])) for vae.encoder_f0 Using per-key quantization points (vae.encoder_f1: torch.Size([256])) for vae.encoder_f1 Using per-key quantization points (vae.decoder: torch.Size([256])) for vae.decoder IndividualUnPacker: text_encoder-item0.clip_pooled_prompt_embeds: torch.Size([768]) text_encoder-item0.clip_prompt_embeds: torch.Size([77, 768]) text_encoder_2-item1.clip_pooled_prompt_embeds: torch.Size([1280]) text_encoder_2-item1.clip_prompt_embeds: torch.Size([77, 1280]) text_encoder_3-item2.t5_prompt_embeds: torch.Size([77, 4096]) text_encoder-item3.clip_pooled_prompt_embeds: torch.Size([768]) text_encoder-item3.clip_prompt_embeds: torch.Size([77, 768]) text_encoder_2-item4.clip_pooled_prompt_embeds: torch.Size([1280]) text_encoder_2-item4.clip_prompt_embeds: torch.Size([77, 1280]) text_encoder_3-item5.t5_prompt_embeds: torch.Size([77, 4096]) vae.decoder: torch.Size([16, 128, 128]) vae.encoder_f0: torch.Size([32, 128, 128]) vae.encoder_f1: torch.Size([32, 128, 128]) Per-key MSE Breakdown (Quant=Truncation+Quantization, Codec=Compression): Key Quant-MSE Total-MSE ------------------------------------------------------------------------------------- text_encoder-item0.clip_pooled_prompt_embeds 0.00036702 0.94154310 text_encoder-item0.clip_prompt_embeds 0.00025651 23.90523962 text_encoder_2-item1.clip_pooled_prompt_embeds 0.00023478 0.95418129 text_encoder_2-item1.clip_prompt_embeds 0.00016148 0.13101699 text_encoder_3-item2.t5_prompt_embeds 0.00000844 0.00344603 text_encoder-item3.clip_pooled_prompt_embeds 0.00841799 1.01420029 text_encoder-item3.clip_prompt_embeds 0.00023247 23.90570887 text_encoder_2-item4.clip_pooled_prompt_embeds 0.00016751 0.67051430 text_encoder_2-item4.clip_prompt_embeds 0.00009830 0.47049568 text_encoder_3-item5.t5_prompt_embeds 0.00001263 0.00227117 vae.encoder_f0 0.00655775 2.89312196 vae.encoder_f1 0.00656268 2.89120626 vae.decoder 0.00020283 0.05328232 ------------------------------------------------------------------------------------- TOTAL 0.00309620 2.62656375 (elements=2,260,992) ---------------------- -------------------------------------------------------- SAMPLE-WISE STATISTICS ---------------------- -------------------------------------------------------- Handler sd35 Strategy individual Architecture hyperprior-featurecoding ---------------------- -------------------------------------------------------- Total Elements 2260992 Total Bytes 129520 BPFP 0.4583 bits/point EBPFP 0.9166 equivalent bits/point MSE 2.626564 ---------------------- -------------------------------------------------------- Time: 0.752s Load: 0.008s, Pack+Encode: 0.293s, Decode+Unpack: 0.451s ---------------------- -------------------------------------------------------- Restored Feature Format: [Dict] with 8 keys key['text_encoder-item0']: [Dict] with 2 keys key['clip_pooled_prompt_embeds']: [Tensor] shape=torch.Size([768]), dtype=torch.float16, device=cpu key['clip_prompt_embeds']: [Tensor] shape=torch.Size([77, 768]), dtype=torch.float16, device=cpu key['text_encoder_2-item1']: [Dict] with 2 keys key['clip_pooled_prompt_embeds']: [Tensor] shape=torch.Size([1280]), dtype=torch.float16, device=cpu key['clip_prompt_embeds']: [Tensor] shape=torch.Size([77, 1280]), dtype=torch.float16, device=cpu key['text_encoder_3-item2']: [Dict] with 1 keys key['t5_prompt_embeds']: [Tensor] shape=torch.Size([77, 4096]), dtype=torch.float16, device=cpu key['text_encoder-item3']: [Dict] with 2 keys key['clip_pooled_prompt_embeds']: [Tensor] shape=torch.Size([768]), dtype=torch.float16, device=cpu key['clip_prompt_embeds']: [Tensor] shape=torch.Size([77, 768]), dtype=torch.float16, device=cpu key['text_encoder_2-item4']: [Dict] with 2 keys key['clip_pooled_prompt_embeds']: [Tensor] shape=torch.Size([1280]), dtype=torch.float16, device=cpu key['clip_prompt_embeds']: [Tensor] shape=torch.Size([77, 1280]), dtype=torch.float16, device=cpu key['text_encoder_3-item5']: [Dict] with 1 keys key['t5_prompt_embeds']: [Tensor] shape=torch.Size([77, 4096]), dtype=torch.float16, device=cpu key['vae.decoder']: [Tensor] shape=torch.Size([16, 128, 128]), dtype=torch.float16, device=cpu key['vae.encoder']: [Dict] with 2 keys key['vae.encoder_f0']: [Tensor] shape=torch.Size([32, 128, 128]), dtype=torch.float16, device=cpu key['vae.encoder_f1']: [Tensor] shape=torch.Size([32, 128, 128]), dtype=torch.float16, device=cpu 💾 Converting with 2.6266 MSE: from ../datasets/FeatureCoding-StableDiffusion3.5/100samples_features/000000027620.zst to output-fixed/sd35/lambda0.004/hyperprior-featurecoding-8bit-individual/sd35cond/000000027620.zst 💪 Processing: ../datasets/FeatureCoding-StableDiffusion3.5/100samples_features/000000030504.zst (7/100) [1/4] FRONTEND: Loading features from ../datasets/FeatureCoding-StableDiffusion3.5/100samples_features/000000030504.zst... Original data structure: root: [Dict] with 8 keys key['text_encoder-item0']: [Dict] with 2 keys key['clip_pooled_prompt_embeds']: [Tensor] shape=torch.Size([768]), dtype=torch.float16, device=cpu key['clip_prompt_embeds']: [Tensor] shape=torch.Size([77, 768]), dtype=torch.float16, device=cpu key['text_encoder_2-item1']: [Dict] with 2 keys key['clip_pooled_prompt_embeds']: [Tensor] shape=torch.Size([1280]), dtype=torch.float16, device=cpu key['clip_prompt_embeds']: [Tensor] shape=torch.Size([77, 1280]), dtype=torch.float16, device=cpu key['text_encoder_3-item2']: [Dict] with 1 keys key['t5_prompt_embeds']: [Tensor] shape=torch.Size([77, 4096]), dtype=torch.float16, device=cpu key['text_encoder-item3']: [Dict] with 2 keys key['clip_pooled_prompt_embeds']: [Tensor] shape=torch.Size([768]), dtype=torch.float16, device=cpu key['clip_prompt_embeds']: [Tensor] shape=torch.Size([77, 768]), dtype=torch.float16, device=cpu key['text_encoder_2-item4']: [Dict] with 2 keys key['clip_pooled_prompt_embeds']: [Tensor] shape=torch.Size([1280]), dtype=torch.float16, device=cpu key['clip_prompt_embeds']: [Tensor] shape=torch.Size([77, 1280]), dtype=torch.float16, device=cpu key['text_encoder_3-item5']: [Dict] with 1 keys key['t5_prompt_embeds']: [Tensor] shape=torch.Size([77, 4096]), dtype=torch.float16, device=cpu key['vae.encoder-item6']: [Dict] with 2 keys key['vae.encoder_f0']: [Tensor] shape=torch.Size([32, 128, 128]), dtype=torch.float16, device=cpu key['vae.encoder_f1']: [Tensor] shape=torch.Size([32, 128, 128]), dtype=torch.float16, device=cpu key['vae.decoder']: [Tensor] shape=torch.Size([16, 128, 128]), dtype=torch.float16, device=cpu ⌛️ [1/4] FRONTEND: Load time: 0.009s ------------------------------------------------------------ SD3.5 Features Summary ------------------------------------------------------------ Number of text encoders: 3 Encoder names: ['text_encoder', 'text_encoder_2', 'text_encoder_3'] Has VAE latents: True Has VAE encoder features: True Data type: torch.float16 text_encoder-item0: clip_pooled_prompt_embeds: torch.Size([768]), torch.float16 clip_prompt_embeds: torch.Size([77, 768]), torch.float16 text_encoder_2-item1: clip_pooled_prompt_embeds: torch.Size([1280]), torch.float16 clip_prompt_embeds: torch.Size([77, 1280]), torch.float16 text_encoder_3-item2: t5_prompt_embeds: torch.Size([77, 4096]), torch.float16 text_encoder-item3: clip_pooled_prompt_embeds: torch.Size([768]), torch.float16 clip_prompt_embeds: torch.Size([77, 768]), torch.float16 text_encoder_2-item4: clip_pooled_prompt_embeds: torch.Size([1280]), torch.float16 clip_prompt_embeds: torch.Size([77, 1280]), torch.float16 text_encoder_3-item5: t5_prompt_embeds: torch.Size([77, 4096]), torch.float16 vae.decoder latents: torch.Size([16, 128, 128]) vae.encoder features: vae.encoder_f0: torch.Size([32, 128, 128]), torch.float16 vae.encoder_f1: torch.Size([32, 128, 128]), torch.float16 ------------------------------------------------------------ [2/4] FRONTEND: Pack + Encode (strategy: individual)... IndividualPacker: text_encoder-item0.clip_pooled_prompt_embeds: torch.Size([768])->torch.Size([1, 1, 1, 768]) text_encoder-item0.clip_prompt_embeds: torch.Size([77, 768])->torch.Size([1, 1, 77, 768]) text_encoder_2-item1.clip_pooled_prompt_embeds: torch.Size([1280])->torch.Size([1, 1, 1, 1280]) text_encoder_2-item1.clip_prompt_embeds: torch.Size([77, 1280])->torch.Size([1, 1, 77, 1280]) text_encoder_3-item2.t5_prompt_embeds: torch.Size([77, 4096])->torch.Size([1, 1, 77, 4096]) text_encoder-item3.clip_pooled_prompt_embeds: torch.Size([768])->torch.Size([1, 1, 1, 768]) text_encoder-item3.clip_prompt_embeds: torch.Size([77, 768])->torch.Size([1, 1, 77, 768]) text_encoder_2-item4.clip_pooled_prompt_embeds: torch.Size([1280])->torch.Size([1, 1, 1, 1280]) text_encoder_2-item4.clip_prompt_embeds: torch.Size([77, 1280])->torch.Size([1, 1, 77, 1280]) text_encoder_3-item5.t5_prompt_embeds: torch.Size([77, 4096])->torch.Size([1, 1, 77, 4096]) vae.encoder_f0: torch.Size([32, 128, 128])->torch.Size([1, 1, 512, 1024]) vae.encoder_f1: torch.Size([32, 128, 128])->torch.Size([1, 1, 512, 1024]) vae.decoder: torch.Size([16, 128, 128])->torch.Size([1, 1, 512, 512]) Using per-key quantization points (text_encoder-item0.clip_pooled_prompt_embeds: torch.Size([256])) for text_encoder-item0.clip_pooled_prompt_embeds text_encoder-item0.clip_pooled_prompt_embeds: 736B, BPFP=7.6667 Using per-key quantization points (text_encoder-item0.clip_prompt_embeds: torch.Size([256])) for text_encoder-item0.clip_prompt_embeds text_encoder-item0.clip_prompt_embeds: 5,456B, BPFP=0.7381 Using per-key quantization points (text_encoder_2-item1.clip_pooled_prompt_embeds: torch.Size([256])) for text_encoder_2-item1.clip_pooled_prompt_embeds text_encoder_2-item1.clip_pooled_prompt_embeds: 1,204B, BPFP=7.5250 Using per-key quantization points (text_encoder_2-item1.clip_prompt_embeds: torch.Size([256])) for text_encoder_2-item1.clip_prompt_embeds text_encoder_2-item1.clip_prompt_embeds: 9,932B, BPFP=0.8062 Using per-key quantization points (text_encoder_3-item2.t5_prompt_embeds: torch.Size([256])) for text_encoder_3-item2.t5_prompt_embeds text_encoder_3-item2.t5_prompt_embeds: 23,936B, BPFP=0.6071 Using per-key quantization points (text_encoder-item3.clip_pooled_prompt_embeds: torch.Size([256])) for text_encoder-item3.clip_pooled_prompt_embeds text_encoder-item3.clip_pooled_prompt_embeds: 760B, BPFP=7.9167 Using per-key quantization points (text_encoder-item3.clip_prompt_embeds: torch.Size([256])) for text_encoder-item3.clip_prompt_embeds text_encoder-item3.clip_prompt_embeds: 4,024B, BPFP=0.5444 Using per-key quantization points (text_encoder_2-item4.clip_pooled: torch.Size([256])) for text_encoder_2-item4.clip_pooled_prompt_embeds text_encoder_2-item4.clip_pooled_prompt_embeds: 1,268B, BPFP=7.9250 Using per-key quantization points (text_encoder_2-item4.clip_prompt: torch.Size([256])) for text_encoder_2-item4.clip_prompt_embeds text_encoder_2-item4.clip_prompt_embeds: 6,996B, BPFP=0.5679 Using per-key quantization points (text_encoder_3-item5.t5_prompt_embeds: torch.Size([256])) for text_encoder_3-item5.t5_prompt_embeds text_encoder_3-item5.t5_prompt_embeds: 18,416B, BPFP=0.4671 Using per-key quantization points (vae.encoder_f0: torch.Size([256])) for vae.encoder_f0 vae.encoder_f0: 18,812B, BPFP=0.2870 Using per-key quantization points (vae.encoder_f1: torch.Size([256])) for vae.encoder_f1 vae.encoder_f1: 18,816B, BPFP=0.2871 Using per-key quantization points (vae.decoder: torch.Size([256])) for vae.decoder vae.decoder: 7,900B, BPFP=0.2411 ⌛️ [2/4] FRONTEND: Frontend time: 0.292s (Pack+Encode) [3/4] BACKEND: Decode + Unpack... Using per-key quantization points (text_encoder-item0.clip_pooled_prompt_embeds: torch.Size([256])) for text_encoder-item0.clip_pooled_prompt_embeds Using per-key quantization points (text_encoder-item0.clip_prompt_embeds: torch.Size([256])) for text_encoder-item0.clip_prompt_embeds Using per-key quantization points (text_encoder_2-item1.clip_pooled_prompt_embeds: torch.Size([256])) for text_encoder_2-item1.clip_pooled_prompt_embeds Using per-key quantization points (text_encoder_2-item1.clip_prompt_embeds: torch.Size([256])) for text_encoder_2-item1.clip_prompt_embeds Using per-key quantization points (text_encoder_3-item2.t5_prompt_embeds: torch.Size([256])) for text_encoder_3-item2.t5_prompt_embeds Using per-key quantization points (text_encoder-item3.clip_pooled_prompt_embeds: torch.Size([256])) for text_encoder-item3.clip_pooled_prompt_embeds Using per-key quantization points (text_encoder-item3.clip_prompt_embeds: torch.Size([256])) for text_encoder-item3.clip_prompt_embeds Using per-key quantization points (text_encoder_2-item4.clip_pooled: torch.Size([256])) for text_encoder_2-item4.clip_pooled_prompt_embeds Using per-key quantization points (text_encoder_2-item4.clip_prompt: torch.Size([256])) for text_encoder_2-item4.clip_prompt_embeds Using per-key quantization points (text_encoder_3-item5.t5_prompt_embeds: torch.Size([256])) for text_encoder_3-item5.t5_prompt_embeds Using per-key quantization points (vae.encoder_f0: torch.Size([256])) for vae.encoder_f0 Using per-key quantization points (vae.encoder_f1: torch.Size([256])) for vae.encoder_f1 Using per-key quantization points (vae.decoder: torch.Size([256])) for vae.decoder IndividualUnPacker: text_encoder-item0.clip_pooled_prompt_embeds: torch.Size([768]) text_encoder-item0.clip_prompt_embeds: torch.Size([77, 768]) text_encoder_2-item1.clip_pooled_prompt_embeds: torch.Size([1280]) text_encoder_2-item1.clip_prompt_embeds: torch.Size([77, 1280]) text_encoder_3-item2.t5_prompt_embeds: torch.Size([77, 4096]) text_encoder-item3.clip_pooled_prompt_embeds: torch.Size([768]) text_encoder-item3.clip_prompt_embeds: torch.Size([77, 768]) text_encoder_2-item4.clip_pooled_prompt_embeds: torch.Size([1280]) text_encoder_2-item4.clip_prompt_embeds: torch.Size([77, 1280]) text_encoder_3-item5.t5_prompt_embeds: torch.Size([77, 4096]) vae.decoder: torch.Size([16, 128, 128]) vae.encoder_f0: torch.Size([32, 128, 128]) vae.encoder_f1: torch.Size([32, 128, 128]) ⌛️ [3/4] BACKEND: Backend time: 0.445s [4/4] METRICS: Computing MSE Breakdown... Using per-key quantization points (text_encoder-item0.clip_pooled_prompt_embeds: torch.Size([256])) for text_encoder-item0.clip_pooled_prompt_embeds Using per-key quantization points (text_encoder-item0.clip_prompt_embeds: torch.Size([256])) for text_encoder-item0.clip_prompt_embeds Using per-key quantization points (text_encoder_2-item1.clip_pooled_prompt_embeds: torch.Size([256])) for text_encoder_2-item1.clip_pooled_prompt_embeds Using per-key quantization points (text_encoder_2-item1.clip_prompt_embeds: torch.Size([256])) for text_encoder_2-item1.clip_prompt_embeds Using per-key quantization points (text_encoder_3-item2.t5_prompt_embeds: torch.Size([256])) for text_encoder_3-item2.t5_prompt_embeds Using per-key quantization points (text_encoder-item3.clip_pooled_prompt_embeds: torch.Size([256])) for text_encoder-item3.clip_pooled_prompt_embeds Using per-key quantization points (text_encoder-item3.clip_prompt_embeds: torch.Size([256])) for text_encoder-item3.clip_prompt_embeds Using per-key quantization points (text_encoder_2-item4.clip_pooled: torch.Size([256])) for text_encoder_2-item4.clip_pooled_prompt_embeds Using per-key quantization points (text_encoder_2-item4.clip_prompt: torch.Size([256])) for text_encoder_2-item4.clip_prompt_embeds Using per-key quantization points (text_encoder_3-item5.t5_prompt_embeds: torch.Size([256])) for text_encoder_3-item5.t5_prompt_embeds Using per-key quantization points (vae.encoder_f0: torch.Size([256])) for vae.encoder_f0 Using per-key quantization points (vae.encoder_f1: torch.Size([256])) for vae.encoder_f1 Using per-key quantization points (vae.decoder: torch.Size([256])) for vae.decoder IndividualUnPacker: text_encoder-item0.clip_pooled_prompt_embeds: torch.Size([768]) text_encoder-item0.clip_prompt_embeds: torch.Size([77, 768]) text_encoder_2-item1.clip_pooled_prompt_embeds: torch.Size([1280]) text_encoder_2-item1.clip_prompt_embeds: torch.Size([77, 1280]) text_encoder_3-item2.t5_prompt_embeds: torch.Size([77, 4096]) text_encoder-item3.clip_pooled_prompt_embeds: torch.Size([768]) text_encoder-item3.clip_prompt_embeds: torch.Size([77, 768]) text_encoder_2-item4.clip_pooled_prompt_embeds: torch.Size([1280]) text_encoder_2-item4.clip_prompt_embeds: torch.Size([77, 1280]) text_encoder_3-item5.t5_prompt_embeds: torch.Size([77, 4096]) vae.decoder: torch.Size([16, 128, 128]) vae.encoder_f0: torch.Size([32, 128, 128]) vae.encoder_f1: torch.Size([32, 128, 128]) Per-key MSE Breakdown (Quant=Truncation+Quantization, Codec=Compression): Key Quant-MSE Total-MSE ------------------------------------------------------------------------------------- text_encoder-item0.clip_pooled_prompt_embeds 0.00036856 0.94820023 text_encoder-item0.clip_prompt_embeds 0.00022242 23.93856323 text_encoder_2-item1.clip_pooled_prompt_embeds 0.00022710 0.94795275 text_encoder_2-item1.clip_prompt_embeds 0.00016311 0.11797708 text_encoder_3-item2.t5_prompt_embeds 0.00000924 0.00337748 text_encoder-item3.clip_pooled_prompt_embeds 0.00841799 1.01420029 text_encoder-item3.clip_prompt_embeds 0.00023247 23.90570887 text_encoder_2-item4.clip_pooled_prompt_embeds 0.00016751 0.67051430 text_encoder_2-item4.clip_prompt_embeds 0.00009830 0.47049568 text_encoder_3-item5.t5_prompt_embeds 0.00001263 0.00227117 vae.encoder_f0 0.00593415 2.23211765 vae.encoder_f1 0.00594307 2.23192954 vae.decoder 0.00018992 0.07523341 ------------------------------------------------------------------------------------- TOTAL 0.00280571 2.32324901 (elements=2,260,992) ---------------------- -------------------------------------------------------- SAMPLE-WISE STATISTICS ---------------------- -------------------------------------------------------- Handler sd35 Strategy individual Architecture hyperprior-featurecoding ---------------------- -------------------------------------------------------- Total Elements 2260992 Total Bytes 118256 BPFP 0.4184 bits/point EBPFP 0.8368 equivalent bits/point MSE 2.323249 ---------------------- -------------------------------------------------------- Time: 0.746s Load: 0.009s, Pack+Encode: 0.292s, Decode+Unpack: 0.445s ---------------------- -------------------------------------------------------- Restored Feature Format: [Dict] with 8 keys key['text_encoder-item0']: [Dict] with 2 keys key['clip_pooled_prompt_embeds']: [Tensor] shape=torch.Size([768]), dtype=torch.float16, device=cpu key['clip_prompt_embeds']: [Tensor] shape=torch.Size([77, 768]), dtype=torch.float16, device=cpu key['text_encoder_2-item1']: [Dict] with 2 keys key['clip_pooled_prompt_embeds']: [Tensor] shape=torch.Size([1280]), dtype=torch.float16, device=cpu key['clip_prompt_embeds']: [Tensor] shape=torch.Size([77, 1280]), dtype=torch.float16, device=cpu key['text_encoder_3-item2']: [Dict] with 1 keys key['t5_prompt_embeds']: [Tensor] shape=torch.Size([77, 4096]), dtype=torch.float16, device=cpu key['text_encoder-item3']: [Dict] with 2 keys key['clip_pooled_prompt_embeds']: [Tensor] shape=torch.Size([768]), dtype=torch.float16, device=cpu key['clip_prompt_embeds']: [Tensor] shape=torch.Size([77, 768]), dtype=torch.float16, device=cpu key['text_encoder_2-item4']: [Dict] with 2 keys key['clip_pooled_prompt_embeds']: [Tensor] shape=torch.Size([1280]), dtype=torch.float16, device=cpu key['clip_prompt_embeds']: [Tensor] shape=torch.Size([77, 1280]), dtype=torch.float16, device=cpu key['text_encoder_3-item5']: [Dict] with 1 keys key['t5_prompt_embeds']: [Tensor] shape=torch.Size([77, 4096]), dtype=torch.float16, device=cpu key['vae.decoder']: [Tensor] shape=torch.Size([16, 128, 128]), dtype=torch.float16, device=cpu key['vae.encoder']: [Dict] with 2 keys key['vae.encoder_f0']: [Tensor] shape=torch.Size([32, 128, 128]), dtype=torch.float16, device=cpu key['vae.encoder_f1']: [Tensor] shape=torch.Size([32, 128, 128]), dtype=torch.float16, device=cpu 💾 Converting with 2.3232 MSE: from ../datasets/FeatureCoding-StableDiffusion3.5/100samples_features/000000030504.zst to output-fixed/sd35/lambda0.004/hyperprior-featurecoding-8bit-individual/sd35cond/000000030504.zst 💪 Processing: ../datasets/FeatureCoding-StableDiffusion3.5/100samples_features/000000031248.zst (8/100) [1/4] FRONTEND: Loading features from ../datasets/FeatureCoding-StableDiffusion3.5/100samples_features/000000031248.zst... Original data structure: root: [Dict] with 8 keys key['text_encoder-item0']: [Dict] with 2 keys key['clip_pooled_prompt_embeds']: [Tensor] shape=torch.Size([768]), dtype=torch.float16, device=cpu key['clip_prompt_embeds']: [Tensor] shape=torch.Size([77, 768]), dtype=torch.float16, device=cpu key['text_encoder_2-item1']: [Dict] with 2 keys key['clip_pooled_prompt_embeds']: [Tensor] shape=torch.Size([1280]), dtype=torch.float16, device=cpu key['clip_prompt_embeds']: [Tensor] shape=torch.Size([77, 1280]), dtype=torch.float16, device=cpu key['text_encoder_3-item2']: [Dict] with 1 keys key['t5_prompt_embeds']: [Tensor] shape=torch.Size([77, 4096]), dtype=torch.float16, device=cpu key['text_encoder-item3']: [Dict] with 2 keys key['clip_pooled_prompt_embeds']: [Tensor] shape=torch.Size([768]), dtype=torch.float16, device=cpu key['clip_prompt_embeds']: [Tensor] shape=torch.Size([77, 768]), dtype=torch.float16, device=cpu key['text_encoder_2-item4']: [Dict] with 2 keys key['clip_pooled_prompt_embeds']: [Tensor] shape=torch.Size([1280]), dtype=torch.float16, device=cpu key['clip_prompt_embeds']: [Tensor] shape=torch.Size([77, 1280]), dtype=torch.float16, device=cpu key['text_encoder_3-item5']: [Dict] with 1 keys key['t5_prompt_embeds']: [Tensor] shape=torch.Size([77, 4096]), dtype=torch.float16, device=cpu key['vae.encoder-item6']: [Dict] with 2 keys key['vae.encoder_f0']: [Tensor] shape=torch.Size([32, 128, 128]), dtype=torch.float16, device=cpu key['vae.encoder_f1']: [Tensor] shape=torch.Size([32, 128, 128]), dtype=torch.float16, device=cpu key['vae.decoder']: [Tensor] shape=torch.Size([16, 128, 128]), dtype=torch.float16, device=cpu ⌛️ [1/4] FRONTEND: Load time: 0.008s ------------------------------------------------------------ SD3.5 Features Summary ------------------------------------------------------------ Number of text encoders: 3 Encoder names: ['text_encoder', 'text_encoder_2', 'text_encoder_3'] Has VAE latents: True Has VAE encoder features: True Data type: torch.float16 text_encoder-item0: clip_pooled_prompt_embeds: torch.Size([768]), torch.float16 clip_prompt_embeds: torch.Size([77, 768]), torch.float16 text_encoder_2-item1: clip_pooled_prompt_embeds: torch.Size([1280]), torch.float16 clip_prompt_embeds: torch.Size([77, 1280]), torch.float16 text_encoder_3-item2: t5_prompt_embeds: torch.Size([77, 4096]), torch.float16 text_encoder-item3: clip_pooled_prompt_embeds: torch.Size([768]), torch.float16 clip_prompt_embeds: torch.Size([77, 768]), torch.float16 text_encoder_2-item4: clip_pooled_prompt_embeds: torch.Size([1280]), torch.float16 clip_prompt_embeds: torch.Size([77, 1280]), torch.float16 text_encoder_3-item5: t5_prompt_embeds: torch.Size([77, 4096]), torch.float16 vae.decoder latents: torch.Size([16, 128, 128]) vae.encoder features: vae.encoder_f0: torch.Size([32, 128, 128]), torch.float16 vae.encoder_f1: torch.Size([32, 128, 128]), torch.float16 ------------------------------------------------------------ [2/4] FRONTEND: Pack + Encode (strategy: individual)... IndividualPacker: text_encoder-item0.clip_pooled_prompt_embeds: torch.Size([768])->torch.Size([1, 1, 1, 768]) text_encoder-item0.clip_prompt_embeds: torch.Size([77, 768])->torch.Size([1, 1, 77, 768]) text_encoder_2-item1.clip_pooled_prompt_embeds: torch.Size([1280])->torch.Size([1, 1, 1, 1280]) text_encoder_2-item1.clip_prompt_embeds: torch.Size([77, 1280])->torch.Size([1, 1, 77, 1280]) text_encoder_3-item2.t5_prompt_embeds: torch.Size([77, 4096])->torch.Size([1, 1, 77, 4096]) text_encoder-item3.clip_pooled_prompt_embeds: torch.Size([768])->torch.Size([1, 1, 1, 768]) text_encoder-item3.clip_prompt_embeds: torch.Size([77, 768])->torch.Size([1, 1, 77, 768]) text_encoder_2-item4.clip_pooled_prompt_embeds: torch.Size([1280])->torch.Size([1, 1, 1, 1280]) text_encoder_2-item4.clip_prompt_embeds: torch.Size([77, 1280])->torch.Size([1, 1, 77, 1280]) text_encoder_3-item5.t5_prompt_embeds: torch.Size([77, 4096])->torch.Size([1, 1, 77, 4096]) vae.encoder_f0: torch.Size([32, 128, 128])->torch.Size([1, 1, 512, 1024]) vae.encoder_f1: torch.Size([32, 128, 128])->torch.Size([1, 1, 512, 1024]) vae.decoder: torch.Size([16, 128, 128])->torch.Size([1, 1, 512, 512]) Using per-key quantization points (text_encoder-item0.clip_pooled_prompt_embeds: torch.Size([256])) for text_encoder-item0.clip_pooled_prompt_embeds text_encoder-item0.clip_pooled_prompt_embeds: 728B, BPFP=7.5833 Using per-key quantization points (text_encoder-item0.clip_prompt_embeds: torch.Size([256])) for text_encoder-item0.clip_prompt_embeds text_encoder-item0.clip_prompt_embeds: 6,676B, BPFP=0.9031 Using per-key quantization points (text_encoder_2-item1.clip_pooled_prompt_embeds: torch.Size([256])) for text_encoder_2-item1.clip_pooled_prompt_embeds text_encoder_2-item1.clip_pooled_prompt_embeds: 1,204B, BPFP=7.5250 Using per-key quantization points (text_encoder_2-item1.clip_prompt_embeds: torch.Size([256])) for text_encoder_2-item1.clip_prompt_embeds text_encoder_2-item1.clip_prompt_embeds: 11,140B, BPFP=0.9042 Using per-key quantization points (text_encoder_3-item2.t5_prompt_embeds: torch.Size([256])) for text_encoder_3-item2.t5_prompt_embeds text_encoder_3-item2.t5_prompt_embeds: 27,472B, BPFP=0.6968 Using per-key quantization points (text_encoder-item3.clip_pooled_prompt_embeds: torch.Size([256])) for text_encoder-item3.clip_pooled_prompt_embeds text_encoder-item3.clip_pooled_prompt_embeds: 760B, BPFP=7.9167 Using per-key quantization points (text_encoder-item3.clip_prompt_embeds: torch.Size([256])) for text_encoder-item3.clip_prompt_embeds text_encoder-item3.clip_prompt_embeds: 4,024B, BPFP=0.5444 Using per-key quantization points (text_encoder_2-item4.clip_pooled: torch.Size([256])) for text_encoder_2-item4.clip_pooled_prompt_embeds text_encoder_2-item4.clip_pooled_prompt_embeds: 1,268B, BPFP=7.9250 Using per-key quantization points (text_encoder_2-item4.clip_prompt: torch.Size([256])) for text_encoder_2-item4.clip_prompt_embeds text_encoder_2-item4.clip_prompt_embeds: 6,996B, BPFP=0.5679 Using per-key quantization points (text_encoder_3-item5.t5_prompt_embeds: torch.Size([256])) for text_encoder_3-item5.t5_prompt_embeds text_encoder_3-item5.t5_prompt_embeds: 18,416B, BPFP=0.4671 Using per-key quantization points (vae.encoder_f0: torch.Size([256])) for vae.encoder_f0 vae.encoder_f0: 18,964B, BPFP=0.2894 Using per-key quantization points (vae.encoder_f1: torch.Size([256])) for vae.encoder_f1 vae.encoder_f1: 18,964B, BPFP=0.2894 Using per-key quantization points (vae.decoder: torch.Size([256])) for vae.decoder vae.decoder: 7,592B, BPFP=0.2317 ⌛️ [2/4] FRONTEND: Frontend time: 0.296s (Pack+Encode) [3/4] BACKEND: Decode + Unpack... Using per-key quantization points (text_encoder-item0.clip_pooled_prompt_embeds: torch.Size([256])) for text_encoder-item0.clip_pooled_prompt_embeds Using per-key quantization points (text_encoder-item0.clip_prompt_embeds: torch.Size([256])) for text_encoder-item0.clip_prompt_embeds Using per-key quantization points (text_encoder_2-item1.clip_pooled_prompt_embeds: torch.Size([256])) for text_encoder_2-item1.clip_pooled_prompt_embeds Using per-key quantization points (text_encoder_2-item1.clip_prompt_embeds: torch.Size([256])) for text_encoder_2-item1.clip_prompt_embeds Using per-key quantization points (text_encoder_3-item2.t5_prompt_embeds: torch.Size([256])) for text_encoder_3-item2.t5_prompt_embeds Using per-key quantization points (text_encoder-item3.clip_pooled_prompt_embeds: torch.Size([256])) for text_encoder-item3.clip_pooled_prompt_embeds Using per-key quantization points (text_encoder-item3.clip_prompt_embeds: torch.Size([256])) for text_encoder-item3.clip_prompt_embeds Using per-key quantization points (text_encoder_2-item4.clip_pooled: torch.Size([256])) for text_encoder_2-item4.clip_pooled_prompt_embeds Using per-key quantization points (text_encoder_2-item4.clip_prompt: torch.Size([256])) for text_encoder_2-item4.clip_prompt_embeds Using per-key quantization points (text_encoder_3-item5.t5_prompt_embeds: torch.Size([256])) for text_encoder_3-item5.t5_prompt_embeds Using per-key quantization points (vae.encoder_f0: torch.Size([256])) for vae.encoder_f0 Using per-key quantization points (vae.encoder_f1: torch.Size([256])) for vae.encoder_f1 Using per-key quantization points (vae.decoder: torch.Size([256])) for vae.decoder IndividualUnPacker: text_encoder-item0.clip_pooled_prompt_embeds: torch.Size([768]) text_encoder-item0.clip_prompt_embeds: torch.Size([77, 768]) text_encoder_2-item1.clip_pooled_prompt_embeds: torch.Size([1280]) text_encoder_2-item1.clip_prompt_embeds: torch.Size([77, 1280]) text_encoder_3-item2.t5_prompt_embeds: torch.Size([77, 4096]) text_encoder-item3.clip_pooled_prompt_embeds: torch.Size([768]) text_encoder-item3.clip_prompt_embeds: torch.Size([77, 768]) text_encoder_2-item4.clip_pooled_prompt_embeds: torch.Size([1280]) text_encoder_2-item4.clip_prompt_embeds: torch.Size([77, 1280]) text_encoder_3-item5.t5_prompt_embeds: torch.Size([77, 4096]) vae.decoder: torch.Size([16, 128, 128]) vae.encoder_f0: torch.Size([32, 128, 128]) vae.encoder_f1: torch.Size([32, 128, 128]) ⌛️ [3/4] BACKEND: Backend time: 0.445s [4/4] METRICS: Computing MSE Breakdown... Using per-key quantization points (text_encoder-item0.clip_pooled_prompt_embeds: torch.Size([256])) for text_encoder-item0.clip_pooled_prompt_embeds Using per-key quantization points (text_encoder-item0.clip_prompt_embeds: torch.Size([256])) for text_encoder-item0.clip_prompt_embeds Using per-key quantization points (text_encoder_2-item1.clip_pooled_prompt_embeds: torch.Size([256])) for text_encoder_2-item1.clip_pooled_prompt_embeds Using per-key quantization points (text_encoder_2-item1.clip_prompt_embeds: torch.Size([256])) for text_encoder_2-item1.clip_prompt_embeds Using per-key quantization points (text_encoder_3-item2.t5_prompt_embeds: torch.Size([256])) for text_encoder_3-item2.t5_prompt_embeds Using per-key quantization points (text_encoder-item3.clip_pooled_prompt_embeds: torch.Size([256])) for text_encoder-item3.clip_pooled_prompt_embeds Using per-key quantization points (text_encoder-item3.clip_prompt_embeds: torch.Size([256])) for text_encoder-item3.clip_prompt_embeds Using per-key quantization points (text_encoder_2-item4.clip_pooled: torch.Size([256])) for text_encoder_2-item4.clip_pooled_prompt_embeds Using per-key quantization points (text_encoder_2-item4.clip_prompt: torch.Size([256])) for text_encoder_2-item4.clip_prompt_embeds Using per-key quantization points (text_encoder_3-item5.t5_prompt_embeds: torch.Size([256])) for text_encoder_3-item5.t5_prompt_embeds Using per-key quantization points (vae.encoder_f0: torch.Size([256])) for vae.encoder_f0 Using per-key quantization points (vae.encoder_f1: torch.Size([256])) for vae.encoder_f1 Using per-key quantization points (vae.decoder: torch.Size([256])) for vae.decoder IndividualUnPacker: text_encoder-item0.clip_pooled_prompt_embeds: torch.Size([768]) text_encoder-item0.clip_prompt_embeds: torch.Size([77, 768]) text_encoder_2-item1.clip_pooled_prompt_embeds: torch.Size([1280]) text_encoder_2-item1.clip_prompt_embeds: torch.Size([77, 1280]) text_encoder_3-item2.t5_prompt_embeds: torch.Size([77, 4096]) text_encoder-item3.clip_pooled_prompt_embeds: torch.Size([768]) text_encoder-item3.clip_prompt_embeds: torch.Size([77, 768]) text_encoder_2-item4.clip_pooled_prompt_embeds: torch.Size([1280]) text_encoder_2-item4.clip_prompt_embeds: torch.Size([77, 1280]) text_encoder_3-item5.t5_prompt_embeds: torch.Size([77, 4096]) vae.decoder: torch.Size([16, 128, 128]) vae.encoder_f0: torch.Size([32, 128, 128]) vae.encoder_f1: torch.Size([32, 128, 128]) Per-key MSE Breakdown (Quant=Truncation+Quantization, Codec=Compression): Key Quant-MSE Total-MSE ------------------------------------------------------------------------------------- text_encoder-item0.clip_pooled_prompt_embeds 0.00036736 0.93140324 text_encoder-item0.clip_prompt_embeds 0.00022110 132.52475649 text_encoder_2-item1.clip_pooled_prompt_embeds 0.00042957 0.89787722 text_encoder_2-item1.clip_prompt_embeds 0.00091506 0.14335624 text_encoder_3-item2.t5_prompt_embeds 0.00000774 0.00320222 text_encoder-item3.clip_pooled_prompt_embeds 0.00841799 1.01420029 text_encoder-item3.clip_prompt_embeds 0.00023247 23.90570887 text_encoder_2-item4.clip_pooled_prompt_embeds 0.00016751 0.67051430 text_encoder_2-item4.clip_prompt_embeds 0.00009830 0.47049568 text_encoder_3-item5.t5_prompt_embeds 0.00001263 0.00227117 vae.encoder_f0 0.00641770 2.36619711 vae.encoder_f1 0.00642053 2.36633277 vae.decoder 0.00017498 0.04471630 ------------------------------------------------------------------------------------- TOTAL 0.00305947 5.22307554 (elements=2,260,992) ---------------------- -------------------------------------------------------- SAMPLE-WISE STATISTICS ---------------------- -------------------------------------------------------- Handler sd35 Strategy individual Architecture hyperprior-featurecoding ---------------------- -------------------------------------------------------- Total Elements 2260992 Total Bytes 124204 BPFP 0.4395 bits/point EBPFP 0.8789 equivalent bits/point MSE 5.223076 ---------------------- -------------------------------------------------------- Time: 0.750s Load: 0.008s, Pack+Encode: 0.296s, Decode+Unpack: 0.445s ---------------------- -------------------------------------------------------- Restored Feature Format: [Dict] with 8 keys key['text_encoder-item0']: [Dict] with 2 keys key['clip_pooled_prompt_embeds']: [Tensor] shape=torch.Size([768]), dtype=torch.float16, device=cpu key['clip_prompt_embeds']: [Tensor] shape=torch.Size([77, 768]), dtype=torch.float16, device=cpu key['text_encoder_2-item1']: [Dict] with 2 keys key['clip_pooled_prompt_embeds']: [Tensor] shape=torch.Size([1280]), dtype=torch.float16, device=cpu key['clip_prompt_embeds']: [Tensor] shape=torch.Size([77, 1280]), dtype=torch.float16, device=cpu key['text_encoder_3-item2']: [Dict] with 1 keys key['t5_prompt_embeds']: [Tensor] shape=torch.Size([77, 4096]), dtype=torch.float16, device=cpu key['text_encoder-item3']: [Dict] with 2 keys key['clip_pooled_prompt_embeds']: [Tensor] shape=torch.Size([768]), dtype=torch.float16, device=cpu key['clip_prompt_embeds']: [Tensor] shape=torch.Size([77, 768]), dtype=torch.float16, device=cpu key['text_encoder_2-item4']: [Dict] with 2 keys key['clip_pooled_prompt_embeds']: [Tensor] shape=torch.Size([1280]), dtype=torch.float16, device=cpu key['clip_prompt_embeds']: [Tensor] shape=torch.Size([77, 1280]), dtype=torch.float16, device=cpu key['text_encoder_3-item5']: [Dict] with 1 keys key['t5_prompt_embeds']: [Tensor] shape=torch.Size([77, 4096]), dtype=torch.float16, device=cpu key['vae.decoder']: [Tensor] shape=torch.Size([16, 128, 128]), dtype=torch.float16, device=cpu key['vae.encoder']: [Dict] with 2 keys key['vae.encoder_f0']: [Tensor] shape=torch.Size([32, 128, 128]), dtype=torch.float16, device=cpu key['vae.encoder_f1']: [Tensor] shape=torch.Size([32, 128, 128]), dtype=torch.float16, device=cpu 💾 Converting with 5.2231 MSE: from ../datasets/FeatureCoding-StableDiffusion3.5/100samples_features/000000031248.zst to output-fixed/sd35/lambda0.004/hyperprior-featurecoding-8bit-individual/sd35cond/000000031248.zst 💪 Processing: ../datasets/FeatureCoding-StableDiffusion3.5/100samples_features/000000055072.zst (9/100) [1/4] FRONTEND: Loading features from ../datasets/FeatureCoding-StableDiffusion3.5/100samples_features/000000055072.zst... Original data structure: root: [Dict] with 8 keys key['text_encoder-item0']: [Dict] with 2 keys key['clip_pooled_prompt_embeds']: [Tensor] shape=torch.Size([768]), dtype=torch.float16, device=cpu key['clip_prompt_embeds']: [Tensor] shape=torch.Size([77, 768]), dtype=torch.float16, device=cpu key['text_encoder_2-item1']: [Dict] with 2 keys key['clip_pooled_prompt_embeds']: [Tensor] shape=torch.Size([1280]), dtype=torch.float16, device=cpu key['clip_prompt_embeds']: [Tensor] shape=torch.Size([77, 1280]), dtype=torch.float16, device=cpu key['text_encoder_3-item2']: [Dict] with 1 keys key['t5_prompt_embeds']: [Tensor] shape=torch.Size([77, 4096]), dtype=torch.float16, device=cpu key['text_encoder-item3']: [Dict] with 2 keys key['clip_pooled_prompt_embeds']: [Tensor] shape=torch.Size([768]), dtype=torch.float16, device=cpu key['clip_prompt_embeds']: [Tensor] shape=torch.Size([77, 768]), dtype=torch.float16, device=cpu key['text_encoder_2-item4']: [Dict] with 2 keys key['clip_pooled_prompt_embeds']: [Tensor] shape=torch.Size([1280]), dtype=torch.float16, device=cpu key['clip_prompt_embeds']: [Tensor] shape=torch.Size([77, 1280]), dtype=torch.float16, device=cpu key['text_encoder_3-item5']: [Dict] with 1 keys key['t5_prompt_embeds']: [Tensor] shape=torch.Size([77, 4096]), dtype=torch.float16, device=cpu key['vae.encoder-item6']: [Dict] with 2 keys key['vae.encoder_f0']: [Tensor] shape=torch.Size([32, 128, 128]), dtype=torch.float16, device=cpu key['vae.encoder_f1']: [Tensor] shape=torch.Size([32, 128, 128]), dtype=torch.float16, device=cpu key['vae.decoder']: [Tensor] shape=torch.Size([16, 128, 128]), dtype=torch.float16, device=cpu ⌛️ [1/4] FRONTEND: Load time: 0.008s ------------------------------------------------------------ SD3.5 Features Summary ------------------------------------------------------------ Number of text encoders: 3 Encoder names: ['text_encoder', 'text_encoder_2', 'text_encoder_3'] Has VAE latents: True Has VAE encoder features: True Data type: torch.float16 text_encoder-item0: clip_pooled_prompt_embeds: torch.Size([768]), torch.float16 clip_prompt_embeds: torch.Size([77, 768]), torch.float16 text_encoder_2-item1: clip_pooled_prompt_embeds: torch.Size([1280]), torch.float16 clip_prompt_embeds: torch.Size([77, 1280]), torch.float16 text_encoder_3-item2: t5_prompt_embeds: torch.Size([77, 4096]), torch.float16 text_encoder-item3: clip_pooled_prompt_embeds: torch.Size([768]), torch.float16 clip_prompt_embeds: torch.Size([77, 768]), torch.float16 text_encoder_2-item4: clip_pooled_prompt_embeds: torch.Size([1280]), torch.float16 clip_prompt_embeds: torch.Size([77, 1280]), torch.float16 text_encoder_3-item5: t5_prompt_embeds: torch.Size([77, 4096]), torch.float16 vae.decoder latents: torch.Size([16, 128, 128]) vae.encoder features: vae.encoder_f0: torch.Size([32, 128, 128]), torch.float16 vae.encoder_f1: torch.Size([32, 128, 128]), torch.float16 ------------------------------------------------------------ [2/4] FRONTEND: Pack + Encode (strategy: individual)... IndividualPacker: text_encoder-item0.clip_pooled_prompt_embeds: torch.Size([768])->torch.Size([1, 1, 1, 768]) text_encoder-item0.clip_prompt_embeds: torch.Size([77, 768])->torch.Size([1, 1, 77, 768]) text_encoder_2-item1.clip_pooled_prompt_embeds: torch.Size([1280])->torch.Size([1, 1, 1, 1280]) text_encoder_2-item1.clip_prompt_embeds: torch.Size([77, 1280])->torch.Size([1, 1, 77, 1280]) text_encoder_3-item2.t5_prompt_embeds: torch.Size([77, 4096])->torch.Size([1, 1, 77, 4096]) text_encoder-item3.clip_pooled_prompt_embeds: torch.Size([768])->torch.Size([1, 1, 1, 768]) text_encoder-item3.clip_prompt_embeds: torch.Size([77, 768])->torch.Size([1, 1, 77, 768]) text_encoder_2-item4.clip_pooled_prompt_embeds: torch.Size([1280])->torch.Size([1, 1, 1, 1280]) text_encoder_2-item4.clip_prompt_embeds: torch.Size([77, 1280])->torch.Size([1, 1, 77, 1280]) text_encoder_3-item5.t5_prompt_embeds: torch.Size([77, 4096])->torch.Size([1, 1, 77, 4096]) vae.encoder_f0: torch.Size([32, 128, 128])->torch.Size([1, 1, 512, 1024]) vae.encoder_f1: torch.Size([32, 128, 128])->torch.Size([1, 1, 512, 1024]) vae.decoder: torch.Size([16, 128, 128])->torch.Size([1, 1, 512, 512]) Using per-key quantization points (text_encoder-item0.clip_pooled_prompt_embeds: torch.Size([256])) for text_encoder-item0.clip_pooled_prompt_embeds text_encoder-item0.clip_pooled_prompt_embeds: 740B, BPFP=7.7083 Using per-key quantization points (text_encoder-item0.clip_prompt_embeds: torch.Size([256])) for text_encoder-item0.clip_prompt_embeds text_encoder-item0.clip_prompt_embeds: 5,440B, BPFP=0.7359 Using per-key quantization points (text_encoder_2-item1.clip_pooled_prompt_embeds: torch.Size([256])) for text_encoder_2-item1.clip_pooled_prompt_embeds text_encoder_2-item1.clip_pooled_prompt_embeds: 1,196B, BPFP=7.4750 Using per-key quantization points (text_encoder_2-item1.clip_prompt_embeds: torch.Size([256])) for text_encoder_2-item1.clip_prompt_embeds text_encoder_2-item1.clip_prompt_embeds: 10,112B, BPFP=0.8208 Using per-key quantization points (text_encoder_3-item2.t5_prompt_embeds: torch.Size([256])) for text_encoder_3-item2.t5_prompt_embeds text_encoder_3-item2.t5_prompt_embeds: 27,452B, BPFP=0.6963 Using per-key quantization points (text_encoder-item3.clip_pooled_prompt_embeds: torch.Size([256])) for text_encoder-item3.clip_pooled_prompt_embeds text_encoder-item3.clip_pooled_prompt_embeds: 760B, BPFP=7.9167 Using per-key quantization points (text_encoder-item3.clip_prompt_embeds: torch.Size([256])) for text_encoder-item3.clip_prompt_embeds text_encoder-item3.clip_prompt_embeds: 4,024B, BPFP=0.5444 Using per-key quantization points (text_encoder_2-item4.clip_pooled: torch.Size([256])) for text_encoder_2-item4.clip_pooled_prompt_embeds text_encoder_2-item4.clip_pooled_prompt_embeds: 1,268B, BPFP=7.9250 Using per-key quantization points (text_encoder_2-item4.clip_prompt: torch.Size([256])) for text_encoder_2-item4.clip_prompt_embeds text_encoder_2-item4.clip_prompt_embeds: 6,996B, BPFP=0.5679 Using per-key quantization points (text_encoder_3-item5.t5_prompt_embeds: torch.Size([256])) for text_encoder_3-item5.t5_prompt_embeds text_encoder_3-item5.t5_prompt_embeds: 18,416B, BPFP=0.4671 Using per-key quantization points (vae.encoder_f0: torch.Size([256])) for vae.encoder_f0 vae.encoder_f0: 13,972B, BPFP=0.2132 Using per-key quantization points (vae.encoder_f1: torch.Size([256])) for vae.encoder_f1 vae.encoder_f1: 13,972B, BPFP=0.2132 Using per-key quantization points (vae.decoder: torch.Size([256])) for vae.decoder vae.decoder: 7,928B, BPFP=0.2419 ⌛️ [2/4] FRONTEND: Frontend time: 0.294s (Pack+Encode) [3/4] BACKEND: Decode + Unpack... Using per-key quantization points (text_encoder-item0.clip_pooled_prompt_embeds: torch.Size([256])) for text_encoder-item0.clip_pooled_prompt_embeds Using per-key quantization points (text_encoder-item0.clip_prompt_embeds: torch.Size([256])) for text_encoder-item0.clip_prompt_embeds Using per-key quantization points (text_encoder_2-item1.clip_pooled_prompt_embeds: torch.Size([256])) for text_encoder_2-item1.clip_pooled_prompt_embeds Using per-key quantization points (text_encoder_2-item1.clip_prompt_embeds: torch.Size([256])) for text_encoder_2-item1.clip_prompt_embeds Using per-key quantization points (text_encoder_3-item2.t5_prompt_embeds: torch.Size([256])) for text_encoder_3-item2.t5_prompt_embeds Using per-key quantization points (text_encoder-item3.clip_pooled_prompt_embeds: torch.Size([256])) for text_encoder-item3.clip_pooled_prompt_embeds Using per-key quantization points (text_encoder-item3.clip_prompt_embeds: torch.Size([256])) for text_encoder-item3.clip_prompt_embeds Using per-key quantization points (text_encoder_2-item4.clip_pooled: torch.Size([256])) for text_encoder_2-item4.clip_pooled_prompt_embeds Using per-key quantization points (text_encoder_2-item4.clip_prompt: torch.Size([256])) for text_encoder_2-item4.clip_prompt_embeds Using per-key quantization points (text_encoder_3-item5.t5_prompt_embeds: torch.Size([256])) for text_encoder_3-item5.t5_prompt_embeds Using per-key quantization points (vae.encoder_f0: torch.Size([256])) for vae.encoder_f0 Using per-key quantization points (vae.encoder_f1: torch.Size([256])) for vae.encoder_f1 Using per-key quantization points (vae.decoder: torch.Size([256])) for vae.decoder IndividualUnPacker: text_encoder-item0.clip_pooled_prompt_embeds: torch.Size([768]) text_encoder-item0.clip_prompt_embeds: torch.Size([77, 768]) text_encoder_2-item1.clip_pooled_prompt_embeds: torch.Size([1280]) text_encoder_2-item1.clip_prompt_embeds: torch.Size([77, 1280]) text_encoder_3-item2.t5_prompt_embeds: torch.Size([77, 4096]) text_encoder-item3.clip_pooled_prompt_embeds: torch.Size([768]) text_encoder-item3.clip_prompt_embeds: torch.Size([77, 768]) text_encoder_2-item4.clip_pooled_prompt_embeds: torch.Size([1280]) text_encoder_2-item4.clip_prompt_embeds: torch.Size([77, 1280]) text_encoder_3-item5.t5_prompt_embeds: torch.Size([77, 4096]) vae.decoder: torch.Size([16, 128, 128]) vae.encoder_f0: torch.Size([32, 128, 128]) vae.encoder_f1: torch.Size([32, 128, 128]) ⌛️ [3/4] BACKEND: Backend time: 0.447s [4/4] METRICS: Computing MSE Breakdown... Using per-key quantization points (text_encoder-item0.clip_pooled_prompt_embeds: torch.Size([256])) for text_encoder-item0.clip_pooled_prompt_embeds Using per-key quantization points (text_encoder-item0.clip_prompt_embeds: torch.Size([256])) for text_encoder-item0.clip_prompt_embeds Using per-key quantization points (text_encoder_2-item1.clip_pooled_prompt_embeds: torch.Size([256])) for text_encoder_2-item1.clip_pooled_prompt_embeds Using per-key quantization points (text_encoder_2-item1.clip_prompt_embeds: torch.Size([256])) for text_encoder_2-item1.clip_prompt_embeds Using per-key quantization points (text_encoder_3-item2.t5_prompt_embeds: torch.Size([256])) for text_encoder_3-item2.t5_prompt_embeds Using per-key quantization points (text_encoder-item3.clip_pooled_prompt_embeds: torch.Size([256])) for text_encoder-item3.clip_pooled_prompt_embeds Using per-key quantization points (text_encoder-item3.clip_prompt_embeds: torch.Size([256])) for text_encoder-item3.clip_prompt_embeds Using per-key quantization points (text_encoder_2-item4.clip_pooled: torch.Size([256])) for text_encoder_2-item4.clip_pooled_prompt_embeds Using per-key quantization points (text_encoder_2-item4.clip_prompt: torch.Size([256])) for text_encoder_2-item4.clip_prompt_embeds Using per-key quantization points (text_encoder_3-item5.t5_prompt_embeds: torch.Size([256])) for text_encoder_3-item5.t5_prompt_embeds Using per-key quantization points (vae.encoder_f0: torch.Size([256])) for vae.encoder_f0 Using per-key quantization points (vae.encoder_f1: torch.Size([256])) for vae.encoder_f1 Using per-key quantization points (vae.decoder: torch.Size([256])) for vae.decoder IndividualUnPacker: text_encoder-item0.clip_pooled_prompt_embeds: torch.Size([768]) text_encoder-item0.clip_prompt_embeds: torch.Size([77, 768]) text_encoder_2-item1.clip_pooled_prompt_embeds: torch.Size([1280]) text_encoder_2-item1.clip_prompt_embeds: torch.Size([77, 1280]) text_encoder_3-item2.t5_prompt_embeds: torch.Size([77, 4096]) text_encoder-item3.clip_pooled_prompt_embeds: torch.Size([768]) text_encoder-item3.clip_prompt_embeds: torch.Size([77, 768]) text_encoder_2-item4.clip_pooled_prompt_embeds: torch.Size([1280]) text_encoder_2-item4.clip_prompt_embeds: torch.Size([77, 1280]) text_encoder_3-item5.t5_prompt_embeds: torch.Size([77, 4096]) vae.decoder: torch.Size([16, 128, 128]) vae.encoder_f0: torch.Size([32, 128, 128]) vae.encoder_f1: torch.Size([32, 128, 128]) Per-key MSE Breakdown (Quant=Truncation+Quantization, Codec=Compression): Key Quant-MSE Total-MSE ------------------------------------------------------------------------------------- text_encoder-item0.clip_pooled_prompt_embeds 0.00030751 0.90416344 text_encoder-item0.clip_prompt_embeds 0.00021654 36.00623985 text_encoder_2-item1.clip_pooled_prompt_embeds 0.00022548 0.98446150 text_encoder_2-item1.clip_prompt_embeds 0.00022218 0.12336559 text_encoder_3-item2.t5_prompt_embeds 0.00000780 0.00362558 text_encoder-item3.clip_pooled_prompt_embeds 0.00841799 1.01420029 text_encoder-item3.clip_prompt_embeds 0.00023247 23.90570887 text_encoder_2-item4.clip_pooled_prompt_embeds 0.00016751 0.67051430 text_encoder_2-item4.clip_prompt_embeds 0.00009830 0.47049568 text_encoder_3-item5.t5_prompt_embeds 0.00001263 0.00227117 vae.encoder_f0 0.00577698 1.94225919 vae.encoder_f1 0.00578348 1.94221735 vae.decoder 0.00017559 0.06701561 ------------------------------------------------------------------------------------- TOTAL 0.00273280 2.50380700 (elements=2,260,992) ---------------------- -------------------------------------------------------- SAMPLE-WISE STATISTICS ---------------------- -------------------------------------------------------- Handler sd35 Strategy individual Architecture hyperprior-featurecoding ---------------------- -------------------------------------------------------- Total Elements 2260992 Total Bytes 112276 BPFP 0.3973 bits/point EBPFP 0.7945 equivalent bits/point MSE 2.503807 ---------------------- -------------------------------------------------------- Time: 0.749s Load: 0.008s, Pack+Encode: 0.294s, Decode+Unpack: 0.447s ---------------------- -------------------------------------------------------- Restored Feature Format: [Dict] with 8 keys key['text_encoder-item0']: [Dict] with 2 keys key['clip_pooled_prompt_embeds']: [Tensor] shape=torch.Size([768]), dtype=torch.float16, device=cpu key['clip_prompt_embeds']: [Tensor] shape=torch.Size([77, 768]), dtype=torch.float16, device=cpu key['text_encoder_2-item1']: [Dict] with 2 keys key['clip_pooled_prompt_embeds']: [Tensor] shape=torch.Size([1280]), dtype=torch.float16, device=cpu key['clip_prompt_embeds']: [Tensor] shape=torch.Size([77, 1280]), dtype=torch.float16, device=cpu key['text_encoder_3-item2']: [Dict] with 1 keys key['t5_prompt_embeds']: [Tensor] shape=torch.Size([77, 4096]), dtype=torch.float16, device=cpu key['text_encoder-item3']: [Dict] with 2 keys key['clip_pooled_prompt_embeds']: [Tensor] shape=torch.Size([768]), dtype=torch.float16, device=cpu key['clip_prompt_embeds']: [Tensor] shape=torch.Size([77, 768]), dtype=torch.float16, device=cpu key['text_encoder_2-item4']: [Dict] with 2 keys key['clip_pooled_prompt_embeds']: [Tensor] shape=torch.Size([1280]), dtype=torch.float16, device=cpu key['clip_prompt_embeds']: [Tensor] shape=torch.Size([77, 1280]), dtype=torch.float16, device=cpu key['text_encoder_3-item5']: [Dict] with 1 keys key['t5_prompt_embeds']: [Tensor] shape=torch.Size([77, 4096]), dtype=torch.float16, device=cpu key['vae.decoder']: [Tensor] shape=torch.Size([16, 128, 128]), dtype=torch.float16, device=cpu key['vae.encoder']: [Dict] with 2 keys key['vae.encoder_f0']: [Tensor] shape=torch.Size([32, 128, 128]), dtype=torch.float16, device=cpu key['vae.encoder_f1']: [Tensor] shape=torch.Size([32, 128, 128]), dtype=torch.float16, device=cpu 💾 Converting with 2.5038 MSE: from ../datasets/FeatureCoding-StableDiffusion3.5/100samples_features/000000055072.zst to output-fixed/sd35/lambda0.004/hyperprior-featurecoding-8bit-individual/sd35cond/000000055072.zst 💪 Processing: ../datasets/FeatureCoding-StableDiffusion3.5/100samples_features/000000060932.zst (10/100) [1/4] FRONTEND: Loading features from ../datasets/FeatureCoding-StableDiffusion3.5/100samples_features/000000060932.zst... Original data structure: root: [Dict] with 8 keys key['text_encoder-item0']: [Dict] with 2 keys key['clip_pooled_prompt_embeds']: [Tensor] shape=torch.Size([768]), dtype=torch.float16, device=cpu key['clip_prompt_embeds']: [Tensor] shape=torch.Size([77, 768]), dtype=torch.float16, device=cpu key['text_encoder_2-item1']: [Dict] with 2 keys key['clip_pooled_prompt_embeds']: [Tensor] shape=torch.Size([1280]), dtype=torch.float16, device=cpu key['clip_prompt_embeds']: [Tensor] shape=torch.Size([77, 1280]), dtype=torch.float16, device=cpu key['text_encoder_3-item2']: [Dict] with 1 keys key['t5_prompt_embeds']: [Tensor] shape=torch.Size([77, 4096]), dtype=torch.float16, device=cpu key['text_encoder-item3']: [Dict] with 2 keys key['clip_pooled_prompt_embeds']: [Tensor] shape=torch.Size([768]), dtype=torch.float16, device=cpu key['clip_prompt_embeds']: [Tensor] shape=torch.Size([77, 768]), dtype=torch.float16, device=cpu key['text_encoder_2-item4']: [Dict] with 2 keys key['clip_pooled_prompt_embeds']: [Tensor] shape=torch.Size([1280]), dtype=torch.float16, device=cpu key['clip_prompt_embeds']: [Tensor] shape=torch.Size([77, 1280]), dtype=torch.float16, device=cpu key['text_encoder_3-item5']: [Dict] with 1 keys key['t5_prompt_embeds']: [Tensor] shape=torch.Size([77, 4096]), dtype=torch.float16, device=cpu key['vae.encoder-item6']: [Dict] with 2 keys key['vae.encoder_f0']: [Tensor] shape=torch.Size([32, 128, 128]), dtype=torch.float16, device=cpu key['vae.encoder_f1']: [Tensor] shape=torch.Size([32, 128, 128]), dtype=torch.float16, device=cpu key['vae.decoder']: [Tensor] shape=torch.Size([16, 128, 128]), dtype=torch.float16, device=cpu ⌛️ [1/4] FRONTEND: Load time: 0.008s ------------------------------------------------------------ SD3.5 Features Summary ------------------------------------------------------------ Number of text encoders: 3 Encoder names: ['text_encoder', 'text_encoder_2', 'text_encoder_3'] Has VAE latents: True Has VAE encoder features: True Data type: torch.float16 text_encoder-item0: clip_pooled_prompt_embeds: torch.Size([768]), torch.float16 clip_prompt_embeds: torch.Size([77, 768]), torch.float16 text_encoder_2-item1: clip_pooled_prompt_embeds: torch.Size([1280]), torch.float16 clip_prompt_embeds: torch.Size([77, 1280]), torch.float16 text_encoder_3-item2: t5_prompt_embeds: torch.Size([77, 4096]), torch.float16 text_encoder-item3: clip_pooled_prompt_embeds: torch.Size([768]), torch.float16 clip_prompt_embeds: torch.Size([77, 768]), torch.float16 text_encoder_2-item4: clip_pooled_prompt_embeds: torch.Size([1280]), torch.float16 clip_prompt_embeds: torch.Size([77, 1280]), torch.float16 text_encoder_3-item5: t5_prompt_embeds: torch.Size([77, 4096]), torch.float16 vae.decoder latents: torch.Size([16, 128, 128]) vae.encoder features: vae.encoder_f0: torch.Size([32, 128, 128]), torch.float16 vae.encoder_f1: torch.Size([32, 128, 128]), torch.float16 ------------------------------------------------------------ [2/4] FRONTEND: Pack + Encode (strategy: individual)... IndividualPacker: text_encoder-item0.clip_pooled_prompt_embeds: torch.Size([768])->torch.Size([1, 1, 1, 768]) text_encoder-item0.clip_prompt_embeds: torch.Size([77, 768])->torch.Size([1, 1, 77, 768]) text_encoder_2-item1.clip_pooled_prompt_embeds: torch.Size([1280])->torch.Size([1, 1, 1, 1280]) text_encoder_2-item1.clip_prompt_embeds: torch.Size([77, 1280])->torch.Size([1, 1, 77, 1280]) text_encoder_3-item2.t5_prompt_embeds: torch.Size([77, 4096])->torch.Size([1, 1, 77, 4096]) text_encoder-item3.clip_pooled_prompt_embeds: torch.Size([768])->torch.Size([1, 1, 1, 768]) text_encoder-item3.clip_prompt_embeds: torch.Size([77, 768])->torch.Size([1, 1, 77, 768]) text_encoder_2-item4.clip_pooled_prompt_embeds: torch.Size([1280])->torch.Size([1, 1, 1, 1280]) text_encoder_2-item4.clip_prompt_embeds: torch.Size([77, 1280])->torch.Size([1, 1, 77, 1280]) text_encoder_3-item5.t5_prompt_embeds: torch.Size([77, 4096])->torch.Size([1, 1, 77, 4096]) vae.encoder_f0: torch.Size([32, 128, 128])->torch.Size([1, 1, 512, 1024]) vae.encoder_f1: torch.Size([32, 128, 128])->torch.Size([1, 1, 512, 1024]) vae.decoder: torch.Size([16, 128, 128])->torch.Size([1, 1, 512, 512]) Using per-key quantization points (text_encoder-item0.clip_pooled_prompt_embeds: torch.Size([256])) for text_encoder-item0.clip_pooled_prompt_embeds text_encoder-item0.clip_pooled_prompt_embeds: 732B, BPFP=7.6250 Using per-key quantization points (text_encoder-item0.clip_prompt_embeds: torch.Size([256])) for text_encoder-item0.clip_prompt_embeds text_encoder-item0.clip_prompt_embeds: 6,268B, BPFP=0.8479 Using per-key quantization points (text_encoder_2-item1.clip_pooled_prompt_embeds: torch.Size([256])) for text_encoder_2-item1.clip_pooled_prompt_embeds text_encoder_2-item1.clip_pooled_prompt_embeds: 1,196B, BPFP=7.4750 Using per-key quantization points (text_encoder_2-item1.clip_prompt_embeds: torch.Size([256])) for text_encoder_2-item1.clip_prompt_embeds text_encoder_2-item1.clip_prompt_embeds: 10,504B, BPFP=0.8526 Using per-key quantization points (text_encoder_3-item2.t5_prompt_embeds: torch.Size([256])) for text_encoder_3-item2.t5_prompt_embeds text_encoder_3-item2.t5_prompt_embeds: 28,648B, BPFP=0.7267 Using per-key quantization points (text_encoder-item3.clip_pooled_prompt_embeds: torch.Size([256])) for text_encoder-item3.clip_pooled_prompt_embeds text_encoder-item3.clip_pooled_prompt_embeds: 760B, BPFP=7.9167 Using per-key quantization points (text_encoder-item3.clip_prompt_embeds: torch.Size([256])) for text_encoder-item3.clip_prompt_embeds text_encoder-item3.clip_prompt_embeds: 4,024B, BPFP=0.5444 Using per-key quantization points (text_encoder_2-item4.clip_pooled: torch.Size([256])) for text_encoder_2-item4.clip_pooled_prompt_embeds text_encoder_2-item4.clip_pooled_prompt_embeds: 1,268B, BPFP=7.9250 Using per-key quantization points (text_encoder_2-item4.clip_prompt: torch.Size([256])) for text_encoder_2-item4.clip_prompt_embeds text_encoder_2-item4.clip_prompt_embeds: 6,996B, BPFP=0.5679 Using per-key quantization points (text_encoder_3-item5.t5_prompt_embeds: torch.Size([256])) for text_encoder_3-item5.t5_prompt_embeds text_encoder_3-item5.t5_prompt_embeds: 18,416B, BPFP=0.4671 Using per-key quantization points (vae.encoder_f0: torch.Size([256])) for vae.encoder_f0 vae.encoder_f0: 17,116B, BPFP=0.2612 Using per-key quantization points (vae.encoder_f1: torch.Size([256])) for vae.encoder_f1 vae.encoder_f1: 17,116B, BPFP=0.2612 Using per-key quantization points (vae.decoder: torch.Size([256])) for vae.decoder vae.decoder: 6,076B, BPFP=0.1854 ⌛️ [2/4] FRONTEND: Frontend time: 0.295s (Pack+Encode) [3/4] BACKEND: Decode + Unpack... Using per-key quantization points (text_encoder-item0.clip_pooled_prompt_embeds: torch.Size([256])) for text_encoder-item0.clip_pooled_prompt_embeds Using per-key quantization points (text_encoder-item0.clip_prompt_embeds: torch.Size([256])) for text_encoder-item0.clip_prompt_embeds Using per-key quantization points (text_encoder_2-item1.clip_pooled_prompt_embeds: torch.Size([256])) for text_encoder_2-item1.clip_pooled_prompt_embeds Using per-key quantization points (text_encoder_2-item1.clip_prompt_embeds: torch.Size([256])) for text_encoder_2-item1.clip_prompt_embeds Using per-key quantization points (text_encoder_3-item2.t5_prompt_embeds: torch.Size([256])) for text_encoder_3-item2.t5_prompt_embeds Using per-key quantization points (text_encoder-item3.clip_pooled_prompt_embeds: torch.Size([256])) for text_encoder-item3.clip_pooled_prompt_embeds Using per-key quantization points (text_encoder-item3.clip_prompt_embeds: torch.Size([256])) for text_encoder-item3.clip_prompt_embeds Using per-key quantization points (text_encoder_2-item4.clip_pooled: torch.Size([256])) for text_encoder_2-item4.clip_pooled_prompt_embeds Using per-key quantization points (text_encoder_2-item4.clip_prompt: torch.Size([256])) for text_encoder_2-item4.clip_prompt_embeds Using per-key quantization points (text_encoder_3-item5.t5_prompt_embeds: torch.Size([256])) for text_encoder_3-item5.t5_prompt_embeds Using per-key quantization points (vae.encoder_f0: torch.Size([256])) for vae.encoder_f0 Using per-key quantization points (vae.encoder_f1: torch.Size([256])) for vae.encoder_f1 Using per-key quantization points (vae.decoder: torch.Size([256])) for vae.decoder IndividualUnPacker: text_encoder-item0.clip_pooled_prompt_embeds: torch.Size([768]) text_encoder-item0.clip_prompt_embeds: torch.Size([77, 768]) text_encoder_2-item1.clip_pooled_prompt_embeds: torch.Size([1280]) text_encoder_2-item1.clip_prompt_embeds: torch.Size([77, 1280]) text_encoder_3-item2.t5_prompt_embeds: torch.Size([77, 4096]) text_encoder-item3.clip_pooled_prompt_embeds: torch.Size([768]) text_encoder-item3.clip_prompt_embeds: torch.Size([77, 768]) text_encoder_2-item4.clip_pooled_prompt_embeds: torch.Size([1280]) text_encoder_2-item4.clip_prompt_embeds: torch.Size([77, 1280]) text_encoder_3-item5.t5_prompt_embeds: torch.Size([77, 4096]) vae.decoder: torch.Size([16, 128, 128]) vae.encoder_f0: torch.Size([32, 128, 128]) vae.encoder_f1: torch.Size([32, 128, 128]) ⌛️ [3/4] BACKEND: Backend time: 0.447s [4/4] METRICS: Computing MSE Breakdown... Using per-key quantization points (text_encoder-item0.clip_pooled_prompt_embeds: torch.Size([256])) for text_encoder-item0.clip_pooled_prompt_embeds Using per-key quantization points (text_encoder-item0.clip_prompt_embeds: torch.Size([256])) for text_encoder-item0.clip_prompt_embeds Using per-key quantization points (text_encoder_2-item1.clip_pooled_prompt_embeds: torch.Size([256])) for text_encoder_2-item1.clip_pooled_prompt_embeds Using per-key quantization points (text_encoder_2-item1.clip_prompt_embeds: torch.Size([256])) for text_encoder_2-item1.clip_prompt_embeds Using per-key quantization points (text_encoder_3-item2.t5_prompt_embeds: torch.Size([256])) for text_encoder_3-item2.t5_prompt_embeds Using per-key quantization points (text_encoder-item3.clip_pooled_prompt_embeds: torch.Size([256])) for text_encoder-item3.clip_pooled_prompt_embeds Using per-key quantization points (text_encoder-item3.clip_prompt_embeds: torch.Size([256])) for text_encoder-item3.clip_prompt_embeds Using per-key quantization points (text_encoder_2-item4.clip_pooled: torch.Size([256])) for text_encoder_2-item4.clip_pooled_prompt_embeds Using per-key quantization points (text_encoder_2-item4.clip_prompt: torch.Size([256])) for text_encoder_2-item4.clip_prompt_embeds Using per-key quantization points (text_encoder_3-item5.t5_prompt_embeds: torch.Size([256])) for text_encoder_3-item5.t5_prompt_embeds Using per-key quantization points (vae.encoder_f0: torch.Size([256])) for vae.encoder_f0 Using per-key quantization points (vae.encoder_f1: torch.Size([256])) for vae.encoder_f1 Using per-key quantization points (vae.decoder: torch.Size([256])) for vae.decoder IndividualUnPacker: text_encoder-item0.clip_pooled_prompt_embeds: torch.Size([768]) text_encoder-item0.clip_prompt_embeds: torch.Size([77, 768]) text_encoder_2-item1.clip_pooled_prompt_embeds: torch.Size([1280]) text_encoder_2-item1.clip_prompt_embeds: torch.Size([77, 1280]) text_encoder_3-item2.t5_prompt_embeds: torch.Size([77, 4096]) text_encoder-item3.clip_pooled_prompt_embeds: torch.Size([768]) text_encoder-item3.clip_prompt_embeds: torch.Size([77, 768]) text_encoder_2-item4.clip_pooled_prompt_embeds: torch.Size([1280]) text_encoder_2-item4.clip_prompt_embeds: torch.Size([77, 1280]) text_encoder_3-item5.t5_prompt_embeds: torch.Size([77, 4096]) vae.decoder: torch.Size([16, 128, 128]) vae.encoder_f0: torch.Size([32, 128, 128]) vae.encoder_f1: torch.Size([32, 128, 128]) Per-key MSE Breakdown (Quant=Truncation+Quantization, Codec=Compression): Key Quant-MSE Total-MSE ------------------------------------------------------------------------------------- text_encoder-item0.clip_pooled_prompt_embeds 0.00030339 0.96714354 text_encoder-item0.clip_prompt_embeds 0.00022160 24.23977780 text_encoder_2-item1.clip_pooled_prompt_embeds 0.00041183 0.83238535 text_encoder_2-item1.clip_prompt_embeds 0.00016827 0.12087520 text_encoder_3-item2.t5_prompt_embeds 0.00000781 0.00338200 text_encoder-item3.clip_pooled_prompt_embeds 0.00841799 1.01420029 text_encoder-item3.clip_prompt_embeds 0.00023247 23.90570887 text_encoder_2-item4.clip_pooled_prompt_embeds 0.00016751 0.67051430 text_encoder_2-item4.clip_prompt_embeds 0.00009830 0.47049568 text_encoder_3-item5.t5_prompt_embeds 0.00001263 0.00227117 vae.encoder_f0 0.00668450 2.29490089 vae.encoder_f1 0.00668875 2.29468369 vae.decoder 0.00023059 0.05260476 ------------------------------------------------------------------------------------- TOTAL 0.00315742 2.35768172 (elements=2,260,992) ---------------------- -------------------------------------------------------- SAMPLE-WISE STATISTICS ---------------------- -------------------------------------------------------- Handler sd35 Strategy individual Architecture hyperprior-featurecoding ---------------------- -------------------------------------------------------- Total Elements 2260992 Total Bytes 119120 BPFP 0.4215 bits/point EBPFP 0.8430 equivalent bits/point MSE 2.357682 ---------------------- -------------------------------------------------------- Time: 0.750s Load: 0.008s, Pack+Encode: 0.295s, Decode+Unpack: 0.447s ---------------------- -------------------------------------------------------- Restored Feature Format: [Dict] with 8 keys key['text_encoder-item0']: [Dict] with 2 keys key['clip_pooled_prompt_embeds']: [Tensor] shape=torch.Size([768]), dtype=torch.float16, device=cpu key['clip_prompt_embeds']: [Tensor] shape=torch.Size([77, 768]), dtype=torch.float16, device=cpu key['text_encoder_2-item1']: [Dict] with 2 keys key['clip_pooled_prompt_embeds']: [Tensor] shape=torch.Size([1280]), dtype=torch.float16, device=cpu key['clip_prompt_embeds']: [Tensor] shape=torch.Size([77, 1280]), dtype=torch.float16, device=cpu key['text_encoder_3-item2']: [Dict] with 1 keys key['t5_prompt_embeds']: [Tensor] shape=torch.Size([77, 4096]), dtype=torch.float16, device=cpu key['text_encoder-item3']: [Dict] with 2 keys key['clip_pooled_prompt_embeds']: [Tensor] shape=torch.Size([768]), dtype=torch.float16, device=cpu key['clip_prompt_embeds']: [Tensor] shape=torch.Size([77, 768]), dtype=torch.float16, device=cpu key['text_encoder_2-item4']: [Dict] with 2 keys key['clip_pooled_prompt_embeds']: [Tensor] shape=torch.Size([1280]), dtype=torch.float16, device=cpu key['clip_prompt_embeds']: [Tensor] shape=torch.Size([77, 1280]), dtype=torch.float16, device=cpu key['text_encoder_3-item5']: [Dict] with 1 keys key['t5_prompt_embeds']: [Tensor] shape=torch.Size([77, 4096]), dtype=torch.float16, device=cpu key['vae.decoder']: [Tensor] shape=torch.Size([16, 128, 128]), dtype=torch.float16, device=cpu key['vae.encoder']: [Dict] with 2 keys key['vae.encoder_f0']: [Tensor] shape=torch.Size([32, 128, 128]), dtype=torch.float16, device=cpu key['vae.encoder_f1']: [Tensor] shape=torch.Size([32, 128, 128]), dtype=torch.float16, device=cpu 💾 Converting with 2.3577 MSE: from ../datasets/FeatureCoding-StableDiffusion3.5/100samples_features/000000060932.zst to output-fixed/sd35/lambda0.004/hyperprior-featurecoding-8bit-individual/sd35cond/000000060932.zst 💪 Processing: ../datasets/FeatureCoding-StableDiffusion3.5/100samples_features/000000062025.zst (11/100) [1/4] FRONTEND: Loading features from ../datasets/FeatureCoding-StableDiffusion3.5/100samples_features/000000062025.zst... Original data structure: root: [Dict] with 8 keys key['text_encoder-item0']: [Dict] with 2 keys key['clip_pooled_prompt_embeds']: [Tensor] shape=torch.Size([768]), dtype=torch.float16, device=cpu key['clip_prompt_embeds']: [Tensor] shape=torch.Size([77, 768]), dtype=torch.float16, device=cpu key['text_encoder_2-item1']: [Dict] with 2 keys key['clip_pooled_prompt_embeds']: [Tensor] shape=torch.Size([1280]), dtype=torch.float16, device=cpu key['clip_prompt_embeds']: [Tensor] shape=torch.Size([77, 1280]), dtype=torch.float16, device=cpu key['text_encoder_3-item2']: [Dict] with 1 keys key['t5_prompt_embeds']: [Tensor] shape=torch.Size([77, 4096]), dtype=torch.float16, device=cpu key['text_encoder-item3']: [Dict] with 2 keys key['clip_pooled_prompt_embeds']: [Tensor] shape=torch.Size([768]), dtype=torch.float16, device=cpu key['clip_prompt_embeds']: [Tensor] shape=torch.Size([77, 768]), dtype=torch.float16, device=cpu key['text_encoder_2-item4']: [Dict] with 2 keys key['clip_pooled_prompt_embeds']: [Tensor] shape=torch.Size([1280]), dtype=torch.float16, device=cpu key['clip_prompt_embeds']: [Tensor] shape=torch.Size([77, 1280]), dtype=torch.float16, device=cpu key['text_encoder_3-item5']: [Dict] with 1 keys key['t5_prompt_embeds']: [Tensor] shape=torch.Size([77, 4096]), dtype=torch.float16, device=cpu key['vae.encoder-item6']: [Dict] with 2 keys key['vae.encoder_f0']: [Tensor] shape=torch.Size([32, 128, 128]), dtype=torch.float16, device=cpu key['vae.encoder_f1']: [Tensor] shape=torch.Size([32, 128, 128]), dtype=torch.float16, device=cpu key['vae.decoder']: [Tensor] shape=torch.Size([16, 128, 128]), dtype=torch.float16, device=cpu ⌛️ [1/4] FRONTEND: Load time: 0.009s ------------------------------------------------------------ SD3.5 Features Summary ------------------------------------------------------------ Number of text encoders: 3 Encoder names: ['text_encoder', 'text_encoder_2', 'text_encoder_3'] Has VAE latents: True Has VAE encoder features: True Data type: torch.float16 text_encoder-item0: clip_pooled_prompt_embeds: torch.Size([768]), torch.float16 clip_prompt_embeds: torch.Size([77, 768]), torch.float16 text_encoder_2-item1: clip_pooled_prompt_embeds: torch.Size([1280]), torch.float16 clip_prompt_embeds: torch.Size([77, 1280]), torch.float16 text_encoder_3-item2: t5_prompt_embeds: torch.Size([77, 4096]), torch.float16 text_encoder-item3: clip_pooled_prompt_embeds: torch.Size([768]), torch.float16 clip_prompt_embeds: torch.Size([77, 768]), torch.float16 text_encoder_2-item4: clip_pooled_prompt_embeds: torch.Size([1280]), torch.float16 clip_prompt_embeds: torch.Size([77, 1280]), torch.float16 text_encoder_3-item5: t5_prompt_embeds: torch.Size([77, 4096]), torch.float16 vae.decoder latents: torch.Size([16, 128, 128]) vae.encoder features: vae.encoder_f0: torch.Size([32, 128, 128]), torch.float16 vae.encoder_f1: torch.Size([32, 128, 128]), torch.float16 ------------------------------------------------------------ [2/4] FRONTEND: Pack + Encode (strategy: individual)... IndividualPacker: text_encoder-item0.clip_pooled_prompt_embeds: torch.Size([768])->torch.Size([1, 1, 1, 768]) text_encoder-item0.clip_prompt_embeds: torch.Size([77, 768])->torch.Size([1, 1, 77, 768]) text_encoder_2-item1.clip_pooled_prompt_embeds: torch.Size([1280])->torch.Size([1, 1, 1, 1280]) text_encoder_2-item1.clip_prompt_embeds: torch.Size([77, 1280])->torch.Size([1, 1, 77, 1280]) text_encoder_3-item2.t5_prompt_embeds: torch.Size([77, 4096])->torch.Size([1, 1, 77, 4096]) text_encoder-item3.clip_pooled_prompt_embeds: torch.Size([768])->torch.Size([1, 1, 1, 768]) text_encoder-item3.clip_prompt_embeds: torch.Size([77, 768])->torch.Size([1, 1, 77, 768]) text_encoder_2-item4.clip_pooled_prompt_embeds: torch.Size([1280])->torch.Size([1, 1, 1, 1280]) text_encoder_2-item4.clip_prompt_embeds: torch.Size([77, 1280])->torch.Size([1, 1, 77, 1280]) text_encoder_3-item5.t5_prompt_embeds: torch.Size([77, 4096])->torch.Size([1, 1, 77, 4096]) vae.encoder_f0: torch.Size([32, 128, 128])->torch.Size([1, 1, 512, 1024]) vae.encoder_f1: torch.Size([32, 128, 128])->torch.Size([1, 1, 512, 1024]) vae.decoder: torch.Size([16, 128, 128])->torch.Size([1, 1, 512, 512]) Using per-key quantization points (text_encoder-item0.clip_pooled_prompt_embeds: torch.Size([256])) for text_encoder-item0.clip_pooled_prompt_embeds text_encoder-item0.clip_pooled_prompt_embeds: 736B, BPFP=7.6667 Using per-key quantization points (text_encoder-item0.clip_prompt_embeds: torch.Size([256])) for text_encoder-item0.clip_prompt_embeds text_encoder-item0.clip_prompt_embeds: 6,696B, BPFP=0.9058 Using per-key quantization points (text_encoder_2-item1.clip_pooled_prompt_embeds: torch.Size([256])) for text_encoder_2-item1.clip_pooled_prompt_embeds text_encoder_2-item1.clip_pooled_prompt_embeds: 1,196B, BPFP=7.4750 Using per-key quantization points (text_encoder_2-item1.clip_prompt_embeds: torch.Size([256])) for text_encoder_2-item1.clip_prompt_embeds text_encoder_2-item1.clip_prompt_embeds: 10,936B, BPFP=0.8877 Using per-key quantization points (text_encoder_3-item2.t5_prompt_embeds: torch.Size([256])) for text_encoder_3-item2.t5_prompt_embeds text_encoder_3-item2.t5_prompt_embeds: 29,528B, BPFP=0.7490 Using per-key quantization points (text_encoder-item3.clip_pooled_prompt_embeds: torch.Size([256])) for text_encoder-item3.clip_pooled_prompt_embeds text_encoder-item3.clip_pooled_prompt_embeds: 760B, BPFP=7.9167 Using per-key quantization points (text_encoder-item3.clip_prompt_embeds: torch.Size([256])) for text_encoder-item3.clip_prompt_embeds text_encoder-item3.clip_prompt_embeds: 4,024B, BPFP=0.5444 Using per-key quantization points (text_encoder_2-item4.clip_pooled: torch.Size([256])) for text_encoder_2-item4.clip_pooled_prompt_embeds text_encoder_2-item4.clip_pooled_prompt_embeds: 1,268B, BPFP=7.9250 Using per-key quantization points (text_encoder_2-item4.clip_prompt: torch.Size([256])) for text_encoder_2-item4.clip_prompt_embeds text_encoder_2-item4.clip_prompt_embeds: 6,996B, BPFP=0.5679 Using per-key quantization points (text_encoder_3-item5.t5_prompt_embeds: torch.Size([256])) for text_encoder_3-item5.t5_prompt_embeds text_encoder_3-item5.t5_prompt_embeds: 18,416B, BPFP=0.4671 Using per-key quantization points (vae.encoder_f0: torch.Size([256])) for vae.encoder_f0 vae.encoder_f0: 16,540B, BPFP=0.2524 Using per-key quantization points (vae.encoder_f1: torch.Size([256])) for vae.encoder_f1 vae.encoder_f1: 16,540B, BPFP=0.2524 Using per-key quantization points (vae.decoder: torch.Size([256])) for vae.decoder vae.decoder: 4,792B, BPFP=0.1462 ⌛️ [2/4] FRONTEND: Frontend time: 0.293s (Pack+Encode) [3/4] BACKEND: Decode + Unpack... Using per-key quantization points (text_encoder-item0.clip_pooled_prompt_embeds: torch.Size([256])) for text_encoder-item0.clip_pooled_prompt_embeds Using per-key quantization points (text_encoder-item0.clip_prompt_embeds: torch.Size([256])) for text_encoder-item0.clip_prompt_embeds Using per-key quantization points (text_encoder_2-item1.clip_pooled_prompt_embeds: torch.Size([256])) for text_encoder_2-item1.clip_pooled_prompt_embeds Using per-key quantization points (text_encoder_2-item1.clip_prompt_embeds: torch.Size([256])) for text_encoder_2-item1.clip_prompt_embeds Using per-key quantization points (text_encoder_3-item2.t5_prompt_embeds: torch.Size([256])) for text_encoder_3-item2.t5_prompt_embeds Using per-key quantization points (text_encoder-item3.clip_pooled_prompt_embeds: torch.Size([256])) for text_encoder-item3.clip_pooled_prompt_embeds Using per-key quantization points (text_encoder-item3.clip_prompt_embeds: torch.Size([256])) for text_encoder-item3.clip_prompt_embeds Using per-key quantization points (text_encoder_2-item4.clip_pooled: torch.Size([256])) for text_encoder_2-item4.clip_pooled_prompt_embeds Using per-key quantization points (text_encoder_2-item4.clip_prompt: torch.Size([256])) for text_encoder_2-item4.clip_prompt_embeds Using per-key quantization points (text_encoder_3-item5.t5_prompt_embeds: torch.Size([256])) for text_encoder_3-item5.t5_prompt_embeds Using per-key quantization points (vae.encoder_f0: torch.Size([256])) for vae.encoder_f0 Using per-key quantization points (vae.encoder_f1: torch.Size([256])) for vae.encoder_f1 Using per-key quantization points (vae.decoder: torch.Size([256])) for vae.decoder IndividualUnPacker: text_encoder-item0.clip_pooled_prompt_embeds: torch.Size([768]) text_encoder-item0.clip_prompt_embeds: torch.Size([77, 768]) text_encoder_2-item1.clip_pooled_prompt_embeds: torch.Size([1280]) text_encoder_2-item1.clip_prompt_embeds: torch.Size([77, 1280]) text_encoder_3-item2.t5_prompt_embeds: torch.Size([77, 4096]) text_encoder-item3.clip_pooled_prompt_embeds: torch.Size([768]) text_encoder-item3.clip_prompt_embeds: torch.Size([77, 768]) text_encoder_2-item4.clip_pooled_prompt_embeds: torch.Size([1280]) text_encoder_2-item4.clip_prompt_embeds: torch.Size([77, 1280]) text_encoder_3-item5.t5_prompt_embeds: torch.Size([77, 4096]) vae.decoder: torch.Size([16, 128, 128]) vae.encoder_f0: torch.Size([32, 128, 128]) vae.encoder_f1: torch.Size([32, 128, 128]) ⌛️ [3/4] BACKEND: Backend time: 0.446s [4/4] METRICS: Computing MSE Breakdown... Using per-key quantization points (text_encoder-item0.clip_pooled_prompt_embeds: torch.Size([256])) for text_encoder-item0.clip_pooled_prompt_embeds Using per-key quantization points (text_encoder-item0.clip_prompt_embeds: torch.Size([256])) for text_encoder-item0.clip_prompt_embeds Using per-key quantization points (text_encoder_2-item1.clip_pooled_prompt_embeds: torch.Size([256])) for text_encoder_2-item1.clip_pooled_prompt_embeds Using per-key quantization points (text_encoder_2-item1.clip_prompt_embeds: torch.Size([256])) for text_encoder_2-item1.clip_prompt_embeds Using per-key quantization points (text_encoder_3-item2.t5_prompt_embeds: torch.Size([256])) for text_encoder_3-item2.t5_prompt_embeds Using per-key quantization points (text_encoder-item3.clip_pooled_prompt_embeds: torch.Size([256])) for text_encoder-item3.clip_pooled_prompt_embeds Using per-key quantization points (text_encoder-item3.clip_prompt_embeds: torch.Size([256])) for text_encoder-item3.clip_prompt_embeds Using per-key quantization points (text_encoder_2-item4.clip_pooled: torch.Size([256])) for text_encoder_2-item4.clip_pooled_prompt_embeds Using per-key quantization points (text_encoder_2-item4.clip_prompt: torch.Size([256])) for text_encoder_2-item4.clip_prompt_embeds Using per-key quantization points (text_encoder_3-item5.t5_prompt_embeds: torch.Size([256])) for text_encoder_3-item5.t5_prompt_embeds Using per-key quantization points (vae.encoder_f0: torch.Size([256])) for vae.encoder_f0 Using per-key quantization points (vae.encoder_f1: torch.Size([256])) for vae.encoder_f1 Using per-key quantization points (vae.decoder: torch.Size([256])) for vae.decoder IndividualUnPacker: text_encoder-item0.clip_pooled_prompt_embeds: torch.Size([768]) text_encoder-item0.clip_prompt_embeds: torch.Size([77, 768]) text_encoder_2-item1.clip_pooled_prompt_embeds: torch.Size([1280]) text_encoder_2-item1.clip_prompt_embeds: torch.Size([77, 1280]) text_encoder_3-item2.t5_prompt_embeds: torch.Size([77, 4096]) text_encoder-item3.clip_pooled_prompt_embeds: torch.Size([768]) text_encoder-item3.clip_prompt_embeds: torch.Size([77, 768]) text_encoder_2-item4.clip_pooled_prompt_embeds: torch.Size([1280]) text_encoder_2-item4.clip_prompt_embeds: torch.Size([77, 1280]) text_encoder_3-item5.t5_prompt_embeds: torch.Size([77, 4096]) vae.decoder: torch.Size([16, 128, 128]) vae.encoder_f0: torch.Size([32, 128, 128]) vae.encoder_f1: torch.Size([32, 128, 128]) Per-key MSE Breakdown (Quant=Truncation+Quantization, Codec=Compression): Key Quant-MSE Total-MSE ------------------------------------------------------------------------------------- text_encoder-item0.clip_pooled_prompt_embeds 0.00017240 0.92981633 text_encoder-item0.clip_prompt_embeds 0.00023190 23.94112723 text_encoder_2-item1.clip_pooled_prompt_embeds 0.00016235 0.79946666 text_encoder_2-item1.clip_prompt_embeds 0.00020162 0.13031174 text_encoder_3-item2.t5_prompt_embeds 0.00000881 0.00381124 text_encoder-item3.clip_pooled_prompt_embeds 0.00841799 1.01420029 text_encoder-item3.clip_prompt_embeds 0.00023247 23.90570887 text_encoder_2-item4.clip_pooled_prompt_embeds 0.00016751 0.67051430 text_encoder_2-item4.clip_prompt_embeds 0.00009830 0.47049568 text_encoder_3-item5.t5_prompt_embeds 0.00001263 0.00227117 vae.encoder_f0 0.04018118 3.47797251 vae.encoder_f1 0.04018488 3.47876120 vae.decoder 0.00016201 0.03279821 ------------------------------------------------------------------------------------- TOTAL 0.01868571 2.89691819 (elements=2,260,992) ---------------------- -------------------------------------------------------- SAMPLE-WISE STATISTICS ---------------------- -------------------------------------------------------- Handler sd35 Strategy individual Architecture hyperprior-featurecoding ---------------------- -------------------------------------------------------- Total Elements 2260992 Total Bytes 118428 BPFP 0.4190 bits/point EBPFP 0.8381 equivalent bits/point MSE 2.896918 ---------------------- -------------------------------------------------------- Time: 0.748s Load: 0.009s, Pack+Encode: 0.293s, Decode+Unpack: 0.446s ---------------------- -------------------------------------------------------- Restored Feature Format: [Dict] with 8 keys key['text_encoder-item0']: [Dict] with 2 keys key['clip_pooled_prompt_embeds']: [Tensor] shape=torch.Size([768]), dtype=torch.float16, device=cpu key['clip_prompt_embeds']: [Tensor] shape=torch.Size([77, 768]), dtype=torch.float16, device=cpu key['text_encoder_2-item1']: [Dict] with 2 keys key['clip_pooled_prompt_embeds']: [Tensor] shape=torch.Size([1280]), dtype=torch.float16, device=cpu key['clip_prompt_embeds']: [Tensor] shape=torch.Size([77, 1280]), dtype=torch.float16, device=cpu key['text_encoder_3-item2']: [Dict] with 1 keys key['t5_prompt_embeds']: [Tensor] shape=torch.Size([77, 4096]), dtype=torch.float16, device=cpu key['text_encoder-item3']: [Dict] with 2 keys key['clip_pooled_prompt_embeds']: [Tensor] shape=torch.Size([768]), dtype=torch.float16, device=cpu key['clip_prompt_embeds']: [Tensor] shape=torch.Size([77, 768]), dtype=torch.float16, device=cpu key['text_encoder_2-item4']: [Dict] with 2 keys key['clip_pooled_prompt_embeds']: [Tensor] shape=torch.Size([1280]), dtype=torch.float16, device=cpu key['clip_prompt_embeds']: [Tensor] shape=torch.Size([77, 1280]), dtype=torch.float16, device=cpu key['text_encoder_3-item5']: [Dict] with 1 keys key['t5_prompt_embeds']: [Tensor] shape=torch.Size([77, 4096]), dtype=torch.float16, device=cpu key['vae.decoder']: [Tensor] shape=torch.Size([16, 128, 128]), dtype=torch.float16, device=cpu key['vae.encoder']: [Dict] with 2 keys key['vae.encoder_f0']: [Tensor] shape=torch.Size([32, 128, 128]), dtype=torch.float16, device=cpu key['vae.encoder_f1']: [Tensor] shape=torch.Size([32, 128, 128]), dtype=torch.float16, device=cpu 💾 Converting with 2.8969 MSE: from ../datasets/FeatureCoding-StableDiffusion3.5/100samples_features/000000062025.zst to output-fixed/sd35/lambda0.004/hyperprior-featurecoding-8bit-individual/sd35cond/000000062025.zst 💪 Processing: ../datasets/FeatureCoding-StableDiffusion3.5/100samples_features/000000064718.zst (12/100) [1/4] FRONTEND: Loading features from ../datasets/FeatureCoding-StableDiffusion3.5/100samples_features/000000064718.zst... Original data structure: root: [Dict] with 8 keys key['text_encoder-item0']: [Dict] with 2 keys key['clip_pooled_prompt_embeds']: [Tensor] shape=torch.Size([768]), dtype=torch.float16, device=cpu key['clip_prompt_embeds']: [Tensor] shape=torch.Size([77, 768]), dtype=torch.float16, device=cpu key['text_encoder_2-item1']: [Dict] with 2 keys key['clip_pooled_prompt_embeds']: [Tensor] shape=torch.Size([1280]), dtype=torch.float16, device=cpu key['clip_prompt_embeds']: [Tensor] shape=torch.Size([77, 1280]), dtype=torch.float16, device=cpu key['text_encoder_3-item2']: [Dict] with 1 keys key['t5_prompt_embeds']: [Tensor] shape=torch.Size([77, 4096]), dtype=torch.float16, device=cpu key['text_encoder-item3']: [Dict] with 2 keys key['clip_pooled_prompt_embeds']: [Tensor] shape=torch.Size([768]), dtype=torch.float16, device=cpu key['clip_prompt_embeds']: [Tensor] shape=torch.Size([77, 768]), dtype=torch.float16, device=cpu key['text_encoder_2-item4']: [Dict] with 2 keys key['clip_pooled_prompt_embeds']: [Tensor] shape=torch.Size([1280]), dtype=torch.float16, device=cpu key['clip_prompt_embeds']: [Tensor] shape=torch.Size([77, 1280]), dtype=torch.float16, device=cpu key['text_encoder_3-item5']: [Dict] with 1 keys key['t5_prompt_embeds']: [Tensor] shape=torch.Size([77, 4096]), dtype=torch.float16, device=cpu key['vae.encoder-item6']: [Dict] with 2 keys key['vae.encoder_f0']: [Tensor] shape=torch.Size([32, 128, 128]), dtype=torch.float16, device=cpu key['vae.encoder_f1']: [Tensor] shape=torch.Size([32, 128, 128]), dtype=torch.float16, device=cpu key['vae.decoder']: [Tensor] shape=torch.Size([16, 128, 128]), dtype=torch.float16, device=cpu ⌛️ [1/4] FRONTEND: Load time: 0.008s ------------------------------------------------------------ SD3.5 Features Summary ------------------------------------------------------------ Number of text encoders: 3 Encoder names: ['text_encoder', 'text_encoder_2', 'text_encoder_3'] Has VAE latents: True Has VAE encoder features: True Data type: torch.float16 text_encoder-item0: clip_pooled_prompt_embeds: torch.Size([768]), torch.float16 clip_prompt_embeds: torch.Size([77, 768]), torch.float16 text_encoder_2-item1: clip_pooled_prompt_embeds: torch.Size([1280]), torch.float16 clip_prompt_embeds: torch.Size([77, 1280]), torch.float16 text_encoder_3-item2: t5_prompt_embeds: torch.Size([77, 4096]), torch.float16 text_encoder-item3: clip_pooled_prompt_embeds: torch.Size([768]), torch.float16 clip_prompt_embeds: torch.Size([77, 768]), torch.float16 text_encoder_2-item4: clip_pooled_prompt_embeds: torch.Size([1280]), torch.float16 clip_prompt_embeds: torch.Size([77, 1280]), torch.float16 text_encoder_3-item5: t5_prompt_embeds: torch.Size([77, 4096]), torch.float16 vae.decoder latents: torch.Size([16, 128, 128]) vae.encoder features: vae.encoder_f0: torch.Size([32, 128, 128]), torch.float16 vae.encoder_f1: torch.Size([32, 128, 128]), torch.float16 ------------------------------------------------------------ [2/4] FRONTEND: Pack + Encode (strategy: individual)... IndividualPacker: text_encoder-item0.clip_pooled_prompt_embeds: torch.Size([768])->torch.Size([1, 1, 1, 768]) text_encoder-item0.clip_prompt_embeds: torch.Size([77, 768])->torch.Size([1, 1, 77, 768]) text_encoder_2-item1.clip_pooled_prompt_embeds: torch.Size([1280])->torch.Size([1, 1, 1, 1280]) text_encoder_2-item1.clip_prompt_embeds: torch.Size([77, 1280])->torch.Size([1, 1, 77, 1280]) text_encoder_3-item2.t5_prompt_embeds: torch.Size([77, 4096])->torch.Size([1, 1, 77, 4096]) text_encoder-item3.clip_pooled_prompt_embeds: torch.Size([768])->torch.Size([1, 1, 1, 768]) text_encoder-item3.clip_prompt_embeds: torch.Size([77, 768])->torch.Size([1, 1, 77, 768]) text_encoder_2-item4.clip_pooled_prompt_embeds: torch.Size([1280])->torch.Size([1, 1, 1, 1280]) text_encoder_2-item4.clip_prompt_embeds: torch.Size([77, 1280])->torch.Size([1, 1, 77, 1280]) text_encoder_3-item5.t5_prompt_embeds: torch.Size([77, 4096])->torch.Size([1, 1, 77, 4096]) vae.encoder_f0: torch.Size([32, 128, 128])->torch.Size([1, 1, 512, 1024]) vae.encoder_f1: torch.Size([32, 128, 128])->torch.Size([1, 1, 512, 1024]) vae.decoder: torch.Size([16, 128, 128])->torch.Size([1, 1, 512, 512]) Using per-key quantization points (text_encoder-item0.clip_pooled_prompt_embeds: torch.Size([256])) for text_encoder-item0.clip_pooled_prompt_embeds text_encoder-item0.clip_pooled_prompt_embeds: 740B, BPFP=7.7083 Using per-key quantization points (text_encoder-item0.clip_prompt_embeds: torch.Size([256])) for text_encoder-item0.clip_prompt_embeds text_encoder-item0.clip_prompt_embeds: 5,944B, BPFP=0.8041 Using per-key quantization points (text_encoder_2-item1.clip_pooled_prompt_embeds: torch.Size([256])) for text_encoder_2-item1.clip_pooled_prompt_embeds text_encoder_2-item1.clip_pooled_prompt_embeds: 1,208B, BPFP=7.5500 Using per-key quantization points (text_encoder_2-item1.clip_prompt_embeds: torch.Size([256])) for text_encoder_2-item1.clip_prompt_embeds text_encoder_2-item1.clip_prompt_embeds: 10,968B, BPFP=0.8903 Using per-key quantization points (text_encoder_3-item2.t5_prompt_embeds: torch.Size([256])) for text_encoder_3-item2.t5_prompt_embeds text_encoder_3-item2.t5_prompt_embeds: 26,700B, BPFP=0.6773 Using per-key quantization points (text_encoder-item3.clip_pooled_prompt_embeds: torch.Size([256])) for text_encoder-item3.clip_pooled_prompt_embeds text_encoder-item3.clip_pooled_prompt_embeds: 760B, BPFP=7.9167 Using per-key quantization points (text_encoder-item3.clip_prompt_embeds: torch.Size([256])) for text_encoder-item3.clip_prompt_embeds text_encoder-item3.clip_prompt_embeds: 4,024B, BPFP=0.5444 Using per-key quantization points (text_encoder_2-item4.clip_pooled: torch.Size([256])) for text_encoder_2-item4.clip_pooled_prompt_embeds text_encoder_2-item4.clip_pooled_prompt_embeds: 1,268B, BPFP=7.9250 Using per-key quantization points (text_encoder_2-item4.clip_prompt: torch.Size([256])) for text_encoder_2-item4.clip_prompt_embeds text_encoder_2-item4.clip_prompt_embeds: 6,996B, BPFP=0.5679 Using per-key quantization points (text_encoder_3-item5.t5_prompt_embeds: torch.Size([256])) for text_encoder_3-item5.t5_prompt_embeds text_encoder_3-item5.t5_prompt_embeds: 18,416B, BPFP=0.4671 Using per-key quantization points (vae.encoder_f0: torch.Size([256])) for vae.encoder_f0 vae.encoder_f0: 18,612B, BPFP=0.2840 Using per-key quantization points (vae.encoder_f1: torch.Size([256])) for vae.encoder_f1 vae.encoder_f1: 18,616B, BPFP=0.2841 Using per-key quantization points (vae.decoder: torch.Size([256])) for vae.decoder vae.decoder: 8,128B, BPFP=0.2480 ⌛️ [2/4] FRONTEND: Frontend time: 0.294s (Pack+Encode) [3/4] BACKEND: Decode + Unpack... Using per-key quantization points (text_encoder-item0.clip_pooled_prompt_embeds: torch.Size([256])) for text_encoder-item0.clip_pooled_prompt_embeds Using per-key quantization points (text_encoder-item0.clip_prompt_embeds: torch.Size([256])) for text_encoder-item0.clip_prompt_embeds Using per-key quantization points (text_encoder_2-item1.clip_pooled_prompt_embeds: torch.Size([256])) for text_encoder_2-item1.clip_pooled_prompt_embeds Using per-key quantization points (text_encoder_2-item1.clip_prompt_embeds: torch.Size([256])) for text_encoder_2-item1.clip_prompt_embeds Using per-key quantization points (text_encoder_3-item2.t5_prompt_embeds: torch.Size([256])) for text_encoder_3-item2.t5_prompt_embeds Using per-key quantization points (text_encoder-item3.clip_pooled_prompt_embeds: torch.Size([256])) for text_encoder-item3.clip_pooled_prompt_embeds Using per-key quantization points (text_encoder-item3.clip_prompt_embeds: torch.Size([256])) for text_encoder-item3.clip_prompt_embeds Using per-key quantization points (text_encoder_2-item4.clip_pooled: torch.Size([256])) for text_encoder_2-item4.clip_pooled_prompt_embeds Using per-key quantization points (text_encoder_2-item4.clip_prompt: torch.Size([256])) for text_encoder_2-item4.clip_prompt_embeds Using per-key quantization points (text_encoder_3-item5.t5_prompt_embeds: torch.Size([256])) for text_encoder_3-item5.t5_prompt_embeds Using per-key quantization points (vae.encoder_f0: torch.Size([256])) for vae.encoder_f0 Using per-key quantization points (vae.encoder_f1: torch.Size([256])) for vae.encoder_f1 Using per-key quantization points (vae.decoder: torch.Size([256])) for vae.decoder IndividualUnPacker: text_encoder-item0.clip_pooled_prompt_embeds: torch.Size([768]) text_encoder-item0.clip_prompt_embeds: torch.Size([77, 768]) text_encoder_2-item1.clip_pooled_prompt_embeds: torch.Size([1280]) text_encoder_2-item1.clip_prompt_embeds: torch.Size([77, 1280]) text_encoder_3-item2.t5_prompt_embeds: torch.Size([77, 4096]) text_encoder-item3.clip_pooled_prompt_embeds: torch.Size([768]) text_encoder-item3.clip_prompt_embeds: torch.Size([77, 768]) text_encoder_2-item4.clip_pooled_prompt_embeds: torch.Size([1280]) text_encoder_2-item4.clip_prompt_embeds: torch.Size([77, 1280]) text_encoder_3-item5.t5_prompt_embeds: torch.Size([77, 4096]) vae.decoder: torch.Size([16, 128, 128]) vae.encoder_f0: torch.Size([32, 128, 128]) vae.encoder_f1: torch.Size([32, 128, 128]) ⌛️ [3/4] BACKEND: Backend time: 0.450s [4/4] METRICS: Computing MSE Breakdown... Using per-key quantization points (text_encoder-item0.clip_pooled_prompt_embeds: torch.Size([256])) for text_encoder-item0.clip_pooled_prompt_embeds Using per-key quantization points (text_encoder-item0.clip_prompt_embeds: torch.Size([256])) for text_encoder-item0.clip_prompt_embeds Using per-key quantization points (text_encoder_2-item1.clip_pooled_prompt_embeds: torch.Size([256])) for text_encoder_2-item1.clip_pooled_prompt_embeds Using per-key quantization points (text_encoder_2-item1.clip_prompt_embeds: torch.Size([256])) for text_encoder_2-item1.clip_prompt_embeds Using per-key quantization points (text_encoder_3-item2.t5_prompt_embeds: torch.Size([256])) for text_encoder_3-item2.t5_prompt_embeds Using per-key quantization points (text_encoder-item3.clip_pooled_prompt_embeds: torch.Size([256])) for text_encoder-item3.clip_pooled_prompt_embeds Using per-key quantization points (text_encoder-item3.clip_prompt_embeds: torch.Size([256])) for text_encoder-item3.clip_prompt_embeds Using per-key quantization points (text_encoder_2-item4.clip_pooled: torch.Size([256])) for text_encoder_2-item4.clip_pooled_prompt_embeds Using per-key quantization points (text_encoder_2-item4.clip_prompt: torch.Size([256])) for text_encoder_2-item4.clip_prompt_embeds Using per-key quantization points (text_encoder_3-item5.t5_prompt_embeds: torch.Size([256])) for text_encoder_3-item5.t5_prompt_embeds Using per-key quantization points (vae.encoder_f0: torch.Size([256])) for vae.encoder_f0 Using per-key quantization points (vae.encoder_f1: torch.Size([256])) for vae.encoder_f1 Using per-key quantization points (vae.decoder: torch.Size([256])) for vae.decoder IndividualUnPacker: text_encoder-item0.clip_pooled_prompt_embeds: torch.Size([768]) text_encoder-item0.clip_prompt_embeds: torch.Size([77, 768]) text_encoder_2-item1.clip_pooled_prompt_embeds: torch.Size([1280]) text_encoder_2-item1.clip_prompt_embeds: torch.Size([77, 1280]) text_encoder_3-item2.t5_prompt_embeds: torch.Size([77, 4096]) text_encoder-item3.clip_pooled_prompt_embeds: torch.Size([768]) text_encoder-item3.clip_prompt_embeds: torch.Size([77, 768]) text_encoder_2-item4.clip_pooled_prompt_embeds: torch.Size([1280]) text_encoder_2-item4.clip_prompt_embeds: torch.Size([77, 1280]) text_encoder_3-item5.t5_prompt_embeds: torch.Size([77, 4096]) vae.decoder: torch.Size([16, 128, 128]) vae.encoder_f0: torch.Size([32, 128, 128]) vae.encoder_f1: torch.Size([32, 128, 128]) Per-key MSE Breakdown (Quant=Truncation+Quantization, Codec=Compression): Key Quant-MSE Total-MSE ------------------------------------------------------------------------------------- text_encoder-item0.clip_pooled_prompt_embeds 0.00038474 0.96030140 text_encoder-item0.clip_prompt_embeds 0.00023140 24.21897195 text_encoder_2-item1.clip_pooled_prompt_embeds 0.00025605 0.87784100 text_encoder_2-item1.clip_prompt_embeds 0.00016636 0.11179552 text_encoder_3-item2.t5_prompt_embeds 0.00000797 0.00377791 text_encoder-item3.clip_pooled_prompt_embeds 0.00841799 1.01420029 text_encoder-item3.clip_prompt_embeds 0.00023247 23.90570887 text_encoder_2-item4.clip_pooled_prompt_embeds 0.00016751 0.67051430 text_encoder_2-item4.clip_prompt_embeds 0.00009830 0.47049568 text_encoder_3-item5.t5_prompt_embeds 0.00001263 0.00227117 vae.encoder_f0 0.04874706 3.34490633 vae.encoder_f1 0.04875064 3.34430313 vae.decoder 0.00019641 0.04781711 ------------------------------------------------------------------------------------- TOTAL 0.02266071 2.84313483 (elements=2,260,992) ---------------------- -------------------------------------------------------- SAMPLE-WISE STATISTICS ---------------------- -------------------------------------------------------- Handler sd35 Strategy individual Architecture hyperprior-featurecoding ---------------------- -------------------------------------------------------- Total Elements 2260992 Total Bytes 122380 BPFP 0.4330 bits/point EBPFP 0.8660 equivalent bits/point MSE 2.843135 ---------------------- -------------------------------------------------------- Time: 0.752s Load: 0.008s, Pack+Encode: 0.294s, Decode+Unpack: 0.450s ---------------------- -------------------------------------------------------- Restored Feature Format: [Dict] with 8 keys key['text_encoder-item0']: [Dict] with 2 keys key['clip_pooled_prompt_embeds']: [Tensor] shape=torch.Size([768]), dtype=torch.float16, device=cpu key['clip_prompt_embeds']: [Tensor] shape=torch.Size([77, 768]), dtype=torch.float16, device=cpu key['text_encoder_2-item1']: [Dict] with 2 keys key['clip_pooled_prompt_embeds']: [Tensor] shape=torch.Size([1280]), dtype=torch.float16, device=cpu key['clip_prompt_embeds']: [Tensor] shape=torch.Size([77, 1280]), dtype=torch.float16, device=cpu key['text_encoder_3-item2']: [Dict] with 1 keys key['t5_prompt_embeds']: [Tensor] shape=torch.Size([77, 4096]), dtype=torch.float16, device=cpu key['text_encoder-item3']: [Dict] with 2 keys key['clip_pooled_prompt_embeds']: [Tensor] shape=torch.Size([768]), dtype=torch.float16, device=cpu key['clip_prompt_embeds']: [Tensor] shape=torch.Size([77, 768]), dtype=torch.float16, device=cpu key['text_encoder_2-item4']: [Dict] with 2 keys key['clip_pooled_prompt_embeds']: [Tensor] shape=torch.Size([1280]), dtype=torch.float16, device=cpu key['clip_prompt_embeds']: [Tensor] shape=torch.Size([77, 1280]), dtype=torch.float16, device=cpu key['text_encoder_3-item5']: [Dict] with 1 keys key['t5_prompt_embeds']: [Tensor] shape=torch.Size([77, 4096]), dtype=torch.float16, device=cpu key['vae.decoder']: [Tensor] shape=torch.Size([16, 128, 128]), dtype=torch.float16, device=cpu key['vae.encoder']: [Dict] with 2 keys key['vae.encoder_f0']: [Tensor] shape=torch.Size([32, 128, 128]), dtype=torch.float16, device=cpu key['vae.encoder_f1']: [Tensor] shape=torch.Size([32, 128, 128]), dtype=torch.float16, device=cpu 💾 Converting with 2.8431 MSE: from ../datasets/FeatureCoding-StableDiffusion3.5/100samples_features/000000064718.zst to output-fixed/sd35/lambda0.004/hyperprior-featurecoding-8bit-individual/sd35cond/000000064718.zst 💪 Processing: ../datasets/FeatureCoding-StableDiffusion3.5/100samples_features/000000070739.zst (13/100) [1/4] FRONTEND: Loading features from ../datasets/FeatureCoding-StableDiffusion3.5/100samples_features/000000070739.zst... Original data structure: root: [Dict] with 8 keys key['text_encoder-item0']: [Dict] with 2 keys key['clip_pooled_prompt_embeds']: [Tensor] shape=torch.Size([768]), dtype=torch.float16, device=cpu key['clip_prompt_embeds']: [Tensor] shape=torch.Size([77, 768]), dtype=torch.float16, device=cpu key['text_encoder_2-item1']: [Dict] with 2 keys key['clip_pooled_prompt_embeds']: [Tensor] shape=torch.Size([1280]), dtype=torch.float16, device=cpu key['clip_prompt_embeds']: [Tensor] shape=torch.Size([77, 1280]), dtype=torch.float16, device=cpu key['text_encoder_3-item2']: [Dict] with 1 keys key['t5_prompt_embeds']: [Tensor] shape=torch.Size([77, 4096]), dtype=torch.float16, device=cpu key['text_encoder-item3']: [Dict] with 2 keys key['clip_pooled_prompt_embeds']: [Tensor] shape=torch.Size([768]), dtype=torch.float16, device=cpu key['clip_prompt_embeds']: [Tensor] shape=torch.Size([77, 768]), dtype=torch.float16, device=cpu key['text_encoder_2-item4']: [Dict] with 2 keys key['clip_pooled_prompt_embeds']: [Tensor] shape=torch.Size([1280]), dtype=torch.float16, device=cpu key['clip_prompt_embeds']: [Tensor] shape=torch.Size([77, 1280]), dtype=torch.float16, device=cpu key['text_encoder_3-item5']: [Dict] with 1 keys key['t5_prompt_embeds']: [Tensor] shape=torch.Size([77, 4096]), dtype=torch.float16, device=cpu key['vae.encoder-item6']: [Dict] with 2 keys key['vae.encoder_f0']: [Tensor] shape=torch.Size([32, 128, 128]), dtype=torch.float16, device=cpu key['vae.encoder_f1']: [Tensor] shape=torch.Size([32, 128, 128]), dtype=torch.float16, device=cpu key['vae.decoder']: [Tensor] shape=torch.Size([16, 128, 128]), dtype=torch.float16, device=cpu ⌛️ [1/4] FRONTEND: Load time: 0.009s ------------------------------------------------------------ SD3.5 Features Summary ------------------------------------------------------------ Number of text encoders: 3 Encoder names: ['text_encoder', 'text_encoder_2', 'text_encoder_3'] Has VAE latents: True Has VAE encoder features: True Data type: torch.float16 text_encoder-item0: clip_pooled_prompt_embeds: torch.Size([768]), torch.float16 clip_prompt_embeds: torch.Size([77, 768]), torch.float16 text_encoder_2-item1: clip_pooled_prompt_embeds: torch.Size([1280]), torch.float16 clip_prompt_embeds: torch.Size([77, 1280]), torch.float16 text_encoder_3-item2: t5_prompt_embeds: torch.Size([77, 4096]), torch.float16 text_encoder-item3: clip_pooled_prompt_embeds: torch.Size([768]), torch.float16 clip_prompt_embeds: torch.Size([77, 768]), torch.float16 text_encoder_2-item4: clip_pooled_prompt_embeds: torch.Size([1280]), torch.float16 clip_prompt_embeds: torch.Size([77, 1280]), torch.float16 text_encoder_3-item5: t5_prompt_embeds: torch.Size([77, 4096]), torch.float16 vae.decoder latents: torch.Size([16, 128, 128]) vae.encoder features: vae.encoder_f0: torch.Size([32, 128, 128]), torch.float16 vae.encoder_f1: torch.Size([32, 128, 128]), torch.float16 ------------------------------------------------------------ [2/4] FRONTEND: Pack + Encode (strategy: individual)... IndividualPacker: text_encoder-item0.clip_pooled_prompt_embeds: torch.Size([768])->torch.Size([1, 1, 1, 768]) text_encoder-item0.clip_prompt_embeds: torch.Size([77, 768])->torch.Size([1, 1, 77, 768]) text_encoder_2-item1.clip_pooled_prompt_embeds: torch.Size([1280])->torch.Size([1, 1, 1, 1280]) text_encoder_2-item1.clip_prompt_embeds: torch.Size([77, 1280])->torch.Size([1, 1, 77, 1280]) text_encoder_3-item2.t5_prompt_embeds: torch.Size([77, 4096])->torch.Size([1, 1, 77, 4096]) text_encoder-item3.clip_pooled_prompt_embeds: torch.Size([768])->torch.Size([1, 1, 1, 768]) text_encoder-item3.clip_prompt_embeds: torch.Size([77, 768])->torch.Size([1, 1, 77, 768]) text_encoder_2-item4.clip_pooled_prompt_embeds: torch.Size([1280])->torch.Size([1, 1, 1, 1280]) text_encoder_2-item4.clip_prompt_embeds: torch.Size([77, 1280])->torch.Size([1, 1, 77, 1280]) text_encoder_3-item5.t5_prompt_embeds: torch.Size([77, 4096])->torch.Size([1, 1, 77, 4096]) vae.encoder_f0: torch.Size([32, 128, 128])->torch.Size([1, 1, 512, 1024]) vae.encoder_f1: torch.Size([32, 128, 128])->torch.Size([1, 1, 512, 1024]) vae.decoder: torch.Size([16, 128, 128])->torch.Size([1, 1, 512, 512]) Using per-key quantization points (text_encoder-item0.clip_pooled_prompt_embeds: torch.Size([256])) for text_encoder-item0.clip_pooled_prompt_embeds text_encoder-item0.clip_pooled_prompt_embeds: 728B, BPFP=7.5833 Using per-key quantization points (text_encoder-item0.clip_prompt_embeds: torch.Size([256])) for text_encoder-item0.clip_prompt_embeds text_encoder-item0.clip_prompt_embeds: 6,484B, BPFP=0.8772 Using per-key quantization points (text_encoder_2-item1.clip_pooled_prompt_embeds: torch.Size([256])) for text_encoder_2-item1.clip_pooled_prompt_embeds text_encoder_2-item1.clip_pooled_prompt_embeds: 1,204B, BPFP=7.5250 Using per-key quantization points (text_encoder_2-item1.clip_prompt_embeds: torch.Size([256])) for text_encoder_2-item1.clip_prompt_embeds text_encoder_2-item1.clip_prompt_embeds: 11,016B, BPFP=0.8942 Using per-key quantization points (text_encoder_3-item2.t5_prompt_embeds: torch.Size([256])) for text_encoder_3-item2.t5_prompt_embeds text_encoder_3-item2.t5_prompt_embeds: 25,644B, BPFP=0.6505 Using per-key quantization points (text_encoder-item3.clip_pooled_prompt_embeds: torch.Size([256])) for text_encoder-item3.clip_pooled_prompt_embeds text_encoder-item3.clip_pooled_prompt_embeds: 760B, BPFP=7.9167 Using per-key quantization points (text_encoder-item3.clip_prompt_embeds: torch.Size([256])) for text_encoder-item3.clip_prompt_embeds text_encoder-item3.clip_prompt_embeds: 4,024B, BPFP=0.5444 Using per-key quantization points (text_encoder_2-item4.clip_pooled: torch.Size([256])) for text_encoder_2-item4.clip_pooled_prompt_embeds text_encoder_2-item4.clip_pooled_prompt_embeds: 1,268B, BPFP=7.9250 Using per-key quantization points (text_encoder_2-item4.clip_prompt: torch.Size([256])) for text_encoder_2-item4.clip_prompt_embeds text_encoder_2-item4.clip_prompt_embeds: 6,996B, BPFP=0.5679 Using per-key quantization points (text_encoder_3-item5.t5_prompt_embeds: torch.Size([256])) for text_encoder_3-item5.t5_prompt_embeds text_encoder_3-item5.t5_prompt_embeds: 18,416B, BPFP=0.4671 Using per-key quantization points (vae.encoder_f0: torch.Size([256])) for vae.encoder_f0 vae.encoder_f0: 22,520B, BPFP=0.3436 Using per-key quantization points (vae.encoder_f1: torch.Size([256])) for vae.encoder_f1 vae.encoder_f1: 22,524B, BPFP=0.3437 Using per-key quantization points (vae.decoder: torch.Size([256])) for vae.decoder vae.decoder: 4,384B, BPFP=0.1338 ⌛️ [2/4] FRONTEND: Frontend time: 0.294s (Pack+Encode) [3/4] BACKEND: Decode + Unpack... Using per-key quantization points (text_encoder-item0.clip_pooled_prompt_embeds: torch.Size([256])) for text_encoder-item0.clip_pooled_prompt_embeds Using per-key quantization points (text_encoder-item0.clip_prompt_embeds: torch.Size([256])) for text_encoder-item0.clip_prompt_embeds Using per-key quantization points (text_encoder_2-item1.clip_pooled_prompt_embeds: torch.Size([256])) for text_encoder_2-item1.clip_pooled_prompt_embeds Using per-key quantization points (text_encoder_2-item1.clip_prompt_embeds: torch.Size([256])) for text_encoder_2-item1.clip_prompt_embeds Using per-key quantization points (text_encoder_3-item2.t5_prompt_embeds: torch.Size([256])) for text_encoder_3-item2.t5_prompt_embeds Using per-key quantization points (text_encoder-item3.clip_pooled_prompt_embeds: torch.Size([256])) for text_encoder-item3.clip_pooled_prompt_embeds Using per-key quantization points (text_encoder-item3.clip_prompt_embeds: torch.Size([256])) for text_encoder-item3.clip_prompt_embeds Using per-key quantization points (text_encoder_2-item4.clip_pooled: torch.Size([256])) for text_encoder_2-item4.clip_pooled_prompt_embeds Using per-key quantization points (text_encoder_2-item4.clip_prompt: torch.Size([256])) for text_encoder_2-item4.clip_prompt_embeds Using per-key quantization points (text_encoder_3-item5.t5_prompt_embeds: torch.Size([256])) for text_encoder_3-item5.t5_prompt_embeds Using per-key quantization points (vae.encoder_f0: torch.Size([256])) for vae.encoder_f0 Using per-key quantization points (vae.encoder_f1: torch.Size([256])) for vae.encoder_f1 Using per-key quantization points (vae.decoder: torch.Size([256])) for vae.decoder IndividualUnPacker: text_encoder-item0.clip_pooled_prompt_embeds: torch.Size([768]) text_encoder-item0.clip_prompt_embeds: torch.Size([77, 768]) text_encoder_2-item1.clip_pooled_prompt_embeds: torch.Size([1280]) text_encoder_2-item1.clip_prompt_embeds: torch.Size([77, 1280]) text_encoder_3-item2.t5_prompt_embeds: torch.Size([77, 4096]) text_encoder-item3.clip_pooled_prompt_embeds: torch.Size([768]) text_encoder-item3.clip_prompt_embeds: torch.Size([77, 768]) text_encoder_2-item4.clip_pooled_prompt_embeds: torch.Size([1280]) text_encoder_2-item4.clip_prompt_embeds: torch.Size([77, 1280]) text_encoder_3-item5.t5_prompt_embeds: torch.Size([77, 4096]) vae.decoder: torch.Size([16, 128, 128]) vae.encoder_f0: torch.Size([32, 128, 128]) vae.encoder_f1: torch.Size([32, 128, 128]) ⌛️ [3/4] BACKEND: Backend time: 0.447s [4/4] METRICS: Computing MSE Breakdown... Using per-key quantization points (text_encoder-item0.clip_pooled_prompt_embeds: torch.Size([256])) for text_encoder-item0.clip_pooled_prompt_embeds Using per-key quantization points (text_encoder-item0.clip_prompt_embeds: torch.Size([256])) for text_encoder-item0.clip_prompt_embeds Using per-key quantization points (text_encoder_2-item1.clip_pooled_prompt_embeds: torch.Size([256])) for text_encoder_2-item1.clip_pooled_prompt_embeds Using per-key quantization points (text_encoder_2-item1.clip_prompt_embeds: torch.Size([256])) for text_encoder_2-item1.clip_prompt_embeds Using per-key quantization points (text_encoder_3-item2.t5_prompt_embeds: torch.Size([256])) for text_encoder_3-item2.t5_prompt_embeds Using per-key quantization points (text_encoder-item3.clip_pooled_prompt_embeds: torch.Size([256])) for text_encoder-item3.clip_pooled_prompt_embeds Using per-key quantization points (text_encoder-item3.clip_prompt_embeds: torch.Size([256])) for text_encoder-item3.clip_prompt_embeds Using per-key quantization points (text_encoder_2-item4.clip_pooled: torch.Size([256])) for text_encoder_2-item4.clip_pooled_prompt_embeds Using per-key quantization points (text_encoder_2-item4.clip_prompt: torch.Size([256])) for text_encoder_2-item4.clip_prompt_embeds Using per-key quantization points (text_encoder_3-item5.t5_prompt_embeds: torch.Size([256])) for text_encoder_3-item5.t5_prompt_embeds Using per-key quantization points (vae.encoder_f0: torch.Size([256])) for vae.encoder_f0 Using per-key quantization points (vae.encoder_f1: torch.Size([256])) for vae.encoder_f1 Using per-key quantization points (vae.decoder: torch.Size([256])) for vae.decoder IndividualUnPacker: text_encoder-item0.clip_pooled_prompt_embeds: torch.Size([768]) text_encoder-item0.clip_prompt_embeds: torch.Size([77, 768]) text_encoder_2-item1.clip_pooled_prompt_embeds: torch.Size([1280]) text_encoder_2-item1.clip_prompt_embeds: torch.Size([77, 1280]) text_encoder_3-item2.t5_prompt_embeds: torch.Size([77, 4096]) text_encoder-item3.clip_pooled_prompt_embeds: torch.Size([768]) text_encoder-item3.clip_prompt_embeds: torch.Size([77, 768]) text_encoder_2-item4.clip_pooled_prompt_embeds: torch.Size([1280]) text_encoder_2-item4.clip_prompt_embeds: torch.Size([77, 1280]) text_encoder_3-item5.t5_prompt_embeds: torch.Size([77, 4096]) vae.decoder: torch.Size([16, 128, 128]) vae.encoder_f0: torch.Size([32, 128, 128]) vae.encoder_f1: torch.Size([32, 128, 128]) Per-key MSE Breakdown (Quant=Truncation+Quantization, Codec=Compression): Key Quant-MSE Total-MSE ------------------------------------------------------------------------------------- text_encoder-item0.clip_pooled_prompt_embeds 0.00017774 0.91339238 text_encoder-item0.clip_prompt_embeds 0.00030893 23.94655540 text_encoder_2-item1.clip_pooled_prompt_embeds 0.00035783 0.92015295 text_encoder_2-item1.clip_prompt_embeds 0.00024047 0.12518289 text_encoder_3-item2.t5_prompt_embeds 0.00000770 0.00317264 text_encoder-item3.clip_pooled_prompt_embeds 0.00841799 1.01420029 text_encoder-item3.clip_prompt_embeds 0.00023247 23.90570887 text_encoder_2-item4.clip_pooled_prompt_embeds 0.00016751 0.67051430 text_encoder_2-item4.clip_prompt_embeds 0.00009830 0.47049568 text_encoder_3-item5.t5_prompt_embeds 0.00001263 0.00227117 vae.encoder_f0 0.01360236 3.38075852 vae.encoder_f1 0.01360807 3.38128018 vae.decoder 0.00023006 0.03794406 ------------------------------------------------------------------------------------- TOTAL 0.00637132 2.85226021 (elements=2,260,992) ---------------------- -------------------------------------------------------- SAMPLE-WISE STATISTICS ---------------------- -------------------------------------------------------- Handler sd35 Strategy individual Architecture hyperprior-featurecoding ---------------------- -------------------------------------------------------- Total Elements 2260992 Total Bytes 125968 BPFP 0.4457 bits/point EBPFP 0.8914 equivalent bits/point MSE 2.852260 ---------------------- -------------------------------------------------------- Time: 0.751s Load: 0.009s, Pack+Encode: 0.294s, Decode+Unpack: 0.447s ---------------------- -------------------------------------------------------- Restored Feature Format: [Dict] with 8 keys key['text_encoder-item0']: [Dict] with 2 keys key['clip_pooled_prompt_embeds']: [Tensor] shape=torch.Size([768]), dtype=torch.float16, device=cpu key['clip_prompt_embeds']: [Tensor] shape=torch.Size([77, 768]), dtype=torch.float16, device=cpu key['text_encoder_2-item1']: [Dict] with 2 keys key['clip_pooled_prompt_embeds']: [Tensor] shape=torch.Size([1280]), dtype=torch.float16, device=cpu key['clip_prompt_embeds']: [Tensor] shape=torch.Size([77, 1280]), dtype=torch.float16, device=cpu key['text_encoder_3-item2']: [Dict] with 1 keys key['t5_prompt_embeds']: [Tensor] shape=torch.Size([77, 4096]), dtype=torch.float16, device=cpu key['text_encoder-item3']: [Dict] with 2 keys key['clip_pooled_prompt_embeds']: [Tensor] shape=torch.Size([768]), dtype=torch.float16, device=cpu key['clip_prompt_embeds']: [Tensor] shape=torch.Size([77, 768]), dtype=torch.float16, device=cpu key['text_encoder_2-item4']: [Dict] with 2 keys key['clip_pooled_prompt_embeds']: [Tensor] shape=torch.Size([1280]), dtype=torch.float16, device=cpu key['clip_prompt_embeds']: [Tensor] shape=torch.Size([77, 1280]), dtype=torch.float16, device=cpu key['text_encoder_3-item5']: [Dict] with 1 keys key['t5_prompt_embeds']: [Tensor] shape=torch.Size([77, 4096]), dtype=torch.float16, device=cpu key['vae.decoder']: [Tensor] shape=torch.Size([16, 128, 128]), dtype=torch.float16, device=cpu key['vae.encoder']: [Dict] with 2 keys key['vae.encoder_f0']: [Tensor] shape=torch.Size([32, 128, 128]), dtype=torch.float16, device=cpu key['vae.encoder_f1']: [Tensor] shape=torch.Size([32, 128, 128]), dtype=torch.float16, device=cpu 💾 Converting with 2.8523 MSE: from ../datasets/FeatureCoding-StableDiffusion3.5/100samples_features/000000070739.zst to output-fixed/sd35/lambda0.004/hyperprior-featurecoding-8bit-individual/sd35cond/000000070739.zst 💪 Processing: ../datasets/FeatureCoding-StableDiffusion3.5/100samples_features/000000074646.zst (14/100) [1/4] FRONTEND: Loading features from ../datasets/FeatureCoding-StableDiffusion3.5/100samples_features/000000074646.zst... Original data structure: root: [Dict] with 8 keys key['text_encoder-item0']: [Dict] with 2 keys key['clip_pooled_prompt_embeds']: [Tensor] shape=torch.Size([768]), dtype=torch.float16, device=cpu key['clip_prompt_embeds']: [Tensor] shape=torch.Size([77, 768]), dtype=torch.float16, device=cpu key['text_encoder_2-item1']: [Dict] with 2 keys key['clip_pooled_prompt_embeds']: [Tensor] shape=torch.Size([1280]), dtype=torch.float16, device=cpu key['clip_prompt_embeds']: [Tensor] shape=torch.Size([77, 1280]), dtype=torch.float16, device=cpu key['text_encoder_3-item2']: [Dict] with 1 keys key['t5_prompt_embeds']: [Tensor] shape=torch.Size([77, 4096]), dtype=torch.float16, device=cpu key['text_encoder-item3']: [Dict] with 2 keys key['clip_pooled_prompt_embeds']: [Tensor] shape=torch.Size([768]), dtype=torch.float16, device=cpu key['clip_prompt_embeds']: [Tensor] shape=torch.Size([77, 768]), dtype=torch.float16, device=cpu key['text_encoder_2-item4']: [Dict] with 2 keys key['clip_pooled_prompt_embeds']: [Tensor] shape=torch.Size([1280]), dtype=torch.float16, device=cpu key['clip_prompt_embeds']: [Tensor] shape=torch.Size([77, 1280]), dtype=torch.float16, device=cpu key['text_encoder_3-item5']: [Dict] with 1 keys key['t5_prompt_embeds']: [Tensor] shape=torch.Size([77, 4096]), dtype=torch.float16, device=cpu key['vae.encoder-item6']: [Dict] with 2 keys key['vae.encoder_f0']: [Tensor] shape=torch.Size([32, 128, 128]), dtype=torch.float16, device=cpu key['vae.encoder_f1']: [Tensor] shape=torch.Size([32, 128, 128]), dtype=torch.float16, device=cpu key['vae.decoder']: [Tensor] shape=torch.Size([16, 128, 128]), dtype=torch.float16, device=cpu ⌛️ [1/4] FRONTEND: Load time: 0.007s ------------------------------------------------------------ SD3.5 Features Summary ------------------------------------------------------------ Number of text encoders: 3 Encoder names: ['text_encoder', 'text_encoder_2', 'text_encoder_3'] Has VAE latents: True Has VAE encoder features: True Data type: torch.float16 text_encoder-item0: clip_pooled_prompt_embeds: torch.Size([768]), torch.float16 clip_prompt_embeds: torch.Size([77, 768]), torch.float16 text_encoder_2-item1: clip_pooled_prompt_embeds: torch.Size([1280]), torch.float16 clip_prompt_embeds: torch.Size([77, 1280]), torch.float16 text_encoder_3-item2: t5_prompt_embeds: torch.Size([77, 4096]), torch.float16 text_encoder-item3: clip_pooled_prompt_embeds: torch.Size([768]), torch.float16 clip_prompt_embeds: torch.Size([77, 768]), torch.float16 text_encoder_2-item4: clip_pooled_prompt_embeds: torch.Size([1280]), torch.float16 clip_prompt_embeds: torch.Size([77, 1280]), torch.float16 text_encoder_3-item5: t5_prompt_embeds: torch.Size([77, 4096]), torch.float16 vae.decoder latents: torch.Size([16, 128, 128]) vae.encoder features: vae.encoder_f0: torch.Size([32, 128, 128]), torch.float16 vae.encoder_f1: torch.Size([32, 128, 128]), torch.float16 ------------------------------------------------------------ [2/4] FRONTEND: Pack + Encode (strategy: individual)... IndividualPacker: text_encoder-item0.clip_pooled_prompt_embeds: torch.Size([768])->torch.Size([1, 1, 1, 768]) text_encoder-item0.clip_prompt_embeds: torch.Size([77, 768])->torch.Size([1, 1, 77, 768]) text_encoder_2-item1.clip_pooled_prompt_embeds: torch.Size([1280])->torch.Size([1, 1, 1, 1280]) text_encoder_2-item1.clip_prompt_embeds: torch.Size([77, 1280])->torch.Size([1, 1, 77, 1280]) text_encoder_3-item2.t5_prompt_embeds: torch.Size([77, 4096])->torch.Size([1, 1, 77, 4096]) text_encoder-item3.clip_pooled_prompt_embeds: torch.Size([768])->torch.Size([1, 1, 1, 768]) text_encoder-item3.clip_prompt_embeds: torch.Size([77, 768])->torch.Size([1, 1, 77, 768]) text_encoder_2-item4.clip_pooled_prompt_embeds: torch.Size([1280])->torch.Size([1, 1, 1, 1280]) text_encoder_2-item4.clip_prompt_embeds: torch.Size([77, 1280])->torch.Size([1, 1, 77, 1280]) text_encoder_3-item5.t5_prompt_embeds: torch.Size([77, 4096])->torch.Size([1, 1, 77, 4096]) vae.encoder_f0: torch.Size([32, 128, 128])->torch.Size([1, 1, 512, 1024]) vae.encoder_f1: torch.Size([32, 128, 128])->torch.Size([1, 1, 512, 1024]) vae.decoder: torch.Size([16, 128, 128])->torch.Size([1, 1, 512, 512]) Using per-key quantization points (text_encoder-item0.clip_pooled_prompt_embeds: torch.Size([256])) for text_encoder-item0.clip_pooled_prompt_embeds text_encoder-item0.clip_pooled_prompt_embeds: 736B, BPFP=7.6667 Using per-key quantization points (text_encoder-item0.clip_prompt_embeds: torch.Size([256])) for text_encoder-item0.clip_prompt_embeds text_encoder-item0.clip_prompt_embeds: 7,236B, BPFP=0.9789 Using per-key quantization points (text_encoder_2-item1.clip_pooled_prompt_embeds: torch.Size([256])) for text_encoder_2-item1.clip_pooled_prompt_embeds text_encoder_2-item1.clip_pooled_prompt_embeds: 1,212B, BPFP=7.5750 Using per-key quantization points (text_encoder_2-item1.clip_prompt_embeds: torch.Size([256])) for text_encoder_2-item1.clip_prompt_embeds text_encoder_2-item1.clip_prompt_embeds: 10,924B, BPFP=0.8867 Using per-key quantization points (text_encoder_3-item2.t5_prompt_embeds: torch.Size([256])) for text_encoder_3-item2.t5_prompt_embeds text_encoder_3-item2.t5_prompt_embeds: 30,672B, BPFP=0.7780 Using per-key quantization points (text_encoder-item3.clip_pooled_prompt_embeds: torch.Size([256])) for text_encoder-item3.clip_pooled_prompt_embeds text_encoder-item3.clip_pooled_prompt_embeds: 760B, BPFP=7.9167 Using per-key quantization points (text_encoder-item3.clip_prompt_embeds: torch.Size([256])) for text_encoder-item3.clip_prompt_embeds text_encoder-item3.clip_prompt_embeds: 4,024B, BPFP=0.5444 Using per-key quantization points (text_encoder_2-item4.clip_pooled: torch.Size([256])) for text_encoder_2-item4.clip_pooled_prompt_embeds text_encoder_2-item4.clip_pooled_prompt_embeds: 1,268B, BPFP=7.9250 Using per-key quantization points (text_encoder_2-item4.clip_prompt: torch.Size([256])) for text_encoder_2-item4.clip_prompt_embeds text_encoder_2-item4.clip_prompt_embeds: 6,996B, BPFP=0.5679 Using per-key quantization points (text_encoder_3-item5.t5_prompt_embeds: torch.Size([256])) for text_encoder_3-item5.t5_prompt_embeds text_encoder_3-item5.t5_prompt_embeds: 18,416B, BPFP=0.4671 Using per-key quantization points (vae.encoder_f0: torch.Size([256])) for vae.encoder_f0 vae.encoder_f0: 8,448B, BPFP=0.1289 Using per-key quantization points (vae.encoder_f1: torch.Size([256])) for vae.encoder_f1 vae.encoder_f1: 8,448B, BPFP=0.1289 Using per-key quantization points (vae.decoder: torch.Size([256])) for vae.decoder vae.decoder: 2,864B, BPFP=0.0874 ⌛️ [2/4] FRONTEND: Frontend time: 0.292s (Pack+Encode) [3/4] BACKEND: Decode + Unpack... Using per-key quantization points (text_encoder-item0.clip_pooled_prompt_embeds: torch.Size([256])) for text_encoder-item0.clip_pooled_prompt_embeds Using per-key quantization points (text_encoder-item0.clip_prompt_embeds: torch.Size([256])) for text_encoder-item0.clip_prompt_embeds Using per-key quantization points (text_encoder_2-item1.clip_pooled_prompt_embeds: torch.Size([256])) for text_encoder_2-item1.clip_pooled_prompt_embeds Using per-key quantization points (text_encoder_2-item1.clip_prompt_embeds: torch.Size([256])) for text_encoder_2-item1.clip_prompt_embeds Using per-key quantization points (text_encoder_3-item2.t5_prompt_embeds: torch.Size([256])) for text_encoder_3-item2.t5_prompt_embeds Using per-key quantization points (text_encoder-item3.clip_pooled_prompt_embeds: torch.Size([256])) for text_encoder-item3.clip_pooled_prompt_embeds Using per-key quantization points (text_encoder-item3.clip_prompt_embeds: torch.Size([256])) for text_encoder-item3.clip_prompt_embeds Using per-key quantization points (text_encoder_2-item4.clip_pooled: torch.Size([256])) for text_encoder_2-item4.clip_pooled_prompt_embeds Using per-key quantization points (text_encoder_2-item4.clip_prompt: torch.Size([256])) for text_encoder_2-item4.clip_prompt_embeds Using per-key quantization points (text_encoder_3-item5.t5_prompt_embeds: torch.Size([256])) for text_encoder_3-item5.t5_prompt_embeds Using per-key quantization points (vae.encoder_f0: torch.Size([256])) for vae.encoder_f0 Using per-key quantization points (vae.encoder_f1: torch.Size([256])) for vae.encoder_f1 Using per-key quantization points (vae.decoder: torch.Size([256])) for vae.decoder IndividualUnPacker: text_encoder-item0.clip_pooled_prompt_embeds: torch.Size([768]) text_encoder-item0.clip_prompt_embeds: torch.Size([77, 768]) text_encoder_2-item1.clip_pooled_prompt_embeds: torch.Size([1280]) text_encoder_2-item1.clip_prompt_embeds: torch.Size([77, 1280]) text_encoder_3-item2.t5_prompt_embeds: torch.Size([77, 4096]) text_encoder-item3.clip_pooled_prompt_embeds: torch.Size([768]) text_encoder-item3.clip_prompt_embeds: torch.Size([77, 768]) text_encoder_2-item4.clip_pooled_prompt_embeds: torch.Size([1280]) text_encoder_2-item4.clip_prompt_embeds: torch.Size([77, 1280]) text_encoder_3-item5.t5_prompt_embeds: torch.Size([77, 4096]) vae.decoder: torch.Size([16, 128, 128]) vae.encoder_f0: torch.Size([32, 128, 128]) vae.encoder_f1: torch.Size([32, 128, 128]) ⌛️ [3/4] BACKEND: Backend time: 0.445s [4/4] METRICS: Computing MSE Breakdown... Using per-key quantization points (text_encoder-item0.clip_pooled_prompt_embeds: torch.Size([256])) for text_encoder-item0.clip_pooled_prompt_embeds Using per-key quantization points (text_encoder-item0.clip_prompt_embeds: torch.Size([256])) for text_encoder-item0.clip_prompt_embeds Using per-key quantization points (text_encoder_2-item1.clip_pooled_prompt_embeds: torch.Size([256])) for text_encoder_2-item1.clip_pooled_prompt_embeds Using per-key quantization points (text_encoder_2-item1.clip_prompt_embeds: torch.Size([256])) for text_encoder_2-item1.clip_prompt_embeds Using per-key quantization points (text_encoder_3-item2.t5_prompt_embeds: torch.Size([256])) for text_encoder_3-item2.t5_prompt_embeds Using per-key quantization points (text_encoder-item3.clip_pooled_prompt_embeds: torch.Size([256])) for text_encoder-item3.clip_pooled_prompt_embeds Using per-key quantization points (text_encoder-item3.clip_prompt_embeds: torch.Size([256])) for text_encoder-item3.clip_prompt_embeds Using per-key quantization points (text_encoder_2-item4.clip_pooled: torch.Size([256])) for text_encoder_2-item4.clip_pooled_prompt_embeds Using per-key quantization points (text_encoder_2-item4.clip_prompt: torch.Size([256])) for text_encoder_2-item4.clip_prompt_embeds Using per-key quantization points (text_encoder_3-item5.t5_prompt_embeds: torch.Size([256])) for text_encoder_3-item5.t5_prompt_embeds Using per-key quantization points (vae.encoder_f0: torch.Size([256])) for vae.encoder_f0 Using per-key quantization points (vae.encoder_f1: torch.Size([256])) for vae.encoder_f1 Using per-key quantization points (vae.decoder: torch.Size([256])) for vae.decoder IndividualUnPacker: text_encoder-item0.clip_pooled_prompt_embeds: torch.Size([768]) text_encoder-item0.clip_prompt_embeds: torch.Size([77, 768]) text_encoder_2-item1.clip_pooled_prompt_embeds: torch.Size([1280]) text_encoder_2-item1.clip_prompt_embeds: torch.Size([77, 1280]) text_encoder_3-item2.t5_prompt_embeds: torch.Size([77, 4096]) text_encoder-item3.clip_pooled_prompt_embeds: torch.Size([768]) text_encoder-item3.clip_prompt_embeds: torch.Size([77, 768]) text_encoder_2-item4.clip_pooled_prompt_embeds: torch.Size([1280]) text_encoder_2-item4.clip_prompt_embeds: torch.Size([77, 1280]) text_encoder_3-item5.t5_prompt_embeds: torch.Size([77, 4096]) vae.decoder: torch.Size([16, 128, 128]) vae.encoder_f0: torch.Size([32, 128, 128]) vae.encoder_f1: torch.Size([32, 128, 128]) Per-key MSE Breakdown (Quant=Truncation+Quantization, Codec=Compression): Key Quant-MSE Total-MSE ------------------------------------------------------------------------------------- text_encoder-item0.clip_pooled_prompt_embeds 0.00059206 0.93097440 text_encoder-item0.clip_prompt_embeds 0.00024198 23.94506942 text_encoder_2-item1.clip_pooled_prompt_embeds 0.00023989 0.91722708 text_encoder_2-item1.clip_prompt_embeds 0.00015983 0.12656511 text_encoder_3-item2.t5_prompt_embeds 0.00000786 0.00341539 text_encoder-item3.clip_pooled_prompt_embeds 0.00841799 1.01420029 text_encoder-item3.clip_prompt_embeds 0.00023247 23.90570887 text_encoder_2-item4.clip_pooled_prompt_embeds 0.00016751 0.67051430 text_encoder_2-item4.clip_prompt_embeds 0.00009830 0.47049568 text_encoder_3-item5.t5_prompt_embeds 0.00001263 0.00227117 vae.encoder_f0 1.67190456 6.81759834 vae.encoder_f1 1.67190480 6.81456280 vae.decoder 0.00017417 0.02075510 ------------------------------------------------------------------------------------- TOTAL 0.77542609 4.44339872 (elements=2,260,992) ---------------------- -------------------------------------------------------- SAMPLE-WISE STATISTICS ---------------------- -------------------------------------------------------- Handler sd35 Strategy individual Architecture hyperprior-featurecoding ---------------------- -------------------------------------------------------- Total Elements 2260992 Total Bytes 102004 BPFP 0.3609 bits/point EBPFP 0.7218 equivalent bits/point MSE 4.443399 ---------------------- -------------------------------------------------------- Time: 0.745s Load: 0.007s, Pack+Encode: 0.292s, Decode+Unpack: 0.445s ---------------------- -------------------------------------------------------- Restored Feature Format: [Dict] with 8 keys key['text_encoder-item0']: [Dict] with 2 keys key['clip_pooled_prompt_embeds']: [Tensor] shape=torch.Size([768]), dtype=torch.float16, device=cpu key['clip_prompt_embeds']: [Tensor] shape=torch.Size([77, 768]), dtype=torch.float16, device=cpu key['text_encoder_2-item1']: [Dict] with 2 keys key['clip_pooled_prompt_embeds']: [Tensor] shape=torch.Size([1280]), dtype=torch.float16, device=cpu key['clip_prompt_embeds']: [Tensor] shape=torch.Size([77, 1280]), dtype=torch.float16, device=cpu key['text_encoder_3-item2']: [Dict] with 1 keys key['t5_prompt_embeds']: [Tensor] shape=torch.Size([77, 4096]), dtype=torch.float16, device=cpu key['text_encoder-item3']: [Dict] with 2 keys key['clip_pooled_prompt_embeds']: [Tensor] shape=torch.Size([768]), dtype=torch.float16, device=cpu key['clip_prompt_embeds']: [Tensor] shape=torch.Size([77, 768]), dtype=torch.float16, device=cpu key['text_encoder_2-item4']: [Dict] with 2 keys key['clip_pooled_prompt_embeds']: [Tensor] shape=torch.Size([1280]), dtype=torch.float16, device=cpu key['clip_prompt_embeds']: [Tensor] shape=torch.Size([77, 1280]), dtype=torch.float16, device=cpu key['text_encoder_3-item5']: [Dict] with 1 keys key['t5_prompt_embeds']: [Tensor] shape=torch.Size([77, 4096]), dtype=torch.float16, device=cpu key['vae.decoder']: [Tensor] shape=torch.Size([16, 128, 128]), dtype=torch.float16, device=cpu key['vae.encoder']: [Dict] with 2 keys key['vae.encoder_f0']: [Tensor] shape=torch.Size([32, 128, 128]), dtype=torch.float16, device=cpu key['vae.encoder_f1']: [Tensor] shape=torch.Size([32, 128, 128]), dtype=torch.float16, device=cpu 💾 Converting with 4.4434 MSE: from ../datasets/FeatureCoding-StableDiffusion3.5/100samples_features/000000074646.zst to output-fixed/sd35/lambda0.004/hyperprior-featurecoding-8bit-individual/sd35cond/000000074646.zst 💪 Processing: ../datasets/FeatureCoding-StableDiffusion3.5/100samples_features/000000085157.zst (15/100) [1/4] FRONTEND: Loading features from ../datasets/FeatureCoding-StableDiffusion3.5/100samples_features/000000085157.zst... Original data structure: root: [Dict] with 8 keys key['text_encoder-item0']: [Dict] with 2 keys key['clip_pooled_prompt_embeds']: [Tensor] shape=torch.Size([768]), dtype=torch.float16, device=cpu key['clip_prompt_embeds']: [Tensor] shape=torch.Size([77, 768]), dtype=torch.float16, device=cpu key['text_encoder_2-item1']: [Dict] with 2 keys key['clip_pooled_prompt_embeds']: [Tensor] shape=torch.Size([1280]), dtype=torch.float16, device=cpu key['clip_prompt_embeds']: [Tensor] shape=torch.Size([77, 1280]), dtype=torch.float16, device=cpu key['text_encoder_3-item2']: [Dict] with 1 keys key['t5_prompt_embeds']: [Tensor] shape=torch.Size([77, 4096]), dtype=torch.float16, device=cpu key['text_encoder-item3']: [Dict] with 2 keys key['clip_pooled_prompt_embeds']: [Tensor] shape=torch.Size([768]), dtype=torch.float16, device=cpu key['clip_prompt_embeds']: [Tensor] shape=torch.Size([77, 768]), dtype=torch.float16, device=cpu key['text_encoder_2-item4']: [Dict] with 2 keys key['clip_pooled_prompt_embeds']: [Tensor] shape=torch.Size([1280]), dtype=torch.float16, device=cpu key['clip_prompt_embeds']: [Tensor] shape=torch.Size([77, 1280]), dtype=torch.float16, device=cpu key['text_encoder_3-item5']: [Dict] with 1 keys key['t5_prompt_embeds']: [Tensor] shape=torch.Size([77, 4096]), dtype=torch.float16, device=cpu key['vae.encoder-item6']: [Dict] with 2 keys key['vae.encoder_f0']: [Tensor] shape=torch.Size([32, 128, 128]), dtype=torch.float16, device=cpu key['vae.encoder_f1']: [Tensor] shape=torch.Size([32, 128, 128]), dtype=torch.float16, device=cpu key['vae.decoder']: [Tensor] shape=torch.Size([16, 128, 128]), dtype=torch.float16, device=cpu ⌛️ [1/4] FRONTEND: Load time: 0.008s ------------------------------------------------------------ SD3.5 Features Summary ------------------------------------------------------------ Number of text encoders: 3 Encoder names: ['text_encoder', 'text_encoder_2', 'text_encoder_3'] Has VAE latents: True Has VAE encoder features: True Data type: torch.float16 text_encoder-item0: clip_pooled_prompt_embeds: torch.Size([768]), torch.float16 clip_prompt_embeds: torch.Size([77, 768]), torch.float16 text_encoder_2-item1: clip_pooled_prompt_embeds: torch.Size([1280]), torch.float16 clip_prompt_embeds: torch.Size([77, 1280]), torch.float16 text_encoder_3-item2: t5_prompt_embeds: torch.Size([77, 4096]), torch.float16 text_encoder-item3: clip_pooled_prompt_embeds: torch.Size([768]), torch.float16 clip_prompt_embeds: torch.Size([77, 768]), torch.float16 text_encoder_2-item4: clip_pooled_prompt_embeds: torch.Size([1280]), torch.float16 clip_prompt_embeds: torch.Size([77, 1280]), torch.float16 text_encoder_3-item5: t5_prompt_embeds: torch.Size([77, 4096]), torch.float16 vae.decoder latents: torch.Size([16, 128, 128]) vae.encoder features: vae.encoder_f0: torch.Size([32, 128, 128]), torch.float16 vae.encoder_f1: torch.Size([32, 128, 128]), torch.float16 ------------------------------------------------------------ [2/4] FRONTEND: Pack + Encode (strategy: individual)... IndividualPacker: text_encoder-item0.clip_pooled_prompt_embeds: torch.Size([768])->torch.Size([1, 1, 1, 768]) text_encoder-item0.clip_prompt_embeds: torch.Size([77, 768])->torch.Size([1, 1, 77, 768]) text_encoder_2-item1.clip_pooled_prompt_embeds: torch.Size([1280])->torch.Size([1, 1, 1, 1280]) text_encoder_2-item1.clip_prompt_embeds: torch.Size([77, 1280])->torch.Size([1, 1, 77, 1280]) text_encoder_3-item2.t5_prompt_embeds: torch.Size([77, 4096])->torch.Size([1, 1, 77, 4096]) text_encoder-item3.clip_pooled_prompt_embeds: torch.Size([768])->torch.Size([1, 1, 1, 768]) text_encoder-item3.clip_prompt_embeds: torch.Size([77, 768])->torch.Size([1, 1, 77, 768]) text_encoder_2-item4.clip_pooled_prompt_embeds: torch.Size([1280])->torch.Size([1, 1, 1, 1280]) text_encoder_2-item4.clip_prompt_embeds: torch.Size([77, 1280])->torch.Size([1, 1, 77, 1280]) text_encoder_3-item5.t5_prompt_embeds: torch.Size([77, 4096])->torch.Size([1, 1, 77, 4096]) vae.encoder_f0: torch.Size([32, 128, 128])->torch.Size([1, 1, 512, 1024]) vae.encoder_f1: torch.Size([32, 128, 128])->torch.Size([1, 1, 512, 1024]) vae.decoder: torch.Size([16, 128, 128])->torch.Size([1, 1, 512, 512]) Using per-key quantization points (text_encoder-item0.clip_pooled_prompt_embeds: torch.Size([256])) for text_encoder-item0.clip_pooled_prompt_embeds text_encoder-item0.clip_pooled_prompt_embeds: 736B, BPFP=7.6667 Using per-key quantization points (text_encoder-item0.clip_prompt_embeds: torch.Size([256])) for text_encoder-item0.clip_prompt_embeds text_encoder-item0.clip_prompt_embeds: 6,676B, BPFP=0.9031 Using per-key quantization points (text_encoder_2-item1.clip_pooled_prompt_embeds: torch.Size([256])) for text_encoder_2-item1.clip_pooled_prompt_embeds text_encoder_2-item1.clip_pooled_prompt_embeds: 1,200B, BPFP=7.5000 Using per-key quantization points (text_encoder_2-item1.clip_prompt_embeds: torch.Size([256])) for text_encoder_2-item1.clip_prompt_embeds text_encoder_2-item1.clip_prompt_embeds: 11,260B, BPFP=0.9140 Using per-key quantization points (text_encoder_3-item2.t5_prompt_embeds: torch.Size([256])) for text_encoder_3-item2.t5_prompt_embeds text_encoder_3-item2.t5_prompt_embeds: 31,412B, BPFP=0.7968 Using per-key quantization points (text_encoder-item3.clip_pooled_prompt_embeds: torch.Size([256])) for text_encoder-item3.clip_pooled_prompt_embeds text_encoder-item3.clip_pooled_prompt_embeds: 760B, BPFP=7.9167 Using per-key quantization points (text_encoder-item3.clip_prompt_embeds: torch.Size([256])) for text_encoder-item3.clip_prompt_embeds text_encoder-item3.clip_prompt_embeds: 4,024B, BPFP=0.5444 Using per-key quantization points (text_encoder_2-item4.clip_pooled: torch.Size([256])) for text_encoder_2-item4.clip_pooled_prompt_embeds text_encoder_2-item4.clip_pooled_prompt_embeds: 1,268B, BPFP=7.9250 Using per-key quantization points (text_encoder_2-item4.clip_prompt: torch.Size([256])) for text_encoder_2-item4.clip_prompt_embeds text_encoder_2-item4.clip_prompt_embeds: 6,996B, BPFP=0.5679 Using per-key quantization points (text_encoder_3-item5.t5_prompt_embeds: torch.Size([256])) for text_encoder_3-item5.t5_prompt_embeds text_encoder_3-item5.t5_prompt_embeds: 18,416B, BPFP=0.4671 Using per-key quantization points (vae.encoder_f0: torch.Size([256])) for vae.encoder_f0 vae.encoder_f0: 21,964B, BPFP=0.3351 Using per-key quantization points (vae.encoder_f1: torch.Size([256])) for vae.encoder_f1 vae.encoder_f1: 21,964B, BPFP=0.3351 Using per-key quantization points (vae.decoder: torch.Size([256])) for vae.decoder vae.decoder: 7,540B, BPFP=0.2301 ⌛️ [2/4] FRONTEND: Frontend time: 0.293s (Pack+Encode) [3/4] BACKEND: Decode + Unpack... Using per-key quantization points (text_encoder-item0.clip_pooled_prompt_embeds: torch.Size([256])) for text_encoder-item0.clip_pooled_prompt_embeds Using per-key quantization points (text_encoder-item0.clip_prompt_embeds: torch.Size([256])) for text_encoder-item0.clip_prompt_embeds Using per-key quantization points (text_encoder_2-item1.clip_pooled_prompt_embeds: torch.Size([256])) for text_encoder_2-item1.clip_pooled_prompt_embeds Using per-key quantization points (text_encoder_2-item1.clip_prompt_embeds: torch.Size([256])) for text_encoder_2-item1.clip_prompt_embeds Using per-key quantization points (text_encoder_3-item2.t5_prompt_embeds: torch.Size([256])) for text_encoder_3-item2.t5_prompt_embeds Using per-key quantization points (text_encoder-item3.clip_pooled_prompt_embeds: torch.Size([256])) for text_encoder-item3.clip_pooled_prompt_embeds Using per-key quantization points (text_encoder-item3.clip_prompt_embeds: torch.Size([256])) for text_encoder-item3.clip_prompt_embeds Using per-key quantization points (text_encoder_2-item4.clip_pooled: torch.Size([256])) for text_encoder_2-item4.clip_pooled_prompt_embeds Using per-key quantization points (text_encoder_2-item4.clip_prompt: torch.Size([256])) for text_encoder_2-item4.clip_prompt_embeds Using per-key quantization points (text_encoder_3-item5.t5_prompt_embeds: torch.Size([256])) for text_encoder_3-item5.t5_prompt_embeds Using per-key quantization points (vae.encoder_f0: torch.Size([256])) for vae.encoder_f0 Using per-key quantization points (vae.encoder_f1: torch.Size([256])) for vae.encoder_f1 Using per-key quantization points (vae.decoder: torch.Size([256])) for vae.decoder IndividualUnPacker: text_encoder-item0.clip_pooled_prompt_embeds: torch.Size([768]) text_encoder-item0.clip_prompt_embeds: torch.Size([77, 768]) text_encoder_2-item1.clip_pooled_prompt_embeds: torch.Size([1280]) text_encoder_2-item1.clip_prompt_embeds: torch.Size([77, 1280]) text_encoder_3-item2.t5_prompt_embeds: torch.Size([77, 4096]) text_encoder-item3.clip_pooled_prompt_embeds: torch.Size([768]) text_encoder-item3.clip_prompt_embeds: torch.Size([77, 768]) text_encoder_2-item4.clip_pooled_prompt_embeds: torch.Size([1280]) text_encoder_2-item4.clip_prompt_embeds: torch.Size([77, 1280]) text_encoder_3-item5.t5_prompt_embeds: torch.Size([77, 4096]) vae.decoder: torch.Size([16, 128, 128]) vae.encoder_f0: torch.Size([32, 128, 128]) vae.encoder_f1: torch.Size([32, 128, 128]) ⌛️ [3/4] BACKEND: Backend time: 0.449s [4/4] METRICS: Computing MSE Breakdown... Using per-key quantization points (text_encoder-item0.clip_pooled_prompt_embeds: torch.Size([256])) for text_encoder-item0.clip_pooled_prompt_embeds Using per-key quantization points (text_encoder-item0.clip_prompt_embeds: torch.Size([256])) for text_encoder-item0.clip_prompt_embeds Using per-key quantization points (text_encoder_2-item1.clip_pooled_prompt_embeds: torch.Size([256])) for text_encoder_2-item1.clip_pooled_prompt_embeds Using per-key quantization points (text_encoder_2-item1.clip_prompt_embeds: torch.Size([256])) for text_encoder_2-item1.clip_prompt_embeds Using per-key quantization points (text_encoder_3-item2.t5_prompt_embeds: torch.Size([256])) for text_encoder_3-item2.t5_prompt_embeds Using per-key quantization points (text_encoder-item3.clip_pooled_prompt_embeds: torch.Size([256])) for text_encoder-item3.clip_pooled_prompt_embeds Using per-key quantization points (text_encoder-item3.clip_prompt_embeds: torch.Size([256])) for text_encoder-item3.clip_prompt_embeds Using per-key quantization points (text_encoder_2-item4.clip_pooled: torch.Size([256])) for text_encoder_2-item4.clip_pooled_prompt_embeds Using per-key quantization points (text_encoder_2-item4.clip_prompt: torch.Size([256])) for text_encoder_2-item4.clip_prompt_embeds Using per-key quantization points (text_encoder_3-item5.t5_prompt_embeds: torch.Size([256])) for text_encoder_3-item5.t5_prompt_embeds Using per-key quantization points (vae.encoder_f0: torch.Size([256])) for vae.encoder_f0 Using per-key quantization points (vae.encoder_f1: torch.Size([256])) for vae.encoder_f1 Using per-key quantization points (vae.decoder: torch.Size([256])) for vae.decoder IndividualUnPacker: text_encoder-item0.clip_pooled_prompt_embeds: torch.Size([768]) text_encoder-item0.clip_prompt_embeds: torch.Size([77, 768]) text_encoder_2-item1.clip_pooled_prompt_embeds: torch.Size([1280]) text_encoder_2-item1.clip_prompt_embeds: torch.Size([77, 1280]) text_encoder_3-item2.t5_prompt_embeds: torch.Size([77, 4096]) text_encoder-item3.clip_pooled_prompt_embeds: torch.Size([768]) text_encoder-item3.clip_prompt_embeds: torch.Size([77, 768]) text_encoder_2-item4.clip_pooled_prompt_embeds: torch.Size([1280]) text_encoder_2-item4.clip_prompt_embeds: torch.Size([77, 1280]) text_encoder_3-item5.t5_prompt_embeds: torch.Size([77, 4096]) vae.decoder: torch.Size([16, 128, 128]) vae.encoder_f0: torch.Size([32, 128, 128]) vae.encoder_f1: torch.Size([32, 128, 128]) Per-key MSE Breakdown (Quant=Truncation+Quantization, Codec=Compression): Key Quant-MSE Total-MSE ------------------------------------------------------------------------------------- text_encoder-item0.clip_pooled_prompt_embeds 0.00021898 0.90416606 text_encoder-item0.clip_prompt_embeds 0.00025129 23.93954824 text_encoder_2-item1.clip_pooled_prompt_embeds 0.00023862 0.90859032 text_encoder_2-item1.clip_prompt_embeds 0.00021627 0.12572472 text_encoder_3-item2.t5_prompt_embeds 0.00000880 0.00359675 text_encoder-item3.clip_pooled_prompt_embeds 0.00841799 1.01420029 text_encoder-item3.clip_prompt_embeds 0.00023247 23.90570887 text_encoder_2-item4.clip_pooled_prompt_embeds 0.00016751 0.67051430 text_encoder_2-item4.clip_prompt_embeds 0.00009830 0.47049568 text_encoder_3-item5.t5_prompt_embeds 0.00001263 0.00227117 vae.encoder_f0 0.00621760 2.49274087 vae.encoder_f1 0.00622505 2.49173641 vae.decoder 0.00025114 0.06197819 ------------------------------------------------------------------------------------- TOTAL 0.00294689 2.44274845 (elements=2,260,992) ---------------------- -------------------------------------------------------- SAMPLE-WISE STATISTICS ---------------------- -------------------------------------------------------- Handler sd35 Strategy individual Architecture hyperprior-featurecoding ---------------------- -------------------------------------------------------- Total Elements 2260992 Total Bytes 134216 BPFP 0.4749 bits/point EBPFP 0.9498 equivalent bits/point MSE 2.442748 ---------------------- -------------------------------------------------------- Time: 0.749s Load: 0.008s, Pack+Encode: 0.293s, Decode+Unpack: 0.449s ---------------------- -------------------------------------------------------- Restored Feature Format: [Dict] with 8 keys key['text_encoder-item0']: [Dict] with 2 keys key['clip_pooled_prompt_embeds']: [Tensor] shape=torch.Size([768]), dtype=torch.float16, device=cpu key['clip_prompt_embeds']: [Tensor] shape=torch.Size([77, 768]), dtype=torch.float16, device=cpu key['text_encoder_2-item1']: [Dict] with 2 keys key['clip_pooled_prompt_embeds']: [Tensor] shape=torch.Size([1280]), dtype=torch.float16, device=cpu key['clip_prompt_embeds']: [Tensor] shape=torch.Size([77, 1280]), dtype=torch.float16, device=cpu key['text_encoder_3-item2']: [Dict] with 1 keys key['t5_prompt_embeds']: [Tensor] shape=torch.Size([77, 4096]), dtype=torch.float16, device=cpu key['text_encoder-item3']: [Dict] with 2 keys key['clip_pooled_prompt_embeds']: [Tensor] shape=torch.Size([768]), dtype=torch.float16, device=cpu key['clip_prompt_embeds']: [Tensor] shape=torch.Size([77, 768]), dtype=torch.float16, device=cpu key['text_encoder_2-item4']: [Dict] with 2 keys key['clip_pooled_prompt_embeds']: [Tensor] shape=torch.Size([1280]), dtype=torch.float16, device=cpu key['clip_prompt_embeds']: [Tensor] shape=torch.Size([77, 1280]), dtype=torch.float16, device=cpu key['text_encoder_3-item5']: [Dict] with 1 keys key['t5_prompt_embeds']: [Tensor] shape=torch.Size([77, 4096]), dtype=torch.float16, device=cpu key['vae.decoder']: [Tensor] shape=torch.Size([16, 128, 128]), dtype=torch.float16, device=cpu key['vae.encoder']: [Dict] with 2 keys key['vae.encoder_f0']: [Tensor] shape=torch.Size([32, 128, 128]), dtype=torch.float16, device=cpu key['vae.encoder_f1']: [Tensor] shape=torch.Size([32, 128, 128]), dtype=torch.float16, device=cpu 💾 Converting with 2.4427 MSE: from ../datasets/FeatureCoding-StableDiffusion3.5/100samples_features/000000085157.zst to output-fixed/sd35/lambda0.004/hyperprior-featurecoding-8bit-individual/sd35cond/000000085157.zst 💪 Processing: ../datasets/FeatureCoding-StableDiffusion3.5/100samples_features/000000089648.zst (16/100) [1/4] FRONTEND: Loading features from ../datasets/FeatureCoding-StableDiffusion3.5/100samples_features/000000089648.zst... Original data structure: root: [Dict] with 8 keys key['text_encoder-item0']: [Dict] with 2 keys key['clip_pooled_prompt_embeds']: [Tensor] shape=torch.Size([768]), dtype=torch.float16, device=cpu key['clip_prompt_embeds']: [Tensor] shape=torch.Size([77, 768]), dtype=torch.float16, device=cpu key['text_encoder_2-item1']: [Dict] with 2 keys key['clip_pooled_prompt_embeds']: [Tensor] shape=torch.Size([1280]), dtype=torch.float16, device=cpu key['clip_prompt_embeds']: [Tensor] shape=torch.Size([77, 1280]), dtype=torch.float16, device=cpu key['text_encoder_3-item2']: [Dict] with 1 keys key['t5_prompt_embeds']: [Tensor] shape=torch.Size([77, 4096]), dtype=torch.float16, device=cpu key['text_encoder-item3']: [Dict] with 2 keys key['clip_pooled_prompt_embeds']: [Tensor] shape=torch.Size([768]), dtype=torch.float16, device=cpu key['clip_prompt_embeds']: [Tensor] shape=torch.Size([77, 768]), dtype=torch.float16, device=cpu key['text_encoder_2-item4']: [Dict] with 2 keys key['clip_pooled_prompt_embeds']: [Tensor] shape=torch.Size([1280]), dtype=torch.float16, device=cpu key['clip_prompt_embeds']: [Tensor] shape=torch.Size([77, 1280]), dtype=torch.float16, device=cpu key['text_encoder_3-item5']: [Dict] with 1 keys key['t5_prompt_embeds']: [Tensor] shape=torch.Size([77, 4096]), dtype=torch.float16, device=cpu key['vae.encoder-item6']: [Dict] with 2 keys key['vae.encoder_f0']: [Tensor] shape=torch.Size([32, 128, 128]), dtype=torch.float16, device=cpu key['vae.encoder_f1']: [Tensor] shape=torch.Size([32, 128, 128]), dtype=torch.float16, device=cpu key['vae.decoder']: [Tensor] shape=torch.Size([16, 128, 128]), dtype=torch.float16, device=cpu ⌛️ [1/4] FRONTEND: Load time: 0.009s ------------------------------------------------------------ SD3.5 Features Summary ------------------------------------------------------------ Number of text encoders: 3 Encoder names: ['text_encoder', 'text_encoder_2', 'text_encoder_3'] Has VAE latents: True Has VAE encoder features: True Data type: torch.float16 text_encoder-item0: clip_pooled_prompt_embeds: torch.Size([768]), torch.float16 clip_prompt_embeds: torch.Size([77, 768]), torch.float16 text_encoder_2-item1: clip_pooled_prompt_embeds: torch.Size([1280]), torch.float16 clip_prompt_embeds: torch.Size([77, 1280]), torch.float16 text_encoder_3-item2: t5_prompt_embeds: torch.Size([77, 4096]), torch.float16 text_encoder-item3: clip_pooled_prompt_embeds: torch.Size([768]), torch.float16 clip_prompt_embeds: torch.Size([77, 768]), torch.float16 text_encoder_2-item4: clip_pooled_prompt_embeds: torch.Size([1280]), torch.float16 clip_prompt_embeds: torch.Size([77, 1280]), torch.float16 text_encoder_3-item5: t5_prompt_embeds: torch.Size([77, 4096]), torch.float16 vae.decoder latents: torch.Size([16, 128, 128]) vae.encoder features: vae.encoder_f0: torch.Size([32, 128, 128]), torch.float16 vae.encoder_f1: torch.Size([32, 128, 128]), torch.float16 ------------------------------------------------------------ [2/4] FRONTEND: Pack + Encode (strategy: individual)... IndividualPacker: text_encoder-item0.clip_pooled_prompt_embeds: torch.Size([768])->torch.Size([1, 1, 1, 768]) text_encoder-item0.clip_prompt_embeds: torch.Size([77, 768])->torch.Size([1, 1, 77, 768]) text_encoder_2-item1.clip_pooled_prompt_embeds: torch.Size([1280])->torch.Size([1, 1, 1, 1280]) text_encoder_2-item1.clip_prompt_embeds: torch.Size([77, 1280])->torch.Size([1, 1, 77, 1280]) text_encoder_3-item2.t5_prompt_embeds: torch.Size([77, 4096])->torch.Size([1, 1, 77, 4096]) text_encoder-item3.clip_pooled_prompt_embeds: torch.Size([768])->torch.Size([1, 1, 1, 768]) text_encoder-item3.clip_prompt_embeds: torch.Size([77, 768])->torch.Size([1, 1, 77, 768]) text_encoder_2-item4.clip_pooled_prompt_embeds: torch.Size([1280])->torch.Size([1, 1, 1, 1280]) text_encoder_2-item4.clip_prompt_embeds: torch.Size([77, 1280])->torch.Size([1, 1, 77, 1280]) text_encoder_3-item5.t5_prompt_embeds: torch.Size([77, 4096])->torch.Size([1, 1, 77, 4096]) vae.encoder_f0: torch.Size([32, 128, 128])->torch.Size([1, 1, 512, 1024]) vae.encoder_f1: torch.Size([32, 128, 128])->torch.Size([1, 1, 512, 1024]) vae.decoder: torch.Size([16, 128, 128])->torch.Size([1, 1, 512, 512]) Using per-key quantization points (text_encoder-item0.clip_pooled_prompt_embeds: torch.Size([256])) for text_encoder-item0.clip_pooled_prompt_embeds text_encoder-item0.clip_pooled_prompt_embeds: 724B, BPFP=7.5417 Using per-key quantization points (text_encoder-item0.clip_prompt_embeds: torch.Size([256])) for text_encoder-item0.clip_prompt_embeds text_encoder-item0.clip_prompt_embeds: 6,184B, BPFP=0.8366 Using per-key quantization points (text_encoder_2-item1.clip_pooled_prompt_embeds: torch.Size([256])) for text_encoder_2-item1.clip_pooled_prompt_embeds text_encoder_2-item1.clip_pooled_prompt_embeds: 1,208B, BPFP=7.5500 Using per-key quantization points (text_encoder_2-item1.clip_prompt_embeds: torch.Size([256])) for text_encoder_2-item1.clip_prompt_embeds text_encoder_2-item1.clip_prompt_embeds: 11,020B, BPFP=0.8945 Using per-key quantization points (text_encoder_3-item2.t5_prompt_embeds: torch.Size([256])) for text_encoder_3-item2.t5_prompt_embeds text_encoder_3-item2.t5_prompt_embeds: 26,292B, BPFP=0.6669 Using per-key quantization points (text_encoder-item3.clip_pooled_prompt_embeds: torch.Size([256])) for text_encoder-item3.clip_pooled_prompt_embeds text_encoder-item3.clip_pooled_prompt_embeds: 760B, BPFP=7.9167 Using per-key quantization points (text_encoder-item3.clip_prompt_embeds: torch.Size([256])) for text_encoder-item3.clip_prompt_embeds text_encoder-item3.clip_prompt_embeds: 4,024B, BPFP=0.5444 Using per-key quantization points (text_encoder_2-item4.clip_pooled: torch.Size([256])) for text_encoder_2-item4.clip_pooled_prompt_embeds text_encoder_2-item4.clip_pooled_prompt_embeds: 1,268B, BPFP=7.9250 Using per-key quantization points (text_encoder_2-item4.clip_prompt: torch.Size([256])) for text_encoder_2-item4.clip_prompt_embeds text_encoder_2-item4.clip_prompt_embeds: 6,996B, BPFP=0.5679 Using per-key quantization points (text_encoder_3-item5.t5_prompt_embeds: torch.Size([256])) for text_encoder_3-item5.t5_prompt_embeds text_encoder_3-item5.t5_prompt_embeds: 18,416B, BPFP=0.4671 Using per-key quantization points (vae.encoder_f0: torch.Size([256])) for vae.encoder_f0 vae.encoder_f0: 23,844B, BPFP=0.3638 Using per-key quantization points (vae.encoder_f1: torch.Size([256])) for vae.encoder_f1 vae.encoder_f1: 23,844B, BPFP=0.3638 Using per-key quantization points (vae.decoder: torch.Size([256])) for vae.decoder vae.decoder: 7,520B, BPFP=0.2295 ⌛️ [2/4] FRONTEND: Frontend time: 0.292s (Pack+Encode) [3/4] BACKEND: Decode + Unpack... Using per-key quantization points (text_encoder-item0.clip_pooled_prompt_embeds: torch.Size([256])) for text_encoder-item0.clip_pooled_prompt_embeds Using per-key quantization points (text_encoder-item0.clip_prompt_embeds: torch.Size([256])) for text_encoder-item0.clip_prompt_embeds Using per-key quantization points (text_encoder_2-item1.clip_pooled_prompt_embeds: torch.Size([256])) for text_encoder_2-item1.clip_pooled_prompt_embeds Using per-key quantization points (text_encoder_2-item1.clip_prompt_embeds: torch.Size([256])) for text_encoder_2-item1.clip_prompt_embeds Using per-key quantization points (text_encoder_3-item2.t5_prompt_embeds: torch.Size([256])) for text_encoder_3-item2.t5_prompt_embeds Using per-key quantization points (text_encoder-item3.clip_pooled_prompt_embeds: torch.Size([256])) for text_encoder-item3.clip_pooled_prompt_embeds Using per-key quantization points (text_encoder-item3.clip_prompt_embeds: torch.Size([256])) for text_encoder-item3.clip_prompt_embeds Using per-key quantization points (text_encoder_2-item4.clip_pooled: torch.Size([256])) for text_encoder_2-item4.clip_pooled_prompt_embeds Using per-key quantization points (text_encoder_2-item4.clip_prompt: torch.Size([256])) for text_encoder_2-item4.clip_prompt_embeds Using per-key quantization points (text_encoder_3-item5.t5_prompt_embeds: torch.Size([256])) for text_encoder_3-item5.t5_prompt_embeds Using per-key quantization points (vae.encoder_f0: torch.Size([256])) for vae.encoder_f0 Using per-key quantization points (vae.encoder_f1: torch.Size([256])) for vae.encoder_f1 Using per-key quantization points (vae.decoder: torch.Size([256])) for vae.decoder IndividualUnPacker: text_encoder-item0.clip_pooled_prompt_embeds: torch.Size([768]) text_encoder-item0.clip_prompt_embeds: torch.Size([77, 768]) text_encoder_2-item1.clip_pooled_prompt_embeds: torch.Size([1280]) text_encoder_2-item1.clip_prompt_embeds: torch.Size([77, 1280]) text_encoder_3-item2.t5_prompt_embeds: torch.Size([77, 4096]) text_encoder-item3.clip_pooled_prompt_embeds: torch.Size([768]) text_encoder-item3.clip_prompt_embeds: torch.Size([77, 768]) text_encoder_2-item4.clip_pooled_prompt_embeds: torch.Size([1280]) text_encoder_2-item4.clip_prompt_embeds: torch.Size([77, 1280]) text_encoder_3-item5.t5_prompt_embeds: torch.Size([77, 4096]) vae.decoder: torch.Size([16, 128, 128]) vae.encoder_f0: torch.Size([32, 128, 128]) vae.encoder_f1: torch.Size([32, 128, 128]) ⌛️ [3/4] BACKEND: Backend time: 0.446s [4/4] METRICS: Computing MSE Breakdown... Using per-key quantization points (text_encoder-item0.clip_pooled_prompt_embeds: torch.Size([256])) for text_encoder-item0.clip_pooled_prompt_embeds Using per-key quantization points (text_encoder-item0.clip_prompt_embeds: torch.Size([256])) for text_encoder-item0.clip_prompt_embeds Using per-key quantization points (text_encoder_2-item1.clip_pooled_prompt_embeds: torch.Size([256])) for text_encoder_2-item1.clip_pooled_prompt_embeds Using per-key quantization points (text_encoder_2-item1.clip_prompt_embeds: torch.Size([256])) for text_encoder_2-item1.clip_prompt_embeds Using per-key quantization points (text_encoder_3-item2.t5_prompt_embeds: torch.Size([256])) for text_encoder_3-item2.t5_prompt_embeds Using per-key quantization points (text_encoder-item3.clip_pooled_prompt_embeds: torch.Size([256])) for text_encoder-item3.clip_pooled_prompt_embeds Using per-key quantization points (text_encoder-item3.clip_prompt_embeds: torch.Size([256])) for text_encoder-item3.clip_prompt_embeds Using per-key quantization points (text_encoder_2-item4.clip_pooled: torch.Size([256])) for text_encoder_2-item4.clip_pooled_prompt_embeds Using per-key quantization points (text_encoder_2-item4.clip_prompt: torch.Size([256])) for text_encoder_2-item4.clip_prompt_embeds Using per-key quantization points (text_encoder_3-item5.t5_prompt_embeds: torch.Size([256])) for text_encoder_3-item5.t5_prompt_embeds Using per-key quantization points (vae.encoder_f0: torch.Size([256])) for vae.encoder_f0 Using per-key quantization points (vae.encoder_f1: torch.Size([256])) for vae.encoder_f1 Using per-key quantization points (vae.decoder: torch.Size([256])) for vae.decoder IndividualUnPacker: text_encoder-item0.clip_pooled_prompt_embeds: torch.Size([768]) text_encoder-item0.clip_prompt_embeds: torch.Size([77, 768]) text_encoder_2-item1.clip_pooled_prompt_embeds: torch.Size([1280]) text_encoder_2-item1.clip_prompt_embeds: torch.Size([77, 1280]) text_encoder_3-item2.t5_prompt_embeds: torch.Size([77, 4096]) text_encoder-item3.clip_pooled_prompt_embeds: torch.Size([768]) text_encoder-item3.clip_prompt_embeds: torch.Size([77, 768]) text_encoder_2-item4.clip_pooled_prompt_embeds: torch.Size([1280]) text_encoder_2-item4.clip_prompt_embeds: torch.Size([77, 1280]) text_encoder_3-item5.t5_prompt_embeds: torch.Size([77, 4096]) vae.decoder: torch.Size([16, 128, 128]) vae.encoder_f0: torch.Size([32, 128, 128]) vae.encoder_f1: torch.Size([32, 128, 128]) Per-key MSE Breakdown (Quant=Truncation+Quantization, Codec=Compression): Key Quant-MSE Total-MSE ------------------------------------------------------------------------------------- text_encoder-item0.clip_pooled_prompt_embeds 0.00241962 0.97099225 text_encoder-item0.clip_prompt_embeds 0.00020838 48.61449455 text_encoder_2-item1.clip_pooled_prompt_embeds 0.00021520 0.92767897 text_encoder_2-item1.clip_prompt_embeds 0.00018543 0.16369402 text_encoder_3-item2.t5_prompt_embeds 0.00000844 0.00270878 text_encoder-item3.clip_pooled_prompt_embeds 0.00841799 1.01420029 text_encoder-item3.clip_prompt_embeds 0.00023247 23.90570887 text_encoder_2-item4.clip_pooled_prompt_embeds 0.00016751 0.67051430 text_encoder_2-item4.clip_prompt_embeds 0.00009830 0.47049568 text_encoder_3-item5.t5_prompt_embeds 0.00001263 0.00227117 vae.encoder_f0 0.00675961 3.14311695 vae.encoder_f1 0.00676652 3.14347458 vae.decoder 0.00021373 0.06481232 ------------------------------------------------------------------------------------- TOTAL 0.00319201 3.39195190 (elements=2,260,992) ---------------------- -------------------------------------------------------- SAMPLE-WISE STATISTICS ---------------------- -------------------------------------------------------- Handler sd35 Strategy individual Architecture hyperprior-featurecoding ---------------------- -------------------------------------------------------- Total Elements 2260992 Total Bytes 132100 BPFP 0.4674 bits/point EBPFP 0.9348 equivalent bits/point MSE 3.391952 ---------------------- -------------------------------------------------------- Time: 0.747s Load: 0.009s, Pack+Encode: 0.292s, Decode+Unpack: 0.446s ---------------------- -------------------------------------------------------- Restored Feature Format: [Dict] with 8 keys key['text_encoder-item0']: [Dict] with 2 keys key['clip_pooled_prompt_embeds']: [Tensor] shape=torch.Size([768]), dtype=torch.float16, device=cpu key['clip_prompt_embeds']: [Tensor] shape=torch.Size([77, 768]), dtype=torch.float16, device=cpu key['text_encoder_2-item1']: [Dict] with 2 keys key['clip_pooled_prompt_embeds']: [Tensor] shape=torch.Size([1280]), dtype=torch.float16, device=cpu key['clip_prompt_embeds']: [Tensor] shape=torch.Size([77, 1280]), dtype=torch.float16, device=cpu key['text_encoder_3-item2']: [Dict] with 1 keys key['t5_prompt_embeds']: [Tensor] shape=torch.Size([77, 4096]), dtype=torch.float16, device=cpu key['text_encoder-item3']: [Dict] with 2 keys key['clip_pooled_prompt_embeds']: [Tensor] shape=torch.Size([768]), dtype=torch.float16, device=cpu key['clip_prompt_embeds']: [Tensor] shape=torch.Size([77, 768]), dtype=torch.float16, device=cpu key['text_encoder_2-item4']: [Dict] with 2 keys key['clip_pooled_prompt_embeds']: [Tensor] shape=torch.Size([1280]), dtype=torch.float16, device=cpu key['clip_prompt_embeds']: [Tensor] shape=torch.Size([77, 1280]), dtype=torch.float16, device=cpu key['text_encoder_3-item5']: [Dict] with 1 keys key['t5_prompt_embeds']: [Tensor] shape=torch.Size([77, 4096]), dtype=torch.float16, device=cpu key['vae.decoder']: [Tensor] shape=torch.Size([16, 128, 128]), dtype=torch.float16, device=cpu key['vae.encoder']: [Dict] with 2 keys key['vae.encoder_f0']: [Tensor] shape=torch.Size([32, 128, 128]), dtype=torch.float16, device=cpu key['vae.encoder_f1']: [Tensor] shape=torch.Size([32, 128, 128]), dtype=torch.float16, device=cpu 💾 Converting with 3.3920 MSE: from ../datasets/FeatureCoding-StableDiffusion3.5/100samples_features/000000089648.zst to output-fixed/sd35/lambda0.004/hyperprior-featurecoding-8bit-individual/sd35cond/000000089648.zst 💪 Processing: ../datasets/FeatureCoding-StableDiffusion3.5/100samples_features/000000093965.zst (17/100) [1/4] FRONTEND: Loading features from ../datasets/FeatureCoding-StableDiffusion3.5/100samples_features/000000093965.zst... Original data structure: root: [Dict] with 8 keys key['text_encoder-item0']: [Dict] with 2 keys key['clip_pooled_prompt_embeds']: [Tensor] shape=torch.Size([768]), dtype=torch.float16, device=cpu key['clip_prompt_embeds']: [Tensor] shape=torch.Size([77, 768]), dtype=torch.float16, device=cpu key['text_encoder_2-item1']: [Dict] with 2 keys key['clip_pooled_prompt_embeds']: [Tensor] shape=torch.Size([1280]), dtype=torch.float16, device=cpu key['clip_prompt_embeds']: [Tensor] shape=torch.Size([77, 1280]), dtype=torch.float16, device=cpu key['text_encoder_3-item2']: [Dict] with 1 keys key['t5_prompt_embeds']: [Tensor] shape=torch.Size([77, 4096]), dtype=torch.float16, device=cpu key['text_encoder-item3']: [Dict] with 2 keys key['clip_pooled_prompt_embeds']: [Tensor] shape=torch.Size([768]), dtype=torch.float16, device=cpu key['clip_prompt_embeds']: [Tensor] shape=torch.Size([77, 768]), dtype=torch.float16, device=cpu key['text_encoder_2-item4']: [Dict] with 2 keys key['clip_pooled_prompt_embeds']: [Tensor] shape=torch.Size([1280]), dtype=torch.float16, device=cpu key['clip_prompt_embeds']: [Tensor] shape=torch.Size([77, 1280]), dtype=torch.float16, device=cpu key['text_encoder_3-item5']: [Dict] with 1 keys key['t5_prompt_embeds']: [Tensor] shape=torch.Size([77, 4096]), dtype=torch.float16, device=cpu key['vae.encoder-item6']: [Dict] with 2 keys key['vae.encoder_f0']: [Tensor] shape=torch.Size([32, 128, 128]), dtype=torch.float16, device=cpu key['vae.encoder_f1']: [Tensor] shape=torch.Size([32, 128, 128]), dtype=torch.float16, device=cpu key['vae.decoder']: [Tensor] shape=torch.Size([16, 128, 128]), dtype=torch.float16, device=cpu ⌛️ [1/4] FRONTEND: Load time: 0.008s ------------------------------------------------------------ SD3.5 Features Summary ------------------------------------------------------------ Number of text encoders: 3 Encoder names: ['text_encoder', 'text_encoder_2', 'text_encoder_3'] Has VAE latents: True Has VAE encoder features: True Data type: torch.float16 text_encoder-item0: clip_pooled_prompt_embeds: torch.Size([768]), torch.float16 clip_prompt_embeds: torch.Size([77, 768]), torch.float16 text_encoder_2-item1: clip_pooled_prompt_embeds: torch.Size([1280]), torch.float16 clip_prompt_embeds: torch.Size([77, 1280]), torch.float16 text_encoder_3-item2: t5_prompt_embeds: torch.Size([77, 4096]), torch.float16 text_encoder-item3: clip_pooled_prompt_embeds: torch.Size([768]), torch.float16 clip_prompt_embeds: torch.Size([77, 768]), torch.float16 text_encoder_2-item4: clip_pooled_prompt_embeds: torch.Size([1280]), torch.float16 clip_prompt_embeds: torch.Size([77, 1280]), torch.float16 text_encoder_3-item5: t5_prompt_embeds: torch.Size([77, 4096]), torch.float16 vae.decoder latents: torch.Size([16, 128, 128]) vae.encoder features: vae.encoder_f0: torch.Size([32, 128, 128]), torch.float16 vae.encoder_f1: torch.Size([32, 128, 128]), torch.float16 ------------------------------------------------------------ [2/4] FRONTEND: Pack + Encode (strategy: individual)... IndividualPacker: text_encoder-item0.clip_pooled_prompt_embeds: torch.Size([768])->torch.Size([1, 1, 1, 768]) text_encoder-item0.clip_prompt_embeds: torch.Size([77, 768])->torch.Size([1, 1, 77, 768]) text_encoder_2-item1.clip_pooled_prompt_embeds: torch.Size([1280])->torch.Size([1, 1, 1, 1280]) text_encoder_2-item1.clip_prompt_embeds: torch.Size([77, 1280])->torch.Size([1, 1, 77, 1280]) text_encoder_3-item2.t5_prompt_embeds: torch.Size([77, 4096])->torch.Size([1, 1, 77, 4096]) text_encoder-item3.clip_pooled_prompt_embeds: torch.Size([768])->torch.Size([1, 1, 1, 768]) text_encoder-item3.clip_prompt_embeds: torch.Size([77, 768])->torch.Size([1, 1, 77, 768]) text_encoder_2-item4.clip_pooled_prompt_embeds: torch.Size([1280])->torch.Size([1, 1, 1, 1280]) text_encoder_2-item4.clip_prompt_embeds: torch.Size([77, 1280])->torch.Size([1, 1, 77, 1280]) text_encoder_3-item5.t5_prompt_embeds: torch.Size([77, 4096])->torch.Size([1, 1, 77, 4096]) vae.encoder_f0: torch.Size([32, 128, 128])->torch.Size([1, 1, 512, 1024]) vae.encoder_f1: torch.Size([32, 128, 128])->torch.Size([1, 1, 512, 1024]) vae.decoder: torch.Size([16, 128, 128])->torch.Size([1, 1, 512, 512]) Using per-key quantization points (text_encoder-item0.clip_pooled_prompt_embeds: torch.Size([256])) for text_encoder-item0.clip_pooled_prompt_embeds text_encoder-item0.clip_pooled_prompt_embeds: 732B, BPFP=7.6250 Using per-key quantization points (text_encoder-item0.clip_prompt_embeds: torch.Size([256])) for text_encoder-item0.clip_prompt_embeds text_encoder-item0.clip_prompt_embeds: 6,904B, BPFP=0.9340 Using per-key quantization points (text_encoder_2-item1.clip_pooled_prompt_embeds: torch.Size([256])) for text_encoder_2-item1.clip_pooled_prompt_embeds text_encoder_2-item1.clip_pooled_prompt_embeds: 1,200B, BPFP=7.5000 Using per-key quantization points (text_encoder_2-item1.clip_prompt_embeds: torch.Size([256])) for text_encoder_2-item1.clip_prompt_embeds text_encoder_2-item1.clip_prompt_embeds: 11,292B, BPFP=0.9166 Using per-key quantization points (text_encoder_3-item2.t5_prompt_embeds: torch.Size([256])) for text_encoder_3-item2.t5_prompt_embeds text_encoder_3-item2.t5_prompt_embeds: 30,416B, BPFP=0.7715 Using per-key quantization points (text_encoder-item3.clip_pooled_prompt_embeds: torch.Size([256])) for text_encoder-item3.clip_pooled_prompt_embeds text_encoder-item3.clip_pooled_prompt_embeds: 760B, BPFP=7.9167 Using per-key quantization points (text_encoder-item3.clip_prompt_embeds: torch.Size([256])) for text_encoder-item3.clip_prompt_embeds text_encoder-item3.clip_prompt_embeds: 4,024B, BPFP=0.5444 Using per-key quantization points (text_encoder_2-item4.clip_pooled: torch.Size([256])) for text_encoder_2-item4.clip_pooled_prompt_embeds text_encoder_2-item4.clip_pooled_prompt_embeds: 1,268B, BPFP=7.9250 Using per-key quantization points (text_encoder_2-item4.clip_prompt: torch.Size([256])) for text_encoder_2-item4.clip_prompt_embeds text_encoder_2-item4.clip_prompt_embeds: 6,996B, BPFP=0.5679 Using per-key quantization points (text_encoder_3-item5.t5_prompt_embeds: torch.Size([256])) for text_encoder_3-item5.t5_prompt_embeds text_encoder_3-item5.t5_prompt_embeds: 18,416B, BPFP=0.4671 Using per-key quantization points (vae.encoder_f0: torch.Size([256])) for vae.encoder_f0 vae.encoder_f0: 10,704B, BPFP=0.1633 Using per-key quantization points (vae.encoder_f1: torch.Size([256])) for vae.encoder_f1 vae.encoder_f1: 10,704B, BPFP=0.1633 Using per-key quantization points (vae.decoder: torch.Size([256])) for vae.decoder vae.decoder: 12,048B, BPFP=0.3677 ⌛️ [2/4] FRONTEND: Frontend time: 0.293s (Pack+Encode) [3/4] BACKEND: Decode + Unpack... Using per-key quantization points (text_encoder-item0.clip_pooled_prompt_embeds: torch.Size([256])) for text_encoder-item0.clip_pooled_prompt_embeds Using per-key quantization points (text_encoder-item0.clip_prompt_embeds: torch.Size([256])) for text_encoder-item0.clip_prompt_embeds Using per-key quantization points (text_encoder_2-item1.clip_pooled_prompt_embeds: torch.Size([256])) for text_encoder_2-item1.clip_pooled_prompt_embeds Using per-key quantization points (text_encoder_2-item1.clip_prompt_embeds: torch.Size([256])) for text_encoder_2-item1.clip_prompt_embeds Using per-key quantization points (text_encoder_3-item2.t5_prompt_embeds: torch.Size([256])) for text_encoder_3-item2.t5_prompt_embeds Using per-key quantization points (text_encoder-item3.clip_pooled_prompt_embeds: torch.Size([256])) for text_encoder-item3.clip_pooled_prompt_embeds Using per-key quantization points (text_encoder-item3.clip_prompt_embeds: torch.Size([256])) for text_encoder-item3.clip_prompt_embeds Using per-key quantization points (text_encoder_2-item4.clip_pooled: torch.Size([256])) for text_encoder_2-item4.clip_pooled_prompt_embeds Using per-key quantization points (text_encoder_2-item4.clip_prompt: torch.Size([256])) for text_encoder_2-item4.clip_prompt_embeds Using per-key quantization points (text_encoder_3-item5.t5_prompt_embeds: torch.Size([256])) for text_encoder_3-item5.t5_prompt_embeds Using per-key quantization points (vae.encoder_f0: torch.Size([256])) for vae.encoder_f0 Using per-key quantization points (vae.encoder_f1: torch.Size([256])) for vae.encoder_f1 Using per-key quantization points (vae.decoder: torch.Size([256])) for vae.decoder IndividualUnPacker: text_encoder-item0.clip_pooled_prompt_embeds: torch.Size([768]) text_encoder-item0.clip_prompt_embeds: torch.Size([77, 768]) text_encoder_2-item1.clip_pooled_prompt_embeds: torch.Size([1280]) text_encoder_2-item1.clip_prompt_embeds: torch.Size([77, 1280]) text_encoder_3-item2.t5_prompt_embeds: torch.Size([77, 4096]) text_encoder-item3.clip_pooled_prompt_embeds: torch.Size([768]) text_encoder-item3.clip_prompt_embeds: torch.Size([77, 768]) text_encoder_2-item4.clip_pooled_prompt_embeds: torch.Size([1280]) text_encoder_2-item4.clip_prompt_embeds: torch.Size([77, 1280]) text_encoder_3-item5.t5_prompt_embeds: torch.Size([77, 4096]) vae.decoder: torch.Size([16, 128, 128]) vae.encoder_f0: torch.Size([32, 128, 128]) vae.encoder_f1: torch.Size([32, 128, 128]) ⌛️ [3/4] BACKEND: Backend time: 0.450s [4/4] METRICS: Computing MSE Breakdown... Using per-key quantization points (text_encoder-item0.clip_pooled_prompt_embeds: torch.Size([256])) for text_encoder-item0.clip_pooled_prompt_embeds Using per-key quantization points (text_encoder-item0.clip_prompt_embeds: torch.Size([256])) for text_encoder-item0.clip_prompt_embeds Using per-key quantization points (text_encoder_2-item1.clip_pooled_prompt_embeds: torch.Size([256])) for text_encoder_2-item1.clip_pooled_prompt_embeds Using per-key quantization points (text_encoder_2-item1.clip_prompt_embeds: torch.Size([256])) for text_encoder_2-item1.clip_prompt_embeds Using per-key quantization points (text_encoder_3-item2.t5_prompt_embeds: torch.Size([256])) for text_encoder_3-item2.t5_prompt_embeds Using per-key quantization points (text_encoder-item3.clip_pooled_prompt_embeds: torch.Size([256])) for text_encoder-item3.clip_pooled_prompt_embeds Using per-key quantization points (text_encoder-item3.clip_prompt_embeds: torch.Size([256])) for text_encoder-item3.clip_prompt_embeds Using per-key quantization points (text_encoder_2-item4.clip_pooled: torch.Size([256])) for text_encoder_2-item4.clip_pooled_prompt_embeds Using per-key quantization points (text_encoder_2-item4.clip_prompt: torch.Size([256])) for text_encoder_2-item4.clip_prompt_embeds Using per-key quantization points (text_encoder_3-item5.t5_prompt_embeds: torch.Size([256])) for text_encoder_3-item5.t5_prompt_embeds Using per-key quantization points (vae.encoder_f0: torch.Size([256])) for vae.encoder_f0 Using per-key quantization points (vae.encoder_f1: torch.Size([256])) for vae.encoder_f1 Using per-key quantization points (vae.decoder: torch.Size([256])) for vae.decoder IndividualUnPacker: text_encoder-item0.clip_pooled_prompt_embeds: torch.Size([768]) text_encoder-item0.clip_prompt_embeds: torch.Size([77, 768]) text_encoder_2-item1.clip_pooled_prompt_embeds: torch.Size([1280]) text_encoder_2-item1.clip_prompt_embeds: torch.Size([77, 1280]) text_encoder_3-item2.t5_prompt_embeds: torch.Size([77, 4096]) text_encoder-item3.clip_pooled_prompt_embeds: torch.Size([768]) text_encoder-item3.clip_prompt_embeds: torch.Size([77, 768]) text_encoder_2-item4.clip_pooled_prompt_embeds: torch.Size([1280]) text_encoder_2-item4.clip_prompt_embeds: torch.Size([77, 1280]) text_encoder_3-item5.t5_prompt_embeds: torch.Size([77, 4096]) vae.decoder: torch.Size([16, 128, 128]) vae.encoder_f0: torch.Size([32, 128, 128]) vae.encoder_f1: torch.Size([32, 128, 128]) Per-key MSE Breakdown (Quant=Truncation+Quantization, Codec=Compression): Key Quant-MSE Total-MSE ------------------------------------------------------------------------------------- text_encoder-item0.clip_pooled_prompt_embeds 0.00020005 0.90248601 text_encoder-item0.clip_prompt_embeds 0.00021387 107.03816626 text_encoder_2-item1.clip_pooled_prompt_embeds 0.00028145 0.88290014 text_encoder_2-item1.clip_prompt_embeds 0.00018115 0.13265011 text_encoder_3-item2.t5_prompt_embeds 0.00000768 0.00363773 text_encoder-item3.clip_pooled_prompt_embeds 0.00841799 1.01420029 text_encoder-item3.clip_prompt_embeds 0.00023247 23.90570887 text_encoder_2-item4.clip_pooled_prompt_embeds 0.00016751 0.67051430 text_encoder_2-item4.clip_prompt_embeds 0.00009830 0.47049568 text_encoder_3-item5.t5_prompt_embeds 0.00001263 0.00227117 vae.encoder_f0 0.00596338 1.31515718 vae.encoder_f1 0.00596322 1.31507826 vae.decoder 0.00018207 0.10055342 ------------------------------------------------------------------------------------- TOTAL 0.00281657 4.07503760 (elements=2,260,992) ---------------------- -------------------------------------------------------- SAMPLE-WISE STATISTICS ---------------------- -------------------------------------------------------- Handler sd35 Strategy individual Architecture hyperprior-featurecoding ---------------------- -------------------------------------------------------- Total Elements 2260992 Total Bytes 115464 BPFP 0.4085 bits/point EBPFP 0.8171 equivalent bits/point MSE 4.075038 ---------------------- -------------------------------------------------------- Time: 0.751s Load: 0.008s, Pack+Encode: 0.293s, Decode+Unpack: 0.450s ---------------------- -------------------------------------------------------- Restored Feature Format: [Dict] with 8 keys key['text_encoder-item0']: [Dict] with 2 keys key['clip_pooled_prompt_embeds']: [Tensor] shape=torch.Size([768]), dtype=torch.float16, device=cpu key['clip_prompt_embeds']: [Tensor] shape=torch.Size([77, 768]), dtype=torch.float16, device=cpu key['text_encoder_2-item1']: [Dict] with 2 keys key['clip_pooled_prompt_embeds']: [Tensor] shape=torch.Size([1280]), dtype=torch.float16, device=cpu key['clip_prompt_embeds']: [Tensor] shape=torch.Size([77, 1280]), dtype=torch.float16, device=cpu key['text_encoder_3-item2']: [Dict] with 1 keys key['t5_prompt_embeds']: [Tensor] shape=torch.Size([77, 4096]), dtype=torch.float16, device=cpu key['text_encoder-item3']: [Dict] with 2 keys key['clip_pooled_prompt_embeds']: [Tensor] shape=torch.Size([768]), dtype=torch.float16, device=cpu key['clip_prompt_embeds']: [Tensor] shape=torch.Size([77, 768]), dtype=torch.float16, device=cpu key['text_encoder_2-item4']: [Dict] with 2 keys key['clip_pooled_prompt_embeds']: [Tensor] shape=torch.Size([1280]), dtype=torch.float16, device=cpu key['clip_prompt_embeds']: [Tensor] shape=torch.Size([77, 1280]), dtype=torch.float16, device=cpu key['text_encoder_3-item5']: [Dict] with 1 keys key['t5_prompt_embeds']: [Tensor] shape=torch.Size([77, 4096]), dtype=torch.float16, device=cpu key['vae.decoder']: [Tensor] shape=torch.Size([16, 128, 128]), dtype=torch.float16, device=cpu key['vae.encoder']: [Dict] with 2 keys key['vae.encoder_f0']: [Tensor] shape=torch.Size([32, 128, 128]), dtype=torch.float16, device=cpu key['vae.encoder_f1']: [Tensor] shape=torch.Size([32, 128, 128]), dtype=torch.float16, device=cpu 💾 Converting with 4.0750 MSE: from ../datasets/FeatureCoding-StableDiffusion3.5/100samples_features/000000093965.zst to output-fixed/sd35/lambda0.004/hyperprior-featurecoding-8bit-individual/sd35cond/000000093965.zst 💪 Processing: ../datasets/FeatureCoding-StableDiffusion3.5/100samples_features/000000094852.zst (18/100) [1/4] FRONTEND: Loading features from ../datasets/FeatureCoding-StableDiffusion3.5/100samples_features/000000094852.zst... Original data structure: root: [Dict] with 8 keys key['text_encoder-item0']: [Dict] with 2 keys key['clip_pooled_prompt_embeds']: [Tensor] shape=torch.Size([768]), dtype=torch.float16, device=cpu key['clip_prompt_embeds']: [Tensor] shape=torch.Size([77, 768]), dtype=torch.float16, device=cpu key['text_encoder_2-item1']: [Dict] with 2 keys key['clip_pooled_prompt_embeds']: [Tensor] shape=torch.Size([1280]), dtype=torch.float16, device=cpu key['clip_prompt_embeds']: [Tensor] shape=torch.Size([77, 1280]), dtype=torch.float16, device=cpu key['text_encoder_3-item2']: [Dict] with 1 keys key['t5_prompt_embeds']: [Tensor] shape=torch.Size([77, 4096]), dtype=torch.float16, device=cpu key['text_encoder-item3']: [Dict] with 2 keys key['clip_pooled_prompt_embeds']: [Tensor] shape=torch.Size([768]), dtype=torch.float16, device=cpu key['clip_prompt_embeds']: [Tensor] shape=torch.Size([77, 768]), dtype=torch.float16, device=cpu key['text_encoder_2-item4']: [Dict] with 2 keys key['clip_pooled_prompt_embeds']: [Tensor] shape=torch.Size([1280]), dtype=torch.float16, device=cpu key['clip_prompt_embeds']: [Tensor] shape=torch.Size([77, 1280]), dtype=torch.float16, device=cpu key['text_encoder_3-item5']: [Dict] with 1 keys key['t5_prompt_embeds']: [Tensor] shape=torch.Size([77, 4096]), dtype=torch.float16, device=cpu key['vae.encoder-item6']: [Dict] with 2 keys key['vae.encoder_f0']: [Tensor] shape=torch.Size([32, 128, 128]), dtype=torch.float16, device=cpu key['vae.encoder_f1']: [Tensor] shape=torch.Size([32, 128, 128]), dtype=torch.float16, device=cpu key['vae.decoder']: [Tensor] shape=torch.Size([16, 128, 128]), dtype=torch.float16, device=cpu ⌛️ [1/4] FRONTEND: Load time: 0.009s ------------------------------------------------------------ SD3.5 Features Summary ------------------------------------------------------------ Number of text encoders: 3 Encoder names: ['text_encoder', 'text_encoder_2', 'text_encoder_3'] Has VAE latents: True Has VAE encoder features: True Data type: torch.float16 text_encoder-item0: clip_pooled_prompt_embeds: torch.Size([768]), torch.float16 clip_prompt_embeds: torch.Size([77, 768]), torch.float16 text_encoder_2-item1: clip_pooled_prompt_embeds: torch.Size([1280]), torch.float16 clip_prompt_embeds: torch.Size([77, 1280]), torch.float16 text_encoder_3-item2: t5_prompt_embeds: torch.Size([77, 4096]), torch.float16 text_encoder-item3: clip_pooled_prompt_embeds: torch.Size([768]), torch.float16 clip_prompt_embeds: torch.Size([77, 768]), torch.float16 text_encoder_2-item4: clip_pooled_prompt_embeds: torch.Size([1280]), torch.float16 clip_prompt_embeds: torch.Size([77, 1280]), torch.float16 text_encoder_3-item5: t5_prompt_embeds: torch.Size([77, 4096]), torch.float16 vae.decoder latents: torch.Size([16, 128, 128]) vae.encoder features: vae.encoder_f0: torch.Size([32, 128, 128]), torch.float16 vae.encoder_f1: torch.Size([32, 128, 128]), torch.float16 ------------------------------------------------------------ [2/4] FRONTEND: Pack + Encode (strategy: individual)... IndividualPacker: text_encoder-item0.clip_pooled_prompt_embeds: torch.Size([768])->torch.Size([1, 1, 1, 768]) text_encoder-item0.clip_prompt_embeds: torch.Size([77, 768])->torch.Size([1, 1, 77, 768]) text_encoder_2-item1.clip_pooled_prompt_embeds: torch.Size([1280])->torch.Size([1, 1, 1, 1280]) text_encoder_2-item1.clip_prompt_embeds: torch.Size([77, 1280])->torch.Size([1, 1, 77, 1280]) text_encoder_3-item2.t5_prompt_embeds: torch.Size([77, 4096])->torch.Size([1, 1, 77, 4096]) text_encoder-item3.clip_pooled_prompt_embeds: torch.Size([768])->torch.Size([1, 1, 1, 768]) text_encoder-item3.clip_prompt_embeds: torch.Size([77, 768])->torch.Size([1, 1, 77, 768]) text_encoder_2-item4.clip_pooled_prompt_embeds: torch.Size([1280])->torch.Size([1, 1, 1, 1280]) text_encoder_2-item4.clip_prompt_embeds: torch.Size([77, 1280])->torch.Size([1, 1, 77, 1280]) text_encoder_3-item5.t5_prompt_embeds: torch.Size([77, 4096])->torch.Size([1, 1, 77, 4096]) vae.encoder_f0: torch.Size([32, 128, 128])->torch.Size([1, 1, 512, 1024]) vae.encoder_f1: torch.Size([32, 128, 128])->torch.Size([1, 1, 512, 1024]) vae.decoder: torch.Size([16, 128, 128])->torch.Size([1, 1, 512, 512]) Using per-key quantization points (text_encoder-item0.clip_pooled_prompt_embeds: torch.Size([256])) for text_encoder-item0.clip_pooled_prompt_embeds text_encoder-item0.clip_pooled_prompt_embeds: 732B, BPFP=7.6250 Using per-key quantization points (text_encoder-item0.clip_prompt_embeds: torch.Size([256])) for text_encoder-item0.clip_prompt_embeds text_encoder-item0.clip_prompt_embeds: 6,460B, BPFP=0.8739 Using per-key quantization points (text_encoder_2-item1.clip_pooled_prompt_embeds: torch.Size([256])) for text_encoder_2-item1.clip_pooled_prompt_embeds text_encoder_2-item1.clip_pooled_prompt_embeds: 1,200B, BPFP=7.5000 Using per-key quantization points (text_encoder_2-item1.clip_prompt_embeds: torch.Size([256])) for text_encoder_2-item1.clip_prompt_embeds text_encoder_2-item1.clip_prompt_embeds: 11,056B, BPFP=0.8974 Using per-key quantization points (text_encoder_3-item2.t5_prompt_embeds: torch.Size([256])) for text_encoder_3-item2.t5_prompt_embeds text_encoder_3-item2.t5_prompt_embeds: 25,628B, BPFP=0.6501 Using per-key quantization points (text_encoder-item3.clip_pooled_prompt_embeds: torch.Size([256])) for text_encoder-item3.clip_pooled_prompt_embeds text_encoder-item3.clip_pooled_prompt_embeds: 760B, BPFP=7.9167 Using per-key quantization points (text_encoder-item3.clip_prompt_embeds: torch.Size([256])) for text_encoder-item3.clip_prompt_embeds text_encoder-item3.clip_prompt_embeds: 4,024B, BPFP=0.5444 Using per-key quantization points (text_encoder_2-item4.clip_pooled: torch.Size([256])) for text_encoder_2-item4.clip_pooled_prompt_embeds text_encoder_2-item4.clip_pooled_prompt_embeds: 1,268B, BPFP=7.9250 Using per-key quantization points (text_encoder_2-item4.clip_prompt: torch.Size([256])) for text_encoder_2-item4.clip_prompt_embeds text_encoder_2-item4.clip_prompt_embeds: 6,996B, BPFP=0.5679 Using per-key quantization points (text_encoder_3-item5.t5_prompt_embeds: torch.Size([256])) for text_encoder_3-item5.t5_prompt_embeds text_encoder_3-item5.t5_prompt_embeds: 18,416B, BPFP=0.4671 Using per-key quantization points (vae.encoder_f0: torch.Size([256])) for vae.encoder_f0 vae.encoder_f0: 8,580B, BPFP=0.1309 Using per-key quantization points (vae.encoder_f1: torch.Size([256])) for vae.encoder_f1 vae.encoder_f1: 8,580B, BPFP=0.1309 Using per-key quantization points (vae.decoder: torch.Size([256])) for vae.decoder vae.decoder: 8,348B, BPFP=0.2548 ⌛️ [2/4] FRONTEND: Frontend time: 0.292s (Pack+Encode) [3/4] BACKEND: Decode + Unpack... Using per-key quantization points (text_encoder-item0.clip_pooled_prompt_embeds: torch.Size([256])) for text_encoder-item0.clip_pooled_prompt_embeds Using per-key quantization points (text_encoder-item0.clip_prompt_embeds: torch.Size([256])) for text_encoder-item0.clip_prompt_embeds Using per-key quantization points (text_encoder_2-item1.clip_pooled_prompt_embeds: torch.Size([256])) for text_encoder_2-item1.clip_pooled_prompt_embeds Using per-key quantization points (text_encoder_2-item1.clip_prompt_embeds: torch.Size([256])) for text_encoder_2-item1.clip_prompt_embeds Using per-key quantization points (text_encoder_3-item2.t5_prompt_embeds: torch.Size([256])) for text_encoder_3-item2.t5_prompt_embeds Using per-key quantization points (text_encoder-item3.clip_pooled_prompt_embeds: torch.Size([256])) for text_encoder-item3.clip_pooled_prompt_embeds Using per-key quantization points (text_encoder-item3.clip_prompt_embeds: torch.Size([256])) for text_encoder-item3.clip_prompt_embeds Using per-key quantization points (text_encoder_2-item4.clip_pooled: torch.Size([256])) for text_encoder_2-item4.clip_pooled_prompt_embeds Using per-key quantization points (text_encoder_2-item4.clip_prompt: torch.Size([256])) for text_encoder_2-item4.clip_prompt_embeds Using per-key quantization points (text_encoder_3-item5.t5_prompt_embeds: torch.Size([256])) for text_encoder_3-item5.t5_prompt_embeds Using per-key quantization points (vae.encoder_f0: torch.Size([256])) for vae.encoder_f0 Using per-key quantization points (vae.encoder_f1: torch.Size([256])) for vae.encoder_f1 Using per-key quantization points (vae.decoder: torch.Size([256])) for vae.decoder IndividualUnPacker: text_encoder-item0.clip_pooled_prompt_embeds: torch.Size([768]) text_encoder-item0.clip_prompt_embeds: torch.Size([77, 768]) text_encoder_2-item1.clip_pooled_prompt_embeds: torch.Size([1280]) text_encoder_2-item1.clip_prompt_embeds: torch.Size([77, 1280]) text_encoder_3-item2.t5_prompt_embeds: torch.Size([77, 4096]) text_encoder-item3.clip_pooled_prompt_embeds: torch.Size([768]) text_encoder-item3.clip_prompt_embeds: torch.Size([77, 768]) text_encoder_2-item4.clip_pooled_prompt_embeds: torch.Size([1280]) text_encoder_2-item4.clip_prompt_embeds: torch.Size([77, 1280]) text_encoder_3-item5.t5_prompt_embeds: torch.Size([77, 4096]) vae.decoder: torch.Size([16, 128, 128]) vae.encoder_f0: torch.Size([32, 128, 128]) vae.encoder_f1: torch.Size([32, 128, 128]) ⌛️ [3/4] BACKEND: Backend time: 0.444s [4/4] METRICS: Computing MSE Breakdown... Using per-key quantization points (text_encoder-item0.clip_pooled_prompt_embeds: torch.Size([256])) for text_encoder-item0.clip_pooled_prompt_embeds Using per-key quantization points (text_encoder-item0.clip_prompt_embeds: torch.Size([256])) for text_encoder-item0.clip_prompt_embeds Using per-key quantization points (text_encoder_2-item1.clip_pooled_prompt_embeds: torch.Size([256])) for text_encoder_2-item1.clip_pooled_prompt_embeds Using per-key quantization points (text_encoder_2-item1.clip_prompt_embeds: torch.Size([256])) for text_encoder_2-item1.clip_prompt_embeds Using per-key quantization points (text_encoder_3-item2.t5_prompt_embeds: torch.Size([256])) for text_encoder_3-item2.t5_prompt_embeds Using per-key quantization points (text_encoder-item3.clip_pooled_prompt_embeds: torch.Size([256])) for text_encoder-item3.clip_pooled_prompt_embeds Using per-key quantization points (text_encoder-item3.clip_prompt_embeds: torch.Size([256])) for text_encoder-item3.clip_prompt_embeds Using per-key quantization points (text_encoder_2-item4.clip_pooled: torch.Size([256])) for text_encoder_2-item4.clip_pooled_prompt_embeds Using per-key quantization points (text_encoder_2-item4.clip_prompt: torch.Size([256])) for text_encoder_2-item4.clip_prompt_embeds Using per-key quantization points (text_encoder_3-item5.t5_prompt_embeds: torch.Size([256])) for text_encoder_3-item5.t5_prompt_embeds Using per-key quantization points (vae.encoder_f0: torch.Size([256])) for vae.encoder_f0 Using per-key quantization points (vae.encoder_f1: torch.Size([256])) for vae.encoder_f1 Using per-key quantization points (vae.decoder: torch.Size([256])) for vae.decoder IndividualUnPacker: text_encoder-item0.clip_pooled_prompt_embeds: torch.Size([768]) text_encoder-item0.clip_prompt_embeds: torch.Size([77, 768]) text_encoder_2-item1.clip_pooled_prompt_embeds: torch.Size([1280]) text_encoder_2-item1.clip_prompt_embeds: torch.Size([77, 1280]) text_encoder_3-item2.t5_prompt_embeds: torch.Size([77, 4096]) text_encoder-item3.clip_pooled_prompt_embeds: torch.Size([768]) text_encoder-item3.clip_prompt_embeds: torch.Size([77, 768]) text_encoder_2-item4.clip_pooled_prompt_embeds: torch.Size([1280]) text_encoder_2-item4.clip_prompt_embeds: torch.Size([77, 1280]) text_encoder_3-item5.t5_prompt_embeds: torch.Size([77, 4096]) vae.decoder: torch.Size([16, 128, 128]) vae.encoder_f0: torch.Size([32, 128, 128]) vae.encoder_f1: torch.Size([32, 128, 128]) Per-key MSE Breakdown (Quant=Truncation+Quantization, Codec=Compression): Key Quant-MSE Total-MSE ------------------------------------------------------------------------------------- text_encoder-item0.clip_pooled_prompt_embeds 0.00022632 0.87907076 text_encoder-item0.clip_prompt_embeds 0.00022138 35.96740564 text_encoder_2-item1.clip_pooled_prompt_embeds 0.00034234 0.89552355 text_encoder_2-item1.clip_prompt_embeds 0.00019942 0.17737611 text_encoder_3-item2.t5_prompt_embeds 0.00000807 0.00283273 text_encoder-item3.clip_pooled_prompt_embeds 0.00841799 1.01420029 text_encoder-item3.clip_prompt_embeds 0.00023247 23.90570887 text_encoder_2-item4.clip_pooled_prompt_embeds 0.00016751 0.67051430 text_encoder_2-item4.clip_prompt_embeds 0.00009830 0.47049568 text_encoder_3-item5.t5_prompt_embeds 0.00001263 0.00227117 vae.encoder_f0 0.00552804 0.96921045 vae.encoder_f1 0.00552758 0.96921027 vae.decoder 0.00018040 0.07720087 ------------------------------------------------------------------------------------- TOTAL 0.00261550 2.05489781 (elements=2,260,992) ---------------------- -------------------------------------------------------- SAMPLE-WISE STATISTICS ---------------------- -------------------------------------------------------- Handler sd35 Strategy individual Architecture hyperprior-featurecoding ---------------------- -------------------------------------------------------- Total Elements 2260992 Total Bytes 102048 BPFP 0.3611 bits/point EBPFP 0.7221 equivalent bits/point MSE 2.054898 ---------------------- -------------------------------------------------------- Time: 0.745s Load: 0.009s, Pack+Encode: 0.292s, Decode+Unpack: 0.444s ---------------------- -------------------------------------------------------- Restored Feature Format: [Dict] with 8 keys key['text_encoder-item0']: [Dict] with 2 keys key['clip_pooled_prompt_embeds']: [Tensor] shape=torch.Size([768]), dtype=torch.float16, device=cpu key['clip_prompt_embeds']: [Tensor] shape=torch.Size([77, 768]), dtype=torch.float16, device=cpu key['text_encoder_2-item1']: [Dict] with 2 keys key['clip_pooled_prompt_embeds']: [Tensor] shape=torch.Size([1280]), dtype=torch.float16, device=cpu key['clip_prompt_embeds']: [Tensor] shape=torch.Size([77, 1280]), dtype=torch.float16, device=cpu key['text_encoder_3-item2']: [Dict] with 1 keys key['t5_prompt_embeds']: [Tensor] shape=torch.Size([77, 4096]), dtype=torch.float16, device=cpu key['text_encoder-item3']: [Dict] with 2 keys key['clip_pooled_prompt_embeds']: [Tensor] shape=torch.Size([768]), dtype=torch.float16, device=cpu key['clip_prompt_embeds']: [Tensor] shape=torch.Size([77, 768]), dtype=torch.float16, device=cpu key['text_encoder_2-item4']: [Dict] with 2 keys key['clip_pooled_prompt_embeds']: [Tensor] shape=torch.Size([1280]), dtype=torch.float16, device=cpu key['clip_prompt_embeds']: [Tensor] shape=torch.Size([77, 1280]), dtype=torch.float16, device=cpu key['text_encoder_3-item5']: [Dict] with 1 keys key['t5_prompt_embeds']: [Tensor] shape=torch.Size([77, 4096]), dtype=torch.float16, device=cpu key['vae.decoder']: [Tensor] shape=torch.Size([16, 128, 128]), dtype=torch.float16, device=cpu key['vae.encoder']: [Dict] with 2 keys key['vae.encoder_f0']: [Tensor] shape=torch.Size([32, 128, 128]), dtype=torch.float16, device=cpu key['vae.encoder_f1']: [Tensor] shape=torch.Size([32, 128, 128]), dtype=torch.float16, device=cpu 💾 Converting with 2.0549 MSE: from ../datasets/FeatureCoding-StableDiffusion3.5/100samples_features/000000094852.zst to output-fixed/sd35/lambda0.004/hyperprior-featurecoding-8bit-individual/sd35cond/000000094852.zst 💪 Processing: ../datasets/FeatureCoding-StableDiffusion3.5/100samples_features/000000117914.zst (19/100) [1/4] FRONTEND: Loading features from ../datasets/FeatureCoding-StableDiffusion3.5/100samples_features/000000117914.zst... Original data structure: root: [Dict] with 8 keys key['text_encoder-item0']: [Dict] with 2 keys key['clip_pooled_prompt_embeds']: [Tensor] shape=torch.Size([768]), dtype=torch.float16, device=cpu key['clip_prompt_embeds']: [Tensor] shape=torch.Size([77, 768]), dtype=torch.float16, device=cpu key['text_encoder_2-item1']: [Dict] with 2 keys key['clip_pooled_prompt_embeds']: [Tensor] shape=torch.Size([1280]), dtype=torch.float16, device=cpu key['clip_prompt_embeds']: [Tensor] shape=torch.Size([77, 1280]), dtype=torch.float16, device=cpu key['text_encoder_3-item2']: [Dict] with 1 keys key['t5_prompt_embeds']: [Tensor] shape=torch.Size([77, 4096]), dtype=torch.float16, device=cpu key['text_encoder-item3']: [Dict] with 2 keys key['clip_pooled_prompt_embeds']: [Tensor] shape=torch.Size([768]), dtype=torch.float16, device=cpu key['clip_prompt_embeds']: [Tensor] shape=torch.Size([77, 768]), dtype=torch.float16, device=cpu key['text_encoder_2-item4']: [Dict] with 2 keys key['clip_pooled_prompt_embeds']: [Tensor] shape=torch.Size([1280]), dtype=torch.float16, device=cpu key['clip_prompt_embeds']: [Tensor] shape=torch.Size([77, 1280]), dtype=torch.float16, device=cpu key['text_encoder_3-item5']: [Dict] with 1 keys key['t5_prompt_embeds']: [Tensor] shape=torch.Size([77, 4096]), dtype=torch.float16, device=cpu key['vae.encoder-item6']: [Dict] with 2 keys key['vae.encoder_f0']: [Tensor] shape=torch.Size([32, 128, 128]), dtype=torch.float16, device=cpu key['vae.encoder_f1']: [Tensor] shape=torch.Size([32, 128, 128]), dtype=torch.float16, device=cpu key['vae.decoder']: [Tensor] shape=torch.Size([16, 128, 128]), dtype=torch.float16, device=cpu ⌛️ [1/4] FRONTEND: Load time: 0.008s ------------------------------------------------------------ SD3.5 Features Summary ------------------------------------------------------------ Number of text encoders: 3 Encoder names: ['text_encoder', 'text_encoder_2', 'text_encoder_3'] Has VAE latents: True Has VAE encoder features: True Data type: torch.float16 text_encoder-item0: clip_pooled_prompt_embeds: torch.Size([768]), torch.float16 clip_prompt_embeds: torch.Size([77, 768]), torch.float16 text_encoder_2-item1: clip_pooled_prompt_embeds: torch.Size([1280]), torch.float16 clip_prompt_embeds: torch.Size([77, 1280]), torch.float16 text_encoder_3-item2: t5_prompt_embeds: torch.Size([77, 4096]), torch.float16 text_encoder-item3: clip_pooled_prompt_embeds: torch.Size([768]), torch.float16 clip_prompt_embeds: torch.Size([77, 768]), torch.float16 text_encoder_2-item4: clip_pooled_prompt_embeds: torch.Size([1280]), torch.float16 clip_prompt_embeds: torch.Size([77, 1280]), torch.float16 text_encoder_3-item5: t5_prompt_embeds: torch.Size([77, 4096]), torch.float16 vae.decoder latents: torch.Size([16, 128, 128]) vae.encoder features: vae.encoder_f0: torch.Size([32, 128, 128]), torch.float16 vae.encoder_f1: torch.Size([32, 128, 128]), torch.float16 ------------------------------------------------------------ [2/4] FRONTEND: Pack + Encode (strategy: individual)... IndividualPacker: text_encoder-item0.clip_pooled_prompt_embeds: torch.Size([768])->torch.Size([1, 1, 1, 768]) text_encoder-item0.clip_prompt_embeds: torch.Size([77, 768])->torch.Size([1, 1, 77, 768]) text_encoder_2-item1.clip_pooled_prompt_embeds: torch.Size([1280])->torch.Size([1, 1, 1, 1280]) text_encoder_2-item1.clip_prompt_embeds: torch.Size([77, 1280])->torch.Size([1, 1, 77, 1280]) text_encoder_3-item2.t5_prompt_embeds: torch.Size([77, 4096])->torch.Size([1, 1, 77, 4096]) text_encoder-item3.clip_pooled_prompt_embeds: torch.Size([768])->torch.Size([1, 1, 1, 768]) text_encoder-item3.clip_prompt_embeds: torch.Size([77, 768])->torch.Size([1, 1, 77, 768]) text_encoder_2-item4.clip_pooled_prompt_embeds: torch.Size([1280])->torch.Size([1, 1, 1, 1280]) text_encoder_2-item4.clip_prompt_embeds: torch.Size([77, 1280])->torch.Size([1, 1, 77, 1280]) text_encoder_3-item5.t5_prompt_embeds: torch.Size([77, 4096])->torch.Size([1, 1, 77, 4096]) vae.encoder_f0: torch.Size([32, 128, 128])->torch.Size([1, 1, 512, 1024]) vae.encoder_f1: torch.Size([32, 128, 128])->torch.Size([1, 1, 512, 1024]) vae.decoder: torch.Size([16, 128, 128])->torch.Size([1, 1, 512, 512]) Using per-key quantization points (text_encoder-item0.clip_pooled_prompt_embeds: torch.Size([256])) for text_encoder-item0.clip_pooled_prompt_embeds text_encoder-item0.clip_pooled_prompt_embeds: 732B, BPFP=7.6250 Using per-key quantization points (text_encoder-item0.clip_prompt_embeds: torch.Size([256])) for text_encoder-item0.clip_prompt_embeds text_encoder-item0.clip_prompt_embeds: 5,696B, BPFP=0.7706 Using per-key quantization points (text_encoder_2-item1.clip_pooled_prompt_embeds: torch.Size([256])) for text_encoder_2-item1.clip_pooled_prompt_embeds text_encoder_2-item1.clip_pooled_prompt_embeds: 1,192B, BPFP=7.4500 Using per-key quantization points (text_encoder_2-item1.clip_prompt_embeds: torch.Size([256])) for text_encoder_2-item1.clip_prompt_embeds text_encoder_2-item1.clip_prompt_embeds: 11,484B, BPFP=0.9321 Using per-key quantization points (text_encoder_3-item2.t5_prompt_embeds: torch.Size([256])) for text_encoder_3-item2.t5_prompt_embeds text_encoder_3-item2.t5_prompt_embeds: 26,244B, BPFP=0.6657 Using per-key quantization points (text_encoder-item3.clip_pooled_prompt_embeds: torch.Size([256])) for text_encoder-item3.clip_pooled_prompt_embeds text_encoder-item3.clip_pooled_prompt_embeds: 760B, BPFP=7.9167 Using per-key quantization points (text_encoder-item3.clip_prompt_embeds: torch.Size([256])) for text_encoder-item3.clip_prompt_embeds text_encoder-item3.clip_prompt_embeds: 4,024B, BPFP=0.5444 Using per-key quantization points (text_encoder_2-item4.clip_pooled: torch.Size([256])) for text_encoder_2-item4.clip_pooled_prompt_embeds text_encoder_2-item4.clip_pooled_prompt_embeds: 1,268B, BPFP=7.9250 Using per-key quantization points (text_encoder_2-item4.clip_prompt: torch.Size([256])) for text_encoder_2-item4.clip_prompt_embeds text_encoder_2-item4.clip_prompt_embeds: 6,996B, BPFP=0.5679 Using per-key quantization points (text_encoder_3-item5.t5_prompt_embeds: torch.Size([256])) for text_encoder_3-item5.t5_prompt_embeds text_encoder_3-item5.t5_prompt_embeds: 18,416B, BPFP=0.4671 Using per-key quantization points (vae.encoder_f0: torch.Size([256])) for vae.encoder_f0 vae.encoder_f0: 12,288B, BPFP=0.1875 Using per-key quantization points (vae.encoder_f1: torch.Size([256])) for vae.encoder_f1 vae.encoder_f1: 12,292B, BPFP=0.1876 Using per-key quantization points (vae.decoder: torch.Size([256])) for vae.decoder vae.decoder: 5,792B, BPFP=0.1768 ⌛️ [2/4] FRONTEND: Frontend time: 0.294s (Pack+Encode) [3/4] BACKEND: Decode + Unpack... Using per-key quantization points (text_encoder-item0.clip_pooled_prompt_embeds: torch.Size([256])) for text_encoder-item0.clip_pooled_prompt_embeds Using per-key quantization points (text_encoder-item0.clip_prompt_embeds: torch.Size([256])) for text_encoder-item0.clip_prompt_embeds Using per-key quantization points (text_encoder_2-item1.clip_pooled_prompt_embeds: torch.Size([256])) for text_encoder_2-item1.clip_pooled_prompt_embeds Using per-key quantization points (text_encoder_2-item1.clip_prompt_embeds: torch.Size([256])) for text_encoder_2-item1.clip_prompt_embeds Using per-key quantization points (text_encoder_3-item2.t5_prompt_embeds: torch.Size([256])) for text_encoder_3-item2.t5_prompt_embeds Using per-key quantization points (text_encoder-item3.clip_pooled_prompt_embeds: torch.Size([256])) for text_encoder-item3.clip_pooled_prompt_embeds Using per-key quantization points (text_encoder-item3.clip_prompt_embeds: torch.Size([256])) for text_encoder-item3.clip_prompt_embeds Using per-key quantization points (text_encoder_2-item4.clip_pooled: torch.Size([256])) for text_encoder_2-item4.clip_pooled_prompt_embeds Using per-key quantization points (text_encoder_2-item4.clip_prompt: torch.Size([256])) for text_encoder_2-item4.clip_prompt_embeds Using per-key quantization points (text_encoder_3-item5.t5_prompt_embeds: torch.Size([256])) for text_encoder_3-item5.t5_prompt_embeds Using per-key quantization points (vae.encoder_f0: torch.Size([256])) for vae.encoder_f0 Using per-key quantization points (vae.encoder_f1: torch.Size([256])) for vae.encoder_f1 Using per-key quantization points (vae.decoder: torch.Size([256])) for vae.decoder IndividualUnPacker: text_encoder-item0.clip_pooled_prompt_embeds: torch.Size([768]) text_encoder-item0.clip_prompt_embeds: torch.Size([77, 768]) text_encoder_2-item1.clip_pooled_prompt_embeds: torch.Size([1280]) text_encoder_2-item1.clip_prompt_embeds: torch.Size([77, 1280]) text_encoder_3-item2.t5_prompt_embeds: torch.Size([77, 4096]) text_encoder-item3.clip_pooled_prompt_embeds: torch.Size([768]) text_encoder-item3.clip_prompt_embeds: torch.Size([77, 768]) text_encoder_2-item4.clip_pooled_prompt_embeds: torch.Size([1280]) text_encoder_2-item4.clip_prompt_embeds: torch.Size([77, 1280]) text_encoder_3-item5.t5_prompt_embeds: torch.Size([77, 4096]) vae.decoder: torch.Size([16, 128, 128]) vae.encoder_f0: torch.Size([32, 128, 128]) vae.encoder_f1: torch.Size([32, 128, 128]) ⌛️ [3/4] BACKEND: Backend time: 0.446s [4/4] METRICS: Computing MSE Breakdown... Using per-key quantization points (text_encoder-item0.clip_pooled_prompt_embeds: torch.Size([256])) for text_encoder-item0.clip_pooled_prompt_embeds Using per-key quantization points (text_encoder-item0.clip_prompt_embeds: torch.Size([256])) for text_encoder-item0.clip_prompt_embeds Using per-key quantization points (text_encoder_2-item1.clip_pooled_prompt_embeds: torch.Size([256])) for text_encoder_2-item1.clip_pooled_prompt_embeds Using per-key quantization points (text_encoder_2-item1.clip_prompt_embeds: torch.Size([256])) for text_encoder_2-item1.clip_prompt_embeds Using per-key quantization points (text_encoder_3-item2.t5_prompt_embeds: torch.Size([256])) for text_encoder_3-item2.t5_prompt_embeds Using per-key quantization points (text_encoder-item3.clip_pooled_prompt_embeds: torch.Size([256])) for text_encoder-item3.clip_pooled_prompt_embeds Using per-key quantization points (text_encoder-item3.clip_prompt_embeds: torch.Size([256])) for text_encoder-item3.clip_prompt_embeds Using per-key quantization points (text_encoder_2-item4.clip_pooled: torch.Size([256])) for text_encoder_2-item4.clip_pooled_prompt_embeds Using per-key quantization points (text_encoder_2-item4.clip_prompt: torch.Size([256])) for text_encoder_2-item4.clip_prompt_embeds Using per-key quantization points (text_encoder_3-item5.t5_prompt_embeds: torch.Size([256])) for text_encoder_3-item5.t5_prompt_embeds Using per-key quantization points (vae.encoder_f0: torch.Size([256])) for vae.encoder_f0 Using per-key quantization points (vae.encoder_f1: torch.Size([256])) for vae.encoder_f1 Using per-key quantization points (vae.decoder: torch.Size([256])) for vae.decoder IndividualUnPacker: text_encoder-item0.clip_pooled_prompt_embeds: torch.Size([768]) text_encoder-item0.clip_prompt_embeds: torch.Size([77, 768]) text_encoder_2-item1.clip_pooled_prompt_embeds: torch.Size([1280]) text_encoder_2-item1.clip_prompt_embeds: torch.Size([77, 1280]) text_encoder_3-item2.t5_prompt_embeds: torch.Size([77, 4096]) text_encoder-item3.clip_pooled_prompt_embeds: torch.Size([768]) text_encoder-item3.clip_prompt_embeds: torch.Size([77, 768]) text_encoder_2-item4.clip_pooled_prompt_embeds: torch.Size([1280]) text_encoder_2-item4.clip_prompt_embeds: torch.Size([77, 1280]) text_encoder_3-item5.t5_prompt_embeds: torch.Size([77, 4096]) vae.decoder: torch.Size([16, 128, 128]) vae.encoder_f0: torch.Size([32, 128, 128]) vae.encoder_f1: torch.Size([32, 128, 128]) Per-key MSE Breakdown (Quant=Truncation+Quantization, Codec=Compression): Key Quant-MSE Total-MSE ------------------------------------------------------------------------------------- text_encoder-item0.clip_pooled_prompt_embeds 0.00019161 0.96792213 text_encoder-item0.clip_prompt_embeds 0.00024507 23.94371237 text_encoder_2-item1.clip_pooled_prompt_embeds 0.00020802 0.94905024 text_encoder_2-item1.clip_prompt_embeds 0.00034897 0.12641346 text_encoder_3-item2.t5_prompt_embeds 0.00000820 0.00307950 text_encoder-item3.clip_pooled_prompt_embeds 0.00841799 1.01420029 text_encoder-item3.clip_prompt_embeds 0.00023247 23.90570887 text_encoder_2-item4.clip_pooled_prompt_embeds 0.00016751 0.67051430 text_encoder_2-item4.clip_prompt_embeds 0.00009830 0.47049568 text_encoder_3-item5.t5_prompt_embeds 0.00001263 0.00227117 vae.encoder_f0 0.00721525 1.92423761 vae.encoder_f1 0.00721777 1.92418337 vae.decoder 0.00018707 0.04186224 ------------------------------------------------------------------------------------- TOTAL 0.00340651 2.17709416 (elements=2,260,992) ---------------------- -------------------------------------------------------- SAMPLE-WISE STATISTICS ---------------------- -------------------------------------------------------- Handler sd35 Strategy individual Architecture hyperprior-featurecoding ---------------------- -------------------------------------------------------- Total Elements 2260992 Total Bytes 107184 BPFP 0.3792 bits/point EBPFP 0.7585 equivalent bits/point MSE 2.177094 ---------------------- -------------------------------------------------------- Time: 0.748s Load: 0.008s, Pack+Encode: 0.294s, Decode+Unpack: 0.446s ---------------------- -------------------------------------------------------- Restored Feature Format: [Dict] with 8 keys key['text_encoder-item0']: [Dict] with 2 keys key['clip_pooled_prompt_embeds']: [Tensor] shape=torch.Size([768]), dtype=torch.float16, device=cpu key['clip_prompt_embeds']: [Tensor] shape=torch.Size([77, 768]), dtype=torch.float16, device=cpu key['text_encoder_2-item1']: [Dict] with 2 keys key['clip_pooled_prompt_embeds']: [Tensor] shape=torch.Size([1280]), dtype=torch.float16, device=cpu key['clip_prompt_embeds']: [Tensor] shape=torch.Size([77, 1280]), dtype=torch.float16, device=cpu key['text_encoder_3-item2']: [Dict] with 1 keys key['t5_prompt_embeds']: [Tensor] shape=torch.Size([77, 4096]), dtype=torch.float16, device=cpu key['text_encoder-item3']: [Dict] with 2 keys key['clip_pooled_prompt_embeds']: [Tensor] shape=torch.Size([768]), dtype=torch.float16, device=cpu key['clip_prompt_embeds']: [Tensor] shape=torch.Size([77, 768]), dtype=torch.float16, device=cpu key['text_encoder_2-item4']: [Dict] with 2 keys key['clip_pooled_prompt_embeds']: [Tensor] shape=torch.Size([1280]), dtype=torch.float16, device=cpu key['clip_prompt_embeds']: [Tensor] shape=torch.Size([77, 1280]), dtype=torch.float16, device=cpu key['text_encoder_3-item5']: [Dict] with 1 keys key['t5_prompt_embeds']: [Tensor] shape=torch.Size([77, 4096]), dtype=torch.float16, device=cpu key['vae.decoder']: [Tensor] shape=torch.Size([16, 128, 128]), dtype=torch.float16, device=cpu key['vae.encoder']: [Dict] with 2 keys key['vae.encoder_f0']: [Tensor] shape=torch.Size([32, 128, 128]), dtype=torch.float16, device=cpu key['vae.encoder_f1']: [Tensor] shape=torch.Size([32, 128, 128]), dtype=torch.float16, device=cpu 💾 Converting with 2.1771 MSE: from ../datasets/FeatureCoding-StableDiffusion3.5/100samples_features/000000117914.zst to output-fixed/sd35/lambda0.004/hyperprior-featurecoding-8bit-individual/sd35cond/000000117914.zst 💪 Processing: ../datasets/FeatureCoding-StableDiffusion3.5/100samples_features/000000123321.zst (20/100) [1/4] FRONTEND: Loading features from ../datasets/FeatureCoding-StableDiffusion3.5/100samples_features/000000123321.zst... Original data structure: root: [Dict] with 8 keys key['text_encoder-item0']: [Dict] with 2 keys key['clip_pooled_prompt_embeds']: [Tensor] shape=torch.Size([768]), dtype=torch.float16, device=cpu key['clip_prompt_embeds']: [Tensor] shape=torch.Size([77, 768]), dtype=torch.float16, device=cpu key['text_encoder_2-item1']: [Dict] with 2 keys key['clip_pooled_prompt_embeds']: [Tensor] shape=torch.Size([1280]), dtype=torch.float16, device=cpu key['clip_prompt_embeds']: [Tensor] shape=torch.Size([77, 1280]), dtype=torch.float16, device=cpu key['text_encoder_3-item2']: [Dict] with 1 keys key['t5_prompt_embeds']: [Tensor] shape=torch.Size([77, 4096]), dtype=torch.float16, device=cpu key['text_encoder-item3']: [Dict] with 2 keys key['clip_pooled_prompt_embeds']: [Tensor] shape=torch.Size([768]), dtype=torch.float16, device=cpu key['clip_prompt_embeds']: [Tensor] shape=torch.Size([77, 768]), dtype=torch.float16, device=cpu key['text_encoder_2-item4']: [Dict] with 2 keys key['clip_pooled_prompt_embeds']: [Tensor] shape=torch.Size([1280]), dtype=torch.float16, device=cpu key['clip_prompt_embeds']: [Tensor] shape=torch.Size([77, 1280]), dtype=torch.float16, device=cpu key['text_encoder_3-item5']: [Dict] with 1 keys key['t5_prompt_embeds']: [Tensor] shape=torch.Size([77, 4096]), dtype=torch.float16, device=cpu key['vae.encoder-item6']: [Dict] with 2 keys key['vae.encoder_f0']: [Tensor] shape=torch.Size([32, 128, 128]), dtype=torch.float16, device=cpu key['vae.encoder_f1']: [Tensor] shape=torch.Size([32, 128, 128]), dtype=torch.float16, device=cpu key['vae.decoder']: [Tensor] shape=torch.Size([16, 128, 128]), dtype=torch.float16, device=cpu ⌛️ [1/4] FRONTEND: Load time: 0.008s ------------------------------------------------------------ SD3.5 Features Summary ------------------------------------------------------------ Number of text encoders: 3 Encoder names: ['text_encoder', 'text_encoder_2', 'text_encoder_3'] Has VAE latents: True Has VAE encoder features: True Data type: torch.float16 text_encoder-item0: clip_pooled_prompt_embeds: torch.Size([768]), torch.float16 clip_prompt_embeds: torch.Size([77, 768]), torch.float16 text_encoder_2-item1: clip_pooled_prompt_embeds: torch.Size([1280]), torch.float16 clip_prompt_embeds: torch.Size([77, 1280]), torch.float16 text_encoder_3-item2: t5_prompt_embeds: torch.Size([77, 4096]), torch.float16 text_encoder-item3: clip_pooled_prompt_embeds: torch.Size([768]), torch.float16 clip_prompt_embeds: torch.Size([77, 768]), torch.float16 text_encoder_2-item4: clip_pooled_prompt_embeds: torch.Size([1280]), torch.float16 clip_prompt_embeds: torch.Size([77, 1280]), torch.float16 text_encoder_3-item5: t5_prompt_embeds: torch.Size([77, 4096]), torch.float16 vae.decoder latents: torch.Size([16, 128, 128]) vae.encoder features: vae.encoder_f0: torch.Size([32, 128, 128]), torch.float16 vae.encoder_f1: torch.Size([32, 128, 128]), torch.float16 ------------------------------------------------------------ [2/4] FRONTEND: Pack + Encode (strategy: individual)... IndividualPacker: text_encoder-item0.clip_pooled_prompt_embeds: torch.Size([768])->torch.Size([1, 1, 1, 768]) text_encoder-item0.clip_prompt_embeds: torch.Size([77, 768])->torch.Size([1, 1, 77, 768]) text_encoder_2-item1.clip_pooled_prompt_embeds: torch.Size([1280])->torch.Size([1, 1, 1, 1280]) text_encoder_2-item1.clip_prompt_embeds: torch.Size([77, 1280])->torch.Size([1, 1, 77, 1280]) text_encoder_3-item2.t5_prompt_embeds: torch.Size([77, 4096])->torch.Size([1, 1, 77, 4096]) text_encoder-item3.clip_pooled_prompt_embeds: torch.Size([768])->torch.Size([1, 1, 1, 768]) text_encoder-item3.clip_prompt_embeds: torch.Size([77, 768])->torch.Size([1, 1, 77, 768]) text_encoder_2-item4.clip_pooled_prompt_embeds: torch.Size([1280])->torch.Size([1, 1, 1, 1280]) text_encoder_2-item4.clip_prompt_embeds: torch.Size([77, 1280])->torch.Size([1, 1, 77, 1280]) text_encoder_3-item5.t5_prompt_embeds: torch.Size([77, 4096])->torch.Size([1, 1, 77, 4096]) vae.encoder_f0: torch.Size([32, 128, 128])->torch.Size([1, 1, 512, 1024]) vae.encoder_f1: torch.Size([32, 128, 128])->torch.Size([1, 1, 512, 1024]) vae.decoder: torch.Size([16, 128, 128])->torch.Size([1, 1, 512, 512]) Using per-key quantization points (text_encoder-item0.clip_pooled_prompt_embeds: torch.Size([256])) for text_encoder-item0.clip_pooled_prompt_embeds text_encoder-item0.clip_pooled_prompt_embeds: 724B, BPFP=7.5417 Using per-key quantization points (text_encoder-item0.clip_prompt_embeds: torch.Size([256])) for text_encoder-item0.clip_prompt_embeds text_encoder-item0.clip_prompt_embeds: 5,160B, BPFP=0.6981 Using per-key quantization points (text_encoder_2-item1.clip_pooled_prompt_embeds: torch.Size([256])) for text_encoder_2-item1.clip_pooled_prompt_embeds text_encoder_2-item1.clip_pooled_prompt_embeds: 1,196B, BPFP=7.4750 Using per-key quantization points (text_encoder_2-item1.clip_prompt_embeds: torch.Size([256])) for text_encoder_2-item1.clip_prompt_embeds text_encoder_2-item1.clip_prompt_embeds: 9,776B, BPFP=0.7935 Using per-key quantization points (text_encoder_3-item2.t5_prompt_embeds: torch.Size([256])) for text_encoder_3-item2.t5_prompt_embeds text_encoder_3-item2.t5_prompt_embeds: 23,832B, BPFP=0.6045 Using per-key quantization points (text_encoder-item3.clip_pooled_prompt_embeds: torch.Size([256])) for text_encoder-item3.clip_pooled_prompt_embeds text_encoder-item3.clip_pooled_prompt_embeds: 760B, BPFP=7.9167 Using per-key quantization points (text_encoder-item3.clip_prompt_embeds: torch.Size([256])) for text_encoder-item3.clip_prompt_embeds text_encoder-item3.clip_prompt_embeds: 4,024B, BPFP=0.5444 Using per-key quantization points (text_encoder_2-item4.clip_pooled: torch.Size([256])) for text_encoder_2-item4.clip_pooled_prompt_embeds text_encoder_2-item4.clip_pooled_prompt_embeds: 1,268B, BPFP=7.9250 Using per-key quantization points (text_encoder_2-item4.clip_prompt: torch.Size([256])) for text_encoder_2-item4.clip_prompt_embeds text_encoder_2-item4.clip_prompt_embeds: 6,996B, BPFP=0.5679 Using per-key quantization points (text_encoder_3-item5.t5_prompt_embeds: torch.Size([256])) for text_encoder_3-item5.t5_prompt_embeds text_encoder_3-item5.t5_prompt_embeds: 18,416B, BPFP=0.4671 Using per-key quantization points (vae.encoder_f0: torch.Size([256])) for vae.encoder_f0 vae.encoder_f0: 18,864B, BPFP=0.2878 Using per-key quantization points (vae.encoder_f1: torch.Size([256])) for vae.encoder_f1 vae.encoder_f1: 18,864B, BPFP=0.2878 Using per-key quantization points (vae.decoder: torch.Size([256])) for vae.decoder vae.decoder: 7,576B, BPFP=0.2312 ⌛️ [2/4] FRONTEND: Frontend time: 0.294s (Pack+Encode) [3/4] BACKEND: Decode + Unpack... Using per-key quantization points (text_encoder-item0.clip_pooled_prompt_embeds: torch.Size([256])) for text_encoder-item0.clip_pooled_prompt_embeds Using per-key quantization points (text_encoder-item0.clip_prompt_embeds: torch.Size([256])) for text_encoder-item0.clip_prompt_embeds Using per-key quantization points (text_encoder_2-item1.clip_pooled_prompt_embeds: torch.Size([256])) for text_encoder_2-item1.clip_pooled_prompt_embeds Using per-key quantization points (text_encoder_2-item1.clip_prompt_embeds: torch.Size([256])) for text_encoder_2-item1.clip_prompt_embeds Using per-key quantization points (text_encoder_3-item2.t5_prompt_embeds: torch.Size([256])) for text_encoder_3-item2.t5_prompt_embeds Using per-key quantization points (text_encoder-item3.clip_pooled_prompt_embeds: torch.Size([256])) for text_encoder-item3.clip_pooled_prompt_embeds Using per-key quantization points (text_encoder-item3.clip_prompt_embeds: torch.Size([256])) for text_encoder-item3.clip_prompt_embeds Using per-key quantization points (text_encoder_2-item4.clip_pooled: torch.Size([256])) for text_encoder_2-item4.clip_pooled_prompt_embeds Using per-key quantization points (text_encoder_2-item4.clip_prompt: torch.Size([256])) for text_encoder_2-item4.clip_prompt_embeds Using per-key quantization points (text_encoder_3-item5.t5_prompt_embeds: torch.Size([256])) for text_encoder_3-item5.t5_prompt_embeds Using per-key quantization points (vae.encoder_f0: torch.Size([256])) for vae.encoder_f0 Using per-key quantization points (vae.encoder_f1: torch.Size([256])) for vae.encoder_f1 Using per-key quantization points (vae.decoder: torch.Size([256])) for vae.decoder IndividualUnPacker: text_encoder-item0.clip_pooled_prompt_embeds: torch.Size([768]) text_encoder-item0.clip_prompt_embeds: torch.Size([77, 768]) text_encoder_2-item1.clip_pooled_prompt_embeds: torch.Size([1280]) text_encoder_2-item1.clip_prompt_embeds: torch.Size([77, 1280]) text_encoder_3-item2.t5_prompt_embeds: torch.Size([77, 4096]) text_encoder-item3.clip_pooled_prompt_embeds: torch.Size([768]) text_encoder-item3.clip_prompt_embeds: torch.Size([77, 768]) text_encoder_2-item4.clip_pooled_prompt_embeds: torch.Size([1280]) text_encoder_2-item4.clip_prompt_embeds: torch.Size([77, 1280]) text_encoder_3-item5.t5_prompt_embeds: torch.Size([77, 4096]) vae.decoder: torch.Size([16, 128, 128]) vae.encoder_f0: torch.Size([32, 128, 128]) vae.encoder_f1: torch.Size([32, 128, 128]) ⌛️ [3/4] BACKEND: Backend time: 0.450s [4/4] METRICS: Computing MSE Breakdown... Using per-key quantization points (text_encoder-item0.clip_pooled_prompt_embeds: torch.Size([256])) for text_encoder-item0.clip_pooled_prompt_embeds Using per-key quantization points (text_encoder-item0.clip_prompt_embeds: torch.Size([256])) for text_encoder-item0.clip_prompt_embeds Using per-key quantization points (text_encoder_2-item1.clip_pooled_prompt_embeds: torch.Size([256])) for text_encoder_2-item1.clip_pooled_prompt_embeds Using per-key quantization points (text_encoder_2-item1.clip_prompt_embeds: torch.Size([256])) for text_encoder_2-item1.clip_prompt_embeds Using per-key quantization points (text_encoder_3-item2.t5_prompt_embeds: torch.Size([256])) for text_encoder_3-item2.t5_prompt_embeds Using per-key quantization points (text_encoder-item3.clip_pooled_prompt_embeds: torch.Size([256])) for text_encoder-item3.clip_pooled_prompt_embeds Using per-key quantization points (text_encoder-item3.clip_prompt_embeds: torch.Size([256])) for text_encoder-item3.clip_prompt_embeds Using per-key quantization points (text_encoder_2-item4.clip_pooled: torch.Size([256])) for text_encoder_2-item4.clip_pooled_prompt_embeds Using per-key quantization points (text_encoder_2-item4.clip_prompt: torch.Size([256])) for text_encoder_2-item4.clip_prompt_embeds Using per-key quantization points (text_encoder_3-item5.t5_prompt_embeds: torch.Size([256])) for text_encoder_3-item5.t5_prompt_embeds Using per-key quantization points (vae.encoder_f0: torch.Size([256])) for vae.encoder_f0 Using per-key quantization points (vae.encoder_f1: torch.Size([256])) for vae.encoder_f1 Using per-key quantization points (vae.decoder: torch.Size([256])) for vae.decoder IndividualUnPacker: text_encoder-item0.clip_pooled_prompt_embeds: torch.Size([768]) text_encoder-item0.clip_prompt_embeds: torch.Size([77, 768]) text_encoder_2-item1.clip_pooled_prompt_embeds: torch.Size([1280]) text_encoder_2-item1.clip_prompt_embeds: torch.Size([77, 1280]) text_encoder_3-item2.t5_prompt_embeds: torch.Size([77, 4096]) text_encoder-item3.clip_pooled_prompt_embeds: torch.Size([768]) text_encoder-item3.clip_prompt_embeds: torch.Size([77, 768]) text_encoder_2-item4.clip_pooled_prompt_embeds: torch.Size([1280]) text_encoder_2-item4.clip_prompt_embeds: torch.Size([77, 1280]) text_encoder_3-item5.t5_prompt_embeds: torch.Size([77, 4096]) vae.decoder: torch.Size([16, 128, 128]) vae.encoder_f0: torch.Size([32, 128, 128]) vae.encoder_f1: torch.Size([32, 128, 128]) Per-key MSE Breakdown (Quant=Truncation+Quantization, Codec=Compression): Key Quant-MSE Total-MSE ------------------------------------------------------------------------------------- text_encoder-item0.clip_pooled_prompt_embeds 0.00018740 0.92997726 text_encoder-item0.clip_prompt_embeds 0.00046272 131.90384876 text_encoder_2-item1.clip_pooled_prompt_embeds 0.00022428 0.98846159 text_encoder_2-item1.clip_prompt_embeds 0.00014574 0.12330389 text_encoder_3-item2.t5_prompt_embeds 0.00000853 0.00301164 text_encoder-item3.clip_pooled_prompt_embeds 0.00841799 1.01420029 text_encoder-item3.clip_prompt_embeds 0.00023247 23.90570887 text_encoder_2-item4.clip_pooled_prompt_embeds 0.00016751 0.67051430 text_encoder_2-item4.clip_prompt_embeds 0.00009830 0.47049568 text_encoder_3-item5.t5_prompt_embeds 0.00001263 0.00227117 vae.encoder_f0 0.01999603 3.47811007 vae.encoder_f1 0.01999529 3.47828436 vae.decoder 0.00024882 0.06096582 ------------------------------------------------------------------------------------- TOTAL 0.00933711 5.72354861 (elements=2,260,992) ---------------------- -------------------------------------------------------- SAMPLE-WISE STATISTICS ---------------------- -------------------------------------------------------- Handler sd35 Strategy individual Architecture hyperprior-featurecoding ---------------------- -------------------------------------------------------- Total Elements 2260992 Total Bytes 117456 BPFP 0.4156 bits/point EBPFP 0.8312 equivalent bits/point MSE 5.723549 ---------------------- -------------------------------------------------------- Time: 0.753s Load: 0.008s, Pack+Encode: 0.294s, Decode+Unpack: 0.450s ---------------------- -------------------------------------------------------- Restored Feature Format: [Dict] with 8 keys key['text_encoder-item0']: [Dict] with 2 keys key['clip_pooled_prompt_embeds']: [Tensor] shape=torch.Size([768]), dtype=torch.float16, device=cpu key['clip_prompt_embeds']: [Tensor] shape=torch.Size([77, 768]), dtype=torch.float16, device=cpu key['text_encoder_2-item1']: [Dict] with 2 keys key['clip_pooled_prompt_embeds']: [Tensor] shape=torch.Size([1280]), dtype=torch.float16, device=cpu key['clip_prompt_embeds']: [Tensor] shape=torch.Size([77, 1280]), dtype=torch.float16, device=cpu key['text_encoder_3-item2']: [Dict] with 1 keys key['t5_prompt_embeds']: [Tensor] shape=torch.Size([77, 4096]), dtype=torch.float16, device=cpu key['text_encoder-item3']: [Dict] with 2 keys key['clip_pooled_prompt_embeds']: [Tensor] shape=torch.Size([768]), dtype=torch.float16, device=cpu key['clip_prompt_embeds']: [Tensor] shape=torch.Size([77, 768]), dtype=torch.float16, device=cpu key['text_encoder_2-item4']: [Dict] with 2 keys key['clip_pooled_prompt_embeds']: [Tensor] shape=torch.Size([1280]), dtype=torch.float16, device=cpu key['clip_prompt_embeds']: [Tensor] shape=torch.Size([77, 1280]), dtype=torch.float16, device=cpu key['text_encoder_3-item5']: [Dict] with 1 keys key['t5_prompt_embeds']: [Tensor] shape=torch.Size([77, 4096]), dtype=torch.float16, device=cpu key['vae.decoder']: [Tensor] shape=torch.Size([16, 128, 128]), dtype=torch.float16, device=cpu key['vae.encoder']: [Dict] with 2 keys key['vae.encoder_f0']: [Tensor] shape=torch.Size([32, 128, 128]), dtype=torch.float16, device=cpu key['vae.encoder_f1']: [Tensor] shape=torch.Size([32, 128, 128]), dtype=torch.float16, device=cpu 💾 Converting with 5.7235 MSE: from ../datasets/FeatureCoding-StableDiffusion3.5/100samples_features/000000123321.zst to output-fixed/sd35/lambda0.004/hyperprior-featurecoding-8bit-individual/sd35cond/000000123321.zst 💪 Processing: ../datasets/FeatureCoding-StableDiffusion3.5/100samples_features/000000127182.zst (21/100) [1/4] FRONTEND: Loading features from ../datasets/FeatureCoding-StableDiffusion3.5/100samples_features/000000127182.zst... Original data structure: root: [Dict] with 8 keys key['text_encoder-item0']: [Dict] with 2 keys key['clip_pooled_prompt_embeds']: [Tensor] shape=torch.Size([768]), dtype=torch.float16, device=cpu key['clip_prompt_embeds']: [Tensor] shape=torch.Size([77, 768]), dtype=torch.float16, device=cpu key['text_encoder_2-item1']: [Dict] with 2 keys key['clip_pooled_prompt_embeds']: [Tensor] shape=torch.Size([1280]), dtype=torch.float16, device=cpu key['clip_prompt_embeds']: [Tensor] shape=torch.Size([77, 1280]), dtype=torch.float16, device=cpu key['text_encoder_3-item2']: [Dict] with 1 keys key['t5_prompt_embeds']: [Tensor] shape=torch.Size([77, 4096]), dtype=torch.float16, device=cpu key['text_encoder-item3']: [Dict] with 2 keys key['clip_pooled_prompt_embeds']: [Tensor] shape=torch.Size([768]), dtype=torch.float16, device=cpu key['clip_prompt_embeds']: [Tensor] shape=torch.Size([77, 768]), dtype=torch.float16, device=cpu key['text_encoder_2-item4']: [Dict] with 2 keys key['clip_pooled_prompt_embeds']: [Tensor] shape=torch.Size([1280]), dtype=torch.float16, device=cpu key['clip_prompt_embeds']: [Tensor] shape=torch.Size([77, 1280]), dtype=torch.float16, device=cpu key['text_encoder_3-item5']: [Dict] with 1 keys key['t5_prompt_embeds']: [Tensor] shape=torch.Size([77, 4096]), dtype=torch.float16, device=cpu key['vae.encoder-item6']: [Dict] with 2 keys key['vae.encoder_f0']: [Tensor] shape=torch.Size([32, 128, 128]), dtype=torch.float16, device=cpu key['vae.encoder_f1']: [Tensor] shape=torch.Size([32, 128, 128]), dtype=torch.float16, device=cpu key['vae.decoder']: [Tensor] shape=torch.Size([16, 128, 128]), dtype=torch.float16, device=cpu ⌛️ [1/4] FRONTEND: Load time: 0.009s ------------------------------------------------------------ SD3.5 Features Summary ------------------------------------------------------------ Number of text encoders: 3 Encoder names: ['text_encoder', 'text_encoder_2', 'text_encoder_3'] Has VAE latents: True Has VAE encoder features: True Data type: torch.float16 text_encoder-item0: clip_pooled_prompt_embeds: torch.Size([768]), torch.float16 clip_prompt_embeds: torch.Size([77, 768]), torch.float16 text_encoder_2-item1: clip_pooled_prompt_embeds: torch.Size([1280]), torch.float16 clip_prompt_embeds: torch.Size([77, 1280]), torch.float16 text_encoder_3-item2: t5_prompt_embeds: torch.Size([77, 4096]), torch.float16 text_encoder-item3: clip_pooled_prompt_embeds: torch.Size([768]), torch.float16 clip_prompt_embeds: torch.Size([77, 768]), torch.float16 text_encoder_2-item4: clip_pooled_prompt_embeds: torch.Size([1280]), torch.float16 clip_prompt_embeds: torch.Size([77, 1280]), torch.float16 text_encoder_3-item5: t5_prompt_embeds: torch.Size([77, 4096]), torch.float16 vae.decoder latents: torch.Size([16, 128, 128]) vae.encoder features: vae.encoder_f0: torch.Size([32, 128, 128]), torch.float16 vae.encoder_f1: torch.Size([32, 128, 128]), torch.float16 ------------------------------------------------------------ [2/4] FRONTEND: Pack + Encode (strategy: individual)... IndividualPacker: text_encoder-item0.clip_pooled_prompt_embeds: torch.Size([768])->torch.Size([1, 1, 1, 768]) text_encoder-item0.clip_prompt_embeds: torch.Size([77, 768])->torch.Size([1, 1, 77, 768]) text_encoder_2-item1.clip_pooled_prompt_embeds: torch.Size([1280])->torch.Size([1, 1, 1, 1280]) text_encoder_2-item1.clip_prompt_embeds: torch.Size([77, 1280])->torch.Size([1, 1, 77, 1280]) text_encoder_3-item2.t5_prompt_embeds: torch.Size([77, 4096])->torch.Size([1, 1, 77, 4096]) text_encoder-item3.clip_pooled_prompt_embeds: torch.Size([768])->torch.Size([1, 1, 1, 768]) text_encoder-item3.clip_prompt_embeds: torch.Size([77, 768])->torch.Size([1, 1, 77, 768]) text_encoder_2-item4.clip_pooled_prompt_embeds: torch.Size([1280])->torch.Size([1, 1, 1, 1280]) text_encoder_2-item4.clip_prompt_embeds: torch.Size([77, 1280])->torch.Size([1, 1, 77, 1280]) text_encoder_3-item5.t5_prompt_embeds: torch.Size([77, 4096])->torch.Size([1, 1, 77, 4096]) vae.encoder_f0: torch.Size([32, 128, 128])->torch.Size([1, 1, 512, 1024]) vae.encoder_f1: torch.Size([32, 128, 128])->torch.Size([1, 1, 512, 1024]) vae.decoder: torch.Size([16, 128, 128])->torch.Size([1, 1, 512, 512]) Using per-key quantization points (text_encoder-item0.clip_pooled_prompt_embeds: torch.Size([256])) for text_encoder-item0.clip_pooled_prompt_embeds text_encoder-item0.clip_pooled_prompt_embeds: 728B, BPFP=7.5833 Using per-key quantization points (text_encoder-item0.clip_prompt_embeds: torch.Size([256])) for text_encoder-item0.clip_prompt_embeds text_encoder-item0.clip_prompt_embeds: 5,608B, BPFP=0.7587 Using per-key quantization points (text_encoder_2-item1.clip_pooled_prompt_embeds: torch.Size([256])) for text_encoder_2-item1.clip_pooled_prompt_embeds text_encoder_2-item1.clip_pooled_prompt_embeds: 1,208B, BPFP=7.5500 Using per-key quantization points (text_encoder_2-item1.clip_prompt_embeds: torch.Size([256])) for text_encoder_2-item1.clip_prompt_embeds text_encoder_2-item1.clip_prompt_embeds: 11,100B, BPFP=0.9010 Using per-key quantization points (text_encoder_3-item2.t5_prompt_embeds: torch.Size([256])) for text_encoder_3-item2.t5_prompt_embeds text_encoder_3-item2.t5_prompt_embeds: 23,740B, BPFP=0.6022 Using per-key quantization points (text_encoder-item3.clip_pooled_prompt_embeds: torch.Size([256])) for text_encoder-item3.clip_pooled_prompt_embeds text_encoder-item3.clip_pooled_prompt_embeds: 760B, BPFP=7.9167 Using per-key quantization points (text_encoder-item3.clip_prompt_embeds: torch.Size([256])) for text_encoder-item3.clip_prompt_embeds text_encoder-item3.clip_prompt_embeds: 4,024B, BPFP=0.5444 Using per-key quantization points (text_encoder_2-item4.clip_pooled: torch.Size([256])) for text_encoder_2-item4.clip_pooled_prompt_embeds text_encoder_2-item4.clip_pooled_prompt_embeds: 1,268B, BPFP=7.9250 Using per-key quantization points (text_encoder_2-item4.clip_prompt: torch.Size([256])) for text_encoder_2-item4.clip_prompt_embeds text_encoder_2-item4.clip_prompt_embeds: 6,996B, BPFP=0.5679 Using per-key quantization points (text_encoder_3-item5.t5_prompt_embeds: torch.Size([256])) for text_encoder_3-item5.t5_prompt_embeds text_encoder_3-item5.t5_prompt_embeds: 18,416B, BPFP=0.4671 Using per-key quantization points (vae.encoder_f0: torch.Size([256])) for vae.encoder_f0 vae.encoder_f0: 19,680B, BPFP=0.3003 Using per-key quantization points (vae.encoder_f1: torch.Size([256])) for vae.encoder_f1 vae.encoder_f1: 19,680B, BPFP=0.3003 Using per-key quantization points (vae.decoder: torch.Size([256])) for vae.decoder vae.decoder: 5,528B, BPFP=0.1687 ⌛️ [2/4] FRONTEND: Frontend time: 0.291s (Pack+Encode) [3/4] BACKEND: Decode + Unpack... Using per-key quantization points (text_encoder-item0.clip_pooled_prompt_embeds: torch.Size([256])) for text_encoder-item0.clip_pooled_prompt_embeds Using per-key quantization points (text_encoder-item0.clip_prompt_embeds: torch.Size([256])) for text_encoder-item0.clip_prompt_embeds Using per-key quantization points (text_encoder_2-item1.clip_pooled_prompt_embeds: torch.Size([256])) for text_encoder_2-item1.clip_pooled_prompt_embeds Using per-key quantization points (text_encoder_2-item1.clip_prompt_embeds: torch.Size([256])) for text_encoder_2-item1.clip_prompt_embeds Using per-key quantization points (text_encoder_3-item2.t5_prompt_embeds: torch.Size([256])) for text_encoder_3-item2.t5_prompt_embeds Using per-key quantization points (text_encoder-item3.clip_pooled_prompt_embeds: torch.Size([256])) for text_encoder-item3.clip_pooled_prompt_embeds Using per-key quantization points (text_encoder-item3.clip_prompt_embeds: torch.Size([256])) for text_encoder-item3.clip_prompt_embeds Using per-key quantization points (text_encoder_2-item4.clip_pooled: torch.Size([256])) for text_encoder_2-item4.clip_pooled_prompt_embeds Using per-key quantization points (text_encoder_2-item4.clip_prompt: torch.Size([256])) for text_encoder_2-item4.clip_prompt_embeds Using per-key quantization points (text_encoder_3-item5.t5_prompt_embeds: torch.Size([256])) for text_encoder_3-item5.t5_prompt_embeds Using per-key quantization points (vae.encoder_f0: torch.Size([256])) for vae.encoder_f0 Using per-key quantization points (vae.encoder_f1: torch.Size([256])) for vae.encoder_f1 Using per-key quantization points (vae.decoder: torch.Size([256])) for vae.decoder IndividualUnPacker: text_encoder-item0.clip_pooled_prompt_embeds: torch.Size([768]) text_encoder-item0.clip_prompt_embeds: torch.Size([77, 768]) text_encoder_2-item1.clip_pooled_prompt_embeds: torch.Size([1280]) text_encoder_2-item1.clip_prompt_embeds: torch.Size([77, 1280]) text_encoder_3-item2.t5_prompt_embeds: torch.Size([77, 4096]) text_encoder-item3.clip_pooled_prompt_embeds: torch.Size([768]) text_encoder-item3.clip_prompt_embeds: torch.Size([77, 768]) text_encoder_2-item4.clip_pooled_prompt_embeds: torch.Size([1280]) text_encoder_2-item4.clip_prompt_embeds: torch.Size([77, 1280]) text_encoder_3-item5.t5_prompt_embeds: torch.Size([77, 4096]) vae.decoder: torch.Size([16, 128, 128]) vae.encoder_f0: torch.Size([32, 128, 128]) vae.encoder_f1: torch.Size([32, 128, 128]) ⌛️ [3/4] BACKEND: Backend time: 0.447s [4/4] METRICS: Computing MSE Breakdown... Using per-key quantization points (text_encoder-item0.clip_pooled_prompt_embeds: torch.Size([256])) for text_encoder-item0.clip_pooled_prompt_embeds Using per-key quantization points (text_encoder-item0.clip_prompt_embeds: torch.Size([256])) for text_encoder-item0.clip_prompt_embeds Using per-key quantization points (text_encoder_2-item1.clip_pooled_prompt_embeds: torch.Size([256])) for text_encoder_2-item1.clip_pooled_prompt_embeds Using per-key quantization points (text_encoder_2-item1.clip_prompt_embeds: torch.Size([256])) for text_encoder_2-item1.clip_prompt_embeds Using per-key quantization points (text_encoder_3-item2.t5_prompt_embeds: torch.Size([256])) for text_encoder_3-item2.t5_prompt_embeds Using per-key quantization points (text_encoder-item3.clip_pooled_prompt_embeds: torch.Size([256])) for text_encoder-item3.clip_pooled_prompt_embeds Using per-key quantization points (text_encoder-item3.clip_prompt_embeds: torch.Size([256])) for text_encoder-item3.clip_prompt_embeds Using per-key quantization points (text_encoder_2-item4.clip_pooled: torch.Size([256])) for text_encoder_2-item4.clip_pooled_prompt_embeds Using per-key quantization points (text_encoder_2-item4.clip_prompt: torch.Size([256])) for text_encoder_2-item4.clip_prompt_embeds Using per-key quantization points (text_encoder_3-item5.t5_prompt_embeds: torch.Size([256])) for text_encoder_3-item5.t5_prompt_embeds Using per-key quantization points (vae.encoder_f0: torch.Size([256])) for vae.encoder_f0 Using per-key quantization points (vae.encoder_f1: torch.Size([256])) for vae.encoder_f1 Using per-key quantization points (vae.decoder: torch.Size([256])) for vae.decoder IndividualUnPacker: text_encoder-item0.clip_pooled_prompt_embeds: torch.Size([768]) text_encoder-item0.clip_prompt_embeds: torch.Size([77, 768]) text_encoder_2-item1.clip_pooled_prompt_embeds: torch.Size([1280]) text_encoder_2-item1.clip_prompt_embeds: torch.Size([77, 1280]) text_encoder_3-item2.t5_prompt_embeds: torch.Size([77, 4096]) text_encoder-item3.clip_pooled_prompt_embeds: torch.Size([768]) text_encoder-item3.clip_prompt_embeds: torch.Size([77, 768]) text_encoder_2-item4.clip_pooled_prompt_embeds: torch.Size([1280]) text_encoder_2-item4.clip_prompt_embeds: torch.Size([77, 1280]) text_encoder_3-item5.t5_prompt_embeds: torch.Size([77, 4096]) vae.decoder: torch.Size([16, 128, 128]) vae.encoder_f0: torch.Size([32, 128, 128]) vae.encoder_f1: torch.Size([32, 128, 128]) Per-key MSE Breakdown (Quant=Truncation+Quantization, Codec=Compression): Key Quant-MSE Total-MSE ------------------------------------------------------------------------------------- text_encoder-item0.clip_pooled_prompt_embeds 0.00062140 0.91544143 text_encoder-item0.clip_prompt_embeds 0.00020334 35.96247210 text_encoder_2-item1.clip_pooled_prompt_embeds 0.00017433 0.86179924 text_encoder_2-item1.clip_prompt_embeds 0.00020202 0.12618976 text_encoder_3-item2.t5_prompt_embeds 0.00000787 0.00312333 text_encoder-item3.clip_pooled_prompt_embeds 0.00841799 1.01420029 text_encoder-item3.clip_prompt_embeds 0.00023247 23.90570887 text_encoder_2-item4.clip_pooled_prompt_embeds 0.00016751 0.67051430 text_encoder_2-item4.clip_prompt_embeds 0.00009830 0.47049568 text_encoder_3-item5.t5_prompt_embeds 0.00001263 0.00227117 vae.encoder_f0 0.01341345 3.12336135 vae.encoder_f1 0.01341645 3.12268400 vae.decoder 0.00018350 0.03317202 ------------------------------------------------------------------------------------- TOTAL 0.00627332 3.04633597 (elements=2,260,992) ---------------------- -------------------------------------------------------- SAMPLE-WISE STATISTICS ---------------------- -------------------------------------------------------- Handler sd35 Strategy individual Architecture hyperprior-featurecoding ---------------------- -------------------------------------------------------- Total Elements 2260992 Total Bytes 118736 BPFP 0.4201 bits/point EBPFP 0.8402 equivalent bits/point MSE 3.046336 ---------------------- -------------------------------------------------------- Time: 0.747s Load: 0.009s, Pack+Encode: 0.291s, Decode+Unpack: 0.447s ---------------------- -------------------------------------------------------- Restored Feature Format: [Dict] with 8 keys key['text_encoder-item0']: [Dict] with 2 keys key['clip_pooled_prompt_embeds']: [Tensor] shape=torch.Size([768]), dtype=torch.float16, device=cpu key['clip_prompt_embeds']: [Tensor] shape=torch.Size([77, 768]), dtype=torch.float16, device=cpu key['text_encoder_2-item1']: [Dict] with 2 keys key['clip_pooled_prompt_embeds']: [Tensor] shape=torch.Size([1280]), dtype=torch.float16, device=cpu key['clip_prompt_embeds']: [Tensor] shape=torch.Size([77, 1280]), dtype=torch.float16, device=cpu key['text_encoder_3-item2']: [Dict] with 1 keys key['t5_prompt_embeds']: [Tensor] shape=torch.Size([77, 4096]), dtype=torch.float16, device=cpu key['text_encoder-item3']: [Dict] with 2 keys key['clip_pooled_prompt_embeds']: [Tensor] shape=torch.Size([768]), dtype=torch.float16, device=cpu key['clip_prompt_embeds']: [Tensor] shape=torch.Size([77, 768]), dtype=torch.float16, device=cpu key['text_encoder_2-item4']: [Dict] with 2 keys key['clip_pooled_prompt_embeds']: [Tensor] shape=torch.Size([1280]), dtype=torch.float16, device=cpu key['clip_prompt_embeds']: [Tensor] shape=torch.Size([77, 1280]), dtype=torch.float16, device=cpu key['text_encoder_3-item5']: [Dict] with 1 keys key['t5_prompt_embeds']: [Tensor] shape=torch.Size([77, 4096]), dtype=torch.float16, device=cpu key['vae.decoder']: [Tensor] shape=torch.Size([16, 128, 128]), dtype=torch.float16, device=cpu key['vae.encoder']: [Dict] with 2 keys key['vae.encoder_f0']: [Tensor] shape=torch.Size([32, 128, 128]), dtype=torch.float16, device=cpu key['vae.encoder_f1']: [Tensor] shape=torch.Size([32, 128, 128]), dtype=torch.float16, device=cpu 💾 Converting with 3.0463 MSE: from ../datasets/FeatureCoding-StableDiffusion3.5/100samples_features/000000127182.zst to output-fixed/sd35/lambda0.004/hyperprior-featurecoding-8bit-individual/sd35cond/000000127182.zst 💪 Processing: ../datasets/FeatureCoding-StableDiffusion3.5/100samples_features/000000127394.zst (22/100) [1/4] FRONTEND: Loading features from ../datasets/FeatureCoding-StableDiffusion3.5/100samples_features/000000127394.zst... Original data structure: root: [Dict] with 8 keys key['text_encoder-item0']: [Dict] with 2 keys key['clip_pooled_prompt_embeds']: [Tensor] shape=torch.Size([768]), dtype=torch.float16, device=cpu key['clip_prompt_embeds']: [Tensor] shape=torch.Size([77, 768]), dtype=torch.float16, device=cpu key['text_encoder_2-item1']: [Dict] with 2 keys key['clip_pooled_prompt_embeds']: [Tensor] shape=torch.Size([1280]), dtype=torch.float16, device=cpu key['clip_prompt_embeds']: [Tensor] shape=torch.Size([77, 1280]), dtype=torch.float16, device=cpu key['text_encoder_3-item2']: [Dict] with 1 keys key['t5_prompt_embeds']: [Tensor] shape=torch.Size([77, 4096]), dtype=torch.float16, device=cpu key['text_encoder-item3']: [Dict] with 2 keys key['clip_pooled_prompt_embeds']: [Tensor] shape=torch.Size([768]), dtype=torch.float16, device=cpu key['clip_prompt_embeds']: [Tensor] shape=torch.Size([77, 768]), dtype=torch.float16, device=cpu key['text_encoder_2-item4']: [Dict] with 2 keys key['clip_pooled_prompt_embeds']: [Tensor] shape=torch.Size([1280]), dtype=torch.float16, device=cpu key['clip_prompt_embeds']: [Tensor] shape=torch.Size([77, 1280]), dtype=torch.float16, device=cpu key['text_encoder_3-item5']: [Dict] with 1 keys key['t5_prompt_embeds']: [Tensor] shape=torch.Size([77, 4096]), dtype=torch.float16, device=cpu key['vae.encoder-item6']: [Dict] with 2 keys key['vae.encoder_f0']: [Tensor] shape=torch.Size([32, 128, 128]), dtype=torch.float16, device=cpu key['vae.encoder_f1']: [Tensor] shape=torch.Size([32, 128, 128]), dtype=torch.float16, device=cpu key['vae.decoder']: [Tensor] shape=torch.Size([16, 128, 128]), dtype=torch.float16, device=cpu ⌛️ [1/4] FRONTEND: Load time: 0.008s ------------------------------------------------------------ SD3.5 Features Summary ------------------------------------------------------------ Number of text encoders: 3 Encoder names: ['text_encoder', 'text_encoder_2', 'text_encoder_3'] Has VAE latents: True Has VAE encoder features: True Data type: torch.float16 text_encoder-item0: clip_pooled_prompt_embeds: torch.Size([768]), torch.float16 clip_prompt_embeds: torch.Size([77, 768]), torch.float16 text_encoder_2-item1: clip_pooled_prompt_embeds: torch.Size([1280]), torch.float16 clip_prompt_embeds: torch.Size([77, 1280]), torch.float16 text_encoder_3-item2: t5_prompt_embeds: torch.Size([77, 4096]), torch.float16 text_encoder-item3: clip_pooled_prompt_embeds: torch.Size([768]), torch.float16 clip_prompt_embeds: torch.Size([77, 768]), torch.float16 text_encoder_2-item4: clip_pooled_prompt_embeds: torch.Size([1280]), torch.float16 clip_prompt_embeds: torch.Size([77, 1280]), torch.float16 text_encoder_3-item5: t5_prompt_embeds: torch.Size([77, 4096]), torch.float16 vae.decoder latents: torch.Size([16, 128, 128]) vae.encoder features: vae.encoder_f0: torch.Size([32, 128, 128]), torch.float16 vae.encoder_f1: torch.Size([32, 128, 128]), torch.float16 ------------------------------------------------------------ [2/4] FRONTEND: Pack + Encode (strategy: individual)... IndividualPacker: text_encoder-item0.clip_pooled_prompt_embeds: torch.Size([768])->torch.Size([1, 1, 1, 768]) text_encoder-item0.clip_prompt_embeds: torch.Size([77, 768])->torch.Size([1, 1, 77, 768]) text_encoder_2-item1.clip_pooled_prompt_embeds: torch.Size([1280])->torch.Size([1, 1, 1, 1280]) text_encoder_2-item1.clip_prompt_embeds: torch.Size([77, 1280])->torch.Size([1, 1, 77, 1280]) text_encoder_3-item2.t5_prompt_embeds: torch.Size([77, 4096])->torch.Size([1, 1, 77, 4096]) text_encoder-item3.clip_pooled_prompt_embeds: torch.Size([768])->torch.Size([1, 1, 1, 768]) text_encoder-item3.clip_prompt_embeds: torch.Size([77, 768])->torch.Size([1, 1, 77, 768]) text_encoder_2-item4.clip_pooled_prompt_embeds: torch.Size([1280])->torch.Size([1, 1, 1, 1280]) text_encoder_2-item4.clip_prompt_embeds: torch.Size([77, 1280])->torch.Size([1, 1, 77, 1280]) text_encoder_3-item5.t5_prompt_embeds: torch.Size([77, 4096])->torch.Size([1, 1, 77, 4096]) vae.encoder_f0: torch.Size([32, 128, 128])->torch.Size([1, 1, 512, 1024]) vae.encoder_f1: torch.Size([32, 128, 128])->torch.Size([1, 1, 512, 1024]) vae.decoder: torch.Size([16, 128, 128])->torch.Size([1, 1, 512, 512]) Using per-key quantization points (text_encoder-item0.clip_pooled_prompt_embeds: torch.Size([256])) for text_encoder-item0.clip_pooled_prompt_embeds text_encoder-item0.clip_pooled_prompt_embeds: 724B, BPFP=7.5417 Using per-key quantization points (text_encoder-item0.clip_prompt_embeds: torch.Size([256])) for text_encoder-item0.clip_prompt_embeds text_encoder-item0.clip_prompt_embeds: 6,312B, BPFP=0.8539 Using per-key quantization points (text_encoder_2-item1.clip_pooled_prompt_embeds: torch.Size([256])) for text_encoder_2-item1.clip_pooled_prompt_embeds text_encoder_2-item1.clip_pooled_prompt_embeds: 1,208B, BPFP=7.5500 Using per-key quantization points (text_encoder_2-item1.clip_prompt_embeds: torch.Size([256])) for text_encoder_2-item1.clip_prompt_embeds text_encoder_2-item1.clip_prompt_embeds: 11,428B, BPFP=0.9276 Using per-key quantization points (text_encoder_3-item2.t5_prompt_embeds: torch.Size([256])) for text_encoder_3-item2.t5_prompt_embeds text_encoder_3-item2.t5_prompt_embeds: 28,148B, BPFP=0.7140 Using per-key quantization points (text_encoder-item3.clip_pooled_prompt_embeds: torch.Size([256])) for text_encoder-item3.clip_pooled_prompt_embeds text_encoder-item3.clip_pooled_prompt_embeds: 760B, BPFP=7.9167 Using per-key quantization points (text_encoder-item3.clip_prompt_embeds: torch.Size([256])) for text_encoder-item3.clip_prompt_embeds text_encoder-item3.clip_prompt_embeds: 4,024B, BPFP=0.5444 Using per-key quantization points (text_encoder_2-item4.clip_pooled: torch.Size([256])) for text_encoder_2-item4.clip_pooled_prompt_embeds text_encoder_2-item4.clip_pooled_prompt_embeds: 1,268B, BPFP=7.9250 Using per-key quantization points (text_encoder_2-item4.clip_prompt: torch.Size([256])) for text_encoder_2-item4.clip_prompt_embeds text_encoder_2-item4.clip_prompt_embeds: 6,996B, BPFP=0.5679 Using per-key quantization points (text_encoder_3-item5.t5_prompt_embeds: torch.Size([256])) for text_encoder_3-item5.t5_prompt_embeds text_encoder_3-item5.t5_prompt_embeds: 18,416B, BPFP=0.4671 Using per-key quantization points (vae.encoder_f0: torch.Size([256])) for vae.encoder_f0 vae.encoder_f0: 18,372B, BPFP=0.2803 Using per-key quantization points (vae.encoder_f1: torch.Size([256])) for vae.encoder_f1 vae.encoder_f1: 18,372B, BPFP=0.2803 Using per-key quantization points (vae.decoder: torch.Size([256])) for vae.decoder vae.decoder: 9,440B, BPFP=0.2881 ⌛️ [2/4] FRONTEND: Frontend time: 0.305s (Pack+Encode) [3/4] BACKEND: Decode + Unpack... Using per-key quantization points (text_encoder-item0.clip_pooled_prompt_embeds: torch.Size([256])) for text_encoder-item0.clip_pooled_prompt_embeds Using per-key quantization points (text_encoder-item0.clip_prompt_embeds: torch.Size([256])) for text_encoder-item0.clip_prompt_embeds Using per-key quantization points (text_encoder_2-item1.clip_pooled_prompt_embeds: torch.Size([256])) for text_encoder_2-item1.clip_pooled_prompt_embeds Using per-key quantization points (text_encoder_2-item1.clip_prompt_embeds: torch.Size([256])) for text_encoder_2-item1.clip_prompt_embeds Using per-key quantization points (text_encoder_3-item2.t5_prompt_embeds: torch.Size([256])) for text_encoder_3-item2.t5_prompt_embeds Using per-key quantization points (text_encoder-item3.clip_pooled_prompt_embeds: torch.Size([256])) for text_encoder-item3.clip_pooled_prompt_embeds Using per-key quantization points (text_encoder-item3.clip_prompt_embeds: torch.Size([256])) for text_encoder-item3.clip_prompt_embeds Using per-key quantization points (text_encoder_2-item4.clip_pooled: torch.Size([256])) for text_encoder_2-item4.clip_pooled_prompt_embeds Using per-key quantization points (text_encoder_2-item4.clip_prompt: torch.Size([256])) for text_encoder_2-item4.clip_prompt_embeds Using per-key quantization points (text_encoder_3-item5.t5_prompt_embeds: torch.Size([256])) for text_encoder_3-item5.t5_prompt_embeds Using per-key quantization points (vae.encoder_f0: torch.Size([256])) for vae.encoder_f0 Using per-key quantization points (vae.encoder_f1: torch.Size([256])) for vae.encoder_f1 Using per-key quantization points (vae.decoder: torch.Size([256])) for vae.decoder IndividualUnPacker: text_encoder-item0.clip_pooled_prompt_embeds: torch.Size([768]) text_encoder-item0.clip_prompt_embeds: torch.Size([77, 768]) text_encoder_2-item1.clip_pooled_prompt_embeds: torch.Size([1280]) text_encoder_2-item1.clip_prompt_embeds: torch.Size([77, 1280]) text_encoder_3-item2.t5_prompt_embeds: torch.Size([77, 4096]) text_encoder-item3.clip_pooled_prompt_embeds: torch.Size([768]) text_encoder-item3.clip_prompt_embeds: torch.Size([77, 768]) text_encoder_2-item4.clip_pooled_prompt_embeds: torch.Size([1280]) text_encoder_2-item4.clip_prompt_embeds: torch.Size([77, 1280]) text_encoder_3-item5.t5_prompt_embeds: torch.Size([77, 4096]) vae.decoder: torch.Size([16, 128, 128]) vae.encoder_f0: torch.Size([32, 128, 128]) vae.encoder_f1: torch.Size([32, 128, 128]) ⌛️ [3/4] BACKEND: Backend time: 0.451s [4/4] METRICS: Computing MSE Breakdown... Using per-key quantization points (text_encoder-item0.clip_pooled_prompt_embeds: torch.Size([256])) for text_encoder-item0.clip_pooled_prompt_embeds Using per-key quantization points (text_encoder-item0.clip_prompt_embeds: torch.Size([256])) for text_encoder-item0.clip_prompt_embeds Using per-key quantization points (text_encoder_2-item1.clip_pooled_prompt_embeds: torch.Size([256])) for text_encoder_2-item1.clip_pooled_prompt_embeds Using per-key quantization points (text_encoder_2-item1.clip_prompt_embeds: torch.Size([256])) for text_encoder_2-item1.clip_prompt_embeds Using per-key quantization points (text_encoder_3-item2.t5_prompt_embeds: torch.Size([256])) for text_encoder_3-item2.t5_prompt_embeds Using per-key quantization points (text_encoder-item3.clip_pooled_prompt_embeds: torch.Size([256])) for text_encoder-item3.clip_pooled_prompt_embeds Using per-key quantization points (text_encoder-item3.clip_prompt_embeds: torch.Size([256])) for text_encoder-item3.clip_prompt_embeds Using per-key quantization points (text_encoder_2-item4.clip_pooled: torch.Size([256])) for text_encoder_2-item4.clip_pooled_prompt_embeds Using per-key quantization points (text_encoder_2-item4.clip_prompt: torch.Size([256])) for text_encoder_2-item4.clip_prompt_embeds Using per-key quantization points (text_encoder_3-item5.t5_prompt_embeds: torch.Size([256])) for text_encoder_3-item5.t5_prompt_embeds Using per-key quantization points (vae.encoder_f0: torch.Size([256])) for vae.encoder_f0 Using per-key quantization points (vae.encoder_f1: torch.Size([256])) for vae.encoder_f1 Using per-key quantization points (vae.decoder: torch.Size([256])) for vae.decoder IndividualUnPacker: text_encoder-item0.clip_pooled_prompt_embeds: torch.Size([768]) text_encoder-item0.clip_prompt_embeds: torch.Size([77, 768]) text_encoder_2-item1.clip_pooled_prompt_embeds: torch.Size([1280]) text_encoder_2-item1.clip_prompt_embeds: torch.Size([77, 1280]) text_encoder_3-item2.t5_prompt_embeds: torch.Size([77, 4096]) text_encoder-item3.clip_pooled_prompt_embeds: torch.Size([768]) text_encoder-item3.clip_prompt_embeds: torch.Size([77, 768]) text_encoder_2-item4.clip_pooled_prompt_embeds: torch.Size([1280]) text_encoder_2-item4.clip_prompt_embeds: torch.Size([77, 1280]) text_encoder_3-item5.t5_prompt_embeds: torch.Size([77, 4096]) vae.decoder: torch.Size([16, 128, 128]) vae.encoder_f0: torch.Size([32, 128, 128]) vae.encoder_f1: torch.Size([32, 128, 128]) Per-key MSE Breakdown (Quant=Truncation+Quantization, Codec=Compression): Key Quant-MSE Total-MSE ------------------------------------------------------------------------------------- text_encoder-item0.clip_pooled_prompt_embeds 0.00063926 0.92774375 text_encoder-item0.clip_prompt_embeds 0.00022316 144.03671199 text_encoder_2-item1.clip_pooled_prompt_embeds 0.00045791 0.92485466 text_encoder_2-item1.clip_prompt_embeds 0.00022852 0.15045479 text_encoder_3-item2.t5_prompt_embeds 0.00000822 0.00335829 text_encoder-item3.clip_pooled_prompt_embeds 0.00841799 1.01420029 text_encoder-item3.clip_prompt_embeds 0.00023247 23.90570887 text_encoder_2-item4.clip_pooled_prompt_embeds 0.00016751 0.67051430 text_encoder_2-item4.clip_prompt_embeds 0.00009830 0.47049568 text_encoder_3-item5.t5_prompt_embeds 0.00001263 0.00227117 vae.encoder_f0 0.00606298 2.17639184 vae.encoder_f1 0.00607096 2.17661667 vae.decoder 0.00023408 0.07605585 ------------------------------------------------------------------------------------- TOTAL 0.00287331 5.44014334 (elements=2,260,992) ---------------------- -------------------------------------------------------- SAMPLE-WISE STATISTICS ---------------------- -------------------------------------------------------- Handler sd35 Strategy individual Architecture hyperprior-featurecoding ---------------------- -------------------------------------------------------- Total Elements 2260992 Total Bytes 125468 BPFP 0.4439 bits/point EBPFP 0.8879 equivalent bits/point MSE 5.440143 ---------------------- -------------------------------------------------------- Time: 0.764s Load: 0.008s, Pack+Encode: 0.305s, Decode+Unpack: 0.451s ---------------------- -------------------------------------------------------- Restored Feature Format: [Dict] with 8 keys key['text_encoder-item0']: [Dict] with 2 keys key['clip_pooled_prompt_embeds']: [Tensor] shape=torch.Size([768]), dtype=torch.float16, device=cpu key['clip_prompt_embeds']: [Tensor] shape=torch.Size([77, 768]), dtype=torch.float16, device=cpu key['text_encoder_2-item1']: [Dict] with 2 keys key['clip_pooled_prompt_embeds']: [Tensor] shape=torch.Size([1280]), dtype=torch.float16, device=cpu key['clip_prompt_embeds']: [Tensor] shape=torch.Size([77, 1280]), dtype=torch.float16, device=cpu key['text_encoder_3-item2']: [Dict] with 1 keys key['t5_prompt_embeds']: [Tensor] shape=torch.Size([77, 4096]), dtype=torch.float16, device=cpu key['text_encoder-item3']: [Dict] with 2 keys key['clip_pooled_prompt_embeds']: [Tensor] shape=torch.Size([768]), dtype=torch.float16, device=cpu key['clip_prompt_embeds']: [Tensor] shape=torch.Size([77, 768]), dtype=torch.float16, device=cpu key['text_encoder_2-item4']: [Dict] with 2 keys key['clip_pooled_prompt_embeds']: [Tensor] shape=torch.Size([1280]), dtype=torch.float16, device=cpu key['clip_prompt_embeds']: [Tensor] shape=torch.Size([77, 1280]), dtype=torch.float16, device=cpu key['text_encoder_3-item5']: [Dict] with 1 keys key['t5_prompt_embeds']: [Tensor] shape=torch.Size([77, 4096]), dtype=torch.float16, device=cpu key['vae.decoder']: [Tensor] shape=torch.Size([16, 128, 128]), dtype=torch.float16, device=cpu key['vae.encoder']: [Dict] with 2 keys key['vae.encoder_f0']: [Tensor] shape=torch.Size([32, 128, 128]), dtype=torch.float16, device=cpu key['vae.encoder_f1']: [Tensor] shape=torch.Size([32, 128, 128]), dtype=torch.float16, device=cpu 💾 Converting with 5.4401 MSE: from ../datasets/FeatureCoding-StableDiffusion3.5/100samples_features/000000127394.zst to output-fixed/sd35/lambda0.004/hyperprior-featurecoding-8bit-individual/sd35cond/000000127394.zst 💪 Processing: ../datasets/FeatureCoding-StableDiffusion3.5/100samples_features/000000133969.zst (23/100) [1/4] FRONTEND: Loading features from ../datasets/FeatureCoding-StableDiffusion3.5/100samples_features/000000133969.zst... Original data structure: root: [Dict] with 8 keys key['text_encoder-item0']: [Dict] with 2 keys key['clip_pooled_prompt_embeds']: [Tensor] shape=torch.Size([768]), dtype=torch.float16, device=cpu key['clip_prompt_embeds']: [Tensor] shape=torch.Size([77, 768]), dtype=torch.float16, device=cpu key['text_encoder_2-item1']: [Dict] with 2 keys key['clip_pooled_prompt_embeds']: [Tensor] shape=torch.Size([1280]), dtype=torch.float16, device=cpu key['clip_prompt_embeds']: [Tensor] shape=torch.Size([77, 1280]), dtype=torch.float16, device=cpu key['text_encoder_3-item2']: [Dict] with 1 keys key['t5_prompt_embeds']: [Tensor] shape=torch.Size([77, 4096]), dtype=torch.float16, device=cpu key['text_encoder-item3']: [Dict] with 2 keys key['clip_pooled_prompt_embeds']: [Tensor] shape=torch.Size([768]), dtype=torch.float16, device=cpu key['clip_prompt_embeds']: [Tensor] shape=torch.Size([77, 768]), dtype=torch.float16, device=cpu key['text_encoder_2-item4']: [Dict] with 2 keys key['clip_pooled_prompt_embeds']: [Tensor] shape=torch.Size([1280]), dtype=torch.float16, device=cpu key['clip_prompt_embeds']: [Tensor] shape=torch.Size([77, 1280]), dtype=torch.float16, device=cpu key['text_encoder_3-item5']: [Dict] with 1 keys key['t5_prompt_embeds']: [Tensor] shape=torch.Size([77, 4096]), dtype=torch.float16, device=cpu key['vae.encoder-item6']: [Dict] with 2 keys key['vae.encoder_f0']: [Tensor] shape=torch.Size([32, 128, 128]), dtype=torch.float16, device=cpu key['vae.encoder_f1']: [Tensor] shape=torch.Size([32, 128, 128]), dtype=torch.float16, device=cpu key['vae.decoder']: [Tensor] shape=torch.Size([16, 128, 128]), dtype=torch.float16, device=cpu ⌛️ [1/4] FRONTEND: Load time: 0.009s ------------------------------------------------------------ SD3.5 Features Summary ------------------------------------------------------------ Number of text encoders: 3 Encoder names: ['text_encoder', 'text_encoder_2', 'text_encoder_3'] Has VAE latents: True Has VAE encoder features: True Data type: torch.float16 text_encoder-item0: clip_pooled_prompt_embeds: torch.Size([768]), torch.float16 clip_prompt_embeds: torch.Size([77, 768]), torch.float16 text_encoder_2-item1: clip_pooled_prompt_embeds: torch.Size([1280]), torch.float16 clip_prompt_embeds: torch.Size([77, 1280]), torch.float16 text_encoder_3-item2: t5_prompt_embeds: torch.Size([77, 4096]), torch.float16 text_encoder-item3: clip_pooled_prompt_embeds: torch.Size([768]), torch.float16 clip_prompt_embeds: torch.Size([77, 768]), torch.float16 text_encoder_2-item4: clip_pooled_prompt_embeds: torch.Size([1280]), torch.float16 clip_prompt_embeds: torch.Size([77, 1280]), torch.float16 text_encoder_3-item5: t5_prompt_embeds: torch.Size([77, 4096]), torch.float16 vae.decoder latents: torch.Size([16, 128, 128]) vae.encoder features: vae.encoder_f0: torch.Size([32, 128, 128]), torch.float16 vae.encoder_f1: torch.Size([32, 128, 128]), torch.float16 ------------------------------------------------------------ [2/4] FRONTEND: Pack + Encode (strategy: individual)... IndividualPacker: text_encoder-item0.clip_pooled_prompt_embeds: torch.Size([768])->torch.Size([1, 1, 1, 768]) text_encoder-item0.clip_prompt_embeds: torch.Size([77, 768])->torch.Size([1, 1, 77, 768]) text_encoder_2-item1.clip_pooled_prompt_embeds: torch.Size([1280])->torch.Size([1, 1, 1, 1280]) text_encoder_2-item1.clip_prompt_embeds: torch.Size([77, 1280])->torch.Size([1, 1, 77, 1280]) text_encoder_3-item2.t5_prompt_embeds: torch.Size([77, 4096])->torch.Size([1, 1, 77, 4096]) text_encoder-item3.clip_pooled_prompt_embeds: torch.Size([768])->torch.Size([1, 1, 1, 768]) text_encoder-item3.clip_prompt_embeds: torch.Size([77, 768])->torch.Size([1, 1, 77, 768]) text_encoder_2-item4.clip_pooled_prompt_embeds: torch.Size([1280])->torch.Size([1, 1, 1, 1280]) text_encoder_2-item4.clip_prompt_embeds: torch.Size([77, 1280])->torch.Size([1, 1, 77, 1280]) text_encoder_3-item5.t5_prompt_embeds: torch.Size([77, 4096])->torch.Size([1, 1, 77, 4096]) vae.encoder_f0: torch.Size([32, 128, 128])->torch.Size([1, 1, 512, 1024]) vae.encoder_f1: torch.Size([32, 128, 128])->torch.Size([1, 1, 512, 1024]) vae.decoder: torch.Size([16, 128, 128])->torch.Size([1, 1, 512, 512]) Using per-key quantization points (text_encoder-item0.clip_pooled_prompt_embeds: torch.Size([256])) for text_encoder-item0.clip_pooled_prompt_embeds text_encoder-item0.clip_pooled_prompt_embeds: 736B, BPFP=7.6667 Using per-key quantization points (text_encoder-item0.clip_prompt_embeds: torch.Size([256])) for text_encoder-item0.clip_prompt_embeds text_encoder-item0.clip_prompt_embeds: 5,784B, BPFP=0.7825 Using per-key quantization points (text_encoder_2-item1.clip_pooled_prompt_embeds: torch.Size([256])) for text_encoder_2-item1.clip_pooled_prompt_embeds text_encoder_2-item1.clip_pooled_prompt_embeds: 1,200B, BPFP=7.5000 Using per-key quantization points (text_encoder_2-item1.clip_prompt_embeds: torch.Size([256])) for text_encoder_2-item1.clip_prompt_embeds text_encoder_2-item1.clip_prompt_embeds: 9,648B, BPFP=0.7831 Using per-key quantization points (text_encoder_3-item2.t5_prompt_embeds: torch.Size([256])) for text_encoder_3-item2.t5_prompt_embeds text_encoder_3-item2.t5_prompt_embeds: 28,864B, BPFP=0.7321 Using per-key quantization points (text_encoder-item3.clip_pooled_prompt_embeds: torch.Size([256])) for text_encoder-item3.clip_pooled_prompt_embeds text_encoder-item3.clip_pooled_prompt_embeds: 760B, BPFP=7.9167 Using per-key quantization points (text_encoder-item3.clip_prompt_embeds: torch.Size([256])) for text_encoder-item3.clip_prompt_embeds text_encoder-item3.clip_prompt_embeds: 4,024B, BPFP=0.5444 Using per-key quantization points (text_encoder_2-item4.clip_pooled: torch.Size([256])) for text_encoder_2-item4.clip_pooled_prompt_embeds text_encoder_2-item4.clip_pooled_prompt_embeds: 1,268B, BPFP=7.9250 Using per-key quantization points (text_encoder_2-item4.clip_prompt: torch.Size([256])) for text_encoder_2-item4.clip_prompt_embeds text_encoder_2-item4.clip_prompt_embeds: 6,996B, BPFP=0.5679 Using per-key quantization points (text_encoder_3-item5.t5_prompt_embeds: torch.Size([256])) for text_encoder_3-item5.t5_prompt_embeds text_encoder_3-item5.t5_prompt_embeds: 18,416B, BPFP=0.4671 Using per-key quantization points (vae.encoder_f0: torch.Size([256])) for vae.encoder_f0 vae.encoder_f0: 17,928B, BPFP=0.2736 Using per-key quantization points (vae.encoder_f1: torch.Size([256])) for vae.encoder_f1 vae.encoder_f1: 17,928B, BPFP=0.2736 Using per-key quantization points (vae.decoder: torch.Size([256])) for vae.decoder vae.decoder: 8,964B, BPFP=0.2736 ⌛️ [2/4] FRONTEND: Frontend time: 0.293s (Pack+Encode) [3/4] BACKEND: Decode + Unpack... Using per-key quantization points (text_encoder-item0.clip_pooled_prompt_embeds: torch.Size([256])) for text_encoder-item0.clip_pooled_prompt_embeds Using per-key quantization points (text_encoder-item0.clip_prompt_embeds: torch.Size([256])) for text_encoder-item0.clip_prompt_embeds Using per-key quantization points (text_encoder_2-item1.clip_pooled_prompt_embeds: torch.Size([256])) for text_encoder_2-item1.clip_pooled_prompt_embeds Using per-key quantization points (text_encoder_2-item1.clip_prompt_embeds: torch.Size([256])) for text_encoder_2-item1.clip_prompt_embeds Using per-key quantization points (text_encoder_3-item2.t5_prompt_embeds: torch.Size([256])) for text_encoder_3-item2.t5_prompt_embeds Using per-key quantization points (text_encoder-item3.clip_pooled_prompt_embeds: torch.Size([256])) for text_encoder-item3.clip_pooled_prompt_embeds Using per-key quantization points (text_encoder-item3.clip_prompt_embeds: torch.Size([256])) for text_encoder-item3.clip_prompt_embeds Using per-key quantization points (text_encoder_2-item4.clip_pooled: torch.Size([256])) for text_encoder_2-item4.clip_pooled_prompt_embeds Using per-key quantization points (text_encoder_2-item4.clip_prompt: torch.Size([256])) for text_encoder_2-item4.clip_prompt_embeds Using per-key quantization points (text_encoder_3-item5.t5_prompt_embeds: torch.Size([256])) for text_encoder_3-item5.t5_prompt_embeds Using per-key quantization points (vae.encoder_f0: torch.Size([256])) for vae.encoder_f0 Using per-key quantization points (vae.encoder_f1: torch.Size([256])) for vae.encoder_f1 Using per-key quantization points (vae.decoder: torch.Size([256])) for vae.decoder IndividualUnPacker: text_encoder-item0.clip_pooled_prompt_embeds: torch.Size([768]) text_encoder-item0.clip_prompt_embeds: torch.Size([77, 768]) text_encoder_2-item1.clip_pooled_prompt_embeds: torch.Size([1280]) text_encoder_2-item1.clip_prompt_embeds: torch.Size([77, 1280]) text_encoder_3-item2.t5_prompt_embeds: torch.Size([77, 4096]) text_encoder-item3.clip_pooled_prompt_embeds: torch.Size([768]) text_encoder-item3.clip_prompt_embeds: torch.Size([77, 768]) text_encoder_2-item4.clip_pooled_prompt_embeds: torch.Size([1280]) text_encoder_2-item4.clip_prompt_embeds: torch.Size([77, 1280]) text_encoder_3-item5.t5_prompt_embeds: torch.Size([77, 4096]) vae.decoder: torch.Size([16, 128, 128]) vae.encoder_f0: torch.Size([32, 128, 128]) vae.encoder_f1: torch.Size([32, 128, 128]) ⌛️ [3/4] BACKEND: Backend time: 0.448s [4/4] METRICS: Computing MSE Breakdown... Using per-key quantization points (text_encoder-item0.clip_pooled_prompt_embeds: torch.Size([256])) for text_encoder-item0.clip_pooled_prompt_embeds Using per-key quantization points (text_encoder-item0.clip_prompt_embeds: torch.Size([256])) for text_encoder-item0.clip_prompt_embeds Using per-key quantization points (text_encoder_2-item1.clip_pooled_prompt_embeds: torch.Size([256])) for text_encoder_2-item1.clip_pooled_prompt_embeds Using per-key quantization points (text_encoder_2-item1.clip_prompt_embeds: torch.Size([256])) for text_encoder_2-item1.clip_prompt_embeds Using per-key quantization points (text_encoder_3-item2.t5_prompt_embeds: torch.Size([256])) for text_encoder_3-item2.t5_prompt_embeds Using per-key quantization points (text_encoder-item3.clip_pooled_prompt_embeds: torch.Size([256])) for text_encoder-item3.clip_pooled_prompt_embeds Using per-key quantization points (text_encoder-item3.clip_prompt_embeds: torch.Size([256])) for text_encoder-item3.clip_prompt_embeds Using per-key quantization points (text_encoder_2-item4.clip_pooled: torch.Size([256])) for text_encoder_2-item4.clip_pooled_prompt_embeds Using per-key quantization points (text_encoder_2-item4.clip_prompt: torch.Size([256])) for text_encoder_2-item4.clip_prompt_embeds Using per-key quantization points (text_encoder_3-item5.t5_prompt_embeds: torch.Size([256])) for text_encoder_3-item5.t5_prompt_embeds Using per-key quantization points (vae.encoder_f0: torch.Size([256])) for vae.encoder_f0 Using per-key quantization points (vae.encoder_f1: torch.Size([256])) for vae.encoder_f1 Using per-key quantization points (vae.decoder: torch.Size([256])) for vae.decoder IndividualUnPacker: text_encoder-item0.clip_pooled_prompt_embeds: torch.Size([768]) text_encoder-item0.clip_prompt_embeds: torch.Size([77, 768]) text_encoder_2-item1.clip_pooled_prompt_embeds: torch.Size([1280]) text_encoder_2-item1.clip_prompt_embeds: torch.Size([77, 1280]) text_encoder_3-item2.t5_prompt_embeds: torch.Size([77, 4096]) text_encoder-item3.clip_pooled_prompt_embeds: torch.Size([768]) text_encoder-item3.clip_prompt_embeds: torch.Size([77, 768]) text_encoder_2-item4.clip_pooled_prompt_embeds: torch.Size([1280]) text_encoder_2-item4.clip_prompt_embeds: torch.Size([77, 1280]) text_encoder_3-item5.t5_prompt_embeds: torch.Size([77, 4096]) vae.decoder: torch.Size([16, 128, 128]) vae.encoder_f0: torch.Size([32, 128, 128]) vae.encoder_f1: torch.Size([32, 128, 128]) Per-key MSE Breakdown (Quant=Truncation+Quantization, Codec=Compression): Key Quant-MSE Total-MSE ------------------------------------------------------------------------------------- text_encoder-item0.clip_pooled_prompt_embeds 0.00054317 0.91855677 text_encoder-item0.clip_prompt_embeds 0.00023597 23.94418797 text_encoder_2-item1.clip_pooled_prompt_embeds 0.00026316 0.92909241 text_encoder_2-item1.clip_prompt_embeds 0.00018757 0.11034792 text_encoder_3-item2.t5_prompt_embeds 0.00000828 0.00300961 text_encoder-item3.clip_pooled_prompt_embeds 0.00841799 1.01420029 text_encoder-item3.clip_prompt_embeds 0.00023247 23.90570887 text_encoder_2-item4.clip_pooled_prompt_embeds 0.00016751 0.67051430 text_encoder_2-item4.clip_prompt_embeds 0.00009830 0.47049568 text_encoder_3-item5.t5_prompt_embeds 0.00001263 0.00227117 vae.encoder_f0 0.00653100 2.34386539 vae.encoder_f1 0.00653745 2.34383154 vae.decoder 0.00020026 0.06500594 ------------------------------------------------------------------------------------- TOTAL 0.00308450 2.37366651 (elements=2,260,992) ---------------------- -------------------------------------------------------- SAMPLE-WISE STATISTICS ---------------------- -------------------------------------------------------- Handler sd35 Strategy individual Architecture hyperprior-featurecoding ---------------------- -------------------------------------------------------- Total Elements 2260992 Total Bytes 122516 BPFP 0.4335 bits/point EBPFP 0.8670 equivalent bits/point MSE 2.373667 ---------------------- -------------------------------------------------------- Time: 0.750s Load: 0.009s, Pack+Encode: 0.293s, Decode+Unpack: 0.448s ---------------------- -------------------------------------------------------- Restored Feature Format: [Dict] with 8 keys key['text_encoder-item0']: [Dict] with 2 keys key['clip_pooled_prompt_embeds']: [Tensor] shape=torch.Size([768]), dtype=torch.float16, device=cpu key['clip_prompt_embeds']: [Tensor] shape=torch.Size([77, 768]), dtype=torch.float16, device=cpu key['text_encoder_2-item1']: [Dict] with 2 keys key['clip_pooled_prompt_embeds']: [Tensor] shape=torch.Size([1280]), dtype=torch.float16, device=cpu key['clip_prompt_embeds']: [Tensor] shape=torch.Size([77, 1280]), dtype=torch.float16, device=cpu key['text_encoder_3-item2']: [Dict] with 1 keys key['t5_prompt_embeds']: [Tensor] shape=torch.Size([77, 4096]), dtype=torch.float16, device=cpu key['text_encoder-item3']: [Dict] with 2 keys key['clip_pooled_prompt_embeds']: [Tensor] shape=torch.Size([768]), dtype=torch.float16, device=cpu key['clip_prompt_embeds']: [Tensor] shape=torch.Size([77, 768]), dtype=torch.float16, device=cpu key['text_encoder_2-item4']: [Dict] with 2 keys key['clip_pooled_prompt_embeds']: [Tensor] shape=torch.Size([1280]), dtype=torch.float16, device=cpu key['clip_prompt_embeds']: [Tensor] shape=torch.Size([77, 1280]), dtype=torch.float16, device=cpu key['text_encoder_3-item5']: [Dict] with 1 keys key['t5_prompt_embeds']: [Tensor] shape=torch.Size([77, 4096]), dtype=torch.float16, device=cpu key['vae.decoder']: [Tensor] shape=torch.Size([16, 128, 128]), dtype=torch.float16, device=cpu key['vae.encoder']: [Dict] with 2 keys key['vae.encoder_f0']: [Tensor] shape=torch.Size([32, 128, 128]), dtype=torch.float16, device=cpu key['vae.encoder_f1']: [Tensor] shape=torch.Size([32, 128, 128]), dtype=torch.float16, device=cpu 💾 Converting with 2.3737 MSE: from ../datasets/FeatureCoding-StableDiffusion3.5/100samples_features/000000133969.zst to output-fixed/sd35/lambda0.004/hyperprior-featurecoding-8bit-individual/sd35cond/000000133969.zst 💪 Processing: ../datasets/FeatureCoding-StableDiffusion3.5/100samples_features/000000140270.zst (24/100) [1/4] FRONTEND: Loading features from ../datasets/FeatureCoding-StableDiffusion3.5/100samples_features/000000140270.zst... Original data structure: root: [Dict] with 8 keys key['text_encoder-item0']: [Dict] with 2 keys key['clip_pooled_prompt_embeds']: [Tensor] shape=torch.Size([768]), dtype=torch.float16, device=cpu key['clip_prompt_embeds']: [Tensor] shape=torch.Size([77, 768]), dtype=torch.float16, device=cpu key['text_encoder_2-item1']: [Dict] with 2 keys key['clip_pooled_prompt_embeds']: [Tensor] shape=torch.Size([1280]), dtype=torch.float16, device=cpu key['clip_prompt_embeds']: [Tensor] shape=torch.Size([77, 1280]), dtype=torch.float16, device=cpu key['text_encoder_3-item2']: [Dict] with 1 keys key['t5_prompt_embeds']: [Tensor] shape=torch.Size([77, 4096]), dtype=torch.float16, device=cpu key['text_encoder-item3']: [Dict] with 2 keys key['clip_pooled_prompt_embeds']: [Tensor] shape=torch.Size([768]), dtype=torch.float16, device=cpu key['clip_prompt_embeds']: [Tensor] shape=torch.Size([77, 768]), dtype=torch.float16, device=cpu key['text_encoder_2-item4']: [Dict] with 2 keys key['clip_pooled_prompt_embeds']: [Tensor] shape=torch.Size([1280]), dtype=torch.float16, device=cpu key['clip_prompt_embeds']: [Tensor] shape=torch.Size([77, 1280]), dtype=torch.float16, device=cpu key['text_encoder_3-item5']: [Dict] with 1 keys key['t5_prompt_embeds']: [Tensor] shape=torch.Size([77, 4096]), dtype=torch.float16, device=cpu key['vae.encoder-item6']: [Dict] with 2 keys key['vae.encoder_f0']: [Tensor] shape=torch.Size([32, 128, 128]), dtype=torch.float16, device=cpu key['vae.encoder_f1']: [Tensor] shape=torch.Size([32, 128, 128]), dtype=torch.float16, device=cpu key['vae.decoder']: [Tensor] shape=torch.Size([16, 128, 128]), dtype=torch.float16, device=cpu ⌛️ [1/4] FRONTEND: Load time: 0.008s ------------------------------------------------------------ SD3.5 Features Summary ------------------------------------------------------------ Number of text encoders: 3 Encoder names: ['text_encoder', 'text_encoder_2', 'text_encoder_3'] Has VAE latents: True Has VAE encoder features: True Data type: torch.float16 text_encoder-item0: clip_pooled_prompt_embeds: torch.Size([768]), torch.float16 clip_prompt_embeds: torch.Size([77, 768]), torch.float16 text_encoder_2-item1: clip_pooled_prompt_embeds: torch.Size([1280]), torch.float16 clip_prompt_embeds: torch.Size([77, 1280]), torch.float16 text_encoder_3-item2: t5_prompt_embeds: torch.Size([77, 4096]), torch.float16 text_encoder-item3: clip_pooled_prompt_embeds: torch.Size([768]), torch.float16 clip_prompt_embeds: torch.Size([77, 768]), torch.float16 text_encoder_2-item4: clip_pooled_prompt_embeds: torch.Size([1280]), torch.float16 clip_prompt_embeds: torch.Size([77, 1280]), torch.float16 text_encoder_3-item5: t5_prompt_embeds: torch.Size([77, 4096]), torch.float16 vae.decoder latents: torch.Size([16, 128, 128]) vae.encoder features: vae.encoder_f0: torch.Size([32, 128, 128]), torch.float16 vae.encoder_f1: torch.Size([32, 128, 128]), torch.float16 ------------------------------------------------------------ [2/4] FRONTEND: Pack + Encode (strategy: individual)... IndividualPacker: text_encoder-item0.clip_pooled_prompt_embeds: torch.Size([768])->torch.Size([1, 1, 1, 768]) text_encoder-item0.clip_prompt_embeds: torch.Size([77, 768])->torch.Size([1, 1, 77, 768]) text_encoder_2-item1.clip_pooled_prompt_embeds: torch.Size([1280])->torch.Size([1, 1, 1, 1280]) text_encoder_2-item1.clip_prompt_embeds: torch.Size([77, 1280])->torch.Size([1, 1, 77, 1280]) text_encoder_3-item2.t5_prompt_embeds: torch.Size([77, 4096])->torch.Size([1, 1, 77, 4096]) text_encoder-item3.clip_pooled_prompt_embeds: torch.Size([768])->torch.Size([1, 1, 1, 768]) text_encoder-item3.clip_prompt_embeds: torch.Size([77, 768])->torch.Size([1, 1, 77, 768]) text_encoder_2-item4.clip_pooled_prompt_embeds: torch.Size([1280])->torch.Size([1, 1, 1, 1280]) text_encoder_2-item4.clip_prompt_embeds: torch.Size([77, 1280])->torch.Size([1, 1, 77, 1280]) text_encoder_3-item5.t5_prompt_embeds: torch.Size([77, 4096])->torch.Size([1, 1, 77, 4096]) vae.encoder_f0: torch.Size([32, 128, 128])->torch.Size([1, 1, 512, 1024]) vae.encoder_f1: torch.Size([32, 128, 128])->torch.Size([1, 1, 512, 1024]) vae.decoder: torch.Size([16, 128, 128])->torch.Size([1, 1, 512, 512]) Using per-key quantization points (text_encoder-item0.clip_pooled_prompt_embeds: torch.Size([256])) for text_encoder-item0.clip_pooled_prompt_embeds text_encoder-item0.clip_pooled_prompt_embeds: 732B, BPFP=7.6250 Using per-key quantization points (text_encoder-item0.clip_prompt_embeds: torch.Size([256])) for text_encoder-item0.clip_prompt_embeds text_encoder-item0.clip_prompt_embeds: 6,704B, BPFP=0.9069 Using per-key quantization points (text_encoder_2-item1.clip_pooled_prompt_embeds: torch.Size([256])) for text_encoder_2-item1.clip_pooled_prompt_embeds text_encoder_2-item1.clip_pooled_prompt_embeds: 1,200B, BPFP=7.5000 Using per-key quantization points (text_encoder_2-item1.clip_prompt_embeds: torch.Size([256])) for text_encoder_2-item1.clip_prompt_embeds text_encoder_2-item1.clip_prompt_embeds: 11,068B, BPFP=0.8984 Using per-key quantization points (text_encoder_3-item2.t5_prompt_embeds: torch.Size([256])) for text_encoder_3-item2.t5_prompt_embeds text_encoder_3-item2.t5_prompt_embeds: 25,896B, BPFP=0.6569 Using per-key quantization points (text_encoder-item3.clip_pooled_prompt_embeds: torch.Size([256])) for text_encoder-item3.clip_pooled_prompt_embeds text_encoder-item3.clip_pooled_prompt_embeds: 760B, BPFP=7.9167 Using per-key quantization points (text_encoder-item3.clip_prompt_embeds: torch.Size([256])) for text_encoder-item3.clip_prompt_embeds text_encoder-item3.clip_prompt_embeds: 4,024B, BPFP=0.5444 Using per-key quantization points (text_encoder_2-item4.clip_pooled: torch.Size([256])) for text_encoder_2-item4.clip_pooled_prompt_embeds text_encoder_2-item4.clip_pooled_prompt_embeds: 1,268B, BPFP=7.9250 Using per-key quantization points (text_encoder_2-item4.clip_prompt: torch.Size([256])) for text_encoder_2-item4.clip_prompt_embeds text_encoder_2-item4.clip_prompt_embeds: 6,996B, BPFP=0.5679 Using per-key quantization points (text_encoder_3-item5.t5_prompt_embeds: torch.Size([256])) for text_encoder_3-item5.t5_prompt_embeds text_encoder_3-item5.t5_prompt_embeds: 18,416B, BPFP=0.4671 Using per-key quantization points (vae.encoder_f0: torch.Size([256])) for vae.encoder_f0 vae.encoder_f0: 18,516B, BPFP=0.2825 Using per-key quantization points (vae.encoder_f1: torch.Size([256])) for vae.encoder_f1 vae.encoder_f1: 18,512B, BPFP=0.2825 Using per-key quantization points (vae.decoder: torch.Size([256])) for vae.decoder vae.decoder: 5,732B, BPFP=0.1749 ⌛️ [2/4] FRONTEND: Frontend time: 0.291s (Pack+Encode) [3/4] BACKEND: Decode + Unpack... Using per-key quantization points (text_encoder-item0.clip_pooled_prompt_embeds: torch.Size([256])) for text_encoder-item0.clip_pooled_prompt_embeds Using per-key quantization points (text_encoder-item0.clip_prompt_embeds: torch.Size([256])) for text_encoder-item0.clip_prompt_embeds Using per-key quantization points (text_encoder_2-item1.clip_pooled_prompt_embeds: torch.Size([256])) for text_encoder_2-item1.clip_pooled_prompt_embeds Using per-key quantization points (text_encoder_2-item1.clip_prompt_embeds: torch.Size([256])) for text_encoder_2-item1.clip_prompt_embeds Using per-key quantization points (text_encoder_3-item2.t5_prompt_embeds: torch.Size([256])) for text_encoder_3-item2.t5_prompt_embeds Using per-key quantization points (text_encoder-item3.clip_pooled_prompt_embeds: torch.Size([256])) for text_encoder-item3.clip_pooled_prompt_embeds Using per-key quantization points (text_encoder-item3.clip_prompt_embeds: torch.Size([256])) for text_encoder-item3.clip_prompt_embeds Using per-key quantization points (text_encoder_2-item4.clip_pooled: torch.Size([256])) for text_encoder_2-item4.clip_pooled_prompt_embeds Using per-key quantization points (text_encoder_2-item4.clip_prompt: torch.Size([256])) for text_encoder_2-item4.clip_prompt_embeds Using per-key quantization points (text_encoder_3-item5.t5_prompt_embeds: torch.Size([256])) for text_encoder_3-item5.t5_prompt_embeds Using per-key quantization points (vae.encoder_f0: torch.Size([256])) for vae.encoder_f0 Using per-key quantization points (vae.encoder_f1: torch.Size([256])) for vae.encoder_f1 Using per-key quantization points (vae.decoder: torch.Size([256])) for vae.decoder IndividualUnPacker: text_encoder-item0.clip_pooled_prompt_embeds: torch.Size([768]) text_encoder-item0.clip_prompt_embeds: torch.Size([77, 768]) text_encoder_2-item1.clip_pooled_prompt_embeds: torch.Size([1280]) text_encoder_2-item1.clip_prompt_embeds: torch.Size([77, 1280]) text_encoder_3-item2.t5_prompt_embeds: torch.Size([77, 4096]) text_encoder-item3.clip_pooled_prompt_embeds: torch.Size([768]) text_encoder-item3.clip_prompt_embeds: torch.Size([77, 768]) text_encoder_2-item4.clip_pooled_prompt_embeds: torch.Size([1280]) text_encoder_2-item4.clip_prompt_embeds: torch.Size([77, 1280]) text_encoder_3-item5.t5_prompt_embeds: torch.Size([77, 4096]) vae.decoder: torch.Size([16, 128, 128]) vae.encoder_f0: torch.Size([32, 128, 128]) vae.encoder_f1: torch.Size([32, 128, 128]) ⌛️ [3/4] BACKEND: Backend time: 0.454s [4/4] METRICS: Computing MSE Breakdown... Using per-key quantization points (text_encoder-item0.clip_pooled_prompt_embeds: torch.Size([256])) for text_encoder-item0.clip_pooled_prompt_embeds Using per-key quantization points (text_encoder-item0.clip_prompt_embeds: torch.Size([256])) for text_encoder-item0.clip_prompt_embeds Using per-key quantization points (text_encoder_2-item1.clip_pooled_prompt_embeds: torch.Size([256])) for text_encoder_2-item1.clip_pooled_prompt_embeds Using per-key quantization points (text_encoder_2-item1.clip_prompt_embeds: torch.Size([256])) for text_encoder_2-item1.clip_prompt_embeds Using per-key quantization points (text_encoder_3-item2.t5_prompt_embeds: torch.Size([256])) for text_encoder_3-item2.t5_prompt_embeds Using per-key quantization points (text_encoder-item3.clip_pooled_prompt_embeds: torch.Size([256])) for text_encoder-item3.clip_pooled_prompt_embeds Using per-key quantization points (text_encoder-item3.clip_prompt_embeds: torch.Size([256])) for text_encoder-item3.clip_prompt_embeds Using per-key quantization points (text_encoder_2-item4.clip_pooled: torch.Size([256])) for text_encoder_2-item4.clip_pooled_prompt_embeds Using per-key quantization points (text_encoder_2-item4.clip_prompt: torch.Size([256])) for text_encoder_2-item4.clip_prompt_embeds Using per-key quantization points (text_encoder_3-item5.t5_prompt_embeds: torch.Size([256])) for text_encoder_3-item5.t5_prompt_embeds Using per-key quantization points (vae.encoder_f0: torch.Size([256])) for vae.encoder_f0 Using per-key quantization points (vae.encoder_f1: torch.Size([256])) for vae.encoder_f1 Using per-key quantization points (vae.decoder: torch.Size([256])) for vae.decoder IndividualUnPacker: text_encoder-item0.clip_pooled_prompt_embeds: torch.Size([768]) text_encoder-item0.clip_prompt_embeds: torch.Size([77, 768]) text_encoder_2-item1.clip_pooled_prompt_embeds: torch.Size([1280]) text_encoder_2-item1.clip_prompt_embeds: torch.Size([77, 1280]) text_encoder_3-item2.t5_prompt_embeds: torch.Size([77, 4096]) text_encoder-item3.clip_pooled_prompt_embeds: torch.Size([768]) text_encoder-item3.clip_prompt_embeds: torch.Size([77, 768]) text_encoder_2-item4.clip_pooled_prompt_embeds: torch.Size([1280]) text_encoder_2-item4.clip_prompt_embeds: torch.Size([77, 1280]) text_encoder_3-item5.t5_prompt_embeds: torch.Size([77, 4096]) vae.decoder: torch.Size([16, 128, 128]) vae.encoder_f0: torch.Size([32, 128, 128]) vae.encoder_f1: torch.Size([32, 128, 128]) Per-key MSE Breakdown (Quant=Truncation+Quantization, Codec=Compression): Key Quant-MSE Total-MSE ------------------------------------------------------------------------------------- text_encoder-item0.clip_pooled_prompt_embeds 0.00018905 0.90260911 text_encoder-item0.clip_prompt_embeds 0.00022433 23.94183957 text_encoder_2-item1.clip_pooled_prompt_embeds 0.00107168 0.94458714 text_encoder_2-item1.clip_prompt_embeds 0.00016492 0.12464044 text_encoder_3-item2.t5_prompt_embeds 0.00000806 0.00310253 text_encoder-item3.clip_pooled_prompt_embeds 0.00841799 1.01420029 text_encoder-item3.clip_prompt_embeds 0.00023247 23.90570887 text_encoder_2-item4.clip_pooled_prompt_embeds 0.00016751 0.67051430 text_encoder_2-item4.clip_prompt_embeds 0.00009830 0.47049568 text_encoder_3-item5.t5_prompt_embeds 0.00001263 0.00227117 vae.encoder_f0 0.00869686 3.59393263 vae.encoder_f1 0.00870063 3.59585857 vae.decoder 0.00021246 0.04679004 ------------------------------------------------------------------------------------- TOTAL 0.00408877 2.95232822 (elements=2,260,992) ---------------------- -------------------------------------------------------- SAMPLE-WISE STATISTICS ---------------------- -------------------------------------------------------- Handler sd35 Strategy individual Architecture hyperprior-featurecoding ---------------------- -------------------------------------------------------- Total Elements 2260992 Total Bytes 119824 BPFP 0.4240 bits/point EBPFP 0.8479 equivalent bits/point MSE 2.952328 ---------------------- -------------------------------------------------------- Time: 0.754s Load: 0.008s, Pack+Encode: 0.291s, Decode+Unpack: 0.454s ---------------------- -------------------------------------------------------- Restored Feature Format: [Dict] with 8 keys key['text_encoder-item0']: [Dict] with 2 keys key['clip_pooled_prompt_embeds']: [Tensor] shape=torch.Size([768]), dtype=torch.float16, device=cpu key['clip_prompt_embeds']: [Tensor] shape=torch.Size([77, 768]), dtype=torch.float16, device=cpu key['text_encoder_2-item1']: [Dict] with 2 keys key['clip_pooled_prompt_embeds']: [Tensor] shape=torch.Size([1280]), dtype=torch.float16, device=cpu key['clip_prompt_embeds']: [Tensor] shape=torch.Size([77, 1280]), dtype=torch.float16, device=cpu key['text_encoder_3-item2']: [Dict] with 1 keys key['t5_prompt_embeds']: [Tensor] shape=torch.Size([77, 4096]), dtype=torch.float16, device=cpu key['text_encoder-item3']: [Dict] with 2 keys key['clip_pooled_prompt_embeds']: [Tensor] shape=torch.Size([768]), dtype=torch.float16, device=cpu key['clip_prompt_embeds']: [Tensor] shape=torch.Size([77, 768]), dtype=torch.float16, device=cpu key['text_encoder_2-item4']: [Dict] with 2 keys key['clip_pooled_prompt_embeds']: [Tensor] shape=torch.Size([1280]), dtype=torch.float16, device=cpu key['clip_prompt_embeds']: [Tensor] shape=torch.Size([77, 1280]), dtype=torch.float16, device=cpu key['text_encoder_3-item5']: [Dict] with 1 keys key['t5_prompt_embeds']: [Tensor] shape=torch.Size([77, 4096]), dtype=torch.float16, device=cpu key['vae.decoder']: [Tensor] shape=torch.Size([16, 128, 128]), dtype=torch.float16, device=cpu key['vae.encoder']: [Dict] with 2 keys key['vae.encoder_f0']: [Tensor] shape=torch.Size([32, 128, 128]), dtype=torch.float16, device=cpu key['vae.encoder_f1']: [Tensor] shape=torch.Size([32, 128, 128]), dtype=torch.float16, device=cpu 💾 Converting with 2.9523 MSE: from ../datasets/FeatureCoding-StableDiffusion3.5/100samples_features/000000140270.zst to output-fixed/sd35/lambda0.004/hyperprior-featurecoding-8bit-individual/sd35cond/000000140270.zst 💪 Processing: ../datasets/FeatureCoding-StableDiffusion3.5/100samples_features/000000146358.zst (25/100) [1/4] FRONTEND: Loading features from ../datasets/FeatureCoding-StableDiffusion3.5/100samples_features/000000146358.zst... Original data structure: root: [Dict] with 8 keys key['text_encoder-item0']: [Dict] with 2 keys key['clip_pooled_prompt_embeds']: [Tensor] shape=torch.Size([768]), dtype=torch.float16, device=cpu key['clip_prompt_embeds']: [Tensor] shape=torch.Size([77, 768]), dtype=torch.float16, device=cpu key['text_encoder_2-item1']: [Dict] with 2 keys key['clip_pooled_prompt_embeds']: [Tensor] shape=torch.Size([1280]), dtype=torch.float16, device=cpu key['clip_prompt_embeds']: [Tensor] shape=torch.Size([77, 1280]), dtype=torch.float16, device=cpu key['text_encoder_3-item2']: [Dict] with 1 keys key['t5_prompt_embeds']: [Tensor] shape=torch.Size([77, 4096]), dtype=torch.float16, device=cpu key['text_encoder-item3']: [Dict] with 2 keys key['clip_pooled_prompt_embeds']: [Tensor] shape=torch.Size([768]), dtype=torch.float16, device=cpu key['clip_prompt_embeds']: [Tensor] shape=torch.Size([77, 768]), dtype=torch.float16, device=cpu key['text_encoder_2-item4']: [Dict] with 2 keys key['clip_pooled_prompt_embeds']: [Tensor] shape=torch.Size([1280]), dtype=torch.float16, device=cpu key['clip_prompt_embeds']: [Tensor] shape=torch.Size([77, 1280]), dtype=torch.float16, device=cpu key['text_encoder_3-item5']: [Dict] with 1 keys key['t5_prompt_embeds']: [Tensor] shape=torch.Size([77, 4096]), dtype=torch.float16, device=cpu key['vae.encoder-item6']: [Dict] with 2 keys key['vae.encoder_f0']: [Tensor] shape=torch.Size([32, 128, 128]), dtype=torch.float16, device=cpu key['vae.encoder_f1']: [Tensor] shape=torch.Size([32, 128, 128]), dtype=torch.float16, device=cpu key['vae.decoder']: [Tensor] shape=torch.Size([16, 128, 128]), dtype=torch.float16, device=cpu ⌛️ [1/4] FRONTEND: Load time: 0.009s ------------------------------------------------------------ SD3.5 Features Summary ------------------------------------------------------------ Number of text encoders: 3 Encoder names: ['text_encoder', 'text_encoder_2', 'text_encoder_3'] Has VAE latents: True Has VAE encoder features: True Data type: torch.float16 text_encoder-item0: clip_pooled_prompt_embeds: torch.Size([768]), torch.float16 clip_prompt_embeds: torch.Size([77, 768]), torch.float16 text_encoder_2-item1: clip_pooled_prompt_embeds: torch.Size([1280]), torch.float16 clip_prompt_embeds: torch.Size([77, 1280]), torch.float16 text_encoder_3-item2: t5_prompt_embeds: torch.Size([77, 4096]), torch.float16 text_encoder-item3: clip_pooled_prompt_embeds: torch.Size([768]), torch.float16 clip_prompt_embeds: torch.Size([77, 768]), torch.float16 text_encoder_2-item4: clip_pooled_prompt_embeds: torch.Size([1280]), torch.float16 clip_prompt_embeds: torch.Size([77, 1280]), torch.float16 text_encoder_3-item5: t5_prompt_embeds: torch.Size([77, 4096]), torch.float16 vae.decoder latents: torch.Size([16, 128, 128]) vae.encoder features: vae.encoder_f0: torch.Size([32, 128, 128]), torch.float16 vae.encoder_f1: torch.Size([32, 128, 128]), torch.float16 ------------------------------------------------------------ [2/4] FRONTEND: Pack + Encode (strategy: individual)... IndividualPacker: text_encoder-item0.clip_pooled_prompt_embeds: torch.Size([768])->torch.Size([1, 1, 1, 768]) text_encoder-item0.clip_prompt_embeds: torch.Size([77, 768])->torch.Size([1, 1, 77, 768]) text_encoder_2-item1.clip_pooled_prompt_embeds: torch.Size([1280])->torch.Size([1, 1, 1, 1280]) text_encoder_2-item1.clip_prompt_embeds: torch.Size([77, 1280])->torch.Size([1, 1, 77, 1280]) text_encoder_3-item2.t5_prompt_embeds: torch.Size([77, 4096])->torch.Size([1, 1, 77, 4096]) text_encoder-item3.clip_pooled_prompt_embeds: torch.Size([768])->torch.Size([1, 1, 1, 768]) text_encoder-item3.clip_prompt_embeds: torch.Size([77, 768])->torch.Size([1, 1, 77, 768]) text_encoder_2-item4.clip_pooled_prompt_embeds: torch.Size([1280])->torch.Size([1, 1, 1, 1280]) text_encoder_2-item4.clip_prompt_embeds: torch.Size([77, 1280])->torch.Size([1, 1, 77, 1280]) text_encoder_3-item5.t5_prompt_embeds: torch.Size([77, 4096])->torch.Size([1, 1, 77, 4096]) vae.encoder_f0: torch.Size([32, 128, 128])->torch.Size([1, 1, 512, 1024]) vae.encoder_f1: torch.Size([32, 128, 128])->torch.Size([1, 1, 512, 1024]) vae.decoder: torch.Size([16, 128, 128])->torch.Size([1, 1, 512, 512]) Using per-key quantization points (text_encoder-item0.clip_pooled_prompt_embeds: torch.Size([256])) for text_encoder-item0.clip_pooled_prompt_embeds text_encoder-item0.clip_pooled_prompt_embeds: 736B, BPFP=7.6667 Using per-key quantization points (text_encoder-item0.clip_prompt_embeds: torch.Size([256])) for text_encoder-item0.clip_prompt_embeds text_encoder-item0.clip_prompt_embeds: 6,716B, BPFP=0.9085 Using per-key quantization points (text_encoder_2-item1.clip_pooled_prompt_embeds: torch.Size([256])) for text_encoder_2-item1.clip_pooled_prompt_embeds text_encoder_2-item1.clip_pooled_prompt_embeds: 1,204B, BPFP=7.5250 Using per-key quantization points (text_encoder_2-item1.clip_prompt_embeds: torch.Size([256])) for text_encoder_2-item1.clip_prompt_embeds text_encoder_2-item1.clip_prompt_embeds: 11,192B, BPFP=0.9084 Using per-key quantization points (text_encoder_3-item2.t5_prompt_embeds: torch.Size([256])) for text_encoder_3-item2.t5_prompt_embeds text_encoder_3-item2.t5_prompt_embeds: 29,404B, BPFP=0.7458 Using per-key quantization points (text_encoder-item3.clip_pooled_prompt_embeds: torch.Size([256])) for text_encoder-item3.clip_pooled_prompt_embeds text_encoder-item3.clip_pooled_prompt_embeds: 760B, BPFP=7.9167 Using per-key quantization points (text_encoder-item3.clip_prompt_embeds: torch.Size([256])) for text_encoder-item3.clip_prompt_embeds text_encoder-item3.clip_prompt_embeds: 4,024B, BPFP=0.5444 Using per-key quantization points (text_encoder_2-item4.clip_pooled: torch.Size([256])) for text_encoder_2-item4.clip_pooled_prompt_embeds text_encoder_2-item4.clip_pooled_prompt_embeds: 1,268B, BPFP=7.9250 Using per-key quantization points (text_encoder_2-item4.clip_prompt: torch.Size([256])) for text_encoder_2-item4.clip_prompt_embeds text_encoder_2-item4.clip_prompt_embeds: 6,996B, BPFP=0.5679 Using per-key quantization points (text_encoder_3-item5.t5_prompt_embeds: torch.Size([256])) for text_encoder_3-item5.t5_prompt_embeds text_encoder_3-item5.t5_prompt_embeds: 18,416B, BPFP=0.4671 Using per-key quantization points (vae.encoder_f0: torch.Size([256])) for vae.encoder_f0 vae.encoder_f0: 21,080B, BPFP=0.3217 Using per-key quantization points (vae.encoder_f1: torch.Size([256])) for vae.encoder_f1 vae.encoder_f1: 21,080B, BPFP=0.3217 Using per-key quantization points (vae.decoder: torch.Size([256])) for vae.decoder vae.decoder: 6,572B, BPFP=0.2006 ⌛️ [2/4] FRONTEND: Frontend time: 0.295s (Pack+Encode) [3/4] BACKEND: Decode + Unpack... Using per-key quantization points (text_encoder-item0.clip_pooled_prompt_embeds: torch.Size([256])) for text_encoder-item0.clip_pooled_prompt_embeds Using per-key quantization points (text_encoder-item0.clip_prompt_embeds: torch.Size([256])) for text_encoder-item0.clip_prompt_embeds Using per-key quantization points (text_encoder_2-item1.clip_pooled_prompt_embeds: torch.Size([256])) for text_encoder_2-item1.clip_pooled_prompt_embeds Using per-key quantization points (text_encoder_2-item1.clip_prompt_embeds: torch.Size([256])) for text_encoder_2-item1.clip_prompt_embeds Using per-key quantization points (text_encoder_3-item2.t5_prompt_embeds: torch.Size([256])) for text_encoder_3-item2.t5_prompt_embeds Using per-key quantization points (text_encoder-item3.clip_pooled_prompt_embeds: torch.Size([256])) for text_encoder-item3.clip_pooled_prompt_embeds Using per-key quantization points (text_encoder-item3.clip_prompt_embeds: torch.Size([256])) for text_encoder-item3.clip_prompt_embeds Using per-key quantization points (text_encoder_2-item4.clip_pooled: torch.Size([256])) for text_encoder_2-item4.clip_pooled_prompt_embeds Using per-key quantization points (text_encoder_2-item4.clip_prompt: torch.Size([256])) for text_encoder_2-item4.clip_prompt_embeds Using per-key quantization points (text_encoder_3-item5.t5_prompt_embeds: torch.Size([256])) for text_encoder_3-item5.t5_prompt_embeds Using per-key quantization points (vae.encoder_f0: torch.Size([256])) for vae.encoder_f0 Using per-key quantization points (vae.encoder_f1: torch.Size([256])) for vae.encoder_f1 Using per-key quantization points (vae.decoder: torch.Size([256])) for vae.decoder IndividualUnPacker: text_encoder-item0.clip_pooled_prompt_embeds: torch.Size([768]) text_encoder-item0.clip_prompt_embeds: torch.Size([77, 768]) text_encoder_2-item1.clip_pooled_prompt_embeds: torch.Size([1280]) text_encoder_2-item1.clip_prompt_embeds: torch.Size([77, 1280]) text_encoder_3-item2.t5_prompt_embeds: torch.Size([77, 4096]) text_encoder-item3.clip_pooled_prompt_embeds: torch.Size([768]) text_encoder-item3.clip_prompt_embeds: torch.Size([77, 768]) text_encoder_2-item4.clip_pooled_prompt_embeds: torch.Size([1280]) text_encoder_2-item4.clip_prompt_embeds: torch.Size([77, 1280]) text_encoder_3-item5.t5_prompt_embeds: torch.Size([77, 4096]) vae.decoder: torch.Size([16, 128, 128]) vae.encoder_f0: torch.Size([32, 128, 128]) vae.encoder_f1: torch.Size([32, 128, 128]) ⌛️ [3/4] BACKEND: Backend time: 0.447s [4/4] METRICS: Computing MSE Breakdown... Using per-key quantization points (text_encoder-item0.clip_pooled_prompt_embeds: torch.Size([256])) for text_encoder-item0.clip_pooled_prompt_embeds Using per-key quantization points (text_encoder-item0.clip_prompt_embeds: torch.Size([256])) for text_encoder-item0.clip_prompt_embeds Using per-key quantization points (text_encoder_2-item1.clip_pooled_prompt_embeds: torch.Size([256])) for text_encoder_2-item1.clip_pooled_prompt_embeds Using per-key quantization points (text_encoder_2-item1.clip_prompt_embeds: torch.Size([256])) for text_encoder_2-item1.clip_prompt_embeds Using per-key quantization points (text_encoder_3-item2.t5_prompt_embeds: torch.Size([256])) for text_encoder_3-item2.t5_prompt_embeds Using per-key quantization points (text_encoder-item3.clip_pooled_prompt_embeds: torch.Size([256])) for text_encoder-item3.clip_pooled_prompt_embeds Using per-key quantization points (text_encoder-item3.clip_prompt_embeds: torch.Size([256])) for text_encoder-item3.clip_prompt_embeds Using per-key quantization points (text_encoder_2-item4.clip_pooled: torch.Size([256])) for text_encoder_2-item4.clip_pooled_prompt_embeds Using per-key quantization points (text_encoder_2-item4.clip_prompt: torch.Size([256])) for text_encoder_2-item4.clip_prompt_embeds Using per-key quantization points (text_encoder_3-item5.t5_prompt_embeds: torch.Size([256])) for text_encoder_3-item5.t5_prompt_embeds Using per-key quantization points (vae.encoder_f0: torch.Size([256])) for vae.encoder_f0 Using per-key quantization points (vae.encoder_f1: torch.Size([256])) for vae.encoder_f1 Using per-key quantization points (vae.decoder: torch.Size([256])) for vae.decoder IndividualUnPacker: text_encoder-item0.clip_pooled_prompt_embeds: torch.Size([768]) text_encoder-item0.clip_prompt_embeds: torch.Size([77, 768]) text_encoder_2-item1.clip_pooled_prompt_embeds: torch.Size([1280]) text_encoder_2-item1.clip_prompt_embeds: torch.Size([77, 1280]) text_encoder_3-item2.t5_prompt_embeds: torch.Size([77, 4096]) text_encoder-item3.clip_pooled_prompt_embeds: torch.Size([768]) text_encoder-item3.clip_prompt_embeds: torch.Size([77, 768]) text_encoder_2-item4.clip_pooled_prompt_embeds: torch.Size([1280]) text_encoder_2-item4.clip_prompt_embeds: torch.Size([77, 1280]) text_encoder_3-item5.t5_prompt_embeds: torch.Size([77, 4096]) vae.decoder: torch.Size([16, 128, 128]) vae.encoder_f0: torch.Size([32, 128, 128]) vae.encoder_f1: torch.Size([32, 128, 128]) Per-key MSE Breakdown (Quant=Truncation+Quantization, Codec=Compression): Key Quant-MSE Total-MSE ------------------------------------------------------------------------------------- text_encoder-item0.clip_pooled_prompt_embeds 0.00020560 0.94367552 text_encoder-item0.clip_prompt_embeds 0.00022433 34.91684000 text_encoder_2-item1.clip_pooled_prompt_embeds 0.00020112 0.89953356 text_encoder_2-item1.clip_prompt_embeds 0.00017331 0.15691746 text_encoder_3-item2.t5_prompt_embeds 0.00000752 0.00357857 text_encoder-item3.clip_pooled_prompt_embeds 0.00841799 1.01420029 text_encoder-item3.clip_prompt_embeds 0.00023247 23.90570887 text_encoder_2-item4.clip_pooled_prompt_embeds 0.00016751 0.67051430 text_encoder_2-item4.clip_prompt_embeds 0.00009830 0.47049568 text_encoder_3-item5.t5_prompt_embeds 0.00001263 0.00227117 vae.encoder_f0 0.00626512 2.94792724 vae.encoder_f1 0.00626949 2.94782734 vae.decoder 0.00018936 0.04929534 ------------------------------------------------------------------------------------- TOTAL 0.00295827 2.94106402 (elements=2,260,992) ---------------------- -------------------------------------------------------- SAMPLE-WISE STATISTICS ---------------------- -------------------------------------------------------- Handler sd35 Strategy individual Architecture hyperprior-featurecoding ---------------------- -------------------------------------------------------- Total Elements 2260992 Total Bytes 129448 BPFP 0.4580 bits/point EBPFP 0.9160 equivalent bits/point MSE 2.941064 ---------------------- -------------------------------------------------------- Time: 0.751s Load: 0.009s, Pack+Encode: 0.295s, Decode+Unpack: 0.447s ---------------------- -------------------------------------------------------- Restored Feature Format: [Dict] with 8 keys key['text_encoder-item0']: [Dict] with 2 keys key['clip_pooled_prompt_embeds']: [Tensor] shape=torch.Size([768]), dtype=torch.float16, device=cpu key['clip_prompt_embeds']: [Tensor] shape=torch.Size([77, 768]), dtype=torch.float16, device=cpu key['text_encoder_2-item1']: [Dict] with 2 keys key['clip_pooled_prompt_embeds']: [Tensor] shape=torch.Size([1280]), dtype=torch.float16, device=cpu key['clip_prompt_embeds']: [Tensor] shape=torch.Size([77, 1280]), dtype=torch.float16, device=cpu key['text_encoder_3-item2']: [Dict] with 1 keys key['t5_prompt_embeds']: [Tensor] shape=torch.Size([77, 4096]), dtype=torch.float16, device=cpu key['text_encoder-item3']: [Dict] with 2 keys key['clip_pooled_prompt_embeds']: [Tensor] shape=torch.Size([768]), dtype=torch.float16, device=cpu key['clip_prompt_embeds']: [Tensor] shape=torch.Size([77, 768]), dtype=torch.float16, device=cpu key['text_encoder_2-item4']: [Dict] with 2 keys key['clip_pooled_prompt_embeds']: [Tensor] shape=torch.Size([1280]), dtype=torch.float16, device=cpu key['clip_prompt_embeds']: [Tensor] shape=torch.Size([77, 1280]), dtype=torch.float16, device=cpu key['text_encoder_3-item5']: [Dict] with 1 keys key['t5_prompt_embeds']: [Tensor] shape=torch.Size([77, 4096]), dtype=torch.float16, device=cpu key['vae.decoder']: [Tensor] shape=torch.Size([16, 128, 128]), dtype=torch.float16, device=cpu key['vae.encoder']: [Dict] with 2 keys key['vae.encoder_f0']: [Tensor] shape=torch.Size([32, 128, 128]), dtype=torch.float16, device=cpu key['vae.encoder_f1']: [Tensor] shape=torch.Size([32, 128, 128]), dtype=torch.float16, device=cpu 💾 Converting with 2.9411 MSE: from ../datasets/FeatureCoding-StableDiffusion3.5/100samples_features/000000146358.zst to output-fixed/sd35/lambda0.004/hyperprior-featurecoding-8bit-individual/sd35cond/000000146358.zst 💪 Processing: ../datasets/FeatureCoding-StableDiffusion3.5/100samples_features/000000148662.zst (26/100) [1/4] FRONTEND: Loading features from ../datasets/FeatureCoding-StableDiffusion3.5/100samples_features/000000148662.zst... Original data structure: root: [Dict] with 8 keys key['text_encoder-item0']: [Dict] with 2 keys key['clip_pooled_prompt_embeds']: [Tensor] shape=torch.Size([768]), dtype=torch.float16, device=cpu key['clip_prompt_embeds']: [Tensor] shape=torch.Size([77, 768]), dtype=torch.float16, device=cpu key['text_encoder_2-item1']: [Dict] with 2 keys key['clip_pooled_prompt_embeds']: [Tensor] shape=torch.Size([1280]), dtype=torch.float16, device=cpu key['clip_prompt_embeds']: [Tensor] shape=torch.Size([77, 1280]), dtype=torch.float16, device=cpu key['text_encoder_3-item2']: [Dict] with 1 keys key['t5_prompt_embeds']: [Tensor] shape=torch.Size([77, 4096]), dtype=torch.float16, device=cpu key['text_encoder-item3']: [Dict] with 2 keys key['clip_pooled_prompt_embeds']: [Tensor] shape=torch.Size([768]), dtype=torch.float16, device=cpu key['clip_prompt_embeds']: [Tensor] shape=torch.Size([77, 768]), dtype=torch.float16, device=cpu key['text_encoder_2-item4']: [Dict] with 2 keys key['clip_pooled_prompt_embeds']: [Tensor] shape=torch.Size([1280]), dtype=torch.float16, device=cpu key['clip_prompt_embeds']: [Tensor] shape=torch.Size([77, 1280]), dtype=torch.float16, device=cpu key['text_encoder_3-item5']: [Dict] with 1 keys key['t5_prompt_embeds']: [Tensor] shape=torch.Size([77, 4096]), dtype=torch.float16, device=cpu key['vae.encoder-item6']: [Dict] with 2 keys key['vae.encoder_f0']: [Tensor] shape=torch.Size([32, 128, 128]), dtype=torch.float16, device=cpu key['vae.encoder_f1']: [Tensor] shape=torch.Size([32, 128, 128]), dtype=torch.float16, device=cpu key['vae.decoder']: [Tensor] shape=torch.Size([16, 128, 128]), dtype=torch.float16, device=cpu ⌛️ [1/4] FRONTEND: Load time: 0.008s ------------------------------------------------------------ SD3.5 Features Summary ------------------------------------------------------------ Number of text encoders: 3 Encoder names: ['text_encoder', 'text_encoder_2', 'text_encoder_3'] Has VAE latents: True Has VAE encoder features: True Data type: torch.float16 text_encoder-item0: clip_pooled_prompt_embeds: torch.Size([768]), torch.float16 clip_prompt_embeds: torch.Size([77, 768]), torch.float16 text_encoder_2-item1: clip_pooled_prompt_embeds: torch.Size([1280]), torch.float16 clip_prompt_embeds: torch.Size([77, 1280]), torch.float16 text_encoder_3-item2: t5_prompt_embeds: torch.Size([77, 4096]), torch.float16 text_encoder-item3: clip_pooled_prompt_embeds: torch.Size([768]), torch.float16 clip_prompt_embeds: torch.Size([77, 768]), torch.float16 text_encoder_2-item4: clip_pooled_prompt_embeds: torch.Size([1280]), torch.float16 clip_prompt_embeds: torch.Size([77, 1280]), torch.float16 text_encoder_3-item5: t5_prompt_embeds: torch.Size([77, 4096]), torch.float16 vae.decoder latents: torch.Size([16, 128, 128]) vae.encoder features: vae.encoder_f0: torch.Size([32, 128, 128]), torch.float16 vae.encoder_f1: torch.Size([32, 128, 128]), torch.float16 ------------------------------------------------------------ [2/4] FRONTEND: Pack + Encode (strategy: individual)... IndividualPacker: text_encoder-item0.clip_pooled_prompt_embeds: torch.Size([768])->torch.Size([1, 1, 1, 768]) text_encoder-item0.clip_prompt_embeds: torch.Size([77, 768])->torch.Size([1, 1, 77, 768]) text_encoder_2-item1.clip_pooled_prompt_embeds: torch.Size([1280])->torch.Size([1, 1, 1, 1280]) text_encoder_2-item1.clip_prompt_embeds: torch.Size([77, 1280])->torch.Size([1, 1, 77, 1280]) text_encoder_3-item2.t5_prompt_embeds: torch.Size([77, 4096])->torch.Size([1, 1, 77, 4096]) text_encoder-item3.clip_pooled_prompt_embeds: torch.Size([768])->torch.Size([1, 1, 1, 768]) text_encoder-item3.clip_prompt_embeds: torch.Size([77, 768])->torch.Size([1, 1, 77, 768]) text_encoder_2-item4.clip_pooled_prompt_embeds: torch.Size([1280])->torch.Size([1, 1, 1, 1280]) text_encoder_2-item4.clip_prompt_embeds: torch.Size([77, 1280])->torch.Size([1, 1, 77, 1280]) text_encoder_3-item5.t5_prompt_embeds: torch.Size([77, 4096])->torch.Size([1, 1, 77, 4096]) vae.encoder_f0: torch.Size([32, 128, 128])->torch.Size([1, 1, 512, 1024]) vae.encoder_f1: torch.Size([32, 128, 128])->torch.Size([1, 1, 512, 1024]) vae.decoder: torch.Size([16, 128, 128])->torch.Size([1, 1, 512, 512]) Using per-key quantization points (text_encoder-item0.clip_pooled_prompt_embeds: torch.Size([256])) for text_encoder-item0.clip_pooled_prompt_embeds text_encoder-item0.clip_pooled_prompt_embeds: 732B, BPFP=7.6250 Using per-key quantization points (text_encoder-item0.clip_prompt_embeds: torch.Size([256])) for text_encoder-item0.clip_prompt_embeds text_encoder-item0.clip_prompt_embeds: 6,916B, BPFP=0.9356 Using per-key quantization points (text_encoder_2-item1.clip_pooled_prompt_embeds: torch.Size([256])) for text_encoder_2-item1.clip_pooled_prompt_embeds text_encoder_2-item1.clip_pooled_prompt_embeds: 1,200B, BPFP=7.5000 Using per-key quantization points (text_encoder_2-item1.clip_prompt_embeds: torch.Size([256])) for text_encoder_2-item1.clip_prompt_embeds text_encoder_2-item1.clip_prompt_embeds: 11,604B, BPFP=0.9419 Using per-key quantization points (text_encoder_3-item2.t5_prompt_embeds: torch.Size([256])) for text_encoder_3-item2.t5_prompt_embeds text_encoder_3-item2.t5_prompt_embeds: 29,036B, BPFP=0.7365 Using per-key quantization points (text_encoder-item3.clip_pooled_prompt_embeds: torch.Size([256])) for text_encoder-item3.clip_pooled_prompt_embeds text_encoder-item3.clip_pooled_prompt_embeds: 760B, BPFP=7.9167 Using per-key quantization points (text_encoder-item3.clip_prompt_embeds: torch.Size([256])) for text_encoder-item3.clip_prompt_embeds text_encoder-item3.clip_prompt_embeds: 4,024B, BPFP=0.5444 Using per-key quantization points (text_encoder_2-item4.clip_pooled: torch.Size([256])) for text_encoder_2-item4.clip_pooled_prompt_embeds text_encoder_2-item4.clip_pooled_prompt_embeds: 1,268B, BPFP=7.9250 Using per-key quantization points (text_encoder_2-item4.clip_prompt: torch.Size([256])) for text_encoder_2-item4.clip_prompt_embeds text_encoder_2-item4.clip_prompt_embeds: 6,996B, BPFP=0.5679 Using per-key quantization points (text_encoder_3-item5.t5_prompt_embeds: torch.Size([256])) for text_encoder_3-item5.t5_prompt_embeds text_encoder_3-item5.t5_prompt_embeds: 18,416B, BPFP=0.4671 Using per-key quantization points (vae.encoder_f0: torch.Size([256])) for vae.encoder_f0 vae.encoder_f0: 15,564B, BPFP=0.2375 Using per-key quantization points (vae.encoder_f1: torch.Size([256])) for vae.encoder_f1 vae.encoder_f1: 15,564B, BPFP=0.2375 Using per-key quantization points (vae.decoder: torch.Size([256])) for vae.decoder vae.decoder: 5,356B, BPFP=0.1635 ⌛️ [2/4] FRONTEND: Frontend time: 0.292s (Pack+Encode) [3/4] BACKEND: Decode + Unpack... Using per-key quantization points (text_encoder-item0.clip_pooled_prompt_embeds: torch.Size([256])) for text_encoder-item0.clip_pooled_prompt_embeds Using per-key quantization points (text_encoder-item0.clip_prompt_embeds: torch.Size([256])) for text_encoder-item0.clip_prompt_embeds Using per-key quantization points (text_encoder_2-item1.clip_pooled_prompt_embeds: torch.Size([256])) for text_encoder_2-item1.clip_pooled_prompt_embeds Using per-key quantization points (text_encoder_2-item1.clip_prompt_embeds: torch.Size([256])) for text_encoder_2-item1.clip_prompt_embeds Using per-key quantization points (text_encoder_3-item2.t5_prompt_embeds: torch.Size([256])) for text_encoder_3-item2.t5_prompt_embeds Using per-key quantization points (text_encoder-item3.clip_pooled_prompt_embeds: torch.Size([256])) for text_encoder-item3.clip_pooled_prompt_embeds Using per-key quantization points (text_encoder-item3.clip_prompt_embeds: torch.Size([256])) for text_encoder-item3.clip_prompt_embeds Using per-key quantization points (text_encoder_2-item4.clip_pooled: torch.Size([256])) for text_encoder_2-item4.clip_pooled_prompt_embeds Using per-key quantization points (text_encoder_2-item4.clip_prompt: torch.Size([256])) for text_encoder_2-item4.clip_prompt_embeds Using per-key quantization points (text_encoder_3-item5.t5_prompt_embeds: torch.Size([256])) for text_encoder_3-item5.t5_prompt_embeds Using per-key quantization points (vae.encoder_f0: torch.Size([256])) for vae.encoder_f0 Using per-key quantization points (vae.encoder_f1: torch.Size([256])) for vae.encoder_f1 Using per-key quantization points (vae.decoder: torch.Size([256])) for vae.decoder IndividualUnPacker: text_encoder-item0.clip_pooled_prompt_embeds: torch.Size([768]) text_encoder-item0.clip_prompt_embeds: torch.Size([77, 768]) text_encoder_2-item1.clip_pooled_prompt_embeds: torch.Size([1280]) text_encoder_2-item1.clip_prompt_embeds: torch.Size([77, 1280]) text_encoder_3-item2.t5_prompt_embeds: torch.Size([77, 4096]) text_encoder-item3.clip_pooled_prompt_embeds: torch.Size([768]) text_encoder-item3.clip_prompt_embeds: torch.Size([77, 768]) text_encoder_2-item4.clip_pooled_prompt_embeds: torch.Size([1280]) text_encoder_2-item4.clip_prompt_embeds: torch.Size([77, 1280]) text_encoder_3-item5.t5_prompt_embeds: torch.Size([77, 4096]) vae.decoder: torch.Size([16, 128, 128]) vae.encoder_f0: torch.Size([32, 128, 128]) vae.encoder_f1: torch.Size([32, 128, 128]) ⌛️ [3/4] BACKEND: Backend time: 0.448s [4/4] METRICS: Computing MSE Breakdown... Using per-key quantization points (text_encoder-item0.clip_pooled_prompt_embeds: torch.Size([256])) for text_encoder-item0.clip_pooled_prompt_embeds Using per-key quantization points (text_encoder-item0.clip_prompt_embeds: torch.Size([256])) for text_encoder-item0.clip_prompt_embeds Using per-key quantization points (text_encoder_2-item1.clip_pooled_prompt_embeds: torch.Size([256])) for text_encoder_2-item1.clip_pooled_prompt_embeds Using per-key quantization points (text_encoder_2-item1.clip_prompt_embeds: torch.Size([256])) for text_encoder_2-item1.clip_prompt_embeds Using per-key quantization points (text_encoder_3-item2.t5_prompt_embeds: torch.Size([256])) for text_encoder_3-item2.t5_prompt_embeds Using per-key quantization points (text_encoder-item3.clip_pooled_prompt_embeds: torch.Size([256])) for text_encoder-item3.clip_pooled_prompt_embeds Using per-key quantization points (text_encoder-item3.clip_prompt_embeds: torch.Size([256])) for text_encoder-item3.clip_prompt_embeds Using per-key quantization points (text_encoder_2-item4.clip_pooled: torch.Size([256])) for text_encoder_2-item4.clip_pooled_prompt_embeds Using per-key quantization points (text_encoder_2-item4.clip_prompt: torch.Size([256])) for text_encoder_2-item4.clip_prompt_embeds Using per-key quantization points (text_encoder_3-item5.t5_prompt_embeds: torch.Size([256])) for text_encoder_3-item5.t5_prompt_embeds Using per-key quantization points (vae.encoder_f0: torch.Size([256])) for vae.encoder_f0 Using per-key quantization points (vae.encoder_f1: torch.Size([256])) for vae.encoder_f1 Using per-key quantization points (vae.decoder: torch.Size([256])) for vae.decoder IndividualUnPacker: text_encoder-item0.clip_pooled_prompt_embeds: torch.Size([768]) text_encoder-item0.clip_prompt_embeds: torch.Size([77, 768]) text_encoder_2-item1.clip_pooled_prompt_embeds: torch.Size([1280]) text_encoder_2-item1.clip_prompt_embeds: torch.Size([77, 1280]) text_encoder_3-item2.t5_prompt_embeds: torch.Size([77, 4096]) text_encoder-item3.clip_pooled_prompt_embeds: torch.Size([768]) text_encoder-item3.clip_prompt_embeds: torch.Size([77, 768]) text_encoder_2-item4.clip_pooled_prompt_embeds: torch.Size([1280]) text_encoder_2-item4.clip_prompt_embeds: torch.Size([77, 1280]) text_encoder_3-item5.t5_prompt_embeds: torch.Size([77, 4096]) vae.decoder: torch.Size([16, 128, 128]) vae.encoder_f0: torch.Size([32, 128, 128]) vae.encoder_f1: torch.Size([32, 128, 128]) Per-key MSE Breakdown (Quant=Truncation+Quantization, Codec=Compression): Key Quant-MSE Total-MSE ------------------------------------------------------------------------------------- text_encoder-item0.clip_pooled_prompt_embeds 0.01261352 0.98434607 text_encoder-item0.clip_prompt_embeds 0.00026137 60.59005513 text_encoder_2-item1.clip_pooled_prompt_embeds 0.00138553 0.84837189 text_encoder_2-item1.clip_prompt_embeds 0.00019680 0.13495667 text_encoder_3-item2.t5_prompt_embeds 0.00000808 0.00326189 text_encoder-item3.clip_pooled_prompt_embeds 0.00841799 1.01420029 text_encoder-item3.clip_prompt_embeds 0.00023247 23.90570887 text_encoder_2-item4.clip_pooled_prompt_embeds 0.00016751 0.67051430 text_encoder_2-item4.clip_prompt_embeds 0.00009830 0.47049568 text_encoder_3-item5.t5_prompt_embeds 0.00001263 0.00227117 vae.encoder_f0 0.35915655 5.12527657 vae.encoder_f1 0.35915723 5.12704611 vae.decoder 0.00024181 0.03853196 ------------------------------------------------------------------------------------- TOTAL 0.16663024 4.62049830 (elements=2,260,992) ---------------------- -------------------------------------------------------- SAMPLE-WISE STATISTICS ---------------------- -------------------------------------------------------- Handler sd35 Strategy individual Architecture hyperprior-featurecoding ---------------------- -------------------------------------------------------- Total Elements 2260992 Total Bytes 117436 BPFP 0.4155 bits/point EBPFP 0.8310 equivalent bits/point MSE 4.620498 ---------------------- -------------------------------------------------------- Time: 0.748s Load: 0.008s, Pack+Encode: 0.292s, Decode+Unpack: 0.448s ---------------------- -------------------------------------------------------- Restored Feature Format: [Dict] with 8 keys key['text_encoder-item0']: [Dict] with 2 keys key['clip_pooled_prompt_embeds']: [Tensor] shape=torch.Size([768]), dtype=torch.float16, device=cpu key['clip_prompt_embeds']: [Tensor] shape=torch.Size([77, 768]), dtype=torch.float16, device=cpu key['text_encoder_2-item1']: [Dict] with 2 keys key['clip_pooled_prompt_embeds']: [Tensor] shape=torch.Size([1280]), dtype=torch.float16, device=cpu key['clip_prompt_embeds']: [Tensor] shape=torch.Size([77, 1280]), dtype=torch.float16, device=cpu key['text_encoder_3-item2']: [Dict] with 1 keys key['t5_prompt_embeds']: [Tensor] shape=torch.Size([77, 4096]), dtype=torch.float16, device=cpu key['text_encoder-item3']: [Dict] with 2 keys key['clip_pooled_prompt_embeds']: [Tensor] shape=torch.Size([768]), dtype=torch.float16, device=cpu key['clip_prompt_embeds']: [Tensor] shape=torch.Size([77, 768]), dtype=torch.float16, device=cpu key['text_encoder_2-item4']: [Dict] with 2 keys key['clip_pooled_prompt_embeds']: [Tensor] shape=torch.Size([1280]), dtype=torch.float16, device=cpu key['clip_prompt_embeds']: [Tensor] shape=torch.Size([77, 1280]), dtype=torch.float16, device=cpu key['text_encoder_3-item5']: [Dict] with 1 keys key['t5_prompt_embeds']: [Tensor] shape=torch.Size([77, 4096]), dtype=torch.float16, device=cpu key['vae.decoder']: [Tensor] shape=torch.Size([16, 128, 128]), dtype=torch.float16, device=cpu key['vae.encoder']: [Dict] with 2 keys key['vae.encoder_f0']: [Tensor] shape=torch.Size([32, 128, 128]), dtype=torch.float16, device=cpu key['vae.encoder_f1']: [Tensor] shape=torch.Size([32, 128, 128]), dtype=torch.float16, device=cpu 💾 Converting with 4.6205 MSE: from ../datasets/FeatureCoding-StableDiffusion3.5/100samples_features/000000148662.zst to output-fixed/sd35/lambda0.004/hyperprior-featurecoding-8bit-individual/sd35cond/000000148662.zst 💪 Processing: ../datasets/FeatureCoding-StableDiffusion3.5/100samples_features/000000151051.zst (27/100) [1/4] FRONTEND: Loading features from ../datasets/FeatureCoding-StableDiffusion3.5/100samples_features/000000151051.zst... Original data structure: root: [Dict] with 8 keys key['text_encoder-item0']: [Dict] with 2 keys key['clip_pooled_prompt_embeds']: [Tensor] shape=torch.Size([768]), dtype=torch.float16, device=cpu key['clip_prompt_embeds']: [Tensor] shape=torch.Size([77, 768]), dtype=torch.float16, device=cpu key['text_encoder_2-item1']: [Dict] with 2 keys key['clip_pooled_prompt_embeds']: [Tensor] shape=torch.Size([1280]), dtype=torch.float16, device=cpu key['clip_prompt_embeds']: [Tensor] shape=torch.Size([77, 1280]), dtype=torch.float16, device=cpu key['text_encoder_3-item2']: [Dict] with 1 keys key['t5_prompt_embeds']: [Tensor] shape=torch.Size([77, 4096]), dtype=torch.float16, device=cpu key['text_encoder-item3']: [Dict] with 2 keys key['clip_pooled_prompt_embeds']: [Tensor] shape=torch.Size([768]), dtype=torch.float16, device=cpu key['clip_prompt_embeds']: [Tensor] shape=torch.Size([77, 768]), dtype=torch.float16, device=cpu key['text_encoder_2-item4']: [Dict] with 2 keys key['clip_pooled_prompt_embeds']: [Tensor] shape=torch.Size([1280]), dtype=torch.float16, device=cpu key['clip_prompt_embeds']: [Tensor] shape=torch.Size([77, 1280]), dtype=torch.float16, device=cpu key['text_encoder_3-item5']: [Dict] with 1 keys key['t5_prompt_embeds']: [Tensor] shape=torch.Size([77, 4096]), dtype=torch.float16, device=cpu key['vae.encoder-item6']: [Dict] with 2 keys key['vae.encoder_f0']: [Tensor] shape=torch.Size([32, 128, 128]), dtype=torch.float16, device=cpu key['vae.encoder_f1']: [Tensor] shape=torch.Size([32, 128, 128]), dtype=torch.float16, device=cpu key['vae.decoder']: [Tensor] shape=torch.Size([16, 128, 128]), dtype=torch.float16, device=cpu ⌛️ [1/4] FRONTEND: Load time: 0.008s ------------------------------------------------------------ SD3.5 Features Summary ------------------------------------------------------------ Number of text encoders: 3 Encoder names: ['text_encoder', 'text_encoder_2', 'text_encoder_3'] Has VAE latents: True Has VAE encoder features: True Data type: torch.float16 text_encoder-item0: clip_pooled_prompt_embeds: torch.Size([768]), torch.float16 clip_prompt_embeds: torch.Size([77, 768]), torch.float16 text_encoder_2-item1: clip_pooled_prompt_embeds: torch.Size([1280]), torch.float16 clip_prompt_embeds: torch.Size([77, 1280]), torch.float16 text_encoder_3-item2: t5_prompt_embeds: torch.Size([77, 4096]), torch.float16 text_encoder-item3: clip_pooled_prompt_embeds: torch.Size([768]), torch.float16 clip_prompt_embeds: torch.Size([77, 768]), torch.float16 text_encoder_2-item4: clip_pooled_prompt_embeds: torch.Size([1280]), torch.float16 clip_prompt_embeds: torch.Size([77, 1280]), torch.float16 text_encoder_3-item5: t5_prompt_embeds: torch.Size([77, 4096]), torch.float16 vae.decoder latents: torch.Size([16, 128, 128]) vae.encoder features: vae.encoder_f0: torch.Size([32, 128, 128]), torch.float16 vae.encoder_f1: torch.Size([32, 128, 128]), torch.float16 ------------------------------------------------------------ [2/4] FRONTEND: Pack + Encode (strategy: individual)... IndividualPacker: text_encoder-item0.clip_pooled_prompt_embeds: torch.Size([768])->torch.Size([1, 1, 1, 768]) text_encoder-item0.clip_prompt_embeds: torch.Size([77, 768])->torch.Size([1, 1, 77, 768]) text_encoder_2-item1.clip_pooled_prompt_embeds: torch.Size([1280])->torch.Size([1, 1, 1, 1280]) text_encoder_2-item1.clip_prompt_embeds: torch.Size([77, 1280])->torch.Size([1, 1, 77, 1280]) text_encoder_3-item2.t5_prompt_embeds: torch.Size([77, 4096])->torch.Size([1, 1, 77, 4096]) text_encoder-item3.clip_pooled_prompt_embeds: torch.Size([768])->torch.Size([1, 1, 1, 768]) text_encoder-item3.clip_prompt_embeds: torch.Size([77, 768])->torch.Size([1, 1, 77, 768]) text_encoder_2-item4.clip_pooled_prompt_embeds: torch.Size([1280])->torch.Size([1, 1, 1, 1280]) text_encoder_2-item4.clip_prompt_embeds: torch.Size([77, 1280])->torch.Size([1, 1, 77, 1280]) text_encoder_3-item5.t5_prompt_embeds: torch.Size([77, 4096])->torch.Size([1, 1, 77, 4096]) vae.encoder_f0: torch.Size([32, 128, 128])->torch.Size([1, 1, 512, 1024]) vae.encoder_f1: torch.Size([32, 128, 128])->torch.Size([1, 1, 512, 1024]) vae.decoder: torch.Size([16, 128, 128])->torch.Size([1, 1, 512, 512]) Using per-key quantization points (text_encoder-item0.clip_pooled_prompt_embeds: torch.Size([256])) for text_encoder-item0.clip_pooled_prompt_embeds text_encoder-item0.clip_pooled_prompt_embeds: 744B, BPFP=7.7500 Using per-key quantization points (text_encoder-item0.clip_prompt_embeds: torch.Size([256])) for text_encoder-item0.clip_prompt_embeds text_encoder-item0.clip_prompt_embeds: 5,252B, BPFP=0.7105 Using per-key quantization points (text_encoder_2-item1.clip_pooled_prompt_embeds: torch.Size([256])) for text_encoder_2-item1.clip_pooled_prompt_embeds text_encoder_2-item1.clip_pooled_prompt_embeds: 1,208B, BPFP=7.5500 Using per-key quantization points (text_encoder_2-item1.clip_prompt_embeds: torch.Size([256])) for text_encoder_2-item1.clip_prompt_embeds text_encoder_2-item1.clip_prompt_embeds: 9,808B, BPFP=0.7961 Using per-key quantization points (text_encoder_3-item2.t5_prompt_embeds: torch.Size([256])) for text_encoder_3-item2.t5_prompt_embeds text_encoder_3-item2.t5_prompt_embeds: 23,972B, BPFP=0.6081 Using per-key quantization points (text_encoder-item3.clip_pooled_prompt_embeds: torch.Size([256])) for text_encoder-item3.clip_pooled_prompt_embeds text_encoder-item3.clip_pooled_prompt_embeds: 760B, BPFP=7.9167 Using per-key quantization points (text_encoder-item3.clip_prompt_embeds: torch.Size([256])) for text_encoder-item3.clip_prompt_embeds text_encoder-item3.clip_prompt_embeds: 4,024B, BPFP=0.5444 Using per-key quantization points (text_encoder_2-item4.clip_pooled: torch.Size([256])) for text_encoder_2-item4.clip_pooled_prompt_embeds text_encoder_2-item4.clip_pooled_prompt_embeds: 1,268B, BPFP=7.9250 Using per-key quantization points (text_encoder_2-item4.clip_prompt: torch.Size([256])) for text_encoder_2-item4.clip_prompt_embeds text_encoder_2-item4.clip_prompt_embeds: 6,996B, BPFP=0.5679 Using per-key quantization points (text_encoder_3-item5.t5_prompt_embeds: torch.Size([256])) for text_encoder_3-item5.t5_prompt_embeds text_encoder_3-item5.t5_prompt_embeds: 18,416B, BPFP=0.4671 Using per-key quantization points (vae.encoder_f0: torch.Size([256])) for vae.encoder_f0 vae.encoder_f0: 7,840B, BPFP=0.1196 Using per-key quantization points (vae.encoder_f1: torch.Size([256])) for vae.encoder_f1 vae.encoder_f1: 7,840B, BPFP=0.1196 Using per-key quantization points (vae.decoder: torch.Size([256])) for vae.decoder vae.decoder: 4,324B, BPFP=0.1320 ⌛️ [2/4] FRONTEND: Frontend time: 0.293s (Pack+Encode) [3/4] BACKEND: Decode + Unpack... Using per-key quantization points (text_encoder-item0.clip_pooled_prompt_embeds: torch.Size([256])) for text_encoder-item0.clip_pooled_prompt_embeds Using per-key quantization points (text_encoder-item0.clip_prompt_embeds: torch.Size([256])) for text_encoder-item0.clip_prompt_embeds Using per-key quantization points (text_encoder_2-item1.clip_pooled_prompt_embeds: torch.Size([256])) for text_encoder_2-item1.clip_pooled_prompt_embeds Using per-key quantization points (text_encoder_2-item1.clip_prompt_embeds: torch.Size([256])) for text_encoder_2-item1.clip_prompt_embeds Using per-key quantization points (text_encoder_3-item2.t5_prompt_embeds: torch.Size([256])) for text_encoder_3-item2.t5_prompt_embeds Using per-key quantization points (text_encoder-item3.clip_pooled_prompt_embeds: torch.Size([256])) for text_encoder-item3.clip_pooled_prompt_embeds Using per-key quantization points (text_encoder-item3.clip_prompt_embeds: torch.Size([256])) for text_encoder-item3.clip_prompt_embeds Using per-key quantization points (text_encoder_2-item4.clip_pooled: torch.Size([256])) for text_encoder_2-item4.clip_pooled_prompt_embeds Using per-key quantization points (text_encoder_2-item4.clip_prompt: torch.Size([256])) for text_encoder_2-item4.clip_prompt_embeds Using per-key quantization points (text_encoder_3-item5.t5_prompt_embeds: torch.Size([256])) for text_encoder_3-item5.t5_prompt_embeds Using per-key quantization points (vae.encoder_f0: torch.Size([256])) for vae.encoder_f0 Using per-key quantization points (vae.encoder_f1: torch.Size([256])) for vae.encoder_f1 Using per-key quantization points (vae.decoder: torch.Size([256])) for vae.decoder IndividualUnPacker: text_encoder-item0.clip_pooled_prompt_embeds: torch.Size([768]) text_encoder-item0.clip_prompt_embeds: torch.Size([77, 768]) text_encoder_2-item1.clip_pooled_prompt_embeds: torch.Size([1280]) text_encoder_2-item1.clip_prompt_embeds: torch.Size([77, 1280]) text_encoder_3-item2.t5_prompt_embeds: torch.Size([77, 4096]) text_encoder-item3.clip_pooled_prompt_embeds: torch.Size([768]) text_encoder-item3.clip_prompt_embeds: torch.Size([77, 768]) text_encoder_2-item4.clip_pooled_prompt_embeds: torch.Size([1280]) text_encoder_2-item4.clip_prompt_embeds: torch.Size([77, 1280]) text_encoder_3-item5.t5_prompt_embeds: torch.Size([77, 4096]) vae.decoder: torch.Size([16, 128, 128]) vae.encoder_f0: torch.Size([32, 128, 128]) vae.encoder_f1: torch.Size([32, 128, 128]) ⌛️ [3/4] BACKEND: Backend time: 0.448s [4/4] METRICS: Computing MSE Breakdown... Using per-key quantization points (text_encoder-item0.clip_pooled_prompt_embeds: torch.Size([256])) for text_encoder-item0.clip_pooled_prompt_embeds Using per-key quantization points (text_encoder-item0.clip_prompt_embeds: torch.Size([256])) for text_encoder-item0.clip_prompt_embeds Using per-key quantization points (text_encoder_2-item1.clip_pooled_prompt_embeds: torch.Size([256])) for text_encoder_2-item1.clip_pooled_prompt_embeds Using per-key quantization points (text_encoder_2-item1.clip_prompt_embeds: torch.Size([256])) for text_encoder_2-item1.clip_prompt_embeds Using per-key quantization points (text_encoder_3-item2.t5_prompt_embeds: torch.Size([256])) for text_encoder_3-item2.t5_prompt_embeds Using per-key quantization points (text_encoder-item3.clip_pooled_prompt_embeds: torch.Size([256])) for text_encoder-item3.clip_pooled_prompt_embeds Using per-key quantization points (text_encoder-item3.clip_prompt_embeds: torch.Size([256])) for text_encoder-item3.clip_prompt_embeds Using per-key quantization points (text_encoder_2-item4.clip_pooled: torch.Size([256])) for text_encoder_2-item4.clip_pooled_prompt_embeds Using per-key quantization points (text_encoder_2-item4.clip_prompt: torch.Size([256])) for text_encoder_2-item4.clip_prompt_embeds Using per-key quantization points (text_encoder_3-item5.t5_prompt_embeds: torch.Size([256])) for text_encoder_3-item5.t5_prompt_embeds Using per-key quantization points (vae.encoder_f0: torch.Size([256])) for vae.encoder_f0 Using per-key quantization points (vae.encoder_f1: torch.Size([256])) for vae.encoder_f1 Using per-key quantization points (vae.decoder: torch.Size([256])) for vae.decoder IndividualUnPacker: text_encoder-item0.clip_pooled_prompt_embeds: torch.Size([768]) text_encoder-item0.clip_prompt_embeds: torch.Size([77, 768]) text_encoder_2-item1.clip_pooled_prompt_embeds: torch.Size([1280]) text_encoder_2-item1.clip_prompt_embeds: torch.Size([77, 1280]) text_encoder_3-item2.t5_prompt_embeds: torch.Size([77, 4096]) text_encoder-item3.clip_pooled_prompt_embeds: torch.Size([768]) text_encoder-item3.clip_prompt_embeds: torch.Size([77, 768]) text_encoder_2-item4.clip_pooled_prompt_embeds: torch.Size([1280]) text_encoder_2-item4.clip_prompt_embeds: torch.Size([77, 1280]) text_encoder_3-item5.t5_prompt_embeds: torch.Size([77, 4096]) vae.decoder: torch.Size([16, 128, 128]) vae.encoder_f0: torch.Size([32, 128, 128]) vae.encoder_f1: torch.Size([32, 128, 128]) Per-key MSE Breakdown (Quant=Truncation+Quantization, Codec=Compression): Key Quant-MSE Total-MSE ------------------------------------------------------------------------------------- text_encoder-item0.clip_pooled_prompt_embeds 0.00032602 0.95771750 text_encoder-item0.clip_prompt_embeds 0.00021656 193.98380005 text_encoder_2-item1.clip_pooled_prompt_embeds 0.00019988 0.94013290 text_encoder_2-item1.clip_prompt_embeds 0.00016555 0.11295096 text_encoder_3-item2.t5_prompt_embeds 0.00000783 0.00303997 text_encoder-item3.clip_pooled_prompt_embeds 0.00841799 1.01420029 text_encoder-item3.clip_prompt_embeds 0.00023247 23.90570887 text_encoder_2-item4.clip_pooled_prompt_embeds 0.00016751 0.67051430 text_encoder_2-item4.clip_prompt_embeds 0.00009830 0.47049568 text_encoder_3-item5.t5_prompt_embeds 0.00001263 0.00227117 vae.encoder_f0 0.29031765 3.89296794 vae.encoder_f1 0.29031771 3.89294100 vae.decoder 0.00019965 0.05006742 ------------------------------------------------------------------------------------- TOTAL 0.13469251 7.53786531 (elements=2,260,992) ---------------------- -------------------------------------------------------- SAMPLE-WISE STATISTICS ---------------------- -------------------------------------------------------- Handler sd35 Strategy individual Architecture hyperprior-featurecoding ---------------------- -------------------------------------------------------- Total Elements 2260992 Total Bytes 92452 BPFP 0.3271 bits/point EBPFP 0.6542 equivalent bits/point MSE 7.537865 ---------------------- -------------------------------------------------------- Time: 0.748s Load: 0.008s, Pack+Encode: 0.293s, Decode+Unpack: 0.448s ---------------------- -------------------------------------------------------- Restored Feature Format: [Dict] with 8 keys key['text_encoder-item0']: [Dict] with 2 keys key['clip_pooled_prompt_embeds']: [Tensor] shape=torch.Size([768]), dtype=torch.float16, device=cpu key['clip_prompt_embeds']: [Tensor] shape=torch.Size([77, 768]), dtype=torch.float16, device=cpu key['text_encoder_2-item1']: [Dict] with 2 keys key['clip_pooled_prompt_embeds']: [Tensor] shape=torch.Size([1280]), dtype=torch.float16, device=cpu key['clip_prompt_embeds']: [Tensor] shape=torch.Size([77, 1280]), dtype=torch.float16, device=cpu key['text_encoder_3-item2']: [Dict] with 1 keys key['t5_prompt_embeds']: [Tensor] shape=torch.Size([77, 4096]), dtype=torch.float16, device=cpu key['text_encoder-item3']: [Dict] with 2 keys key['clip_pooled_prompt_embeds']: [Tensor] shape=torch.Size([768]), dtype=torch.float16, device=cpu key['clip_prompt_embeds']: [Tensor] shape=torch.Size([77, 768]), dtype=torch.float16, device=cpu key['text_encoder_2-item4']: [Dict] with 2 keys key['clip_pooled_prompt_embeds']: [Tensor] shape=torch.Size([1280]), dtype=torch.float16, device=cpu key['clip_prompt_embeds']: [Tensor] shape=torch.Size([77, 1280]), dtype=torch.float16, device=cpu key['text_encoder_3-item5']: [Dict] with 1 keys key['t5_prompt_embeds']: [Tensor] shape=torch.Size([77, 4096]), dtype=torch.float16, device=cpu key['vae.decoder']: [Tensor] shape=torch.Size([16, 128, 128]), dtype=torch.float16, device=cpu key['vae.encoder']: [Dict] with 2 keys key['vae.encoder_f0']: [Tensor] shape=torch.Size([32, 128, 128]), dtype=torch.float16, device=cpu key['vae.encoder_f1']: [Tensor] shape=torch.Size([32, 128, 128]), dtype=torch.float16, device=cpu 💾 Converting with 7.5379 MSE: from ../datasets/FeatureCoding-StableDiffusion3.5/100samples_features/000000151051.zst to output-fixed/sd35/lambda0.004/hyperprior-featurecoding-8bit-individual/sd35cond/000000151051.zst 💪 Processing: ../datasets/FeatureCoding-StableDiffusion3.5/100samples_features/000000155443.zst (28/100) [1/4] FRONTEND: Loading features from ../datasets/FeatureCoding-StableDiffusion3.5/100samples_features/000000155443.zst... Original data structure: root: [Dict] with 8 keys key['text_encoder-item0']: [Dict] with 2 keys key['clip_pooled_prompt_embeds']: [Tensor] shape=torch.Size([768]), dtype=torch.float16, device=cpu key['clip_prompt_embeds']: [Tensor] shape=torch.Size([77, 768]), dtype=torch.float16, device=cpu key['text_encoder_2-item1']: [Dict] with 2 keys key['clip_pooled_prompt_embeds']: [Tensor] shape=torch.Size([1280]), dtype=torch.float16, device=cpu key['clip_prompt_embeds']: [Tensor] shape=torch.Size([77, 1280]), dtype=torch.float16, device=cpu key['text_encoder_3-item2']: [Dict] with 1 keys key['t5_prompt_embeds']: [Tensor] shape=torch.Size([77, 4096]), dtype=torch.float16, device=cpu key['text_encoder-item3']: [Dict] with 2 keys key['clip_pooled_prompt_embeds']: [Tensor] shape=torch.Size([768]), dtype=torch.float16, device=cpu key['clip_prompt_embeds']: [Tensor] shape=torch.Size([77, 768]), dtype=torch.float16, device=cpu key['text_encoder_2-item4']: [Dict] with 2 keys key['clip_pooled_prompt_embeds']: [Tensor] shape=torch.Size([1280]), dtype=torch.float16, device=cpu key['clip_prompt_embeds']: [Tensor] shape=torch.Size([77, 1280]), dtype=torch.float16, device=cpu key['text_encoder_3-item5']: [Dict] with 1 keys key['t5_prompt_embeds']: [Tensor] shape=torch.Size([77, 4096]), dtype=torch.float16, device=cpu key['vae.encoder-item6']: [Dict] with 2 keys key['vae.encoder_f0']: [Tensor] shape=torch.Size([32, 128, 128]), dtype=torch.float16, device=cpu key['vae.encoder_f1']: [Tensor] shape=torch.Size([32, 128, 128]), dtype=torch.float16, device=cpu key['vae.decoder']: [Tensor] shape=torch.Size([16, 128, 128]), dtype=torch.float16, device=cpu ⌛️ [1/4] FRONTEND: Load time: 0.009s ------------------------------------------------------------ SD3.5 Features Summary ------------------------------------------------------------ Number of text encoders: 3 Encoder names: ['text_encoder', 'text_encoder_2', 'text_encoder_3'] Has VAE latents: True Has VAE encoder features: True Data type: torch.float16 text_encoder-item0: clip_pooled_prompt_embeds: torch.Size([768]), torch.float16 clip_prompt_embeds: torch.Size([77, 768]), torch.float16 text_encoder_2-item1: clip_pooled_prompt_embeds: torch.Size([1280]), torch.float16 clip_prompt_embeds: torch.Size([77, 1280]), torch.float16 text_encoder_3-item2: t5_prompt_embeds: torch.Size([77, 4096]), torch.float16 text_encoder-item3: clip_pooled_prompt_embeds: torch.Size([768]), torch.float16 clip_prompt_embeds: torch.Size([77, 768]), torch.float16 text_encoder_2-item4: clip_pooled_prompt_embeds: torch.Size([1280]), torch.float16 clip_prompt_embeds: torch.Size([77, 1280]), torch.float16 text_encoder_3-item5: t5_prompt_embeds: torch.Size([77, 4096]), torch.float16 vae.decoder latents: torch.Size([16, 128, 128]) vae.encoder features: vae.encoder_f0: torch.Size([32, 128, 128]), torch.float16 vae.encoder_f1: torch.Size([32, 128, 128]), torch.float16 ------------------------------------------------------------ [2/4] FRONTEND: Pack + Encode (strategy: individual)... IndividualPacker: text_encoder-item0.clip_pooled_prompt_embeds: torch.Size([768])->torch.Size([1, 1, 1, 768]) text_encoder-item0.clip_prompt_embeds: torch.Size([77, 768])->torch.Size([1, 1, 77, 768]) text_encoder_2-item1.clip_pooled_prompt_embeds: torch.Size([1280])->torch.Size([1, 1, 1, 1280]) text_encoder_2-item1.clip_prompt_embeds: torch.Size([77, 1280])->torch.Size([1, 1, 77, 1280]) text_encoder_3-item2.t5_prompt_embeds: torch.Size([77, 4096])->torch.Size([1, 1, 77, 4096]) text_encoder-item3.clip_pooled_prompt_embeds: torch.Size([768])->torch.Size([1, 1, 1, 768]) text_encoder-item3.clip_prompt_embeds: torch.Size([77, 768])->torch.Size([1, 1, 77, 768]) text_encoder_2-item4.clip_pooled_prompt_embeds: torch.Size([1280])->torch.Size([1, 1, 1, 1280]) text_encoder_2-item4.clip_prompt_embeds: torch.Size([77, 1280])->torch.Size([1, 1, 77, 1280]) text_encoder_3-item5.t5_prompt_embeds: torch.Size([77, 4096])->torch.Size([1, 1, 77, 4096]) vae.encoder_f0: torch.Size([32, 128, 128])->torch.Size([1, 1, 512, 1024]) vae.encoder_f1: torch.Size([32, 128, 128])->torch.Size([1, 1, 512, 1024]) vae.decoder: torch.Size([16, 128, 128])->torch.Size([1, 1, 512, 512]) Using per-key quantization points (text_encoder-item0.clip_pooled_prompt_embeds: torch.Size([256])) for text_encoder-item0.clip_pooled_prompt_embeds text_encoder-item0.clip_pooled_prompt_embeds: 732B, BPFP=7.6250 Using per-key quantization points (text_encoder-item0.clip_prompt_embeds: torch.Size([256])) for text_encoder-item0.clip_prompt_embeds text_encoder-item0.clip_prompt_embeds: 5,056B, BPFP=0.6840 Using per-key quantization points (text_encoder_2-item1.clip_pooled_prompt_embeds: torch.Size([256])) for text_encoder_2-item1.clip_pooled_prompt_embeds text_encoder_2-item1.clip_pooled_prompt_embeds: 1,192B, BPFP=7.4500 Using per-key quantization points (text_encoder_2-item1.clip_prompt_embeds: torch.Size([256])) for text_encoder_2-item1.clip_prompt_embeds text_encoder_2-item1.clip_prompt_embeds: 10,592B, BPFP=0.8597 Using per-key quantization points (text_encoder_3-item2.t5_prompt_embeds: torch.Size([256])) for text_encoder_3-item2.t5_prompt_embeds text_encoder_3-item2.t5_prompt_embeds: 23,504B, BPFP=0.5962 Using per-key quantization points (text_encoder-item3.clip_pooled_prompt_embeds: torch.Size([256])) for text_encoder-item3.clip_pooled_prompt_embeds text_encoder-item3.clip_pooled_prompt_embeds: 760B, BPFP=7.9167 Using per-key quantization points (text_encoder-item3.clip_prompt_embeds: torch.Size([256])) for text_encoder-item3.clip_prompt_embeds text_encoder-item3.clip_prompt_embeds: 4,024B, BPFP=0.5444 Using per-key quantization points (text_encoder_2-item4.clip_pooled: torch.Size([256])) for text_encoder_2-item4.clip_pooled_prompt_embeds text_encoder_2-item4.clip_pooled_prompt_embeds: 1,268B, BPFP=7.9250 Using per-key quantization points (text_encoder_2-item4.clip_prompt: torch.Size([256])) for text_encoder_2-item4.clip_prompt_embeds text_encoder_2-item4.clip_prompt_embeds: 6,996B, BPFP=0.5679 Using per-key quantization points (text_encoder_3-item5.t5_prompt_embeds: torch.Size([256])) for text_encoder_3-item5.t5_prompt_embeds text_encoder_3-item5.t5_prompt_embeds: 18,416B, BPFP=0.4671 Using per-key quantization points (vae.encoder_f0: torch.Size([256])) for vae.encoder_f0 vae.encoder_f0: 14,124B, BPFP=0.2155 Using per-key quantization points (vae.encoder_f1: torch.Size([256])) for vae.encoder_f1 vae.encoder_f1: 14,128B, BPFP=0.2156 Using per-key quantization points (vae.decoder: torch.Size([256])) for vae.decoder vae.decoder: 10,764B, BPFP=0.3285 ⌛️ [2/4] FRONTEND: Frontend time: 0.296s (Pack+Encode) [3/4] BACKEND: Decode + Unpack... Using per-key quantization points (text_encoder-item0.clip_pooled_prompt_embeds: torch.Size([256])) for text_encoder-item0.clip_pooled_prompt_embeds Using per-key quantization points (text_encoder-item0.clip_prompt_embeds: torch.Size([256])) for text_encoder-item0.clip_prompt_embeds Using per-key quantization points (text_encoder_2-item1.clip_pooled_prompt_embeds: torch.Size([256])) for text_encoder_2-item1.clip_pooled_prompt_embeds Using per-key quantization points (text_encoder_2-item1.clip_prompt_embeds: torch.Size([256])) for text_encoder_2-item1.clip_prompt_embeds Using per-key quantization points (text_encoder_3-item2.t5_prompt_embeds: torch.Size([256])) for text_encoder_3-item2.t5_prompt_embeds Using per-key quantization points (text_encoder-item3.clip_pooled_prompt_embeds: torch.Size([256])) for text_encoder-item3.clip_pooled_prompt_embeds Using per-key quantization points (text_encoder-item3.clip_prompt_embeds: torch.Size([256])) for text_encoder-item3.clip_prompt_embeds Using per-key quantization points (text_encoder_2-item4.clip_pooled: torch.Size([256])) for text_encoder_2-item4.clip_pooled_prompt_embeds Using per-key quantization points (text_encoder_2-item4.clip_prompt: torch.Size([256])) for text_encoder_2-item4.clip_prompt_embeds Using per-key quantization points (text_encoder_3-item5.t5_prompt_embeds: torch.Size([256])) for text_encoder_3-item5.t5_prompt_embeds Using per-key quantization points (vae.encoder_f0: torch.Size([256])) for vae.encoder_f0 Using per-key quantization points (vae.encoder_f1: torch.Size([256])) for vae.encoder_f1 Using per-key quantization points (vae.decoder: torch.Size([256])) for vae.decoder IndividualUnPacker: text_encoder-item0.clip_pooled_prompt_embeds: torch.Size([768]) text_encoder-item0.clip_prompt_embeds: torch.Size([77, 768]) text_encoder_2-item1.clip_pooled_prompt_embeds: torch.Size([1280]) text_encoder_2-item1.clip_prompt_embeds: torch.Size([77, 1280]) text_encoder_3-item2.t5_prompt_embeds: torch.Size([77, 4096]) text_encoder-item3.clip_pooled_prompt_embeds: torch.Size([768]) text_encoder-item3.clip_prompt_embeds: torch.Size([77, 768]) text_encoder_2-item4.clip_pooled_prompt_embeds: torch.Size([1280]) text_encoder_2-item4.clip_prompt_embeds: torch.Size([77, 1280]) text_encoder_3-item5.t5_prompt_embeds: torch.Size([77, 4096]) vae.decoder: torch.Size([16, 128, 128]) vae.encoder_f0: torch.Size([32, 128, 128]) vae.encoder_f1: torch.Size([32, 128, 128]) ⌛️ [3/4] BACKEND: Backend time: 0.445s [4/4] METRICS: Computing MSE Breakdown... Using per-key quantization points (text_encoder-item0.clip_pooled_prompt_embeds: torch.Size([256])) for text_encoder-item0.clip_pooled_prompt_embeds Using per-key quantization points (text_encoder-item0.clip_prompt_embeds: torch.Size([256])) for text_encoder-item0.clip_prompt_embeds Using per-key quantization points (text_encoder_2-item1.clip_pooled_prompt_embeds: torch.Size([256])) for text_encoder_2-item1.clip_pooled_prompt_embeds Using per-key quantization points (text_encoder_2-item1.clip_prompt_embeds: torch.Size([256])) for text_encoder_2-item1.clip_prompt_embeds Using per-key quantization points (text_encoder_3-item2.t5_prompt_embeds: torch.Size([256])) for text_encoder_3-item2.t5_prompt_embeds Using per-key quantization points (text_encoder-item3.clip_pooled_prompt_embeds: torch.Size([256])) for text_encoder-item3.clip_pooled_prompt_embeds Using per-key quantization points (text_encoder-item3.clip_prompt_embeds: torch.Size([256])) for text_encoder-item3.clip_prompt_embeds Using per-key quantization points (text_encoder_2-item4.clip_pooled: torch.Size([256])) for text_encoder_2-item4.clip_pooled_prompt_embeds Using per-key quantization points (text_encoder_2-item4.clip_prompt: torch.Size([256])) for text_encoder_2-item4.clip_prompt_embeds Using per-key quantization points (text_encoder_3-item5.t5_prompt_embeds: torch.Size([256])) for text_encoder_3-item5.t5_prompt_embeds Using per-key quantization points (vae.encoder_f0: torch.Size([256])) for vae.encoder_f0 Using per-key quantization points (vae.encoder_f1: torch.Size([256])) for vae.encoder_f1 Using per-key quantization points (vae.decoder: torch.Size([256])) for vae.decoder IndividualUnPacker: text_encoder-item0.clip_pooled_prompt_embeds: torch.Size([768]) text_encoder-item0.clip_prompt_embeds: torch.Size([77, 768]) text_encoder_2-item1.clip_pooled_prompt_embeds: torch.Size([1280]) text_encoder_2-item1.clip_prompt_embeds: torch.Size([77, 1280]) text_encoder_3-item2.t5_prompt_embeds: torch.Size([77, 4096]) text_encoder-item3.clip_pooled_prompt_embeds: torch.Size([768]) text_encoder-item3.clip_prompt_embeds: torch.Size([77, 768]) text_encoder_2-item4.clip_pooled_prompt_embeds: torch.Size([1280]) text_encoder_2-item4.clip_prompt_embeds: torch.Size([77, 1280]) text_encoder_3-item5.t5_prompt_embeds: torch.Size([77, 4096]) vae.decoder: torch.Size([16, 128, 128]) vae.encoder_f0: torch.Size([32, 128, 128]) vae.encoder_f1: torch.Size([32, 128, 128]) Per-key MSE Breakdown (Quant=Truncation+Quantization, Codec=Compression): Key Quant-MSE Total-MSE ------------------------------------------------------------------------------------- text_encoder-item0.clip_pooled_prompt_embeds 0.00199158 0.96516999 text_encoder-item0.clip_prompt_embeds 0.00025451 36.83176069 text_encoder_2-item1.clip_pooled_prompt_embeds 0.00023552 0.87337875 text_encoder_2-item1.clip_prompt_embeds 0.00017758 0.11525207 text_encoder_3-item2.t5_prompt_embeds 0.00000816 0.00289896 text_encoder-item3.clip_pooled_prompt_embeds 0.00841799 1.01420029 text_encoder-item3.clip_prompt_embeds 0.00023247 23.90570887 text_encoder_2-item4.clip_pooled_prompt_embeds 0.00016751 0.67051430 text_encoder_2-item4.clip_prompt_embeds 0.00009830 0.47049568 text_encoder_3-item5.t5_prompt_embeds 0.00001263 0.00227117 vae.encoder_f0 0.00595764 1.53567481 vae.encoder_f1 0.00596395 1.53542089 vae.decoder 0.00019845 0.08592859 ------------------------------------------------------------------------------------- TOTAL 0.00281886 2.33848397 (elements=2,260,992) ---------------------- -------------------------------------------------------- SAMPLE-WISE STATISTICS ---------------------- -------------------------------------------------------- Handler sd35 Strategy individual Architecture hyperprior-featurecoding ---------------------- -------------------------------------------------------- Total Elements 2260992 Total Bytes 111556 BPFP 0.3947 bits/point EBPFP 0.7894 equivalent bits/point MSE 2.338484 ---------------------- -------------------------------------------------------- Time: 0.749s Load: 0.009s, Pack+Encode: 0.296s, Decode+Unpack: 0.445s ---------------------- -------------------------------------------------------- Restored Feature Format: [Dict] with 8 keys key['text_encoder-item0']: [Dict] with 2 keys key['clip_pooled_prompt_embeds']: [Tensor] shape=torch.Size([768]), dtype=torch.float16, device=cpu key['clip_prompt_embeds']: [Tensor] shape=torch.Size([77, 768]), dtype=torch.float16, device=cpu key['text_encoder_2-item1']: [Dict] with 2 keys key['clip_pooled_prompt_embeds']: [Tensor] shape=torch.Size([1280]), dtype=torch.float16, device=cpu key['clip_prompt_embeds']: [Tensor] shape=torch.Size([77, 1280]), dtype=torch.float16, device=cpu key['text_encoder_3-item2']: [Dict] with 1 keys key['t5_prompt_embeds']: [Tensor] shape=torch.Size([77, 4096]), dtype=torch.float16, device=cpu key['text_encoder-item3']: [Dict] with 2 keys key['clip_pooled_prompt_embeds']: [Tensor] shape=torch.Size([768]), dtype=torch.float16, device=cpu key['clip_prompt_embeds']: [Tensor] shape=torch.Size([77, 768]), dtype=torch.float16, device=cpu key['text_encoder_2-item4']: [Dict] with 2 keys key['clip_pooled_prompt_embeds']: [Tensor] shape=torch.Size([1280]), dtype=torch.float16, device=cpu key['clip_prompt_embeds']: [Tensor] shape=torch.Size([77, 1280]), dtype=torch.float16, device=cpu key['text_encoder_3-item5']: [Dict] with 1 keys key['t5_prompt_embeds']: [Tensor] shape=torch.Size([77, 4096]), dtype=torch.float16, device=cpu key['vae.decoder']: [Tensor] shape=torch.Size([16, 128, 128]), dtype=torch.float16, device=cpu key['vae.encoder']: [Dict] with 2 keys key['vae.encoder_f0']: [Tensor] shape=torch.Size([32, 128, 128]), dtype=torch.float16, device=cpu key['vae.encoder_f1']: [Tensor] shape=torch.Size([32, 128, 128]), dtype=torch.float16, device=cpu 💾 Converting with 2.3385 MSE: from ../datasets/FeatureCoding-StableDiffusion3.5/100samples_features/000000155443.zst to output-fixed/sd35/lambda0.004/hyperprior-featurecoding-8bit-individual/sd35cond/000000155443.zst 💪 Processing: ../datasets/FeatureCoding-StableDiffusion3.5/100samples_features/000000159458.zst (29/100) [1/4] FRONTEND: Loading features from ../datasets/FeatureCoding-StableDiffusion3.5/100samples_features/000000159458.zst... Original data structure: root: [Dict] with 8 keys key['text_encoder-item0']: [Dict] with 2 keys key['clip_pooled_prompt_embeds']: [Tensor] shape=torch.Size([768]), dtype=torch.float16, device=cpu key['clip_prompt_embeds']: [Tensor] shape=torch.Size([77, 768]), dtype=torch.float16, device=cpu key['text_encoder_2-item1']: [Dict] with 2 keys key['clip_pooled_prompt_embeds']: [Tensor] shape=torch.Size([1280]), dtype=torch.float16, device=cpu key['clip_prompt_embeds']: [Tensor] shape=torch.Size([77, 1280]), dtype=torch.float16, device=cpu key['text_encoder_3-item2']: [Dict] with 1 keys key['t5_prompt_embeds']: [Tensor] shape=torch.Size([77, 4096]), dtype=torch.float16, device=cpu key['text_encoder-item3']: [Dict] with 2 keys key['clip_pooled_prompt_embeds']: [Tensor] shape=torch.Size([768]), dtype=torch.float16, device=cpu key['clip_prompt_embeds']: [Tensor] shape=torch.Size([77, 768]), dtype=torch.float16, device=cpu key['text_encoder_2-item4']: [Dict] with 2 keys key['clip_pooled_prompt_embeds']: [Tensor] shape=torch.Size([1280]), dtype=torch.float16, device=cpu key['clip_prompt_embeds']: [Tensor] shape=torch.Size([77, 1280]), dtype=torch.float16, device=cpu key['text_encoder_3-item5']: [Dict] with 1 keys key['t5_prompt_embeds']: [Tensor] shape=torch.Size([77, 4096]), dtype=torch.float16, device=cpu key['vae.encoder-item6']: [Dict] with 2 keys key['vae.encoder_f0']: [Tensor] shape=torch.Size([32, 128, 128]), dtype=torch.float16, device=cpu key['vae.encoder_f1']: [Tensor] shape=torch.Size([32, 128, 128]), dtype=torch.float16, device=cpu key['vae.decoder']: [Tensor] shape=torch.Size([16, 128, 128]), dtype=torch.float16, device=cpu ⌛️ [1/4] FRONTEND: Load time: 0.008s ------------------------------------------------------------ SD3.5 Features Summary ------------------------------------------------------------ Number of text encoders: 3 Encoder names: ['text_encoder', 'text_encoder_2', 'text_encoder_3'] Has VAE latents: True Has VAE encoder features: True Data type: torch.float16 text_encoder-item0: clip_pooled_prompt_embeds: torch.Size([768]), torch.float16 clip_prompt_embeds: torch.Size([77, 768]), torch.float16 text_encoder_2-item1: clip_pooled_prompt_embeds: torch.Size([1280]), torch.float16 clip_prompt_embeds: torch.Size([77, 1280]), torch.float16 text_encoder_3-item2: t5_prompt_embeds: torch.Size([77, 4096]), torch.float16 text_encoder-item3: clip_pooled_prompt_embeds: torch.Size([768]), torch.float16 clip_prompt_embeds: torch.Size([77, 768]), torch.float16 text_encoder_2-item4: clip_pooled_prompt_embeds: torch.Size([1280]), torch.float16 clip_prompt_embeds: torch.Size([77, 1280]), torch.float16 text_encoder_3-item5: t5_prompt_embeds: torch.Size([77, 4096]), torch.float16 vae.decoder latents: torch.Size([16, 128, 128]) vae.encoder features: vae.encoder_f0: torch.Size([32, 128, 128]), torch.float16 vae.encoder_f1: torch.Size([32, 128, 128]), torch.float16 ------------------------------------------------------------ [2/4] FRONTEND: Pack + Encode (strategy: individual)... IndividualPacker: text_encoder-item0.clip_pooled_prompt_embeds: torch.Size([768])->torch.Size([1, 1, 1, 768]) text_encoder-item0.clip_prompt_embeds: torch.Size([77, 768])->torch.Size([1, 1, 77, 768]) text_encoder_2-item1.clip_pooled_prompt_embeds: torch.Size([1280])->torch.Size([1, 1, 1, 1280]) text_encoder_2-item1.clip_prompt_embeds: torch.Size([77, 1280])->torch.Size([1, 1, 77, 1280]) text_encoder_3-item2.t5_prompt_embeds: torch.Size([77, 4096])->torch.Size([1, 1, 77, 4096]) text_encoder-item3.clip_pooled_prompt_embeds: torch.Size([768])->torch.Size([1, 1, 1, 768]) text_encoder-item3.clip_prompt_embeds: torch.Size([77, 768])->torch.Size([1, 1, 77, 768]) text_encoder_2-item4.clip_pooled_prompt_embeds: torch.Size([1280])->torch.Size([1, 1, 1, 1280]) text_encoder_2-item4.clip_prompt_embeds: torch.Size([77, 1280])->torch.Size([1, 1, 77, 1280]) text_encoder_3-item5.t5_prompt_embeds: torch.Size([77, 4096])->torch.Size([1, 1, 77, 4096]) vae.encoder_f0: torch.Size([32, 128, 128])->torch.Size([1, 1, 512, 1024]) vae.encoder_f1: torch.Size([32, 128, 128])->torch.Size([1, 1, 512, 1024]) vae.decoder: torch.Size([16, 128, 128])->torch.Size([1, 1, 512, 512]) Using per-key quantization points (text_encoder-item0.clip_pooled_prompt_embeds: torch.Size([256])) for text_encoder-item0.clip_pooled_prompt_embeds text_encoder-item0.clip_pooled_prompt_embeds: 728B, BPFP=7.5833 Using per-key quantization points (text_encoder-item0.clip_prompt_embeds: torch.Size([256])) for text_encoder-item0.clip_prompt_embeds text_encoder-item0.clip_prompt_embeds: 6,360B, BPFP=0.8604 Using per-key quantization points (text_encoder_2-item1.clip_pooled_prompt_embeds: torch.Size([256])) for text_encoder_2-item1.clip_pooled_prompt_embeds text_encoder_2-item1.clip_pooled_prompt_embeds: 1,200B, BPFP=7.5000 Using per-key quantization points (text_encoder_2-item1.clip_prompt_embeds: torch.Size([256])) for text_encoder_2-item1.clip_prompt_embeds text_encoder_2-item1.clip_prompt_embeds: 10,872B, BPFP=0.8825 Using per-key quantization points (text_encoder_3-item2.t5_prompt_embeds: torch.Size([256])) for text_encoder_3-item2.t5_prompt_embeds text_encoder_3-item2.t5_prompt_embeds: 29,800B, BPFP=0.7559 Using per-key quantization points (text_encoder-item3.clip_pooled_prompt_embeds: torch.Size([256])) for text_encoder-item3.clip_pooled_prompt_embeds text_encoder-item3.clip_pooled_prompt_embeds: 760B, BPFP=7.9167 Using per-key quantization points (text_encoder-item3.clip_prompt_embeds: torch.Size([256])) for text_encoder-item3.clip_prompt_embeds text_encoder-item3.clip_prompt_embeds: 4,024B, BPFP=0.5444 Using per-key quantization points (text_encoder_2-item4.clip_pooled: torch.Size([256])) for text_encoder_2-item4.clip_pooled_prompt_embeds text_encoder_2-item4.clip_pooled_prompt_embeds: 1,268B, BPFP=7.9250 Using per-key quantization points (text_encoder_2-item4.clip_prompt: torch.Size([256])) for text_encoder_2-item4.clip_prompt_embeds text_encoder_2-item4.clip_prompt_embeds: 6,996B, BPFP=0.5679 Using per-key quantization points (text_encoder_3-item5.t5_prompt_embeds: torch.Size([256])) for text_encoder_3-item5.t5_prompt_embeds text_encoder_3-item5.t5_prompt_embeds: 18,416B, BPFP=0.4671 Using per-key quantization points (vae.encoder_f0: torch.Size([256])) for vae.encoder_f0 vae.encoder_f0: 11,952B, BPFP=0.1824 Using per-key quantization points (vae.encoder_f1: torch.Size([256])) for vae.encoder_f1 vae.encoder_f1: 11,952B, BPFP=0.1824 Using per-key quantization points (vae.decoder: torch.Size([256])) for vae.decoder vae.decoder: 4,896B, BPFP=0.1494 ⌛️ [2/4] FRONTEND: Frontend time: 0.293s (Pack+Encode) [3/4] BACKEND: Decode + Unpack... Using per-key quantization points (text_encoder-item0.clip_pooled_prompt_embeds: torch.Size([256])) for text_encoder-item0.clip_pooled_prompt_embeds Using per-key quantization points (text_encoder-item0.clip_prompt_embeds: torch.Size([256])) for text_encoder-item0.clip_prompt_embeds Using per-key quantization points (text_encoder_2-item1.clip_pooled_prompt_embeds: torch.Size([256])) for text_encoder_2-item1.clip_pooled_prompt_embeds Using per-key quantization points (text_encoder_2-item1.clip_prompt_embeds: torch.Size([256])) for text_encoder_2-item1.clip_prompt_embeds Using per-key quantization points (text_encoder_3-item2.t5_prompt_embeds: torch.Size([256])) for text_encoder_3-item2.t5_prompt_embeds Using per-key quantization points (text_encoder-item3.clip_pooled_prompt_embeds: torch.Size([256])) for text_encoder-item3.clip_pooled_prompt_embeds Using per-key quantization points (text_encoder-item3.clip_prompt_embeds: torch.Size([256])) for text_encoder-item3.clip_prompt_embeds Using per-key quantization points (text_encoder_2-item4.clip_pooled: torch.Size([256])) for text_encoder_2-item4.clip_pooled_prompt_embeds Using per-key quantization points (text_encoder_2-item4.clip_prompt: torch.Size([256])) for text_encoder_2-item4.clip_prompt_embeds Using per-key quantization points (text_encoder_3-item5.t5_prompt_embeds: torch.Size([256])) for text_encoder_3-item5.t5_prompt_embeds Using per-key quantization points (vae.encoder_f0: torch.Size([256])) for vae.encoder_f0 Using per-key quantization points (vae.encoder_f1: torch.Size([256])) for vae.encoder_f1 Using per-key quantization points (vae.decoder: torch.Size([256])) for vae.decoder IndividualUnPacker: text_encoder-item0.clip_pooled_prompt_embeds: torch.Size([768]) text_encoder-item0.clip_prompt_embeds: torch.Size([77, 768]) text_encoder_2-item1.clip_pooled_prompt_embeds: torch.Size([1280]) text_encoder_2-item1.clip_prompt_embeds: torch.Size([77, 1280]) text_encoder_3-item2.t5_prompt_embeds: torch.Size([77, 4096]) text_encoder-item3.clip_pooled_prompt_embeds: torch.Size([768]) text_encoder-item3.clip_prompt_embeds: torch.Size([77, 768]) text_encoder_2-item4.clip_pooled_prompt_embeds: torch.Size([1280]) text_encoder_2-item4.clip_prompt_embeds: torch.Size([77, 1280]) text_encoder_3-item5.t5_prompt_embeds: torch.Size([77, 4096]) vae.decoder: torch.Size([16, 128, 128]) vae.encoder_f0: torch.Size([32, 128, 128]) vae.encoder_f1: torch.Size([32, 128, 128]) ⌛️ [3/4] BACKEND: Backend time: 0.446s [4/4] METRICS: Computing MSE Breakdown... Using per-key quantization points (text_encoder-item0.clip_pooled_prompt_embeds: torch.Size([256])) for text_encoder-item0.clip_pooled_prompt_embeds Using per-key quantization points (text_encoder-item0.clip_prompt_embeds: torch.Size([256])) for text_encoder-item0.clip_prompt_embeds Using per-key quantization points (text_encoder_2-item1.clip_pooled_prompt_embeds: torch.Size([256])) for text_encoder_2-item1.clip_pooled_prompt_embeds Using per-key quantization points (text_encoder_2-item1.clip_prompt_embeds: torch.Size([256])) for text_encoder_2-item1.clip_prompt_embeds Using per-key quantization points (text_encoder_3-item2.t5_prompt_embeds: torch.Size([256])) for text_encoder_3-item2.t5_prompt_embeds Using per-key quantization points (text_encoder-item3.clip_pooled_prompt_embeds: torch.Size([256])) for text_encoder-item3.clip_pooled_prompt_embeds Using per-key quantization points (text_encoder-item3.clip_prompt_embeds: torch.Size([256])) for text_encoder-item3.clip_prompt_embeds Using per-key quantization points (text_encoder_2-item4.clip_pooled: torch.Size([256])) for text_encoder_2-item4.clip_pooled_prompt_embeds Using per-key quantization points (text_encoder_2-item4.clip_prompt: torch.Size([256])) for text_encoder_2-item4.clip_prompt_embeds Using per-key quantization points (text_encoder_3-item5.t5_prompt_embeds: torch.Size([256])) for text_encoder_3-item5.t5_prompt_embeds Using per-key quantization points (vae.encoder_f0: torch.Size([256])) for vae.encoder_f0 Using per-key quantization points (vae.encoder_f1: torch.Size([256])) for vae.encoder_f1 Using per-key quantization points (vae.decoder: torch.Size([256])) for vae.decoder IndividualUnPacker: text_encoder-item0.clip_pooled_prompt_embeds: torch.Size([768]) text_encoder-item0.clip_prompt_embeds: torch.Size([77, 768]) text_encoder_2-item1.clip_pooled_prompt_embeds: torch.Size([1280]) text_encoder_2-item1.clip_prompt_embeds: torch.Size([77, 1280]) text_encoder_3-item2.t5_prompt_embeds: torch.Size([77, 4096]) text_encoder-item3.clip_pooled_prompt_embeds: torch.Size([768]) text_encoder-item3.clip_prompt_embeds: torch.Size([77, 768]) text_encoder_2-item4.clip_pooled_prompt_embeds: torch.Size([1280]) text_encoder_2-item4.clip_prompt_embeds: torch.Size([77, 1280]) text_encoder_3-item5.t5_prompt_embeds: torch.Size([77, 4096]) vae.decoder: torch.Size([16, 128, 128]) vae.encoder_f0: torch.Size([32, 128, 128]) vae.encoder_f1: torch.Size([32, 128, 128]) Per-key MSE Breakdown (Quant=Truncation+Quantization, Codec=Compression): Key Quant-MSE Total-MSE ------------------------------------------------------------------------------------- text_encoder-item0.clip_pooled_prompt_embeds 0.00029967 0.92405295 text_encoder-item0.clip_prompt_embeds 0.00026157 23.94255191 text_encoder_2-item1.clip_pooled_prompt_embeds 0.00022221 0.91029253 text_encoder_2-item1.clip_prompt_embeds 0.00022582 0.13882221 text_encoder_3-item2.t5_prompt_embeds 0.00000776 0.00367807 text_encoder-item3.clip_pooled_prompt_embeds 0.00841799 1.01420029 text_encoder-item3.clip_prompt_embeds 0.00023247 23.90570887 text_encoder_2-item4.clip_pooled_prompt_embeds 0.00016751 0.67051430 text_encoder_2-item4.clip_prompt_embeds 0.00009830 0.47049568 text_encoder_3-item5.t5_prompt_embeds 0.00001263 0.00227117 vae.encoder_f0 0.40456498 6.01515341 vae.encoder_f1 0.40456539 6.01513529 vae.decoder 0.00020503 0.03923426 ------------------------------------------------------------------------------------- TOTAL 0.18768128 4.07459138 (elements=2,260,992) ---------------------- -------------------------------------------------------- SAMPLE-WISE STATISTICS ---------------------- -------------------------------------------------------- Handler sd35 Strategy individual Architecture hyperprior-featurecoding ---------------------- -------------------------------------------------------- Total Elements 2260992 Total Bytes 109224 BPFP 0.3865 bits/point EBPFP 0.7729 equivalent bits/point MSE 4.074591 ---------------------- -------------------------------------------------------- Time: 0.747s Load: 0.008s, Pack+Encode: 0.293s, Decode+Unpack: 0.446s ---------------------- -------------------------------------------------------- Restored Feature Format: [Dict] with 8 keys key['text_encoder-item0']: [Dict] with 2 keys key['clip_pooled_prompt_embeds']: [Tensor] shape=torch.Size([768]), dtype=torch.float16, device=cpu key['clip_prompt_embeds']: [Tensor] shape=torch.Size([77, 768]), dtype=torch.float16, device=cpu key['text_encoder_2-item1']: [Dict] with 2 keys key['clip_pooled_prompt_embeds']: [Tensor] shape=torch.Size([1280]), dtype=torch.float16, device=cpu key['clip_prompt_embeds']: [Tensor] shape=torch.Size([77, 1280]), dtype=torch.float16, device=cpu key['text_encoder_3-item2']: [Dict] with 1 keys key['t5_prompt_embeds']: [Tensor] shape=torch.Size([77, 4096]), dtype=torch.float16, device=cpu key['text_encoder-item3']: [Dict] with 2 keys key['clip_pooled_prompt_embeds']: [Tensor] shape=torch.Size([768]), dtype=torch.float16, device=cpu key['clip_prompt_embeds']: [Tensor] shape=torch.Size([77, 768]), dtype=torch.float16, device=cpu key['text_encoder_2-item4']: [Dict] with 2 keys key['clip_pooled_prompt_embeds']: [Tensor] shape=torch.Size([1280]), dtype=torch.float16, device=cpu key['clip_prompt_embeds']: [Tensor] shape=torch.Size([77, 1280]), dtype=torch.float16, device=cpu key['text_encoder_3-item5']: [Dict] with 1 keys key['t5_prompt_embeds']: [Tensor] shape=torch.Size([77, 4096]), dtype=torch.float16, device=cpu key['vae.decoder']: [Tensor] shape=torch.Size([16, 128, 128]), dtype=torch.float16, device=cpu key['vae.encoder']: [Dict] with 2 keys key['vae.encoder_f0']: [Tensor] shape=torch.Size([32, 128, 128]), dtype=torch.float16, device=cpu key['vae.encoder_f1']: [Tensor] shape=torch.Size([32, 128, 128]), dtype=torch.float16, device=cpu 💾 Converting with 4.0746 MSE: from ../datasets/FeatureCoding-StableDiffusion3.5/100samples_features/000000159458.zst to output-fixed/sd35/lambda0.004/hyperprior-featurecoding-8bit-individual/sd35cond/000000159458.zst 💪 Processing: ../datasets/FeatureCoding-StableDiffusion3.5/100samples_features/000000161128.zst (30/100) [1/4] FRONTEND: Loading features from ../datasets/FeatureCoding-StableDiffusion3.5/100samples_features/000000161128.zst... Original data structure: root: [Dict] with 8 keys key['text_encoder-item0']: [Dict] with 2 keys key['clip_pooled_prompt_embeds']: [Tensor] shape=torch.Size([768]), dtype=torch.float16, device=cpu key['clip_prompt_embeds']: [Tensor] shape=torch.Size([77, 768]), dtype=torch.float16, device=cpu key['text_encoder_2-item1']: [Dict] with 2 keys key['clip_pooled_prompt_embeds']: [Tensor] shape=torch.Size([1280]), dtype=torch.float16, device=cpu key['clip_prompt_embeds']: [Tensor] shape=torch.Size([77, 1280]), dtype=torch.float16, device=cpu key['text_encoder_3-item2']: [Dict] with 1 keys key['t5_prompt_embeds']: [Tensor] shape=torch.Size([77, 4096]), dtype=torch.float16, device=cpu key['text_encoder-item3']: [Dict] with 2 keys key['clip_pooled_prompt_embeds']: [Tensor] shape=torch.Size([768]), dtype=torch.float16, device=cpu key['clip_prompt_embeds']: [Tensor] shape=torch.Size([77, 768]), dtype=torch.float16, device=cpu key['text_encoder_2-item4']: [Dict] with 2 keys key['clip_pooled_prompt_embeds']: [Tensor] shape=torch.Size([1280]), dtype=torch.float16, device=cpu key['clip_prompt_embeds']: [Tensor] shape=torch.Size([77, 1280]), dtype=torch.float16, device=cpu key['text_encoder_3-item5']: [Dict] with 1 keys key['t5_prompt_embeds']: [Tensor] shape=torch.Size([77, 4096]), dtype=torch.float16, device=cpu key['vae.encoder-item6']: [Dict] with 2 keys key['vae.encoder_f0']: [Tensor] shape=torch.Size([32, 128, 128]), dtype=torch.float16, device=cpu key['vae.encoder_f1']: [Tensor] shape=torch.Size([32, 128, 128]), dtype=torch.float16, device=cpu key['vae.decoder']: [Tensor] shape=torch.Size([16, 128, 128]), dtype=torch.float16, device=cpu ⌛️ [1/4] FRONTEND: Load time: 0.009s ------------------------------------------------------------ SD3.5 Features Summary ------------------------------------------------------------ Number of text encoders: 3 Encoder names: ['text_encoder', 'text_encoder_2', 'text_encoder_3'] Has VAE latents: True Has VAE encoder features: True Data type: torch.float16 text_encoder-item0: clip_pooled_prompt_embeds: torch.Size([768]), torch.float16 clip_prompt_embeds: torch.Size([77, 768]), torch.float16 text_encoder_2-item1: clip_pooled_prompt_embeds: torch.Size([1280]), torch.float16 clip_prompt_embeds: torch.Size([77, 1280]), torch.float16 text_encoder_3-item2: t5_prompt_embeds: torch.Size([77, 4096]), torch.float16 text_encoder-item3: clip_pooled_prompt_embeds: torch.Size([768]), torch.float16 clip_prompt_embeds: torch.Size([77, 768]), torch.float16 text_encoder_2-item4: clip_pooled_prompt_embeds: torch.Size([1280]), torch.float16 clip_prompt_embeds: torch.Size([77, 1280]), torch.float16 text_encoder_3-item5: t5_prompt_embeds: torch.Size([77, 4096]), torch.float16 vae.decoder latents: torch.Size([16, 128, 128]) vae.encoder features: vae.encoder_f0: torch.Size([32, 128, 128]), torch.float16 vae.encoder_f1: torch.Size([32, 128, 128]), torch.float16 ------------------------------------------------------------ [2/4] FRONTEND: Pack + Encode (strategy: individual)... IndividualPacker: text_encoder-item0.clip_pooled_prompt_embeds: torch.Size([768])->torch.Size([1, 1, 1, 768]) text_encoder-item0.clip_prompt_embeds: torch.Size([77, 768])->torch.Size([1, 1, 77, 768]) text_encoder_2-item1.clip_pooled_prompt_embeds: torch.Size([1280])->torch.Size([1, 1, 1, 1280]) text_encoder_2-item1.clip_prompt_embeds: torch.Size([77, 1280])->torch.Size([1, 1, 77, 1280]) text_encoder_3-item2.t5_prompt_embeds: torch.Size([77, 4096])->torch.Size([1, 1, 77, 4096]) text_encoder-item3.clip_pooled_prompt_embeds: torch.Size([768])->torch.Size([1, 1, 1, 768]) text_encoder-item3.clip_prompt_embeds: torch.Size([77, 768])->torch.Size([1, 1, 77, 768]) text_encoder_2-item4.clip_pooled_prompt_embeds: torch.Size([1280])->torch.Size([1, 1, 1, 1280]) text_encoder_2-item4.clip_prompt_embeds: torch.Size([77, 1280])->torch.Size([1, 1, 77, 1280]) text_encoder_3-item5.t5_prompt_embeds: torch.Size([77, 4096])->torch.Size([1, 1, 77, 4096]) vae.encoder_f0: torch.Size([32, 128, 128])->torch.Size([1, 1, 512, 1024]) vae.encoder_f1: torch.Size([32, 128, 128])->torch.Size([1, 1, 512, 1024]) vae.decoder: torch.Size([16, 128, 128])->torch.Size([1, 1, 512, 512]) Using per-key quantization points (text_encoder-item0.clip_pooled_prompt_embeds: torch.Size([256])) for text_encoder-item0.clip_pooled_prompt_embeds text_encoder-item0.clip_pooled_prompt_embeds: 732B, BPFP=7.6250 Using per-key quantization points (text_encoder-item0.clip_prompt_embeds: torch.Size([256])) for text_encoder-item0.clip_prompt_embeds text_encoder-item0.clip_prompt_embeds: 6,008B, BPFP=0.8128 Using per-key quantization points (text_encoder_2-item1.clip_pooled_prompt_embeds: torch.Size([256])) for text_encoder_2-item1.clip_pooled_prompt_embeds text_encoder_2-item1.clip_pooled_prompt_embeds: 1,196B, BPFP=7.4750 Using per-key quantization points (text_encoder_2-item1.clip_prompt_embeds: torch.Size([256])) for text_encoder_2-item1.clip_prompt_embeds text_encoder_2-item1.clip_prompt_embeds: 10,504B, BPFP=0.8526 Using per-key quantization points (text_encoder_3-item2.t5_prompt_embeds: torch.Size([256])) for text_encoder_3-item2.t5_prompt_embeds text_encoder_3-item2.t5_prompt_embeds: 24,968B, BPFP=0.6333 Using per-key quantization points (text_encoder-item3.clip_pooled_prompt_embeds: torch.Size([256])) for text_encoder-item3.clip_pooled_prompt_embeds text_encoder-item3.clip_pooled_prompt_embeds: 760B, BPFP=7.9167 Using per-key quantization points (text_encoder-item3.clip_prompt_embeds: torch.Size([256])) for text_encoder-item3.clip_prompt_embeds text_encoder-item3.clip_prompt_embeds: 4,024B, BPFP=0.5444 Using per-key quantization points (text_encoder_2-item4.clip_pooled: torch.Size([256])) for text_encoder_2-item4.clip_pooled_prompt_embeds text_encoder_2-item4.clip_pooled_prompt_embeds: 1,268B, BPFP=7.9250 Using per-key quantization points (text_encoder_2-item4.clip_prompt: torch.Size([256])) for text_encoder_2-item4.clip_prompt_embeds text_encoder_2-item4.clip_prompt_embeds: 6,996B, BPFP=0.5679 Using per-key quantization points (text_encoder_3-item5.t5_prompt_embeds: torch.Size([256])) for text_encoder_3-item5.t5_prompt_embeds text_encoder_3-item5.t5_prompt_embeds: 18,416B, BPFP=0.4671 Using per-key quantization points (vae.encoder_f0: torch.Size([256])) for vae.encoder_f0 vae.encoder_f0: 19,356B, BPFP=0.2953 Using per-key quantization points (vae.encoder_f1: torch.Size([256])) for vae.encoder_f1 vae.encoder_f1: 19,356B, BPFP=0.2953 Using per-key quantization points (vae.decoder: torch.Size([256])) for vae.decoder vae.decoder: 8,832B, BPFP=0.2695 ⌛️ [2/4] FRONTEND: Frontend time: 0.294s (Pack+Encode) [3/4] BACKEND: Decode + Unpack... Using per-key quantization points (text_encoder-item0.clip_pooled_prompt_embeds: torch.Size([256])) for text_encoder-item0.clip_pooled_prompt_embeds Using per-key quantization points (text_encoder-item0.clip_prompt_embeds: torch.Size([256])) for text_encoder-item0.clip_prompt_embeds Using per-key quantization points (text_encoder_2-item1.clip_pooled_prompt_embeds: torch.Size([256])) for text_encoder_2-item1.clip_pooled_prompt_embeds Using per-key quantization points (text_encoder_2-item1.clip_prompt_embeds: torch.Size([256])) for text_encoder_2-item1.clip_prompt_embeds Using per-key quantization points (text_encoder_3-item2.t5_prompt_embeds: torch.Size([256])) for text_encoder_3-item2.t5_prompt_embeds Using per-key quantization points (text_encoder-item3.clip_pooled_prompt_embeds: torch.Size([256])) for text_encoder-item3.clip_pooled_prompt_embeds Using per-key quantization points (text_encoder-item3.clip_prompt_embeds: torch.Size([256])) for text_encoder-item3.clip_prompt_embeds Using per-key quantization points (text_encoder_2-item4.clip_pooled: torch.Size([256])) for text_encoder_2-item4.clip_pooled_prompt_embeds Using per-key quantization points (text_encoder_2-item4.clip_prompt: torch.Size([256])) for text_encoder_2-item4.clip_prompt_embeds Using per-key quantization points (text_encoder_3-item5.t5_prompt_embeds: torch.Size([256])) for text_encoder_3-item5.t5_prompt_embeds Using per-key quantization points (vae.encoder_f0: torch.Size([256])) for vae.encoder_f0 Using per-key quantization points (vae.encoder_f1: torch.Size([256])) for vae.encoder_f1 Using per-key quantization points (vae.decoder: torch.Size([256])) for vae.decoder IndividualUnPacker: text_encoder-item0.clip_pooled_prompt_embeds: torch.Size([768]) text_encoder-item0.clip_prompt_embeds: torch.Size([77, 768]) text_encoder_2-item1.clip_pooled_prompt_embeds: torch.Size([1280]) text_encoder_2-item1.clip_prompt_embeds: torch.Size([77, 1280]) text_encoder_3-item2.t5_prompt_embeds: torch.Size([77, 4096]) text_encoder-item3.clip_pooled_prompt_embeds: torch.Size([768]) text_encoder-item3.clip_prompt_embeds: torch.Size([77, 768]) text_encoder_2-item4.clip_pooled_prompt_embeds: torch.Size([1280]) text_encoder_2-item4.clip_prompt_embeds: torch.Size([77, 1280]) text_encoder_3-item5.t5_prompt_embeds: torch.Size([77, 4096]) vae.decoder: torch.Size([16, 128, 128]) vae.encoder_f0: torch.Size([32, 128, 128]) vae.encoder_f1: torch.Size([32, 128, 128]) ⌛️ [3/4] BACKEND: Backend time: 0.450s [4/4] METRICS: Computing MSE Breakdown... Using per-key quantization points (text_encoder-item0.clip_pooled_prompt_embeds: torch.Size([256])) for text_encoder-item0.clip_pooled_prompt_embeds Using per-key quantization points (text_encoder-item0.clip_prompt_embeds: torch.Size([256])) for text_encoder-item0.clip_prompt_embeds Using per-key quantization points (text_encoder_2-item1.clip_pooled_prompt_embeds: torch.Size([256])) for text_encoder_2-item1.clip_pooled_prompt_embeds Using per-key quantization points (text_encoder_2-item1.clip_prompt_embeds: torch.Size([256])) for text_encoder_2-item1.clip_prompt_embeds Using per-key quantization points (text_encoder_3-item2.t5_prompt_embeds: torch.Size([256])) for text_encoder_3-item2.t5_prompt_embeds Using per-key quantization points (text_encoder-item3.clip_pooled_prompt_embeds: torch.Size([256])) for text_encoder-item3.clip_pooled_prompt_embeds Using per-key quantization points (text_encoder-item3.clip_prompt_embeds: torch.Size([256])) for text_encoder-item3.clip_prompt_embeds Using per-key quantization points (text_encoder_2-item4.clip_pooled: torch.Size([256])) for text_encoder_2-item4.clip_pooled_prompt_embeds Using per-key quantization points (text_encoder_2-item4.clip_prompt: torch.Size([256])) for text_encoder_2-item4.clip_prompt_embeds Using per-key quantization points (text_encoder_3-item5.t5_prompt_embeds: torch.Size([256])) for text_encoder_3-item5.t5_prompt_embeds Using per-key quantization points (vae.encoder_f0: torch.Size([256])) for vae.encoder_f0 Using per-key quantization points (vae.encoder_f1: torch.Size([256])) for vae.encoder_f1 Using per-key quantization points (vae.decoder: torch.Size([256])) for vae.decoder IndividualUnPacker: text_encoder-item0.clip_pooled_prompt_embeds: torch.Size([768]) text_encoder-item0.clip_prompt_embeds: torch.Size([77, 768]) text_encoder_2-item1.clip_pooled_prompt_embeds: torch.Size([1280]) text_encoder_2-item1.clip_prompt_embeds: torch.Size([77, 1280]) text_encoder_3-item2.t5_prompt_embeds: torch.Size([77, 4096]) text_encoder-item3.clip_pooled_prompt_embeds: torch.Size([768]) text_encoder-item3.clip_prompt_embeds: torch.Size([77, 768]) text_encoder_2-item4.clip_pooled_prompt_embeds: torch.Size([1280]) text_encoder_2-item4.clip_prompt_embeds: torch.Size([77, 1280]) text_encoder_3-item5.t5_prompt_embeds: torch.Size([77, 4096]) vae.decoder: torch.Size([16, 128, 128]) vae.encoder_f0: torch.Size([32, 128, 128]) vae.encoder_f1: torch.Size([32, 128, 128]) Per-key MSE Breakdown (Quant=Truncation+Quantization, Codec=Compression): Key Quant-MSE Total-MSE ------------------------------------------------------------------------------------- text_encoder-item0.clip_pooled_prompt_embeds 0.00063306 0.91871460 text_encoder-item0.clip_prompt_embeds 0.00027179 23.94517933 text_encoder_2-item1.clip_pooled_prompt_embeds 0.00025795 0.93989544 text_encoder_2-item1.clip_prompt_embeds 0.00015124 0.12223185 text_encoder_3-item2.t5_prompt_embeds 0.00000794 0.00254879 text_encoder-item3.clip_pooled_prompt_embeds 0.00841799 1.01420029 text_encoder-item3.clip_prompt_embeds 0.00023247 23.90570887 text_encoder_2-item4.clip_pooled_prompt_embeds 0.00016751 0.67051430 text_encoder_2-item4.clip_prompt_embeds 0.00009830 0.47049568 text_encoder_3-item5.t5_prompt_embeds 0.00001263 0.00227117 vae.encoder_f0 0.00673531 3.22262645 vae.encoder_f1 0.00673732 3.22259521 vae.decoder 0.00020129 0.07213721 ------------------------------------------------------------------------------------- TOTAL 0.00317768 2.78252114 (elements=2,260,992) ---------------------- -------------------------------------------------------- SAMPLE-WISE STATISTICS ---------------------- -------------------------------------------------------- Handler sd35 Strategy individual Architecture hyperprior-featurecoding ---------------------- -------------------------------------------------------- Total Elements 2260992 Total Bytes 122416 BPFP 0.4331 bits/point EBPFP 0.8663 equivalent bits/point MSE 2.782521 ---------------------- -------------------------------------------------------- Time: 0.752s Load: 0.009s, Pack+Encode: 0.294s, Decode+Unpack: 0.450s ---------------------- -------------------------------------------------------- Restored Feature Format: [Dict] with 8 keys key['text_encoder-item0']: [Dict] with 2 keys key['clip_pooled_prompt_embeds']: [Tensor] shape=torch.Size([768]), dtype=torch.float16, device=cpu key['clip_prompt_embeds']: [Tensor] shape=torch.Size([77, 768]), dtype=torch.float16, device=cpu key['text_encoder_2-item1']: [Dict] with 2 keys key['clip_pooled_prompt_embeds']: [Tensor] shape=torch.Size([1280]), dtype=torch.float16, device=cpu key['clip_prompt_embeds']: [Tensor] shape=torch.Size([77, 1280]), dtype=torch.float16, device=cpu key['text_encoder_3-item2']: [Dict] with 1 keys key['t5_prompt_embeds']: [Tensor] shape=torch.Size([77, 4096]), dtype=torch.float16, device=cpu key['text_encoder-item3']: [Dict] with 2 keys key['clip_pooled_prompt_embeds']: [Tensor] shape=torch.Size([768]), dtype=torch.float16, device=cpu key['clip_prompt_embeds']: [Tensor] shape=torch.Size([77, 768]), dtype=torch.float16, device=cpu key['text_encoder_2-item4']: [Dict] with 2 keys key['clip_pooled_prompt_embeds']: [Tensor] shape=torch.Size([1280]), dtype=torch.float16, device=cpu key['clip_prompt_embeds']: [Tensor] shape=torch.Size([77, 1280]), dtype=torch.float16, device=cpu key['text_encoder_3-item5']: [Dict] with 1 keys key['t5_prompt_embeds']: [Tensor] shape=torch.Size([77, 4096]), dtype=torch.float16, device=cpu key['vae.decoder']: [Tensor] shape=torch.Size([16, 128, 128]), dtype=torch.float16, device=cpu key['vae.encoder']: [Dict] with 2 keys key['vae.encoder_f0']: [Tensor] shape=torch.Size([32, 128, 128]), dtype=torch.float16, device=cpu key['vae.encoder_f1']: [Tensor] shape=torch.Size([32, 128, 128]), dtype=torch.float16, device=cpu 💾 Converting with 2.7825 MSE: from ../datasets/FeatureCoding-StableDiffusion3.5/100samples_features/000000161128.zst to output-fixed/sd35/lambda0.004/hyperprior-featurecoding-8bit-individual/sd35cond/000000161128.zst 💪 Processing: ../datasets/FeatureCoding-StableDiffusion3.5/100samples_features/000000168458.zst (31/100) [1/4] FRONTEND: Loading features from ../datasets/FeatureCoding-StableDiffusion3.5/100samples_features/000000168458.zst... Original data structure: root: [Dict] with 8 keys key['text_encoder-item0']: [Dict] with 2 keys key['clip_pooled_prompt_embeds']: [Tensor] shape=torch.Size([768]), dtype=torch.float16, device=cpu key['clip_prompt_embeds']: [Tensor] shape=torch.Size([77, 768]), dtype=torch.float16, device=cpu key['text_encoder_2-item1']: [Dict] with 2 keys key['clip_pooled_prompt_embeds']: [Tensor] shape=torch.Size([1280]), dtype=torch.float16, device=cpu key['clip_prompt_embeds']: [Tensor] shape=torch.Size([77, 1280]), dtype=torch.float16, device=cpu key['text_encoder_3-item2']: [Dict] with 1 keys key['t5_prompt_embeds']: [Tensor] shape=torch.Size([77, 4096]), dtype=torch.float16, device=cpu key['text_encoder-item3']: [Dict] with 2 keys key['clip_pooled_prompt_embeds']: [Tensor] shape=torch.Size([768]), dtype=torch.float16, device=cpu key['clip_prompt_embeds']: [Tensor] shape=torch.Size([77, 768]), dtype=torch.float16, device=cpu key['text_encoder_2-item4']: [Dict] with 2 keys key['clip_pooled_prompt_embeds']: [Tensor] shape=torch.Size([1280]), dtype=torch.float16, device=cpu key['clip_prompt_embeds']: [Tensor] shape=torch.Size([77, 1280]), dtype=torch.float16, device=cpu key['text_encoder_3-item5']: [Dict] with 1 keys key['t5_prompt_embeds']: [Tensor] shape=torch.Size([77, 4096]), dtype=torch.float16, device=cpu key['vae.encoder-item6']: [Dict] with 2 keys key['vae.encoder_f0']: [Tensor] shape=torch.Size([32, 128, 128]), dtype=torch.float16, device=cpu key['vae.encoder_f1']: [Tensor] shape=torch.Size([32, 128, 128]), dtype=torch.float16, device=cpu key['vae.decoder']: [Tensor] shape=torch.Size([16, 128, 128]), dtype=torch.float16, device=cpu ⌛️ [1/4] FRONTEND: Load time: 0.008s ------------------------------------------------------------ SD3.5 Features Summary ------------------------------------------------------------ Number of text encoders: 3 Encoder names: ['text_encoder', 'text_encoder_2', 'text_encoder_3'] Has VAE latents: True Has VAE encoder features: True Data type: torch.float16 text_encoder-item0: clip_pooled_prompt_embeds: torch.Size([768]), torch.float16 clip_prompt_embeds: torch.Size([77, 768]), torch.float16 text_encoder_2-item1: clip_pooled_prompt_embeds: torch.Size([1280]), torch.float16 clip_prompt_embeds: torch.Size([77, 1280]), torch.float16 text_encoder_3-item2: t5_prompt_embeds: torch.Size([77, 4096]), torch.float16 text_encoder-item3: clip_pooled_prompt_embeds: torch.Size([768]), torch.float16 clip_prompt_embeds: torch.Size([77, 768]), torch.float16 text_encoder_2-item4: clip_pooled_prompt_embeds: torch.Size([1280]), torch.float16 clip_prompt_embeds: torch.Size([77, 1280]), torch.float16 text_encoder_3-item5: t5_prompt_embeds: torch.Size([77, 4096]), torch.float16 vae.decoder latents: torch.Size([16, 128, 128]) vae.encoder features: vae.encoder_f0: torch.Size([32, 128, 128]), torch.float16 vae.encoder_f1: torch.Size([32, 128, 128]), torch.float16 ------------------------------------------------------------ [2/4] FRONTEND: Pack + Encode (strategy: individual)... IndividualPacker: text_encoder-item0.clip_pooled_prompt_embeds: torch.Size([768])->torch.Size([1, 1, 1, 768]) text_encoder-item0.clip_prompt_embeds: torch.Size([77, 768])->torch.Size([1, 1, 77, 768]) text_encoder_2-item1.clip_pooled_prompt_embeds: torch.Size([1280])->torch.Size([1, 1, 1, 1280]) text_encoder_2-item1.clip_prompt_embeds: torch.Size([77, 1280])->torch.Size([1, 1, 77, 1280]) text_encoder_3-item2.t5_prompt_embeds: torch.Size([77, 4096])->torch.Size([1, 1, 77, 4096]) text_encoder-item3.clip_pooled_prompt_embeds: torch.Size([768])->torch.Size([1, 1, 1, 768]) text_encoder-item3.clip_prompt_embeds: torch.Size([77, 768])->torch.Size([1, 1, 77, 768]) text_encoder_2-item4.clip_pooled_prompt_embeds: torch.Size([1280])->torch.Size([1, 1, 1, 1280]) text_encoder_2-item4.clip_prompt_embeds: torch.Size([77, 1280])->torch.Size([1, 1, 77, 1280]) text_encoder_3-item5.t5_prompt_embeds: torch.Size([77, 4096])->torch.Size([1, 1, 77, 4096]) vae.encoder_f0: torch.Size([32, 128, 128])->torch.Size([1, 1, 512, 1024]) vae.encoder_f1: torch.Size([32, 128, 128])->torch.Size([1, 1, 512, 1024]) vae.decoder: torch.Size([16, 128, 128])->torch.Size([1, 1, 512, 512]) Using per-key quantization points (text_encoder-item0.clip_pooled_prompt_embeds: torch.Size([256])) for text_encoder-item0.clip_pooled_prompt_embeds text_encoder-item0.clip_pooled_prompt_embeds: 740B, BPFP=7.7083 Using per-key quantization points (text_encoder-item0.clip_prompt_embeds: torch.Size([256])) for text_encoder-item0.clip_prompt_embeds text_encoder-item0.clip_prompt_embeds: 6,572B, BPFP=0.8891 Using per-key quantization points (text_encoder_2-item1.clip_pooled_prompt_embeds: torch.Size([256])) for text_encoder_2-item1.clip_pooled_prompt_embeds text_encoder_2-item1.clip_pooled_prompt_embeds: 1,188B, BPFP=7.4250 Using per-key quantization points (text_encoder_2-item1.clip_prompt_embeds: torch.Size([256])) for text_encoder_2-item1.clip_prompt_embeds text_encoder_2-item1.clip_prompt_embeds: 11,000B, BPFP=0.8929 Using per-key quantization points (text_encoder_3-item2.t5_prompt_embeds: torch.Size([256])) for text_encoder_3-item2.t5_prompt_embeds text_encoder_3-item2.t5_prompt_embeds: 30,152B, BPFP=0.7648 Using per-key quantization points (text_encoder-item3.clip_pooled_prompt_embeds: torch.Size([256])) for text_encoder-item3.clip_pooled_prompt_embeds text_encoder-item3.clip_pooled_prompt_embeds: 760B, BPFP=7.9167 Using per-key quantization points (text_encoder-item3.clip_prompt_embeds: torch.Size([256])) for text_encoder-item3.clip_prompt_embeds text_encoder-item3.clip_prompt_embeds: 4,024B, BPFP=0.5444 Using per-key quantization points (text_encoder_2-item4.clip_pooled: torch.Size([256])) for text_encoder_2-item4.clip_pooled_prompt_embeds text_encoder_2-item4.clip_pooled_prompt_embeds: 1,268B, BPFP=7.9250 Using per-key quantization points (text_encoder_2-item4.clip_prompt: torch.Size([256])) for text_encoder_2-item4.clip_prompt_embeds text_encoder_2-item4.clip_prompt_embeds: 6,996B, BPFP=0.5679 Using per-key quantization points (text_encoder_3-item5.t5_prompt_embeds: torch.Size([256])) for text_encoder_3-item5.t5_prompt_embeds text_encoder_3-item5.t5_prompt_embeds: 18,416B, BPFP=0.4671 Using per-key quantization points (vae.encoder_f0: torch.Size([256])) for vae.encoder_f0 vae.encoder_f0: 16,756B, BPFP=0.2557 Using per-key quantization points (vae.encoder_f1: torch.Size([256])) for vae.encoder_f1 vae.encoder_f1: 16,756B, BPFP=0.2557 Using per-key quantization points (vae.decoder: torch.Size([256])) for vae.decoder vae.decoder: 4,916B, BPFP=0.1500 ⌛️ [2/4] FRONTEND: Frontend time: 0.293s (Pack+Encode) [3/4] BACKEND: Decode + Unpack... Using per-key quantization points (text_encoder-item0.clip_pooled_prompt_embeds: torch.Size([256])) for text_encoder-item0.clip_pooled_prompt_embeds Using per-key quantization points (text_encoder-item0.clip_prompt_embeds: torch.Size([256])) for text_encoder-item0.clip_prompt_embeds Using per-key quantization points (text_encoder_2-item1.clip_pooled_prompt_embeds: torch.Size([256])) for text_encoder_2-item1.clip_pooled_prompt_embeds Using per-key quantization points (text_encoder_2-item1.clip_prompt_embeds: torch.Size([256])) for text_encoder_2-item1.clip_prompt_embeds Using per-key quantization points (text_encoder_3-item2.t5_prompt_embeds: torch.Size([256])) for text_encoder_3-item2.t5_prompt_embeds Using per-key quantization points (text_encoder-item3.clip_pooled_prompt_embeds: torch.Size([256])) for text_encoder-item3.clip_pooled_prompt_embeds Using per-key quantization points (text_encoder-item3.clip_prompt_embeds: torch.Size([256])) for text_encoder-item3.clip_prompt_embeds Using per-key quantization points (text_encoder_2-item4.clip_pooled: torch.Size([256])) for text_encoder_2-item4.clip_pooled_prompt_embeds Using per-key quantization points (text_encoder_2-item4.clip_prompt: torch.Size([256])) for text_encoder_2-item4.clip_prompt_embeds Using per-key quantization points (text_encoder_3-item5.t5_prompt_embeds: torch.Size([256])) for text_encoder_3-item5.t5_prompt_embeds Using per-key quantization points (vae.encoder_f0: torch.Size([256])) for vae.encoder_f0 Using per-key quantization points (vae.encoder_f1: torch.Size([256])) for vae.encoder_f1 Using per-key quantization points (vae.decoder: torch.Size([256])) for vae.decoder IndividualUnPacker: text_encoder-item0.clip_pooled_prompt_embeds: torch.Size([768]) text_encoder-item0.clip_prompt_embeds: torch.Size([77, 768]) text_encoder_2-item1.clip_pooled_prompt_embeds: torch.Size([1280]) text_encoder_2-item1.clip_prompt_embeds: torch.Size([77, 1280]) text_encoder_3-item2.t5_prompt_embeds: torch.Size([77, 4096]) text_encoder-item3.clip_pooled_prompt_embeds: torch.Size([768]) text_encoder-item3.clip_prompt_embeds: torch.Size([77, 768]) text_encoder_2-item4.clip_pooled_prompt_embeds: torch.Size([1280]) text_encoder_2-item4.clip_prompt_embeds: torch.Size([77, 1280]) text_encoder_3-item5.t5_prompt_embeds: torch.Size([77, 4096]) vae.decoder: torch.Size([16, 128, 128]) vae.encoder_f0: torch.Size([32, 128, 128]) vae.encoder_f1: torch.Size([32, 128, 128]) ⌛️ [3/4] BACKEND: Backend time: 0.453s [4/4] METRICS: Computing MSE Breakdown... Using per-key quantization points (text_encoder-item0.clip_pooled_prompt_embeds: torch.Size([256])) for text_encoder-item0.clip_pooled_prompt_embeds Using per-key quantization points (text_encoder-item0.clip_prompt_embeds: torch.Size([256])) for text_encoder-item0.clip_prompt_embeds Using per-key quantization points (text_encoder_2-item1.clip_pooled_prompt_embeds: torch.Size([256])) for text_encoder_2-item1.clip_pooled_prompt_embeds Using per-key quantization points (text_encoder_2-item1.clip_prompt_embeds: torch.Size([256])) for text_encoder_2-item1.clip_prompt_embeds Using per-key quantization points (text_encoder_3-item2.t5_prompt_embeds: torch.Size([256])) for text_encoder_3-item2.t5_prompt_embeds Using per-key quantization points (text_encoder-item3.clip_pooled_prompt_embeds: torch.Size([256])) for text_encoder-item3.clip_pooled_prompt_embeds Using per-key quantization points (text_encoder-item3.clip_prompt_embeds: torch.Size([256])) for text_encoder-item3.clip_prompt_embeds Using per-key quantization points (text_encoder_2-item4.clip_pooled: torch.Size([256])) for text_encoder_2-item4.clip_pooled_prompt_embeds Using per-key quantization points (text_encoder_2-item4.clip_prompt: torch.Size([256])) for text_encoder_2-item4.clip_prompt_embeds Using per-key quantization points (text_encoder_3-item5.t5_prompt_embeds: torch.Size([256])) for text_encoder_3-item5.t5_prompt_embeds Using per-key quantization points (vae.encoder_f0: torch.Size([256])) for vae.encoder_f0 Using per-key quantization points (vae.encoder_f1: torch.Size([256])) for vae.encoder_f1 Using per-key quantization points (vae.decoder: torch.Size([256])) for vae.decoder IndividualUnPacker: text_encoder-item0.clip_pooled_prompt_embeds: torch.Size([768]) text_encoder-item0.clip_prompt_embeds: torch.Size([77, 768]) text_encoder_2-item1.clip_pooled_prompt_embeds: torch.Size([1280]) text_encoder_2-item1.clip_prompt_embeds: torch.Size([77, 1280]) text_encoder_3-item2.t5_prompt_embeds: torch.Size([77, 4096]) text_encoder-item3.clip_pooled_prompt_embeds: torch.Size([768]) text_encoder-item3.clip_prompt_embeds: torch.Size([77, 768]) text_encoder_2-item4.clip_pooled_prompt_embeds: torch.Size([1280]) text_encoder_2-item4.clip_prompt_embeds: torch.Size([77, 1280]) text_encoder_3-item5.t5_prompt_embeds: torch.Size([77, 4096]) vae.decoder: torch.Size([16, 128, 128]) vae.encoder_f0: torch.Size([32, 128, 128]) vae.encoder_f1: torch.Size([32, 128, 128]) Per-key MSE Breakdown (Quant=Truncation+Quantization, Codec=Compression): Key Quant-MSE Total-MSE ------------------------------------------------------------------------------------- text_encoder-item0.clip_pooled_prompt_embeds 0.00023681 0.93672069 text_encoder-item0.clip_prompt_embeds 0.00023057 23.92751243 text_encoder_2-item1.clip_pooled_prompt_embeds 0.00023879 0.94562416 text_encoder_2-item1.clip_prompt_embeds 0.00123217 0.13309284 text_encoder_3-item2.t5_prompt_embeds 0.00000768 0.00336660 text_encoder-item3.clip_pooled_prompt_embeds 0.00841799 1.01420029 text_encoder-item3.clip_prompt_embeds 0.00023247 23.90570887 text_encoder_2-item4.clip_pooled_prompt_embeds 0.00016751 0.67051430 text_encoder_2-item4.clip_prompt_embeds 0.00009830 0.47049568 text_encoder_3-item5.t5_prompt_embeds 0.00001263 0.00227117 vae.encoder_f0 0.00881784 3.07203364 vae.encoder_f1 0.00882136 3.07392502 vae.decoder 0.00017598 0.03536221 ------------------------------------------------------------------------------------- TOTAL 0.00418676 2.70899786 (elements=2,260,992) ---------------------- -------------------------------------------------------- SAMPLE-WISE STATISTICS ---------------------- -------------------------------------------------------- Handler sd35 Strategy individual Architecture hyperprior-featurecoding ---------------------- -------------------------------------------------------- Total Elements 2260992 Total Bytes 119544 BPFP 0.4230 bits/point EBPFP 0.8460 equivalent bits/point MSE 2.708998 ---------------------- -------------------------------------------------------- Time: 0.754s Load: 0.008s, Pack+Encode: 0.293s, Decode+Unpack: 0.453s ---------------------- -------------------------------------------------------- Restored Feature Format: [Dict] with 8 keys key['text_encoder-item0']: [Dict] with 2 keys key['clip_pooled_prompt_embeds']: [Tensor] shape=torch.Size([768]), dtype=torch.float16, device=cpu key['clip_prompt_embeds']: [Tensor] shape=torch.Size([77, 768]), dtype=torch.float16, device=cpu key['text_encoder_2-item1']: [Dict] with 2 keys key['clip_pooled_prompt_embeds']: [Tensor] shape=torch.Size([1280]), dtype=torch.float16, device=cpu key['clip_prompt_embeds']: [Tensor] shape=torch.Size([77, 1280]), dtype=torch.float16, device=cpu key['text_encoder_3-item2']: [Dict] with 1 keys key['t5_prompt_embeds']: [Tensor] shape=torch.Size([77, 4096]), dtype=torch.float16, device=cpu key['text_encoder-item3']: [Dict] with 2 keys key['clip_pooled_prompt_embeds']: [Tensor] shape=torch.Size([768]), dtype=torch.float16, device=cpu key['clip_prompt_embeds']: [Tensor] shape=torch.Size([77, 768]), dtype=torch.float16, device=cpu key['text_encoder_2-item4']: [Dict] with 2 keys key['clip_pooled_prompt_embeds']: [Tensor] shape=torch.Size([1280]), dtype=torch.float16, device=cpu key['clip_prompt_embeds']: [Tensor] shape=torch.Size([77, 1280]), dtype=torch.float16, device=cpu key['text_encoder_3-item5']: [Dict] with 1 keys key['t5_prompt_embeds']: [Tensor] shape=torch.Size([77, 4096]), dtype=torch.float16, device=cpu key['vae.decoder']: [Tensor] shape=torch.Size([16, 128, 128]), dtype=torch.float16, device=cpu key['vae.encoder']: [Dict] with 2 keys key['vae.encoder_f0']: [Tensor] shape=torch.Size([32, 128, 128]), dtype=torch.float16, device=cpu key['vae.encoder_f1']: [Tensor] shape=torch.Size([32, 128, 128]), dtype=torch.float16, device=cpu 💾 Converting with 2.7090 MSE: from ../datasets/FeatureCoding-StableDiffusion3.5/100samples_features/000000168458.zst to output-fixed/sd35/lambda0.004/hyperprior-featurecoding-8bit-individual/sd35cond/000000168458.zst 💪 Processing: ../datasets/FeatureCoding-StableDiffusion3.5/100samples_features/000000171788.zst (32/100) [1/4] FRONTEND: Loading features from ../datasets/FeatureCoding-StableDiffusion3.5/100samples_features/000000171788.zst... Original data structure: root: [Dict] with 8 keys key['text_encoder-item0']: [Dict] with 2 keys key['clip_pooled_prompt_embeds']: [Tensor] shape=torch.Size([768]), dtype=torch.float16, device=cpu key['clip_prompt_embeds']: [Tensor] shape=torch.Size([77, 768]), dtype=torch.float16, device=cpu key['text_encoder_2-item1']: [Dict] with 2 keys key['clip_pooled_prompt_embeds']: [Tensor] shape=torch.Size([1280]), dtype=torch.float16, device=cpu key['clip_prompt_embeds']: [Tensor] shape=torch.Size([77, 1280]), dtype=torch.float16, device=cpu key['text_encoder_3-item2']: [Dict] with 1 keys key['t5_prompt_embeds']: [Tensor] shape=torch.Size([77, 4096]), dtype=torch.float16, device=cpu key['text_encoder-item3']: [Dict] with 2 keys key['clip_pooled_prompt_embeds']: [Tensor] shape=torch.Size([768]), dtype=torch.float16, device=cpu key['clip_prompt_embeds']: [Tensor] shape=torch.Size([77, 768]), dtype=torch.float16, device=cpu key['text_encoder_2-item4']: [Dict] with 2 keys key['clip_pooled_prompt_embeds']: [Tensor] shape=torch.Size([1280]), dtype=torch.float16, device=cpu key['clip_prompt_embeds']: [Tensor] shape=torch.Size([77, 1280]), dtype=torch.float16, device=cpu key['text_encoder_3-item5']: [Dict] with 1 keys key['t5_prompt_embeds']: [Tensor] shape=torch.Size([77, 4096]), dtype=torch.float16, device=cpu key['vae.encoder-item6']: [Dict] with 2 keys key['vae.encoder_f0']: [Tensor] shape=torch.Size([32, 128, 128]), dtype=torch.float16, device=cpu key['vae.encoder_f1']: [Tensor] shape=torch.Size([32, 128, 128]), dtype=torch.float16, device=cpu key['vae.decoder']: [Tensor] shape=torch.Size([16, 128, 128]), dtype=torch.float16, device=cpu ⌛️ [1/4] FRONTEND: Load time: 0.009s ------------------------------------------------------------ SD3.5 Features Summary ------------------------------------------------------------ Number of text encoders: 3 Encoder names: ['text_encoder', 'text_encoder_2', 'text_encoder_3'] Has VAE latents: True Has VAE encoder features: True Data type: torch.float16 text_encoder-item0: clip_pooled_prompt_embeds: torch.Size([768]), torch.float16 clip_prompt_embeds: torch.Size([77, 768]), torch.float16 text_encoder_2-item1: clip_pooled_prompt_embeds: torch.Size([1280]), torch.float16 clip_prompt_embeds: torch.Size([77, 1280]), torch.float16 text_encoder_3-item2: t5_prompt_embeds: torch.Size([77, 4096]), torch.float16 text_encoder-item3: clip_pooled_prompt_embeds: torch.Size([768]), torch.float16 clip_prompt_embeds: torch.Size([77, 768]), torch.float16 text_encoder_2-item4: clip_pooled_prompt_embeds: torch.Size([1280]), torch.float16 clip_prompt_embeds: torch.Size([77, 1280]), torch.float16 text_encoder_3-item5: t5_prompt_embeds: torch.Size([77, 4096]), torch.float16 vae.decoder latents: torch.Size([16, 128, 128]) vae.encoder features: vae.encoder_f0: torch.Size([32, 128, 128]), torch.float16 vae.encoder_f1: torch.Size([32, 128, 128]), torch.float16 ------------------------------------------------------------ [2/4] FRONTEND: Pack + Encode (strategy: individual)... IndividualPacker: text_encoder-item0.clip_pooled_prompt_embeds: torch.Size([768])->torch.Size([1, 1, 1, 768]) text_encoder-item0.clip_prompt_embeds: torch.Size([77, 768])->torch.Size([1, 1, 77, 768]) text_encoder_2-item1.clip_pooled_prompt_embeds: torch.Size([1280])->torch.Size([1, 1, 1, 1280]) text_encoder_2-item1.clip_prompt_embeds: torch.Size([77, 1280])->torch.Size([1, 1, 77, 1280]) text_encoder_3-item2.t5_prompt_embeds: torch.Size([77, 4096])->torch.Size([1, 1, 77, 4096]) text_encoder-item3.clip_pooled_prompt_embeds: torch.Size([768])->torch.Size([1, 1, 1, 768]) text_encoder-item3.clip_prompt_embeds: torch.Size([77, 768])->torch.Size([1, 1, 77, 768]) text_encoder_2-item4.clip_pooled_prompt_embeds: torch.Size([1280])->torch.Size([1, 1, 1, 1280]) text_encoder_2-item4.clip_prompt_embeds: torch.Size([77, 1280])->torch.Size([1, 1, 77, 1280]) text_encoder_3-item5.t5_prompt_embeds: torch.Size([77, 4096])->torch.Size([1, 1, 77, 4096]) vae.encoder_f0: torch.Size([32, 128, 128])->torch.Size([1, 1, 512, 1024]) vae.encoder_f1: torch.Size([32, 128, 128])->torch.Size([1, 1, 512, 1024]) vae.decoder: torch.Size([16, 128, 128])->torch.Size([1, 1, 512, 512]) Using per-key quantization points (text_encoder-item0.clip_pooled_prompt_embeds: torch.Size([256])) for text_encoder-item0.clip_pooled_prompt_embeds text_encoder-item0.clip_pooled_prompt_embeds: 724B, BPFP=7.5417 Using per-key quantization points (text_encoder-item0.clip_prompt_embeds: torch.Size([256])) for text_encoder-item0.clip_prompt_embeds text_encoder-item0.clip_prompt_embeds: 6,428B, BPFP=0.8696 Using per-key quantization points (text_encoder_2-item1.clip_pooled_prompt_embeds: torch.Size([256])) for text_encoder_2-item1.clip_pooled_prompt_embeds text_encoder_2-item1.clip_pooled_prompt_embeds: 1,204B, BPFP=7.5250 Using per-key quantization points (text_encoder_2-item1.clip_prompt_embeds: torch.Size([256])) for text_encoder_2-item1.clip_prompt_embeds text_encoder_2-item1.clip_prompt_embeds: 11,268B, BPFP=0.9146 Using per-key quantization points (text_encoder_3-item2.t5_prompt_embeds: torch.Size([256])) for text_encoder_3-item2.t5_prompt_embeds text_encoder_3-item2.t5_prompt_embeds: 27,604B, BPFP=0.7002 Using per-key quantization points (text_encoder-item3.clip_pooled_prompt_embeds: torch.Size([256])) for text_encoder-item3.clip_pooled_prompt_embeds text_encoder-item3.clip_pooled_prompt_embeds: 760B, BPFP=7.9167 Using per-key quantization points (text_encoder-item3.clip_prompt_embeds: torch.Size([256])) for text_encoder-item3.clip_prompt_embeds text_encoder-item3.clip_prompt_embeds: 4,024B, BPFP=0.5444 Using per-key quantization points (text_encoder_2-item4.clip_pooled: torch.Size([256])) for text_encoder_2-item4.clip_pooled_prompt_embeds text_encoder_2-item4.clip_pooled_prompt_embeds: 1,268B, BPFP=7.9250 Using per-key quantization points (text_encoder_2-item4.clip_prompt: torch.Size([256])) for text_encoder_2-item4.clip_prompt_embeds text_encoder_2-item4.clip_prompt_embeds: 6,996B, BPFP=0.5679 Using per-key quantization points (text_encoder_3-item5.t5_prompt_embeds: torch.Size([256])) for text_encoder_3-item5.t5_prompt_embeds text_encoder_3-item5.t5_prompt_embeds: 18,416B, BPFP=0.4671 Using per-key quantization points (vae.encoder_f0: torch.Size([256])) for vae.encoder_f0 vae.encoder_f0: 14,000B, BPFP=0.2136 Using per-key quantization points (vae.encoder_f1: torch.Size([256])) for vae.encoder_f1 vae.encoder_f1: 14,000B, BPFP=0.2136 Using per-key quantization points (vae.decoder: torch.Size([256])) for vae.decoder vae.decoder: 8,228B, BPFP=0.2511 ⌛️ [2/4] FRONTEND: Frontend time: 0.293s (Pack+Encode) [3/4] BACKEND: Decode + Unpack... Using per-key quantization points (text_encoder-item0.clip_pooled_prompt_embeds: torch.Size([256])) for text_encoder-item0.clip_pooled_prompt_embeds Using per-key quantization points (text_encoder-item0.clip_prompt_embeds: torch.Size([256])) for text_encoder-item0.clip_prompt_embeds Using per-key quantization points (text_encoder_2-item1.clip_pooled_prompt_embeds: torch.Size([256])) for text_encoder_2-item1.clip_pooled_prompt_embeds Using per-key quantization points (text_encoder_2-item1.clip_prompt_embeds: torch.Size([256])) for text_encoder_2-item1.clip_prompt_embeds Using per-key quantization points (text_encoder_3-item2.t5_prompt_embeds: torch.Size([256])) for text_encoder_3-item2.t5_prompt_embeds Using per-key quantization points (text_encoder-item3.clip_pooled_prompt_embeds: torch.Size([256])) for text_encoder-item3.clip_pooled_prompt_embeds Using per-key quantization points (text_encoder-item3.clip_prompt_embeds: torch.Size([256])) for text_encoder-item3.clip_prompt_embeds Using per-key quantization points (text_encoder_2-item4.clip_pooled: torch.Size([256])) for text_encoder_2-item4.clip_pooled_prompt_embeds Using per-key quantization points (text_encoder_2-item4.clip_prompt: torch.Size([256])) for text_encoder_2-item4.clip_prompt_embeds Using per-key quantization points (text_encoder_3-item5.t5_prompt_embeds: torch.Size([256])) for text_encoder_3-item5.t5_prompt_embeds Using per-key quantization points (vae.encoder_f0: torch.Size([256])) for vae.encoder_f0 Using per-key quantization points (vae.encoder_f1: torch.Size([256])) for vae.encoder_f1 Using per-key quantization points (vae.decoder: torch.Size([256])) for vae.decoder IndividualUnPacker: text_encoder-item0.clip_pooled_prompt_embeds: torch.Size([768]) text_encoder-item0.clip_prompt_embeds: torch.Size([77, 768]) text_encoder_2-item1.clip_pooled_prompt_embeds: torch.Size([1280]) text_encoder_2-item1.clip_prompt_embeds: torch.Size([77, 1280]) text_encoder_3-item2.t5_prompt_embeds: torch.Size([77, 4096]) text_encoder-item3.clip_pooled_prompt_embeds: torch.Size([768]) text_encoder-item3.clip_prompt_embeds: torch.Size([77, 768]) text_encoder_2-item4.clip_pooled_prompt_embeds: torch.Size([1280]) text_encoder_2-item4.clip_prompt_embeds: torch.Size([77, 1280]) text_encoder_3-item5.t5_prompt_embeds: torch.Size([77, 4096]) vae.decoder: torch.Size([16, 128, 128]) vae.encoder_f0: torch.Size([32, 128, 128]) vae.encoder_f1: torch.Size([32, 128, 128]) ⌛️ [3/4] BACKEND: Backend time: 0.447s [4/4] METRICS: Computing MSE Breakdown... Using per-key quantization points (text_encoder-item0.clip_pooled_prompt_embeds: torch.Size([256])) for text_encoder-item0.clip_pooled_prompt_embeds Using per-key quantization points (text_encoder-item0.clip_prompt_embeds: torch.Size([256])) for text_encoder-item0.clip_prompt_embeds Using per-key quantization points (text_encoder_2-item1.clip_pooled_prompt_embeds: torch.Size([256])) for text_encoder_2-item1.clip_pooled_prompt_embeds Using per-key quantization points (text_encoder_2-item1.clip_prompt_embeds: torch.Size([256])) for text_encoder_2-item1.clip_prompt_embeds Using per-key quantization points (text_encoder_3-item2.t5_prompt_embeds: torch.Size([256])) for text_encoder_3-item2.t5_prompt_embeds Using per-key quantization points (text_encoder-item3.clip_pooled_prompt_embeds: torch.Size([256])) for text_encoder-item3.clip_pooled_prompt_embeds Using per-key quantization points (text_encoder-item3.clip_prompt_embeds: torch.Size([256])) for text_encoder-item3.clip_prompt_embeds Using per-key quantization points (text_encoder_2-item4.clip_pooled: torch.Size([256])) for text_encoder_2-item4.clip_pooled_prompt_embeds Using per-key quantization points (text_encoder_2-item4.clip_prompt: torch.Size([256])) for text_encoder_2-item4.clip_prompt_embeds Using per-key quantization points (text_encoder_3-item5.t5_prompt_embeds: torch.Size([256])) for text_encoder_3-item5.t5_prompt_embeds Using per-key quantization points (vae.encoder_f0: torch.Size([256])) for vae.encoder_f0 Using per-key quantization points (vae.encoder_f1: torch.Size([256])) for vae.encoder_f1 Using per-key quantization points (vae.decoder: torch.Size([256])) for vae.decoder IndividualUnPacker: text_encoder-item0.clip_pooled_prompt_embeds: torch.Size([768]) text_encoder-item0.clip_prompt_embeds: torch.Size([77, 768]) text_encoder_2-item1.clip_pooled_prompt_embeds: torch.Size([1280]) text_encoder_2-item1.clip_prompt_embeds: torch.Size([77, 1280]) text_encoder_3-item2.t5_prompt_embeds: torch.Size([77, 4096]) text_encoder-item3.clip_pooled_prompt_embeds: torch.Size([768]) text_encoder-item3.clip_prompt_embeds: torch.Size([77, 768]) text_encoder_2-item4.clip_pooled_prompt_embeds: torch.Size([1280]) text_encoder_2-item4.clip_prompt_embeds: torch.Size([77, 1280]) text_encoder_3-item5.t5_prompt_embeds: torch.Size([77, 4096]) vae.decoder: torch.Size([16, 128, 128]) vae.encoder_f0: torch.Size([32, 128, 128]) vae.encoder_f1: torch.Size([32, 128, 128]) Per-key MSE Breakdown (Quant=Truncation+Quantization, Codec=Compression): Key Quant-MSE Total-MSE ------------------------------------------------------------------------------------- text_encoder-item0.clip_pooled_prompt_embeds 0.00038174 0.93624973 text_encoder-item0.clip_prompt_embeds 0.00025208 34.95843057 text_encoder_2-item1.clip_pooled_prompt_embeds 0.00047028 0.90315208 text_encoder_2-item1.clip_prompt_embeds 0.00113921 0.13290990 text_encoder_3-item2.t5_prompt_embeds 0.00000794 0.00324598 text_encoder-item3.clip_pooled_prompt_embeds 0.00841799 1.01420029 text_encoder-item3.clip_prompt_embeds 0.00023247 23.90570887 text_encoder_2-item4.clip_pooled_prompt_embeds 0.00016751 0.67051430 text_encoder_2-item4.clip_prompt_embeds 0.00009830 0.47049568 text_encoder_3-item5.t5_prompt_embeds 0.00001263 0.00227117 vae.encoder_f0 0.00582247 1.67626643 vae.encoder_f1 0.00582996 1.67604637 vae.decoder 0.00016099 0.07558111 ------------------------------------------------------------------------------------- TOTAL 0.00279351 2.35432245 (elements=2,260,992) ---------------------- -------------------------------------------------------- SAMPLE-WISE STATISTICS ---------------------- -------------------------------------------------------- Handler sd35 Strategy individual Architecture hyperprior-featurecoding ---------------------- -------------------------------------------------------- Total Elements 2260992 Total Bytes 114920 BPFP 0.4066 bits/point EBPFP 0.8132 equivalent bits/point MSE 2.354322 ---------------------- -------------------------------------------------------- Time: 0.749s Load: 0.009s, Pack+Encode: 0.293s, Decode+Unpack: 0.447s ---------------------- -------------------------------------------------------- Restored Feature Format: [Dict] with 8 keys key['text_encoder-item0']: [Dict] with 2 keys key['clip_pooled_prompt_embeds']: [Tensor] shape=torch.Size([768]), dtype=torch.float16, device=cpu key['clip_prompt_embeds']: [Tensor] shape=torch.Size([77, 768]), dtype=torch.float16, device=cpu key['text_encoder_2-item1']: [Dict] with 2 keys key['clip_pooled_prompt_embeds']: [Tensor] shape=torch.Size([1280]), dtype=torch.float16, device=cpu key['clip_prompt_embeds']: [Tensor] shape=torch.Size([77, 1280]), dtype=torch.float16, device=cpu key['text_encoder_3-item2']: [Dict] with 1 keys key['t5_prompt_embeds']: [Tensor] shape=torch.Size([77, 4096]), dtype=torch.float16, device=cpu key['text_encoder-item3']: [Dict] with 2 keys key['clip_pooled_prompt_embeds']: [Tensor] shape=torch.Size([768]), dtype=torch.float16, device=cpu key['clip_prompt_embeds']: [Tensor] shape=torch.Size([77, 768]), dtype=torch.float16, device=cpu key['text_encoder_2-item4']: [Dict] with 2 keys key['clip_pooled_prompt_embeds']: [Tensor] shape=torch.Size([1280]), dtype=torch.float16, device=cpu key['clip_prompt_embeds']: [Tensor] shape=torch.Size([77, 1280]), dtype=torch.float16, device=cpu key['text_encoder_3-item5']: [Dict] with 1 keys key['t5_prompt_embeds']: [Tensor] shape=torch.Size([77, 4096]), dtype=torch.float16, device=cpu key['vae.decoder']: [Tensor] shape=torch.Size([16, 128, 128]), dtype=torch.float16, device=cpu key['vae.encoder']: [Dict] with 2 keys key['vae.encoder_f0']: [Tensor] shape=torch.Size([32, 128, 128]), dtype=torch.float16, device=cpu key['vae.encoder_f1']: [Tensor] shape=torch.Size([32, 128, 128]), dtype=torch.float16, device=cpu 💾 Converting with 2.3543 MSE: from ../datasets/FeatureCoding-StableDiffusion3.5/100samples_features/000000171788.zst to output-fixed/sd35/lambda0.004/hyperprior-featurecoding-8bit-individual/sd35cond/000000171788.zst 💪 Processing: ../datasets/FeatureCoding-StableDiffusion3.5/100samples_features/000000179265.zst (33/100) [1/4] FRONTEND: Loading features from ../datasets/FeatureCoding-StableDiffusion3.5/100samples_features/000000179265.zst... Original data structure: root: [Dict] with 8 keys key['text_encoder-item0']: [Dict] with 2 keys key['clip_pooled_prompt_embeds']: [Tensor] shape=torch.Size([768]), dtype=torch.float16, device=cpu key['clip_prompt_embeds']: [Tensor] shape=torch.Size([77, 768]), dtype=torch.float16, device=cpu key['text_encoder_2-item1']: [Dict] with 2 keys key['clip_pooled_prompt_embeds']: [Tensor] shape=torch.Size([1280]), dtype=torch.float16, device=cpu key['clip_prompt_embeds']: [Tensor] shape=torch.Size([77, 1280]), dtype=torch.float16, device=cpu key['text_encoder_3-item2']: [Dict] with 1 keys key['t5_prompt_embeds']: [Tensor] shape=torch.Size([77, 4096]), dtype=torch.float16, device=cpu key['text_encoder-item3']: [Dict] with 2 keys key['clip_pooled_prompt_embeds']: [Tensor] shape=torch.Size([768]), dtype=torch.float16, device=cpu key['clip_prompt_embeds']: [Tensor] shape=torch.Size([77, 768]), dtype=torch.float16, device=cpu key['text_encoder_2-item4']: [Dict] with 2 keys key['clip_pooled_prompt_embeds']: [Tensor] shape=torch.Size([1280]), dtype=torch.float16, device=cpu key['clip_prompt_embeds']: [Tensor] shape=torch.Size([77, 1280]), dtype=torch.float16, device=cpu key['text_encoder_3-item5']: [Dict] with 1 keys key['t5_prompt_embeds']: [Tensor] shape=torch.Size([77, 4096]), dtype=torch.float16, device=cpu key['vae.encoder-item6']: [Dict] with 2 keys key['vae.encoder_f0']: [Tensor] shape=torch.Size([32, 128, 128]), dtype=torch.float16, device=cpu key['vae.encoder_f1']: [Tensor] shape=torch.Size([32, 128, 128]), dtype=torch.float16, device=cpu key['vae.decoder']: [Tensor] shape=torch.Size([16, 128, 128]), dtype=torch.float16, device=cpu ⌛️ [1/4] FRONTEND: Load time: 0.009s ------------------------------------------------------------ SD3.5 Features Summary ------------------------------------------------------------ Number of text encoders: 3 Encoder names: ['text_encoder', 'text_encoder_2', 'text_encoder_3'] Has VAE latents: True Has VAE encoder features: True Data type: torch.float16 text_encoder-item0: clip_pooled_prompt_embeds: torch.Size([768]), torch.float16 clip_prompt_embeds: torch.Size([77, 768]), torch.float16 text_encoder_2-item1: clip_pooled_prompt_embeds: torch.Size([1280]), torch.float16 clip_prompt_embeds: torch.Size([77, 1280]), torch.float16 text_encoder_3-item2: t5_prompt_embeds: torch.Size([77, 4096]), torch.float16 text_encoder-item3: clip_pooled_prompt_embeds: torch.Size([768]), torch.float16 clip_prompt_embeds: torch.Size([77, 768]), torch.float16 text_encoder_2-item4: clip_pooled_prompt_embeds: torch.Size([1280]), torch.float16 clip_prompt_embeds: torch.Size([77, 1280]), torch.float16 text_encoder_3-item5: t5_prompt_embeds: torch.Size([77, 4096]), torch.float16 vae.decoder latents: torch.Size([16, 128, 128]) vae.encoder features: vae.encoder_f0: torch.Size([32, 128, 128]), torch.float16 vae.encoder_f1: torch.Size([32, 128, 128]), torch.float16 ------------------------------------------------------------ [2/4] FRONTEND: Pack + Encode (strategy: individual)... IndividualPacker: text_encoder-item0.clip_pooled_prompt_embeds: torch.Size([768])->torch.Size([1, 1, 1, 768]) text_encoder-item0.clip_prompt_embeds: torch.Size([77, 768])->torch.Size([1, 1, 77, 768]) text_encoder_2-item1.clip_pooled_prompt_embeds: torch.Size([1280])->torch.Size([1, 1, 1, 1280]) text_encoder_2-item1.clip_prompt_embeds: torch.Size([77, 1280])->torch.Size([1, 1, 77, 1280]) text_encoder_3-item2.t5_prompt_embeds: torch.Size([77, 4096])->torch.Size([1, 1, 77, 4096]) text_encoder-item3.clip_pooled_prompt_embeds: torch.Size([768])->torch.Size([1, 1, 1, 768]) text_encoder-item3.clip_prompt_embeds: torch.Size([77, 768])->torch.Size([1, 1, 77, 768]) text_encoder_2-item4.clip_pooled_prompt_embeds: torch.Size([1280])->torch.Size([1, 1, 1, 1280]) text_encoder_2-item4.clip_prompt_embeds: torch.Size([77, 1280])->torch.Size([1, 1, 77, 1280]) text_encoder_3-item5.t5_prompt_embeds: torch.Size([77, 4096])->torch.Size([1, 1, 77, 4096]) vae.encoder_f0: torch.Size([32, 128, 128])->torch.Size([1, 1, 512, 1024]) vae.encoder_f1: torch.Size([32, 128, 128])->torch.Size([1, 1, 512, 1024]) vae.decoder: torch.Size([16, 128, 128])->torch.Size([1, 1, 512, 512]) Using per-key quantization points (text_encoder-item0.clip_pooled_prompt_embeds: torch.Size([256])) for text_encoder-item0.clip_pooled_prompt_embeds text_encoder-item0.clip_pooled_prompt_embeds: 744B, BPFP=7.7500 Using per-key quantization points (text_encoder-item0.clip_prompt_embeds: torch.Size([256])) for text_encoder-item0.clip_prompt_embeds text_encoder-item0.clip_prompt_embeds: 6,204B, BPFP=0.8393 Using per-key quantization points (text_encoder_2-item1.clip_pooled_prompt_embeds: torch.Size([256])) for text_encoder_2-item1.clip_pooled_prompt_embeds text_encoder_2-item1.clip_pooled_prompt_embeds: 1,184B, BPFP=7.4000 Using per-key quantization points (text_encoder_2-item1.clip_prompt_embeds: torch.Size([256])) for text_encoder_2-item1.clip_prompt_embeds text_encoder_2-item1.clip_prompt_embeds: 11,236B, BPFP=0.9120 Using per-key quantization points (text_encoder_3-item2.t5_prompt_embeds: torch.Size([256])) for text_encoder_3-item2.t5_prompt_embeds text_encoder_3-item2.t5_prompt_embeds: 26,156B, BPFP=0.6635 Using per-key quantization points (text_encoder-item3.clip_pooled_prompt_embeds: torch.Size([256])) for text_encoder-item3.clip_pooled_prompt_embeds text_encoder-item3.clip_pooled_prompt_embeds: 760B, BPFP=7.9167 Using per-key quantization points (text_encoder-item3.clip_prompt_embeds: torch.Size([256])) for text_encoder-item3.clip_prompt_embeds text_encoder-item3.clip_prompt_embeds: 4,024B, BPFP=0.5444 Using per-key quantization points (text_encoder_2-item4.clip_pooled: torch.Size([256])) for text_encoder_2-item4.clip_pooled_prompt_embeds text_encoder_2-item4.clip_pooled_prompt_embeds: 1,268B, BPFP=7.9250 Using per-key quantization points (text_encoder_2-item4.clip_prompt: torch.Size([256])) for text_encoder_2-item4.clip_prompt_embeds text_encoder_2-item4.clip_prompt_embeds: 6,996B, BPFP=0.5679 Using per-key quantization points (text_encoder_3-item5.t5_prompt_embeds: torch.Size([256])) for text_encoder_3-item5.t5_prompt_embeds text_encoder_3-item5.t5_prompt_embeds: 18,416B, BPFP=0.4671 Using per-key quantization points (vae.encoder_f0: torch.Size([256])) for vae.encoder_f0 vae.encoder_f0: 21,128B, BPFP=0.3224 Using per-key quantization points (vae.encoder_f1: torch.Size([256])) for vae.encoder_f1 vae.encoder_f1: 21,128B, BPFP=0.3224 Using per-key quantization points (vae.decoder: torch.Size([256])) for vae.decoder vae.decoder: 6,992B, BPFP=0.2134 ⌛️ [2/4] FRONTEND: Frontend time: 0.296s (Pack+Encode) [3/4] BACKEND: Decode + Unpack... Using per-key quantization points (text_encoder-item0.clip_pooled_prompt_embeds: torch.Size([256])) for text_encoder-item0.clip_pooled_prompt_embeds Using per-key quantization points (text_encoder-item0.clip_prompt_embeds: torch.Size([256])) for text_encoder-item0.clip_prompt_embeds Using per-key quantization points (text_encoder_2-item1.clip_pooled_prompt_embeds: torch.Size([256])) for text_encoder_2-item1.clip_pooled_prompt_embeds Using per-key quantization points (text_encoder_2-item1.clip_prompt_embeds: torch.Size([256])) for text_encoder_2-item1.clip_prompt_embeds Using per-key quantization points (text_encoder_3-item2.t5_prompt_embeds: torch.Size([256])) for text_encoder_3-item2.t5_prompt_embeds Using per-key quantization points (text_encoder-item3.clip_pooled_prompt_embeds: torch.Size([256])) for text_encoder-item3.clip_pooled_prompt_embeds Using per-key quantization points (text_encoder-item3.clip_prompt_embeds: torch.Size([256])) for text_encoder-item3.clip_prompt_embeds Using per-key quantization points (text_encoder_2-item4.clip_pooled: torch.Size([256])) for text_encoder_2-item4.clip_pooled_prompt_embeds Using per-key quantization points (text_encoder_2-item4.clip_prompt: torch.Size([256])) for text_encoder_2-item4.clip_prompt_embeds Using per-key quantization points (text_encoder_3-item5.t5_prompt_embeds: torch.Size([256])) for text_encoder_3-item5.t5_prompt_embeds Using per-key quantization points (vae.encoder_f0: torch.Size([256])) for vae.encoder_f0 Using per-key quantization points (vae.encoder_f1: torch.Size([256])) for vae.encoder_f1 Using per-key quantization points (vae.decoder: torch.Size([256])) for vae.decoder IndividualUnPacker: text_encoder-item0.clip_pooled_prompt_embeds: torch.Size([768]) text_encoder-item0.clip_prompt_embeds: torch.Size([77, 768]) text_encoder_2-item1.clip_pooled_prompt_embeds: torch.Size([1280]) text_encoder_2-item1.clip_prompt_embeds: torch.Size([77, 1280]) text_encoder_3-item2.t5_prompt_embeds: torch.Size([77, 4096]) text_encoder-item3.clip_pooled_prompt_embeds: torch.Size([768]) text_encoder-item3.clip_prompt_embeds: torch.Size([77, 768]) text_encoder_2-item4.clip_pooled_prompt_embeds: torch.Size([1280]) text_encoder_2-item4.clip_prompt_embeds: torch.Size([77, 1280]) text_encoder_3-item5.t5_prompt_embeds: torch.Size([77, 4096]) vae.decoder: torch.Size([16, 128, 128]) vae.encoder_f0: torch.Size([32, 128, 128]) vae.encoder_f1: torch.Size([32, 128, 128]) ⌛️ [3/4] BACKEND: Backend time: 0.448s [4/4] METRICS: Computing MSE Breakdown... Using per-key quantization points (text_encoder-item0.clip_pooled_prompt_embeds: torch.Size([256])) for text_encoder-item0.clip_pooled_prompt_embeds Using per-key quantization points (text_encoder-item0.clip_prompt_embeds: torch.Size([256])) for text_encoder-item0.clip_prompt_embeds Using per-key quantization points (text_encoder_2-item1.clip_pooled_prompt_embeds: torch.Size([256])) for text_encoder_2-item1.clip_pooled_prompt_embeds Using per-key quantization points (text_encoder_2-item1.clip_prompt_embeds: torch.Size([256])) for text_encoder_2-item1.clip_prompt_embeds Using per-key quantization points (text_encoder_3-item2.t5_prompt_embeds: torch.Size([256])) for text_encoder_3-item2.t5_prompt_embeds Using per-key quantization points (text_encoder-item3.clip_pooled_prompt_embeds: torch.Size([256])) for text_encoder-item3.clip_pooled_prompt_embeds Using per-key quantization points (text_encoder-item3.clip_prompt_embeds: torch.Size([256])) for text_encoder-item3.clip_prompt_embeds Using per-key quantization points (text_encoder_2-item4.clip_pooled: torch.Size([256])) for text_encoder_2-item4.clip_pooled_prompt_embeds Using per-key quantization points (text_encoder_2-item4.clip_prompt: torch.Size([256])) for text_encoder_2-item4.clip_prompt_embeds Using per-key quantization points (text_encoder_3-item5.t5_prompt_embeds: torch.Size([256])) for text_encoder_3-item5.t5_prompt_embeds Using per-key quantization points (vae.encoder_f0: torch.Size([256])) for vae.encoder_f0 Using per-key quantization points (vae.encoder_f1: torch.Size([256])) for vae.encoder_f1 Using per-key quantization points (vae.decoder: torch.Size([256])) for vae.decoder IndividualUnPacker: text_encoder-item0.clip_pooled_prompt_embeds: torch.Size([768]) text_encoder-item0.clip_prompt_embeds: torch.Size([77, 768]) text_encoder_2-item1.clip_pooled_prompt_embeds: torch.Size([1280]) text_encoder_2-item1.clip_prompt_embeds: torch.Size([77, 1280]) text_encoder_3-item2.t5_prompt_embeds: torch.Size([77, 4096]) text_encoder-item3.clip_pooled_prompt_embeds: torch.Size([768]) text_encoder-item3.clip_prompt_embeds: torch.Size([77, 768]) text_encoder_2-item4.clip_pooled_prompt_embeds: torch.Size([1280]) text_encoder_2-item4.clip_prompt_embeds: torch.Size([77, 1280]) text_encoder_3-item5.t5_prompt_embeds: torch.Size([77, 4096]) vae.decoder: torch.Size([16, 128, 128]) vae.encoder_f0: torch.Size([32, 128, 128]) vae.encoder_f1: torch.Size([32, 128, 128]) Per-key MSE Breakdown (Quant=Truncation+Quantization, Codec=Compression): Key Quant-MSE Total-MSE ------------------------------------------------------------------------------------- text_encoder-item0.clip_pooled_prompt_embeds 0.00017989 0.93581033 text_encoder-item0.clip_prompt_embeds 0.00020809 36.01152006 text_encoder_2-item1.clip_pooled_prompt_embeds 0.00035925 0.92333622 text_encoder_2-item1.clip_prompt_embeds 0.00112984 0.13325733 text_encoder_3-item2.t5_prompt_embeds 0.00000832 0.00319981 text_encoder-item3.clip_pooled_prompt_embeds 0.00841799 1.01420029 text_encoder-item3.clip_prompt_embeds 0.00023247 23.90570887 text_encoder_2-item4.clip_pooled_prompt_embeds 0.00016751 0.67051430 text_encoder_2-item4.clip_prompt_embeds 0.00009830 0.47049568 text_encoder_3-item5.t5_prompt_embeds 0.00001263 0.00227117 vae.encoder_f0 0.00602745 2.87793422 vae.encoder_f1 0.00603159 2.87791896 vae.decoder 0.00017526 0.05981690 ------------------------------------------------------------------------------------- TOTAL 0.00288782 2.93740083 (elements=2,260,992) ---------------------- -------------------------------------------------------- SAMPLE-WISE STATISTICS ---------------------- -------------------------------------------------------- Handler sd35 Strategy individual Architecture hyperprior-featurecoding ---------------------- -------------------------------------------------------- Total Elements 2260992 Total Bytes 126236 BPFP 0.4467 bits/point EBPFP 0.8933 equivalent bits/point MSE 2.937401 ---------------------- -------------------------------------------------------- Time: 0.753s Load: 0.009s, Pack+Encode: 0.296s, Decode+Unpack: 0.448s ---------------------- -------------------------------------------------------- Restored Feature Format: [Dict] with 8 keys key['text_encoder-item0']: [Dict] with 2 keys key['clip_pooled_prompt_embeds']: [Tensor] shape=torch.Size([768]), dtype=torch.float16, device=cpu key['clip_prompt_embeds']: [Tensor] shape=torch.Size([77, 768]), dtype=torch.float16, device=cpu key['text_encoder_2-item1']: [Dict] with 2 keys key['clip_pooled_prompt_embeds']: [Tensor] shape=torch.Size([1280]), dtype=torch.float16, device=cpu key['clip_prompt_embeds']: [Tensor] shape=torch.Size([77, 1280]), dtype=torch.float16, device=cpu key['text_encoder_3-item2']: [Dict] with 1 keys key['t5_prompt_embeds']: [Tensor] shape=torch.Size([77, 4096]), dtype=torch.float16, device=cpu key['text_encoder-item3']: [Dict] with 2 keys key['clip_pooled_prompt_embeds']: [Tensor] shape=torch.Size([768]), dtype=torch.float16, device=cpu key['clip_prompt_embeds']: [Tensor] shape=torch.Size([77, 768]), dtype=torch.float16, device=cpu key['text_encoder_2-item4']: [Dict] with 2 keys key['clip_pooled_prompt_embeds']: [Tensor] shape=torch.Size([1280]), dtype=torch.float16, device=cpu key['clip_prompt_embeds']: [Tensor] shape=torch.Size([77, 1280]), dtype=torch.float16, device=cpu key['text_encoder_3-item5']: [Dict] with 1 keys key['t5_prompt_embeds']: [Tensor] shape=torch.Size([77, 4096]), dtype=torch.float16, device=cpu key['vae.decoder']: [Tensor] shape=torch.Size([16, 128, 128]), dtype=torch.float16, device=cpu key['vae.encoder']: [Dict] with 2 keys key['vae.encoder_f0']: [Tensor] shape=torch.Size([32, 128, 128]), dtype=torch.float16, device=cpu key['vae.encoder_f1']: [Tensor] shape=torch.Size([32, 128, 128]), dtype=torch.float16, device=cpu 💾 Converting with 2.9374 MSE: from ../datasets/FeatureCoding-StableDiffusion3.5/100samples_features/000000179265.zst to output-fixed/sd35/lambda0.004/hyperprior-featurecoding-8bit-individual/sd35cond/000000179265.zst 💪 Processing: ../datasets/FeatureCoding-StableDiffusion3.5/100samples_features/000000189752.zst (34/100) [1/4] FRONTEND: Loading features from ../datasets/FeatureCoding-StableDiffusion3.5/100samples_features/000000189752.zst... Original data structure: root: [Dict] with 8 keys key['text_encoder-item0']: [Dict] with 2 keys key['clip_pooled_prompt_embeds']: [Tensor] shape=torch.Size([768]), dtype=torch.float16, device=cpu key['clip_prompt_embeds']: [Tensor] shape=torch.Size([77, 768]), dtype=torch.float16, device=cpu key['text_encoder_2-item1']: [Dict] with 2 keys key['clip_pooled_prompt_embeds']: [Tensor] shape=torch.Size([1280]), dtype=torch.float16, device=cpu key['clip_prompt_embeds']: [Tensor] shape=torch.Size([77, 1280]), dtype=torch.float16, device=cpu key['text_encoder_3-item2']: [Dict] with 1 keys key['t5_prompt_embeds']: [Tensor] shape=torch.Size([77, 4096]), dtype=torch.float16, device=cpu key['text_encoder-item3']: [Dict] with 2 keys key['clip_pooled_prompt_embeds']: [Tensor] shape=torch.Size([768]), dtype=torch.float16, device=cpu key['clip_prompt_embeds']: [Tensor] shape=torch.Size([77, 768]), dtype=torch.float16, device=cpu key['text_encoder_2-item4']: [Dict] with 2 keys key['clip_pooled_prompt_embeds']: [Tensor] shape=torch.Size([1280]), dtype=torch.float16, device=cpu key['clip_prompt_embeds']: [Tensor] shape=torch.Size([77, 1280]), dtype=torch.float16, device=cpu key['text_encoder_3-item5']: [Dict] with 1 keys key['t5_prompt_embeds']: [Tensor] shape=torch.Size([77, 4096]), dtype=torch.float16, device=cpu key['vae.encoder-item6']: [Dict] with 2 keys key['vae.encoder_f0']: [Tensor] shape=torch.Size([32, 128, 128]), dtype=torch.float16, device=cpu key['vae.encoder_f1']: [Tensor] shape=torch.Size([32, 128, 128]), dtype=torch.float16, device=cpu key['vae.decoder']: [Tensor] shape=torch.Size([16, 128, 128]), dtype=torch.float16, device=cpu ⌛️ [1/4] FRONTEND: Load time: 0.009s ------------------------------------------------------------ SD3.5 Features Summary ------------------------------------------------------------ Number of text encoders: 3 Encoder names: ['text_encoder', 'text_encoder_2', 'text_encoder_3'] Has VAE latents: True Has VAE encoder features: True Data type: torch.float16 text_encoder-item0: clip_pooled_prompt_embeds: torch.Size([768]), torch.float16 clip_prompt_embeds: torch.Size([77, 768]), torch.float16 text_encoder_2-item1: clip_pooled_prompt_embeds: torch.Size([1280]), torch.float16 clip_prompt_embeds: torch.Size([77, 1280]), torch.float16 text_encoder_3-item2: t5_prompt_embeds: torch.Size([77, 4096]), torch.float16 text_encoder-item3: clip_pooled_prompt_embeds: torch.Size([768]), torch.float16 clip_prompt_embeds: torch.Size([77, 768]), torch.float16 text_encoder_2-item4: clip_pooled_prompt_embeds: torch.Size([1280]), torch.float16 clip_prompt_embeds: torch.Size([77, 1280]), torch.float16 text_encoder_3-item5: t5_prompt_embeds: torch.Size([77, 4096]), torch.float16 vae.decoder latents: torch.Size([16, 128, 128]) vae.encoder features: vae.encoder_f0: torch.Size([32, 128, 128]), torch.float16 vae.encoder_f1: torch.Size([32, 128, 128]), torch.float16 ------------------------------------------------------------ [2/4] FRONTEND: Pack + Encode (strategy: individual)... IndividualPacker: text_encoder-item0.clip_pooled_prompt_embeds: torch.Size([768])->torch.Size([1, 1, 1, 768]) text_encoder-item0.clip_prompt_embeds: torch.Size([77, 768])->torch.Size([1, 1, 77, 768]) text_encoder_2-item1.clip_pooled_prompt_embeds: torch.Size([1280])->torch.Size([1, 1, 1, 1280]) text_encoder_2-item1.clip_prompt_embeds: torch.Size([77, 1280])->torch.Size([1, 1, 77, 1280]) text_encoder_3-item2.t5_prompt_embeds: torch.Size([77, 4096])->torch.Size([1, 1, 77, 4096]) text_encoder-item3.clip_pooled_prompt_embeds: torch.Size([768])->torch.Size([1, 1, 1, 768]) text_encoder-item3.clip_prompt_embeds: torch.Size([77, 768])->torch.Size([1, 1, 77, 768]) text_encoder_2-item4.clip_pooled_prompt_embeds: torch.Size([1280])->torch.Size([1, 1, 1, 1280]) text_encoder_2-item4.clip_prompt_embeds: torch.Size([77, 1280])->torch.Size([1, 1, 77, 1280]) text_encoder_3-item5.t5_prompt_embeds: torch.Size([77, 4096])->torch.Size([1, 1, 77, 4096]) vae.encoder_f0: torch.Size([32, 128, 128])->torch.Size([1, 1, 512, 1024]) vae.encoder_f1: torch.Size([32, 128, 128])->torch.Size([1, 1, 512, 1024]) vae.decoder: torch.Size([16, 128, 128])->torch.Size([1, 1, 512, 512]) Using per-key quantization points (text_encoder-item0.clip_pooled_prompt_embeds: torch.Size([256])) for text_encoder-item0.clip_pooled_prompt_embeds text_encoder-item0.clip_pooled_prompt_embeds: 740B, BPFP=7.7083 Using per-key quantization points (text_encoder-item0.clip_prompt_embeds: torch.Size([256])) for text_encoder-item0.clip_prompt_embeds text_encoder-item0.clip_prompt_embeds: 5,564B, BPFP=0.7527 Using per-key quantization points (text_encoder_2-item1.clip_pooled_prompt_embeds: torch.Size([256])) for text_encoder_2-item1.clip_pooled_prompt_embeds text_encoder_2-item1.clip_pooled_prompt_embeds: 1,204B, BPFP=7.5250 Using per-key quantization points (text_encoder_2-item1.clip_prompt_embeds: torch.Size([256])) for text_encoder_2-item1.clip_prompt_embeds text_encoder_2-item1.clip_prompt_embeds: 10,632B, BPFP=0.8630 Using per-key quantization points (text_encoder_3-item2.t5_prompt_embeds: torch.Size([256])) for text_encoder_3-item2.t5_prompt_embeds text_encoder_3-item2.t5_prompt_embeds: 25,700B, BPFP=0.6519 Using per-key quantization points (text_encoder-item3.clip_pooled_prompt_embeds: torch.Size([256])) for text_encoder-item3.clip_pooled_prompt_embeds text_encoder-item3.clip_pooled_prompt_embeds: 760B, BPFP=7.9167 Using per-key quantization points (text_encoder-item3.clip_prompt_embeds: torch.Size([256])) for text_encoder-item3.clip_prompt_embeds text_encoder-item3.clip_prompt_embeds: 4,024B, BPFP=0.5444 Using per-key quantization points (text_encoder_2-item4.clip_pooled: torch.Size([256])) for text_encoder_2-item4.clip_pooled_prompt_embeds text_encoder_2-item4.clip_pooled_prompt_embeds: 1,268B, BPFP=7.9250 Using per-key quantization points (text_encoder_2-item4.clip_prompt: torch.Size([256])) for text_encoder_2-item4.clip_prompt_embeds text_encoder_2-item4.clip_prompt_embeds: 6,996B, BPFP=0.5679 Using per-key quantization points (text_encoder_3-item5.t5_prompt_embeds: torch.Size([256])) for text_encoder_3-item5.t5_prompt_embeds text_encoder_3-item5.t5_prompt_embeds: 18,416B, BPFP=0.4671 Using per-key quantization points (vae.encoder_f0: torch.Size([256])) for vae.encoder_f0 vae.encoder_f0: 21,640B, BPFP=0.3302 Using per-key quantization points (vae.encoder_f1: torch.Size([256])) for vae.encoder_f1 vae.encoder_f1: 21,636B, BPFP=0.3301 Using per-key quantization points (vae.decoder: torch.Size([256])) for vae.decoder vae.decoder: 8,584B, BPFP=0.2620 ⌛️ [2/4] FRONTEND: Frontend time: 0.294s (Pack+Encode) [3/4] BACKEND: Decode + Unpack... Using per-key quantization points (text_encoder-item0.clip_pooled_prompt_embeds: torch.Size([256])) for text_encoder-item0.clip_pooled_prompt_embeds Using per-key quantization points (text_encoder-item0.clip_prompt_embeds: torch.Size([256])) for text_encoder-item0.clip_prompt_embeds Using per-key quantization points (text_encoder_2-item1.clip_pooled_prompt_embeds: torch.Size([256])) for text_encoder_2-item1.clip_pooled_prompt_embeds Using per-key quantization points (text_encoder_2-item1.clip_prompt_embeds: torch.Size([256])) for text_encoder_2-item1.clip_prompt_embeds Using per-key quantization points (text_encoder_3-item2.t5_prompt_embeds: torch.Size([256])) for text_encoder_3-item2.t5_prompt_embeds Using per-key quantization points (text_encoder-item3.clip_pooled_prompt_embeds: torch.Size([256])) for text_encoder-item3.clip_pooled_prompt_embeds Using per-key quantization points (text_encoder-item3.clip_prompt_embeds: torch.Size([256])) for text_encoder-item3.clip_prompt_embeds Using per-key quantization points (text_encoder_2-item4.clip_pooled: torch.Size([256])) for text_encoder_2-item4.clip_pooled_prompt_embeds Using per-key quantization points (text_encoder_2-item4.clip_prompt: torch.Size([256])) for text_encoder_2-item4.clip_prompt_embeds Using per-key quantization points (text_encoder_3-item5.t5_prompt_embeds: torch.Size([256])) for text_encoder_3-item5.t5_prompt_embeds Using per-key quantization points (vae.encoder_f0: torch.Size([256])) for vae.encoder_f0 Using per-key quantization points (vae.encoder_f1: torch.Size([256])) for vae.encoder_f1 Using per-key quantization points (vae.decoder: torch.Size([256])) for vae.decoder IndividualUnPacker: text_encoder-item0.clip_pooled_prompt_embeds: torch.Size([768]) text_encoder-item0.clip_prompt_embeds: torch.Size([77, 768]) text_encoder_2-item1.clip_pooled_prompt_embeds: torch.Size([1280]) text_encoder_2-item1.clip_prompt_embeds: torch.Size([77, 1280]) text_encoder_3-item2.t5_prompt_embeds: torch.Size([77, 4096]) text_encoder-item3.clip_pooled_prompt_embeds: torch.Size([768]) text_encoder-item3.clip_prompt_embeds: torch.Size([77, 768]) text_encoder_2-item4.clip_pooled_prompt_embeds: torch.Size([1280]) text_encoder_2-item4.clip_prompt_embeds: torch.Size([77, 1280]) text_encoder_3-item5.t5_prompt_embeds: torch.Size([77, 4096]) vae.decoder: torch.Size([16, 128, 128]) vae.encoder_f0: torch.Size([32, 128, 128]) vae.encoder_f1: torch.Size([32, 128, 128]) ⌛️ [3/4] BACKEND: Backend time: 0.451s [4/4] METRICS: Computing MSE Breakdown... Using per-key quantization points (text_encoder-item0.clip_pooled_prompt_embeds: torch.Size([256])) for text_encoder-item0.clip_pooled_prompt_embeds Using per-key quantization points (text_encoder-item0.clip_prompt_embeds: torch.Size([256])) for text_encoder-item0.clip_prompt_embeds Using per-key quantization points (text_encoder_2-item1.clip_pooled_prompt_embeds: torch.Size([256])) for text_encoder_2-item1.clip_pooled_prompt_embeds Using per-key quantization points (text_encoder_2-item1.clip_prompt_embeds: torch.Size([256])) for text_encoder_2-item1.clip_prompt_embeds Using per-key quantization points (text_encoder_3-item2.t5_prompt_embeds: torch.Size([256])) for text_encoder_3-item2.t5_prompt_embeds Using per-key quantization points (text_encoder-item3.clip_pooled_prompt_embeds: torch.Size([256])) for text_encoder-item3.clip_pooled_prompt_embeds Using per-key quantization points (text_encoder-item3.clip_prompt_embeds: torch.Size([256])) for text_encoder-item3.clip_prompt_embeds Using per-key quantization points (text_encoder_2-item4.clip_pooled: torch.Size([256])) for text_encoder_2-item4.clip_pooled_prompt_embeds Using per-key quantization points (text_encoder_2-item4.clip_prompt: torch.Size([256])) for text_encoder_2-item4.clip_prompt_embeds Using per-key quantization points (text_encoder_3-item5.t5_prompt_embeds: torch.Size([256])) for text_encoder_3-item5.t5_prompt_embeds Using per-key quantization points (vae.encoder_f0: torch.Size([256])) for vae.encoder_f0 Using per-key quantization points (vae.encoder_f1: torch.Size([256])) for vae.encoder_f1 Using per-key quantization points (vae.decoder: torch.Size([256])) for vae.decoder IndividualUnPacker: text_encoder-item0.clip_pooled_prompt_embeds: torch.Size([768]) text_encoder-item0.clip_prompt_embeds: torch.Size([77, 768]) text_encoder_2-item1.clip_pooled_prompt_embeds: torch.Size([1280]) text_encoder_2-item1.clip_prompt_embeds: torch.Size([77, 1280]) text_encoder_3-item2.t5_prompt_embeds: torch.Size([77, 4096]) text_encoder-item3.clip_pooled_prompt_embeds: torch.Size([768]) text_encoder-item3.clip_prompt_embeds: torch.Size([77, 768]) text_encoder_2-item4.clip_pooled_prompt_embeds: torch.Size([1280]) text_encoder_2-item4.clip_prompt_embeds: torch.Size([77, 1280]) text_encoder_3-item5.t5_prompt_embeds: torch.Size([77, 4096]) vae.decoder: torch.Size([16, 128, 128]) vae.encoder_f0: torch.Size([32, 128, 128]) vae.encoder_f1: torch.Size([32, 128, 128]) Per-key MSE Breakdown (Quant=Truncation+Quantization, Codec=Compression): Key Quant-MSE Total-MSE ------------------------------------------------------------------------------------- text_encoder-item0.clip_pooled_prompt_embeds 0.00019078 0.90850528 text_encoder-item0.clip_prompt_embeds 0.00020908 24.22980291 text_encoder_2-item1.clip_pooled_prompt_embeds 0.00048701 0.91163120 text_encoder_2-item1.clip_prompt_embeds 0.00016227 0.11880111 text_encoder_3-item2.t5_prompt_embeds 0.00000845 0.00244882 text_encoder-item3.clip_pooled_prompt_embeds 0.00841799 1.01420029 text_encoder-item3.clip_prompt_embeds 0.00023247 23.90570887 text_encoder_2-item4.clip_pooled_prompt_embeds 0.00016751 0.67051430 text_encoder_2-item4.clip_prompt_embeds 0.00009830 0.47049568 text_encoder_3-item5.t5_prompt_embeds 0.00001263 0.00227117 vae.encoder_f0 0.00634616 2.90769339 vae.encoder_f1 0.00635208 2.90866613 vae.decoder 0.00022721 0.06450719 ------------------------------------------------------------------------------------- TOTAL 0.00300000 2.64307473 (elements=2,260,992) ---------------------- -------------------------------------------------------- SAMPLE-WISE STATISTICS ---------------------- -------------------------------------------------------- Handler sd35 Strategy individual Architecture hyperprior-featurecoding ---------------------- -------------------------------------------------------- Total Elements 2260992 Total Bytes 127164 BPFP 0.4499 bits/point EBPFP 0.8999 equivalent bits/point MSE 2.643075 ---------------------- -------------------------------------------------------- Time: 0.753s Load: 0.009s, Pack+Encode: 0.294s, Decode+Unpack: 0.451s ---------------------- -------------------------------------------------------- Restored Feature Format: [Dict] with 8 keys key['text_encoder-item0']: [Dict] with 2 keys key['clip_pooled_prompt_embeds']: [Tensor] shape=torch.Size([768]), dtype=torch.float16, device=cpu key['clip_prompt_embeds']: [Tensor] shape=torch.Size([77, 768]), dtype=torch.float16, device=cpu key['text_encoder_2-item1']: [Dict] with 2 keys key['clip_pooled_prompt_embeds']: [Tensor] shape=torch.Size([1280]), dtype=torch.float16, device=cpu key['clip_prompt_embeds']: [Tensor] shape=torch.Size([77, 1280]), dtype=torch.float16, device=cpu key['text_encoder_3-item2']: [Dict] with 1 keys key['t5_prompt_embeds']: [Tensor] shape=torch.Size([77, 4096]), dtype=torch.float16, device=cpu key['text_encoder-item3']: [Dict] with 2 keys key['clip_pooled_prompt_embeds']: [Tensor] shape=torch.Size([768]), dtype=torch.float16, device=cpu key['clip_prompt_embeds']: [Tensor] shape=torch.Size([77, 768]), dtype=torch.float16, device=cpu key['text_encoder_2-item4']: [Dict] with 2 keys key['clip_pooled_prompt_embeds']: [Tensor] shape=torch.Size([1280]), dtype=torch.float16, device=cpu key['clip_prompt_embeds']: [Tensor] shape=torch.Size([77, 1280]), dtype=torch.float16, device=cpu key['text_encoder_3-item5']: [Dict] with 1 keys key['t5_prompt_embeds']: [Tensor] shape=torch.Size([77, 4096]), dtype=torch.float16, device=cpu key['vae.decoder']: [Tensor] shape=torch.Size([16, 128, 128]), dtype=torch.float16, device=cpu key['vae.encoder']: [Dict] with 2 keys key['vae.encoder_f0']: [Tensor] shape=torch.Size([32, 128, 128]), dtype=torch.float16, device=cpu key['vae.encoder_f1']: [Tensor] shape=torch.Size([32, 128, 128]), dtype=torch.float16, device=cpu 💾 Converting with 2.6431 MSE: from ../datasets/FeatureCoding-StableDiffusion3.5/100samples_features/000000189752.zst to output-fixed/sd35/lambda0.004/hyperprior-featurecoding-8bit-individual/sd35cond/000000189752.zst 💪 Processing: ../datasets/FeatureCoding-StableDiffusion3.5/100samples_features/000000222118.zst (35/100) [1/4] FRONTEND: Loading features from ../datasets/FeatureCoding-StableDiffusion3.5/100samples_features/000000222118.zst... Original data structure: root: [Dict] with 8 keys key['text_encoder-item0']: [Dict] with 2 keys key['clip_pooled_prompt_embeds']: [Tensor] shape=torch.Size([768]), dtype=torch.float16, device=cpu key['clip_prompt_embeds']: [Tensor] shape=torch.Size([77, 768]), dtype=torch.float16, device=cpu key['text_encoder_2-item1']: [Dict] with 2 keys key['clip_pooled_prompt_embeds']: [Tensor] shape=torch.Size([1280]), dtype=torch.float16, device=cpu key['clip_prompt_embeds']: [Tensor] shape=torch.Size([77, 1280]), dtype=torch.float16, device=cpu key['text_encoder_3-item2']: [Dict] with 1 keys key['t5_prompt_embeds']: [Tensor] shape=torch.Size([77, 4096]), dtype=torch.float16, device=cpu key['text_encoder-item3']: [Dict] with 2 keys key['clip_pooled_prompt_embeds']: [Tensor] shape=torch.Size([768]), dtype=torch.float16, device=cpu key['clip_prompt_embeds']: [Tensor] shape=torch.Size([77, 768]), dtype=torch.float16, device=cpu key['text_encoder_2-item4']: [Dict] with 2 keys key['clip_pooled_prompt_embeds']: [Tensor] shape=torch.Size([1280]), dtype=torch.float16, device=cpu key['clip_prompt_embeds']: [Tensor] shape=torch.Size([77, 1280]), dtype=torch.float16, device=cpu key['text_encoder_3-item5']: [Dict] with 1 keys key['t5_prompt_embeds']: [Tensor] shape=torch.Size([77, 4096]), dtype=torch.float16, device=cpu key['vae.encoder-item6']: [Dict] with 2 keys key['vae.encoder_f0']: [Tensor] shape=torch.Size([32, 128, 128]), dtype=torch.float16, device=cpu key['vae.encoder_f1']: [Tensor] shape=torch.Size([32, 128, 128]), dtype=torch.float16, device=cpu key['vae.decoder']: [Tensor] shape=torch.Size([16, 128, 128]), dtype=torch.float16, device=cpu ⌛️ [1/4] FRONTEND: Load time: 0.008s ------------------------------------------------------------ SD3.5 Features Summary ------------------------------------------------------------ Number of text encoders: 3 Encoder names: ['text_encoder', 'text_encoder_2', 'text_encoder_3'] Has VAE latents: True Has VAE encoder features: True Data type: torch.float16 text_encoder-item0: clip_pooled_prompt_embeds: torch.Size([768]), torch.float16 clip_prompt_embeds: torch.Size([77, 768]), torch.float16 text_encoder_2-item1: clip_pooled_prompt_embeds: torch.Size([1280]), torch.float16 clip_prompt_embeds: torch.Size([77, 1280]), torch.float16 text_encoder_3-item2: t5_prompt_embeds: torch.Size([77, 4096]), torch.float16 text_encoder-item3: clip_pooled_prompt_embeds: torch.Size([768]), torch.float16 clip_prompt_embeds: torch.Size([77, 768]), torch.float16 text_encoder_2-item4: clip_pooled_prompt_embeds: torch.Size([1280]), torch.float16 clip_prompt_embeds: torch.Size([77, 1280]), torch.float16 text_encoder_3-item5: t5_prompt_embeds: torch.Size([77, 4096]), torch.float16 vae.decoder latents: torch.Size([16, 128, 128]) vae.encoder features: vae.encoder_f0: torch.Size([32, 128, 128]), torch.float16 vae.encoder_f1: torch.Size([32, 128, 128]), torch.float16 ------------------------------------------------------------ [2/4] FRONTEND: Pack + Encode (strategy: individual)... IndividualPacker: text_encoder-item0.clip_pooled_prompt_embeds: torch.Size([768])->torch.Size([1, 1, 1, 768]) text_encoder-item0.clip_prompt_embeds: torch.Size([77, 768])->torch.Size([1, 1, 77, 768]) text_encoder_2-item1.clip_pooled_prompt_embeds: torch.Size([1280])->torch.Size([1, 1, 1, 1280]) text_encoder_2-item1.clip_prompt_embeds: torch.Size([77, 1280])->torch.Size([1, 1, 77, 1280]) text_encoder_3-item2.t5_prompt_embeds: torch.Size([77, 4096])->torch.Size([1, 1, 77, 4096]) text_encoder-item3.clip_pooled_prompt_embeds: torch.Size([768])->torch.Size([1, 1, 1, 768]) text_encoder-item3.clip_prompt_embeds: torch.Size([77, 768])->torch.Size([1, 1, 77, 768]) text_encoder_2-item4.clip_pooled_prompt_embeds: torch.Size([1280])->torch.Size([1, 1, 1, 1280]) text_encoder_2-item4.clip_prompt_embeds: torch.Size([77, 1280])->torch.Size([1, 1, 77, 1280]) text_encoder_3-item5.t5_prompt_embeds: torch.Size([77, 4096])->torch.Size([1, 1, 77, 4096]) vae.encoder_f0: torch.Size([32, 128, 128])->torch.Size([1, 1, 512, 1024]) vae.encoder_f1: torch.Size([32, 128, 128])->torch.Size([1, 1, 512, 1024]) vae.decoder: torch.Size([16, 128, 128])->torch.Size([1, 1, 512, 512]) Using per-key quantization points (text_encoder-item0.clip_pooled_prompt_embeds: torch.Size([256])) for text_encoder-item0.clip_pooled_prompt_embeds text_encoder-item0.clip_pooled_prompt_embeds: 732B, BPFP=7.6250 Using per-key quantization points (text_encoder-item0.clip_prompt_embeds: torch.Size([256])) for text_encoder-item0.clip_prompt_embeds text_encoder-item0.clip_prompt_embeds: 5,732B, BPFP=0.7754 Using per-key quantization points (text_encoder_2-item1.clip_pooled_prompt_embeds: torch.Size([256])) for text_encoder_2-item1.clip_pooled_prompt_embeds text_encoder_2-item1.clip_pooled_prompt_embeds: 1,208B, BPFP=7.5500 Using per-key quantization points (text_encoder_2-item1.clip_prompt_embeds: torch.Size([256])) for text_encoder_2-item1.clip_prompt_embeds text_encoder_2-item1.clip_prompt_embeds: 10,308B, BPFP=0.8367 Using per-key quantization points (text_encoder_3-item2.t5_prompt_embeds: torch.Size([256])) for text_encoder_3-item2.t5_prompt_embeds text_encoder_3-item2.t5_prompt_embeds: 24,976B, BPFP=0.6335 Using per-key quantization points (text_encoder-item3.clip_pooled_prompt_embeds: torch.Size([256])) for text_encoder-item3.clip_pooled_prompt_embeds text_encoder-item3.clip_pooled_prompt_embeds: 760B, BPFP=7.9167 Using per-key quantization points (text_encoder-item3.clip_prompt_embeds: torch.Size([256])) for text_encoder-item3.clip_prompt_embeds text_encoder-item3.clip_prompt_embeds: 4,024B, BPFP=0.5444 Using per-key quantization points (text_encoder_2-item4.clip_pooled: torch.Size([256])) for text_encoder_2-item4.clip_pooled_prompt_embeds text_encoder_2-item4.clip_pooled_prompt_embeds: 1,268B, BPFP=7.9250 Using per-key quantization points (text_encoder_2-item4.clip_prompt: torch.Size([256])) for text_encoder_2-item4.clip_prompt_embeds text_encoder_2-item4.clip_prompt_embeds: 6,996B, BPFP=0.5679 Using per-key quantization points (text_encoder_3-item5.t5_prompt_embeds: torch.Size([256])) for text_encoder_3-item5.t5_prompt_embeds text_encoder_3-item5.t5_prompt_embeds: 18,416B, BPFP=0.4671 Using per-key quantization points (vae.encoder_f0: torch.Size([256])) for vae.encoder_f0 vae.encoder_f0: 11,628B, BPFP=0.1774 Using per-key quantization points (vae.encoder_f1: torch.Size([256])) for vae.encoder_f1 vae.encoder_f1: 11,624B, BPFP=0.1774 Using per-key quantization points (vae.decoder: torch.Size([256])) for vae.decoder vae.decoder: 4,276B, BPFP=0.1305 ⌛️ [2/4] FRONTEND: Frontend time: 0.294s (Pack+Encode) [3/4] BACKEND: Decode + Unpack... Using per-key quantization points (text_encoder-item0.clip_pooled_prompt_embeds: torch.Size([256])) for text_encoder-item0.clip_pooled_prompt_embeds Using per-key quantization points (text_encoder-item0.clip_prompt_embeds: torch.Size([256])) for text_encoder-item0.clip_prompt_embeds Using per-key quantization points (text_encoder_2-item1.clip_pooled_prompt_embeds: torch.Size([256])) for text_encoder_2-item1.clip_pooled_prompt_embeds Using per-key quantization points (text_encoder_2-item1.clip_prompt_embeds: torch.Size([256])) for text_encoder_2-item1.clip_prompt_embeds Using per-key quantization points (text_encoder_3-item2.t5_prompt_embeds: torch.Size([256])) for text_encoder_3-item2.t5_prompt_embeds Using per-key quantization points (text_encoder-item3.clip_pooled_prompt_embeds: torch.Size([256])) for text_encoder-item3.clip_pooled_prompt_embeds Using per-key quantization points (text_encoder-item3.clip_prompt_embeds: torch.Size([256])) for text_encoder-item3.clip_prompt_embeds Using per-key quantization points (text_encoder_2-item4.clip_pooled: torch.Size([256])) for text_encoder_2-item4.clip_pooled_prompt_embeds Using per-key quantization points (text_encoder_2-item4.clip_prompt: torch.Size([256])) for text_encoder_2-item4.clip_prompt_embeds Using per-key quantization points (text_encoder_3-item5.t5_prompt_embeds: torch.Size([256])) for text_encoder_3-item5.t5_prompt_embeds Using per-key quantization points (vae.encoder_f0: torch.Size([256])) for vae.encoder_f0 Using per-key quantization points (vae.encoder_f1: torch.Size([256])) for vae.encoder_f1 Using per-key quantization points (vae.decoder: torch.Size([256])) for vae.decoder IndividualUnPacker: text_encoder-item0.clip_pooled_prompt_embeds: torch.Size([768]) text_encoder-item0.clip_prompt_embeds: torch.Size([77, 768]) text_encoder_2-item1.clip_pooled_prompt_embeds: torch.Size([1280]) text_encoder_2-item1.clip_prompt_embeds: torch.Size([77, 1280]) text_encoder_3-item2.t5_prompt_embeds: torch.Size([77, 4096]) text_encoder-item3.clip_pooled_prompt_embeds: torch.Size([768]) text_encoder-item3.clip_prompt_embeds: torch.Size([77, 768]) text_encoder_2-item4.clip_pooled_prompt_embeds: torch.Size([1280]) text_encoder_2-item4.clip_prompt_embeds: torch.Size([77, 1280]) text_encoder_3-item5.t5_prompt_embeds: torch.Size([77, 4096]) vae.decoder: torch.Size([16, 128, 128]) vae.encoder_f0: torch.Size([32, 128, 128]) vae.encoder_f1: torch.Size([32, 128, 128]) ⌛️ [3/4] BACKEND: Backend time: 0.447s [4/4] METRICS: Computing MSE Breakdown... Using per-key quantization points (text_encoder-item0.clip_pooled_prompt_embeds: torch.Size([256])) for text_encoder-item0.clip_pooled_prompt_embeds Using per-key quantization points (text_encoder-item0.clip_prompt_embeds: torch.Size([256])) for text_encoder-item0.clip_prompt_embeds Using per-key quantization points (text_encoder_2-item1.clip_pooled_prompt_embeds: torch.Size([256])) for text_encoder_2-item1.clip_pooled_prompt_embeds Using per-key quantization points (text_encoder_2-item1.clip_prompt_embeds: torch.Size([256])) for text_encoder_2-item1.clip_prompt_embeds Using per-key quantization points (text_encoder_3-item2.t5_prompt_embeds: torch.Size([256])) for text_encoder_3-item2.t5_prompt_embeds Using per-key quantization points (text_encoder-item3.clip_pooled_prompt_embeds: torch.Size([256])) for text_encoder-item3.clip_pooled_prompt_embeds Using per-key quantization points (text_encoder-item3.clip_prompt_embeds: torch.Size([256])) for text_encoder-item3.clip_prompt_embeds Using per-key quantization points (text_encoder_2-item4.clip_pooled: torch.Size([256])) for text_encoder_2-item4.clip_pooled_prompt_embeds Using per-key quantization points (text_encoder_2-item4.clip_prompt: torch.Size([256])) for text_encoder_2-item4.clip_prompt_embeds Using per-key quantization points (text_encoder_3-item5.t5_prompt_embeds: torch.Size([256])) for text_encoder_3-item5.t5_prompt_embeds Using per-key quantization points (vae.encoder_f0: torch.Size([256])) for vae.encoder_f0 Using per-key quantization points (vae.encoder_f1: torch.Size([256])) for vae.encoder_f1 Using per-key quantization points (vae.decoder: torch.Size([256])) for vae.decoder IndividualUnPacker: text_encoder-item0.clip_pooled_prompt_embeds: torch.Size([768]) text_encoder-item0.clip_prompt_embeds: torch.Size([77, 768]) text_encoder_2-item1.clip_pooled_prompt_embeds: torch.Size([1280]) text_encoder_2-item1.clip_prompt_embeds: torch.Size([77, 1280]) text_encoder_3-item2.t5_prompt_embeds: torch.Size([77, 4096]) text_encoder-item3.clip_pooled_prompt_embeds: torch.Size([768]) text_encoder-item3.clip_prompt_embeds: torch.Size([77, 768]) text_encoder_2-item4.clip_pooled_prompt_embeds: torch.Size([1280]) text_encoder_2-item4.clip_prompt_embeds: torch.Size([77, 1280]) text_encoder_3-item5.t5_prompt_embeds: torch.Size([77, 4096]) vae.decoder: torch.Size([16, 128, 128]) vae.encoder_f0: torch.Size([32, 128, 128]) vae.encoder_f1: torch.Size([32, 128, 128]) Per-key MSE Breakdown (Quant=Truncation+Quantization, Codec=Compression): Key Quant-MSE Total-MSE ------------------------------------------------------------------------------------- text_encoder-item0.clip_pooled_prompt_embeds 0.00020745 0.95460097 text_encoder-item0.clip_prompt_embeds 0.00022947 84.56356534 text_encoder_2-item1.clip_pooled_prompt_embeds 0.00031292 0.99602556 text_encoder_2-item1.clip_prompt_embeds 0.00017460 0.11531809 text_encoder_3-item2.t5_prompt_embeds 0.00000789 0.00249646 text_encoder-item3.clip_pooled_prompt_embeds 0.00841799 1.01420029 text_encoder-item3.clip_prompt_embeds 0.00023247 23.90570887 text_encoder_2-item4.clip_pooled_prompt_embeds 0.00016751 0.67051430 text_encoder_2-item4.clip_prompt_embeds 0.00009830 0.47049568 text_encoder_3-item5.t5_prompt_embeds 0.00001263 0.00227117 vae.encoder_f0 0.05448642 2.74767041 vae.encoder_f1 0.05448771 2.74699283 vae.decoder 0.00017748 0.03015359 ------------------------------------------------------------------------------------- TOTAL 0.02531999 4.14243671 (elements=2,260,992) ---------------------- -------------------------------------------------------- SAMPLE-WISE STATISTICS ---------------------- -------------------------------------------------------- Handler sd35 Strategy individual Architecture hyperprior-featurecoding ---------------------- -------------------------------------------------------- Total Elements 2260992 Total Bytes 101948 BPFP 0.3607 bits/point EBPFP 0.7214 equivalent bits/point MSE 4.142437 ---------------------- -------------------------------------------------------- Time: 0.749s Load: 0.008s, Pack+Encode: 0.294s, Decode+Unpack: 0.447s ---------------------- -------------------------------------------------------- Restored Feature Format: [Dict] with 8 keys key['text_encoder-item0']: [Dict] with 2 keys key['clip_pooled_prompt_embeds']: [Tensor] shape=torch.Size([768]), dtype=torch.float16, device=cpu key['clip_prompt_embeds']: [Tensor] shape=torch.Size([77, 768]), dtype=torch.float16, device=cpu key['text_encoder_2-item1']: [Dict] with 2 keys key['clip_pooled_prompt_embeds']: [Tensor] shape=torch.Size([1280]), dtype=torch.float16, device=cpu key['clip_prompt_embeds']: [Tensor] shape=torch.Size([77, 1280]), dtype=torch.float16, device=cpu key['text_encoder_3-item2']: [Dict] with 1 keys key['t5_prompt_embeds']: [Tensor] shape=torch.Size([77, 4096]), dtype=torch.float16, device=cpu key['text_encoder-item3']: [Dict] with 2 keys key['clip_pooled_prompt_embeds']: [Tensor] shape=torch.Size([768]), dtype=torch.float16, device=cpu key['clip_prompt_embeds']: [Tensor] shape=torch.Size([77, 768]), dtype=torch.float16, device=cpu key['text_encoder_2-item4']: [Dict] with 2 keys key['clip_pooled_prompt_embeds']: [Tensor] shape=torch.Size([1280]), dtype=torch.float16, device=cpu key['clip_prompt_embeds']: [Tensor] shape=torch.Size([77, 1280]), dtype=torch.float16, device=cpu key['text_encoder_3-item5']: [Dict] with 1 keys key['t5_prompt_embeds']: [Tensor] shape=torch.Size([77, 4096]), dtype=torch.float16, device=cpu key['vae.decoder']: [Tensor] shape=torch.Size([16, 128, 128]), dtype=torch.float16, device=cpu key['vae.encoder']: [Dict] with 2 keys key['vae.encoder_f0']: [Tensor] shape=torch.Size([32, 128, 128]), dtype=torch.float16, device=cpu key['vae.encoder_f1']: [Tensor] shape=torch.Size([32, 128, 128]), dtype=torch.float16, device=cpu 💾 Converting with 4.1424 MSE: from ../datasets/FeatureCoding-StableDiffusion3.5/100samples_features/000000222118.zst to output-fixed/sd35/lambda0.004/hyperprior-featurecoding-8bit-individual/sd35cond/000000222118.zst 💪 Processing: ../datasets/FeatureCoding-StableDiffusion3.5/100samples_features/000000222825.zst (36/100) [1/4] FRONTEND: Loading features from ../datasets/FeatureCoding-StableDiffusion3.5/100samples_features/000000222825.zst... Original data structure: root: [Dict] with 8 keys key['text_encoder-item0']: [Dict] with 2 keys key['clip_pooled_prompt_embeds']: [Tensor] shape=torch.Size([768]), dtype=torch.float16, device=cpu key['clip_prompt_embeds']: [Tensor] shape=torch.Size([77, 768]), dtype=torch.float16, device=cpu key['text_encoder_2-item1']: [Dict] with 2 keys key['clip_pooled_prompt_embeds']: [Tensor] shape=torch.Size([1280]), dtype=torch.float16, device=cpu key['clip_prompt_embeds']: [Tensor] shape=torch.Size([77, 1280]), dtype=torch.float16, device=cpu key['text_encoder_3-item2']: [Dict] with 1 keys key['t5_prompt_embeds']: [Tensor] shape=torch.Size([77, 4096]), dtype=torch.float16, device=cpu key['text_encoder-item3']: [Dict] with 2 keys key['clip_pooled_prompt_embeds']: [Tensor] shape=torch.Size([768]), dtype=torch.float16, device=cpu key['clip_prompt_embeds']: [Tensor] shape=torch.Size([77, 768]), dtype=torch.float16, device=cpu key['text_encoder_2-item4']: [Dict] with 2 keys key['clip_pooled_prompt_embeds']: [Tensor] shape=torch.Size([1280]), dtype=torch.float16, device=cpu key['clip_prompt_embeds']: [Tensor] shape=torch.Size([77, 1280]), dtype=torch.float16, device=cpu key['text_encoder_3-item5']: [Dict] with 1 keys key['t5_prompt_embeds']: [Tensor] shape=torch.Size([77, 4096]), dtype=torch.float16, device=cpu key['vae.encoder-item6']: [Dict] with 2 keys key['vae.encoder_f0']: [Tensor] shape=torch.Size([32, 128, 128]), dtype=torch.float16, device=cpu key['vae.encoder_f1']: [Tensor] shape=torch.Size([32, 128, 128]), dtype=torch.float16, device=cpu key['vae.decoder']: [Tensor] shape=torch.Size([16, 128, 128]), dtype=torch.float16, device=cpu ⌛️ [1/4] FRONTEND: Load time: 0.008s ------------------------------------------------------------ SD3.5 Features Summary ------------------------------------------------------------ Number of text encoders: 3 Encoder names: ['text_encoder', 'text_encoder_2', 'text_encoder_3'] Has VAE latents: True Has VAE encoder features: True Data type: torch.float16 text_encoder-item0: clip_pooled_prompt_embeds: torch.Size([768]), torch.float16 clip_prompt_embeds: torch.Size([77, 768]), torch.float16 text_encoder_2-item1: clip_pooled_prompt_embeds: torch.Size([1280]), torch.float16 clip_prompt_embeds: torch.Size([77, 1280]), torch.float16 text_encoder_3-item2: t5_prompt_embeds: torch.Size([77, 4096]), torch.float16 text_encoder-item3: clip_pooled_prompt_embeds: torch.Size([768]), torch.float16 clip_prompt_embeds: torch.Size([77, 768]), torch.float16 text_encoder_2-item4: clip_pooled_prompt_embeds: torch.Size([1280]), torch.float16 clip_prompt_embeds: torch.Size([77, 1280]), torch.float16 text_encoder_3-item5: t5_prompt_embeds: torch.Size([77, 4096]), torch.float16 vae.decoder latents: torch.Size([16, 128, 128]) vae.encoder features: vae.encoder_f0: torch.Size([32, 128, 128]), torch.float16 vae.encoder_f1: torch.Size([32, 128, 128]), torch.float16 ------------------------------------------------------------ [2/4] FRONTEND: Pack + Encode (strategy: individual)... IndividualPacker: text_encoder-item0.clip_pooled_prompt_embeds: torch.Size([768])->torch.Size([1, 1, 1, 768]) text_encoder-item0.clip_prompt_embeds: torch.Size([77, 768])->torch.Size([1, 1, 77, 768]) text_encoder_2-item1.clip_pooled_prompt_embeds: torch.Size([1280])->torch.Size([1, 1, 1, 1280]) text_encoder_2-item1.clip_prompt_embeds: torch.Size([77, 1280])->torch.Size([1, 1, 77, 1280]) text_encoder_3-item2.t5_prompt_embeds: torch.Size([77, 4096])->torch.Size([1, 1, 77, 4096]) text_encoder-item3.clip_pooled_prompt_embeds: torch.Size([768])->torch.Size([1, 1, 1, 768]) text_encoder-item3.clip_prompt_embeds: torch.Size([77, 768])->torch.Size([1, 1, 77, 768]) text_encoder_2-item4.clip_pooled_prompt_embeds: torch.Size([1280])->torch.Size([1, 1, 1, 1280]) text_encoder_2-item4.clip_prompt_embeds: torch.Size([77, 1280])->torch.Size([1, 1, 77, 1280]) text_encoder_3-item5.t5_prompt_embeds: torch.Size([77, 4096])->torch.Size([1, 1, 77, 4096]) vae.encoder_f0: torch.Size([32, 128, 128])->torch.Size([1, 1, 512, 1024]) vae.encoder_f1: torch.Size([32, 128, 128])->torch.Size([1, 1, 512, 1024]) vae.decoder: torch.Size([16, 128, 128])->torch.Size([1, 1, 512, 512]) Using per-key quantization points (text_encoder-item0.clip_pooled_prompt_embeds: torch.Size([256])) for text_encoder-item0.clip_pooled_prompt_embeds text_encoder-item0.clip_pooled_prompt_embeds: 732B, BPFP=7.6250 Using per-key quantization points (text_encoder-item0.clip_prompt_embeds: torch.Size([256])) for text_encoder-item0.clip_prompt_embeds text_encoder-item0.clip_prompt_embeds: 6,348B, BPFP=0.8588 Using per-key quantization points (text_encoder_2-item1.clip_pooled_prompt_embeds: torch.Size([256])) for text_encoder_2-item1.clip_pooled_prompt_embeds text_encoder_2-item1.clip_pooled_prompt_embeds: 1,208B, BPFP=7.5500 Using per-key quantization points (text_encoder_2-item1.clip_prompt_embeds: torch.Size([256])) for text_encoder_2-item1.clip_prompt_embeds text_encoder_2-item1.clip_prompt_embeds: 11,172B, BPFP=0.9068 Using per-key quantization points (text_encoder_3-item2.t5_prompt_embeds: torch.Size([256])) for text_encoder_3-item2.t5_prompt_embeds text_encoder_3-item2.t5_prompt_embeds: 27,852B, BPFP=0.7065 Using per-key quantization points (text_encoder-item3.clip_pooled_prompt_embeds: torch.Size([256])) for text_encoder-item3.clip_pooled_prompt_embeds text_encoder-item3.clip_pooled_prompt_embeds: 760B, BPFP=7.9167 Using per-key quantization points (text_encoder-item3.clip_prompt_embeds: torch.Size([256])) for text_encoder-item3.clip_prompt_embeds text_encoder-item3.clip_prompt_embeds: 4,024B, BPFP=0.5444 Using per-key quantization points (text_encoder_2-item4.clip_pooled: torch.Size([256])) for text_encoder_2-item4.clip_pooled_prompt_embeds text_encoder_2-item4.clip_pooled_prompt_embeds: 1,268B, BPFP=7.9250 Using per-key quantization points (text_encoder_2-item4.clip_prompt: torch.Size([256])) for text_encoder_2-item4.clip_prompt_embeds text_encoder_2-item4.clip_prompt_embeds: 6,996B, BPFP=0.5679 Using per-key quantization points (text_encoder_3-item5.t5_prompt_embeds: torch.Size([256])) for text_encoder_3-item5.t5_prompt_embeds text_encoder_3-item5.t5_prompt_embeds: 18,416B, BPFP=0.4671 Using per-key quantization points (vae.encoder_f0: torch.Size([256])) for vae.encoder_f0 vae.encoder_f0: 15,676B, BPFP=0.2392 Using per-key quantization points (vae.encoder_f1: torch.Size([256])) for vae.encoder_f1 vae.encoder_f1: 15,676B, BPFP=0.2392 Using per-key quantization points (vae.decoder: torch.Size([256])) for vae.decoder vae.decoder: 5,180B, BPFP=0.1581 ⌛️ [2/4] FRONTEND: Frontend time: 0.294s (Pack+Encode) [3/4] BACKEND: Decode + Unpack... Using per-key quantization points (text_encoder-item0.clip_pooled_prompt_embeds: torch.Size([256])) for text_encoder-item0.clip_pooled_prompt_embeds Using per-key quantization points (text_encoder-item0.clip_prompt_embeds: torch.Size([256])) for text_encoder-item0.clip_prompt_embeds Using per-key quantization points (text_encoder_2-item1.clip_pooled_prompt_embeds: torch.Size([256])) for text_encoder_2-item1.clip_pooled_prompt_embeds Using per-key quantization points (text_encoder_2-item1.clip_prompt_embeds: torch.Size([256])) for text_encoder_2-item1.clip_prompt_embeds Using per-key quantization points (text_encoder_3-item2.t5_prompt_embeds: torch.Size([256])) for text_encoder_3-item2.t5_prompt_embeds Using per-key quantization points (text_encoder-item3.clip_pooled_prompt_embeds: torch.Size([256])) for text_encoder-item3.clip_pooled_prompt_embeds Using per-key quantization points (text_encoder-item3.clip_prompt_embeds: torch.Size([256])) for text_encoder-item3.clip_prompt_embeds Using per-key quantization points (text_encoder_2-item4.clip_pooled: torch.Size([256])) for text_encoder_2-item4.clip_pooled_prompt_embeds Using per-key quantization points (text_encoder_2-item4.clip_prompt: torch.Size([256])) for text_encoder_2-item4.clip_prompt_embeds Using per-key quantization points (text_encoder_3-item5.t5_prompt_embeds: torch.Size([256])) for text_encoder_3-item5.t5_prompt_embeds Using per-key quantization points (vae.encoder_f0: torch.Size([256])) for vae.encoder_f0 Using per-key quantization points (vae.encoder_f1: torch.Size([256])) for vae.encoder_f1 Using per-key quantization points (vae.decoder: torch.Size([256])) for vae.decoder IndividualUnPacker: text_encoder-item0.clip_pooled_prompt_embeds: torch.Size([768]) text_encoder-item0.clip_prompt_embeds: torch.Size([77, 768]) text_encoder_2-item1.clip_pooled_prompt_embeds: torch.Size([1280]) text_encoder_2-item1.clip_prompt_embeds: torch.Size([77, 1280]) text_encoder_3-item2.t5_prompt_embeds: torch.Size([77, 4096]) text_encoder-item3.clip_pooled_prompt_embeds: torch.Size([768]) text_encoder-item3.clip_prompt_embeds: torch.Size([77, 768]) text_encoder_2-item4.clip_pooled_prompt_embeds: torch.Size([1280]) text_encoder_2-item4.clip_prompt_embeds: torch.Size([77, 1280]) text_encoder_3-item5.t5_prompt_embeds: torch.Size([77, 4096]) vae.decoder: torch.Size([16, 128, 128]) vae.encoder_f0: torch.Size([32, 128, 128]) vae.encoder_f1: torch.Size([32, 128, 128]) ⌛️ [3/4] BACKEND: Backend time: 0.449s [4/4] METRICS: Computing MSE Breakdown... Using per-key quantization points (text_encoder-item0.clip_pooled_prompt_embeds: torch.Size([256])) for text_encoder-item0.clip_pooled_prompt_embeds Using per-key quantization points (text_encoder-item0.clip_prompt_embeds: torch.Size([256])) for text_encoder-item0.clip_prompt_embeds Using per-key quantization points (text_encoder_2-item1.clip_pooled_prompt_embeds: torch.Size([256])) for text_encoder_2-item1.clip_pooled_prompt_embeds Using per-key quantization points (text_encoder_2-item1.clip_prompt_embeds: torch.Size([256])) for text_encoder_2-item1.clip_prompt_embeds Using per-key quantization points (text_encoder_3-item2.t5_prompt_embeds: torch.Size([256])) for text_encoder_3-item2.t5_prompt_embeds Using per-key quantization points (text_encoder-item3.clip_pooled_prompt_embeds: torch.Size([256])) for text_encoder-item3.clip_pooled_prompt_embeds Using per-key quantization points (text_encoder-item3.clip_prompt_embeds: torch.Size([256])) for text_encoder-item3.clip_prompt_embeds Using per-key quantization points (text_encoder_2-item4.clip_pooled: torch.Size([256])) for text_encoder_2-item4.clip_pooled_prompt_embeds Using per-key quantization points (text_encoder_2-item4.clip_prompt: torch.Size([256])) for text_encoder_2-item4.clip_prompt_embeds Using per-key quantization points (text_encoder_3-item5.t5_prompt_embeds: torch.Size([256])) for text_encoder_3-item5.t5_prompt_embeds Using per-key quantization points (vae.encoder_f0: torch.Size([256])) for vae.encoder_f0 Using per-key quantization points (vae.encoder_f1: torch.Size([256])) for vae.encoder_f1 Using per-key quantization points (vae.decoder: torch.Size([256])) for vae.decoder IndividualUnPacker: text_encoder-item0.clip_pooled_prompt_embeds: torch.Size([768]) text_encoder-item0.clip_prompt_embeds: torch.Size([77, 768]) text_encoder_2-item1.clip_pooled_prompt_embeds: torch.Size([1280]) text_encoder_2-item1.clip_prompt_embeds: torch.Size([77, 1280]) text_encoder_3-item2.t5_prompt_embeds: torch.Size([77, 4096]) text_encoder-item3.clip_pooled_prompt_embeds: torch.Size([768]) text_encoder-item3.clip_prompt_embeds: torch.Size([77, 768]) text_encoder_2-item4.clip_pooled_prompt_embeds: torch.Size([1280]) text_encoder_2-item4.clip_prompt_embeds: torch.Size([77, 1280]) text_encoder_3-item5.t5_prompt_embeds: torch.Size([77, 4096]) vae.decoder: torch.Size([16, 128, 128]) vae.encoder_f0: torch.Size([32, 128, 128]) vae.encoder_f1: torch.Size([32, 128, 128]) Per-key MSE Breakdown (Quant=Truncation+Quantization, Codec=Compression): Key Quant-MSE Total-MSE ------------------------------------------------------------------------------------- text_encoder-item0.clip_pooled_prompt_embeds 0.00026664 0.93440914 text_encoder-item0.clip_prompt_embeds 0.00020169 23.95497455 text_encoder_2-item1.clip_pooled_prompt_embeds 0.00017591 0.89337587 text_encoder_2-item1.clip_prompt_embeds 0.00015739 0.13715159 text_encoder_3-item2.t5_prompt_embeds 0.00000811 0.00369594 text_encoder-item3.clip_pooled_prompt_embeds 0.00841799 1.01420029 text_encoder-item3.clip_prompt_embeds 0.00023247 23.90570887 text_encoder_2-item4.clip_pooled_prompt_embeds 0.00016751 0.67051430 text_encoder_2-item4.clip_prompt_embeds 0.00009830 0.47049568 text_encoder_3-item5.t5_prompt_embeds 0.00001263 0.00227117 vae.encoder_f0 0.06876971 3.81661367 vae.encoder_f1 0.06877109 3.81584835 vae.decoder 0.00023999 0.02866444 ------------------------------------------------------------------------------------- TOTAL 0.03194988 3.05382852 (elements=2,260,992) ---------------------- -------------------------------------------------------- SAMPLE-WISE STATISTICS ---------------------- -------------------------------------------------------- Handler sd35 Strategy individual Architecture hyperprior-featurecoding ---------------------- -------------------------------------------------------- Total Elements 2260992 Total Bytes 115308 BPFP 0.4080 bits/point EBPFP 0.8160 equivalent bits/point MSE 3.053829 ---------------------- -------------------------------------------------------- Time: 0.752s Load: 0.008s, Pack+Encode: 0.294s, Decode+Unpack: 0.449s ---------------------- -------------------------------------------------------- Restored Feature Format: [Dict] with 8 keys key['text_encoder-item0']: [Dict] with 2 keys key['clip_pooled_prompt_embeds']: [Tensor] shape=torch.Size([768]), dtype=torch.float16, device=cpu key['clip_prompt_embeds']: [Tensor] shape=torch.Size([77, 768]), dtype=torch.float16, device=cpu key['text_encoder_2-item1']: [Dict] with 2 keys key['clip_pooled_prompt_embeds']: [Tensor] shape=torch.Size([1280]), dtype=torch.float16, device=cpu key['clip_prompt_embeds']: [Tensor] shape=torch.Size([77, 1280]), dtype=torch.float16, device=cpu key['text_encoder_3-item2']: [Dict] with 1 keys key['t5_prompt_embeds']: [Tensor] shape=torch.Size([77, 4096]), dtype=torch.float16, device=cpu key['text_encoder-item3']: [Dict] with 2 keys key['clip_pooled_prompt_embeds']: [Tensor] shape=torch.Size([768]), dtype=torch.float16, device=cpu key['clip_prompt_embeds']: [Tensor] shape=torch.Size([77, 768]), dtype=torch.float16, device=cpu key['text_encoder_2-item4']: [Dict] with 2 keys key['clip_pooled_prompt_embeds']: [Tensor] shape=torch.Size([1280]), dtype=torch.float16, device=cpu key['clip_prompt_embeds']: [Tensor] shape=torch.Size([77, 1280]), dtype=torch.float16, device=cpu key['text_encoder_3-item5']: [Dict] with 1 keys key['t5_prompt_embeds']: [Tensor] shape=torch.Size([77, 4096]), dtype=torch.float16, device=cpu key['vae.decoder']: [Tensor] shape=torch.Size([16, 128, 128]), dtype=torch.float16, device=cpu key['vae.encoder']: [Dict] with 2 keys key['vae.encoder_f0']: [Tensor] shape=torch.Size([32, 128, 128]), dtype=torch.float16, device=cpu key['vae.encoder_f1']: [Tensor] shape=torch.Size([32, 128, 128]), dtype=torch.float16, device=cpu 💾 Converting with 3.0538 MSE: from ../datasets/FeatureCoding-StableDiffusion3.5/100samples_features/000000222825.zst to output-fixed/sd35/lambda0.004/hyperprior-featurecoding-8bit-individual/sd35cond/000000222825.zst 💪 Processing: ../datasets/FeatureCoding-StableDiffusion3.5/100samples_features/000000227478.zst (37/100) [1/4] FRONTEND: Loading features from ../datasets/FeatureCoding-StableDiffusion3.5/100samples_features/000000227478.zst... Original data structure: root: [Dict] with 8 keys key['text_encoder-item0']: [Dict] with 2 keys key['clip_pooled_prompt_embeds']: [Tensor] shape=torch.Size([768]), dtype=torch.float16, device=cpu key['clip_prompt_embeds']: [Tensor] shape=torch.Size([77, 768]), dtype=torch.float16, device=cpu key['text_encoder_2-item1']: [Dict] with 2 keys key['clip_pooled_prompt_embeds']: [Tensor] shape=torch.Size([1280]), dtype=torch.float16, device=cpu key['clip_prompt_embeds']: [Tensor] shape=torch.Size([77, 1280]), dtype=torch.float16, device=cpu key['text_encoder_3-item2']: [Dict] with 1 keys key['t5_prompt_embeds']: [Tensor] shape=torch.Size([77, 4096]), dtype=torch.float16, device=cpu key['text_encoder-item3']: [Dict] with 2 keys key['clip_pooled_prompt_embeds']: [Tensor] shape=torch.Size([768]), dtype=torch.float16, device=cpu key['clip_prompt_embeds']: [Tensor] shape=torch.Size([77, 768]), dtype=torch.float16, device=cpu key['text_encoder_2-item4']: [Dict] with 2 keys key['clip_pooled_prompt_embeds']: [Tensor] shape=torch.Size([1280]), dtype=torch.float16, device=cpu key['clip_prompt_embeds']: [Tensor] shape=torch.Size([77, 1280]), dtype=torch.float16, device=cpu key['text_encoder_3-item5']: [Dict] with 1 keys key['t5_prompt_embeds']: [Tensor] shape=torch.Size([77, 4096]), dtype=torch.float16, device=cpu key['vae.encoder-item6']: [Dict] with 2 keys key['vae.encoder_f0']: [Tensor] shape=torch.Size([32, 128, 128]), dtype=torch.float16, device=cpu key['vae.encoder_f1']: [Tensor] shape=torch.Size([32, 128, 128]), dtype=torch.float16, device=cpu key['vae.decoder']: [Tensor] shape=torch.Size([16, 128, 128]), dtype=torch.float16, device=cpu ⌛️ [1/4] FRONTEND: Load time: 0.009s ------------------------------------------------------------ SD3.5 Features Summary ------------------------------------------------------------ Number of text encoders: 3 Encoder names: ['text_encoder', 'text_encoder_2', 'text_encoder_3'] Has VAE latents: True Has VAE encoder features: True Data type: torch.float16 text_encoder-item0: clip_pooled_prompt_embeds: torch.Size([768]), torch.float16 clip_prompt_embeds: torch.Size([77, 768]), torch.float16 text_encoder_2-item1: clip_pooled_prompt_embeds: torch.Size([1280]), torch.float16 clip_prompt_embeds: torch.Size([77, 1280]), torch.float16 text_encoder_3-item2: t5_prompt_embeds: torch.Size([77, 4096]), torch.float16 text_encoder-item3: clip_pooled_prompt_embeds: torch.Size([768]), torch.float16 clip_prompt_embeds: torch.Size([77, 768]), torch.float16 text_encoder_2-item4: clip_pooled_prompt_embeds: torch.Size([1280]), torch.float16 clip_prompt_embeds: torch.Size([77, 1280]), torch.float16 text_encoder_3-item5: t5_prompt_embeds: torch.Size([77, 4096]), torch.float16 vae.decoder latents: torch.Size([16, 128, 128]) vae.encoder features: vae.encoder_f0: torch.Size([32, 128, 128]), torch.float16 vae.encoder_f1: torch.Size([32, 128, 128]), torch.float16 ------------------------------------------------------------ [2/4] FRONTEND: Pack + Encode (strategy: individual)... IndividualPacker: text_encoder-item0.clip_pooled_prompt_embeds: torch.Size([768])->torch.Size([1, 1, 1, 768]) text_encoder-item0.clip_prompt_embeds: torch.Size([77, 768])->torch.Size([1, 1, 77, 768]) text_encoder_2-item1.clip_pooled_prompt_embeds: torch.Size([1280])->torch.Size([1, 1, 1, 1280]) text_encoder_2-item1.clip_prompt_embeds: torch.Size([77, 1280])->torch.Size([1, 1, 77, 1280]) text_encoder_3-item2.t5_prompt_embeds: torch.Size([77, 4096])->torch.Size([1, 1, 77, 4096]) text_encoder-item3.clip_pooled_prompt_embeds: torch.Size([768])->torch.Size([1, 1, 1, 768]) text_encoder-item3.clip_prompt_embeds: torch.Size([77, 768])->torch.Size([1, 1, 77, 768]) text_encoder_2-item4.clip_pooled_prompt_embeds: torch.Size([1280])->torch.Size([1, 1, 1, 1280]) text_encoder_2-item4.clip_prompt_embeds: torch.Size([77, 1280])->torch.Size([1, 1, 77, 1280]) text_encoder_3-item5.t5_prompt_embeds: torch.Size([77, 4096])->torch.Size([1, 1, 77, 4096]) vae.encoder_f0: torch.Size([32, 128, 128])->torch.Size([1, 1, 512, 1024]) vae.encoder_f1: torch.Size([32, 128, 128])->torch.Size([1, 1, 512, 1024]) vae.decoder: torch.Size([16, 128, 128])->torch.Size([1, 1, 512, 512]) Using per-key quantization points (text_encoder-item0.clip_pooled_prompt_embeds: torch.Size([256])) for text_encoder-item0.clip_pooled_prompt_embeds text_encoder-item0.clip_pooled_prompt_embeds: 728B, BPFP=7.5833 Using per-key quantization points (text_encoder-item0.clip_prompt_embeds: torch.Size([256])) for text_encoder-item0.clip_prompt_embeds text_encoder-item0.clip_prompt_embeds: 6,540B, BPFP=0.8847 Using per-key quantization points (text_encoder_2-item1.clip_pooled_prompt_embeds: torch.Size([256])) for text_encoder_2-item1.clip_pooled_prompt_embeds text_encoder_2-item1.clip_pooled_prompt_embeds: 1,208B, BPFP=7.5500 Using per-key quantization points (text_encoder_2-item1.clip_prompt_embeds: torch.Size([256])) for text_encoder_2-item1.clip_prompt_embeds text_encoder_2-item1.clip_prompt_embeds: 11,408B, BPFP=0.9260 Using per-key quantization points (text_encoder_3-item2.t5_prompt_embeds: torch.Size([256])) for text_encoder_3-item2.t5_prompt_embeds text_encoder_3-item2.t5_prompt_embeds: 28,136B, BPFP=0.7137 Using per-key quantization points (text_encoder-item3.clip_pooled_prompt_embeds: torch.Size([256])) for text_encoder-item3.clip_pooled_prompt_embeds text_encoder-item3.clip_pooled_prompt_embeds: 760B, BPFP=7.9167 Using per-key quantization points (text_encoder-item3.clip_prompt_embeds: torch.Size([256])) for text_encoder-item3.clip_prompt_embeds text_encoder-item3.clip_prompt_embeds: 4,024B, BPFP=0.5444 Using per-key quantization points (text_encoder_2-item4.clip_pooled: torch.Size([256])) for text_encoder_2-item4.clip_pooled_prompt_embeds text_encoder_2-item4.clip_pooled_prompt_embeds: 1,268B, BPFP=7.9250 Using per-key quantization points (text_encoder_2-item4.clip_prompt: torch.Size([256])) for text_encoder_2-item4.clip_prompt_embeds text_encoder_2-item4.clip_prompt_embeds: 6,996B, BPFP=0.5679 Using per-key quantization points (text_encoder_3-item5.t5_prompt_embeds: torch.Size([256])) for text_encoder_3-item5.t5_prompt_embeds text_encoder_3-item5.t5_prompt_embeds: 18,416B, BPFP=0.4671 Using per-key quantization points (vae.encoder_f0: torch.Size([256])) for vae.encoder_f0 vae.encoder_f0: 13,432B, BPFP=0.2050 Using per-key quantization points (vae.encoder_f1: torch.Size([256])) for vae.encoder_f1 vae.encoder_f1: 13,432B, BPFP=0.2050 Using per-key quantization points (vae.decoder: torch.Size([256])) for vae.decoder vae.decoder: 8,980B, BPFP=0.2740 ⌛️ [2/4] FRONTEND: Frontend time: 0.295s (Pack+Encode) [3/4] BACKEND: Decode + Unpack... Using per-key quantization points (text_encoder-item0.clip_pooled_prompt_embeds: torch.Size([256])) for text_encoder-item0.clip_pooled_prompt_embeds Using per-key quantization points (text_encoder-item0.clip_prompt_embeds: torch.Size([256])) for text_encoder-item0.clip_prompt_embeds Using per-key quantization points (text_encoder_2-item1.clip_pooled_prompt_embeds: torch.Size([256])) for text_encoder_2-item1.clip_pooled_prompt_embeds Using per-key quantization points (text_encoder_2-item1.clip_prompt_embeds: torch.Size([256])) for text_encoder_2-item1.clip_prompt_embeds Using per-key quantization points (text_encoder_3-item2.t5_prompt_embeds: torch.Size([256])) for text_encoder_3-item2.t5_prompt_embeds Using per-key quantization points (text_encoder-item3.clip_pooled_prompt_embeds: torch.Size([256])) for text_encoder-item3.clip_pooled_prompt_embeds Using per-key quantization points (text_encoder-item3.clip_prompt_embeds: torch.Size([256])) for text_encoder-item3.clip_prompt_embeds Using per-key quantization points (text_encoder_2-item4.clip_pooled: torch.Size([256])) for text_encoder_2-item4.clip_pooled_prompt_embeds Using per-key quantization points (text_encoder_2-item4.clip_prompt: torch.Size([256])) for text_encoder_2-item4.clip_prompt_embeds Using per-key quantization points (text_encoder_3-item5.t5_prompt_embeds: torch.Size([256])) for text_encoder_3-item5.t5_prompt_embeds Using per-key quantization points (vae.encoder_f0: torch.Size([256])) for vae.encoder_f0 Using per-key quantization points (vae.encoder_f1: torch.Size([256])) for vae.encoder_f1 Using per-key quantization points (vae.decoder: torch.Size([256])) for vae.decoder IndividualUnPacker: text_encoder-item0.clip_pooled_prompt_embeds: torch.Size([768]) text_encoder-item0.clip_prompt_embeds: torch.Size([77, 768]) text_encoder_2-item1.clip_pooled_prompt_embeds: torch.Size([1280]) text_encoder_2-item1.clip_prompt_embeds: torch.Size([77, 1280]) text_encoder_3-item2.t5_prompt_embeds: torch.Size([77, 4096]) text_encoder-item3.clip_pooled_prompt_embeds: torch.Size([768]) text_encoder-item3.clip_prompt_embeds: torch.Size([77, 768]) text_encoder_2-item4.clip_pooled_prompt_embeds: torch.Size([1280]) text_encoder_2-item4.clip_prompt_embeds: torch.Size([77, 1280]) text_encoder_3-item5.t5_prompt_embeds: torch.Size([77, 4096]) vae.decoder: torch.Size([16, 128, 128]) vae.encoder_f0: torch.Size([32, 128, 128]) vae.encoder_f1: torch.Size([32, 128, 128]) ⌛️ [3/4] BACKEND: Backend time: 0.451s [4/4] METRICS: Computing MSE Breakdown... Using per-key quantization points (text_encoder-item0.clip_pooled_prompt_embeds: torch.Size([256])) for text_encoder-item0.clip_pooled_prompt_embeds Using per-key quantization points (text_encoder-item0.clip_prompt_embeds: torch.Size([256])) for text_encoder-item0.clip_prompt_embeds Using per-key quantization points (text_encoder_2-item1.clip_pooled_prompt_embeds: torch.Size([256])) for text_encoder_2-item1.clip_pooled_prompt_embeds Using per-key quantization points (text_encoder_2-item1.clip_prompt_embeds: torch.Size([256])) for text_encoder_2-item1.clip_prompt_embeds Using per-key quantization points (text_encoder_3-item2.t5_prompt_embeds: torch.Size([256])) for text_encoder_3-item2.t5_prompt_embeds Using per-key quantization points (text_encoder-item3.clip_pooled_prompt_embeds: torch.Size([256])) for text_encoder-item3.clip_pooled_prompt_embeds Using per-key quantization points (text_encoder-item3.clip_prompt_embeds: torch.Size([256])) for text_encoder-item3.clip_prompt_embeds Using per-key quantization points (text_encoder_2-item4.clip_pooled: torch.Size([256])) for text_encoder_2-item4.clip_pooled_prompt_embeds Using per-key quantization points (text_encoder_2-item4.clip_prompt: torch.Size([256])) for text_encoder_2-item4.clip_prompt_embeds Using per-key quantization points (text_encoder_3-item5.t5_prompt_embeds: torch.Size([256])) for text_encoder_3-item5.t5_prompt_embeds Using per-key quantization points (vae.encoder_f0: torch.Size([256])) for vae.encoder_f0 Using per-key quantization points (vae.encoder_f1: torch.Size([256])) for vae.encoder_f1 Using per-key quantization points (vae.decoder: torch.Size([256])) for vae.decoder IndividualUnPacker: text_encoder-item0.clip_pooled_prompt_embeds: torch.Size([768]) text_encoder-item0.clip_prompt_embeds: torch.Size([77, 768]) text_encoder_2-item1.clip_pooled_prompt_embeds: torch.Size([1280]) text_encoder_2-item1.clip_prompt_embeds: torch.Size([77, 1280]) text_encoder_3-item2.t5_prompt_embeds: torch.Size([77, 4096]) text_encoder-item3.clip_pooled_prompt_embeds: torch.Size([768]) text_encoder-item3.clip_prompt_embeds: torch.Size([77, 768]) text_encoder_2-item4.clip_pooled_prompt_embeds: torch.Size([1280]) text_encoder_2-item4.clip_prompt_embeds: torch.Size([77, 1280]) text_encoder_3-item5.t5_prompt_embeds: torch.Size([77, 4096]) vae.decoder: torch.Size([16, 128, 128]) vae.encoder_f0: torch.Size([32, 128, 128]) vae.encoder_f1: torch.Size([32, 128, 128]) Per-key MSE Breakdown (Quant=Truncation+Quantization, Codec=Compression): Key Quant-MSE Total-MSE ------------------------------------------------------------------------------------- text_encoder-item0.clip_pooled_prompt_embeds 0.00028326 0.92201018 text_encoder-item0.clip_prompt_embeds 0.00025253 23.93719984 text_encoder_2-item1.clip_pooled_prompt_embeds 0.00041073 0.96261625 text_encoder_2-item1.clip_prompt_embeds 0.00018825 0.12794205 text_encoder_3-item2.t5_prompt_embeds 0.00000859 0.00280123 text_encoder-item3.clip_pooled_prompt_embeds 0.00841799 1.01420029 text_encoder-item3.clip_prompt_embeds 0.00023247 23.90570887 text_encoder_2-item4.clip_pooled_prompt_embeds 0.00016751 0.67051430 text_encoder_2-item4.clip_prompt_embeds 0.00009830 0.47049568 text_encoder_3-item5.t5_prompt_embeds 0.00001263 0.00227117 vae.encoder_f0 0.00595097 1.49915934 vae.encoder_f1 0.00595882 1.49926889 vae.decoder 0.00020134 0.08623081 ------------------------------------------------------------------------------------- TOTAL 0.00281645 1.98498814 (elements=2,260,992) ---------------------- -------------------------------------------------------- SAMPLE-WISE STATISTICS ---------------------- -------------------------------------------------------- Handler sd35 Strategy individual Architecture hyperprior-featurecoding ---------------------- -------------------------------------------------------- Total Elements 2260992 Total Bytes 115328 BPFP 0.4081 bits/point EBPFP 0.8161 equivalent bits/point MSE 1.984988 ---------------------- -------------------------------------------------------- Time: 0.755s Load: 0.009s, Pack+Encode: 0.295s, Decode+Unpack: 0.451s ---------------------- -------------------------------------------------------- Restored Feature Format: [Dict] with 8 keys key['text_encoder-item0']: [Dict] with 2 keys key['clip_pooled_prompt_embeds']: [Tensor] shape=torch.Size([768]), dtype=torch.float16, device=cpu key['clip_prompt_embeds']: [Tensor] shape=torch.Size([77, 768]), dtype=torch.float16, device=cpu key['text_encoder_2-item1']: [Dict] with 2 keys key['clip_pooled_prompt_embeds']: [Tensor] shape=torch.Size([1280]), dtype=torch.float16, device=cpu key['clip_prompt_embeds']: [Tensor] shape=torch.Size([77, 1280]), dtype=torch.float16, device=cpu key['text_encoder_3-item2']: [Dict] with 1 keys key['t5_prompt_embeds']: [Tensor] shape=torch.Size([77, 4096]), dtype=torch.float16, device=cpu key['text_encoder-item3']: [Dict] with 2 keys key['clip_pooled_prompt_embeds']: [Tensor] shape=torch.Size([768]), dtype=torch.float16, device=cpu key['clip_prompt_embeds']: [Tensor] shape=torch.Size([77, 768]), dtype=torch.float16, device=cpu key['text_encoder_2-item4']: [Dict] with 2 keys key['clip_pooled_prompt_embeds']: [Tensor] shape=torch.Size([1280]), dtype=torch.float16, device=cpu key['clip_prompt_embeds']: [Tensor] shape=torch.Size([77, 1280]), dtype=torch.float16, device=cpu key['text_encoder_3-item5']: [Dict] with 1 keys key['t5_prompt_embeds']: [Tensor] shape=torch.Size([77, 4096]), dtype=torch.float16, device=cpu key['vae.decoder']: [Tensor] shape=torch.Size([16, 128, 128]), dtype=torch.float16, device=cpu key['vae.encoder']: [Dict] with 2 keys key['vae.encoder_f0']: [Tensor] shape=torch.Size([32, 128, 128]), dtype=torch.float16, device=cpu key['vae.encoder_f1']: [Tensor] shape=torch.Size([32, 128, 128]), dtype=torch.float16, device=cpu 💾 Converting with 1.9850 MSE: from ../datasets/FeatureCoding-StableDiffusion3.5/100samples_features/000000227478.zst to output-fixed/sd35/lambda0.004/hyperprior-featurecoding-8bit-individual/sd35cond/000000227478.zst 💪 Processing: ../datasets/FeatureCoding-StableDiffusion3.5/100samples_features/000000239843.zst (38/100) [1/4] FRONTEND: Loading features from ../datasets/FeatureCoding-StableDiffusion3.5/100samples_features/000000239843.zst... Original data structure: root: [Dict] with 8 keys key['text_encoder-item0']: [Dict] with 2 keys key['clip_pooled_prompt_embeds']: [Tensor] shape=torch.Size([768]), dtype=torch.float16, device=cpu key['clip_prompt_embeds']: [Tensor] shape=torch.Size([77, 768]), dtype=torch.float16, device=cpu key['text_encoder_2-item1']: [Dict] with 2 keys key['clip_pooled_prompt_embeds']: [Tensor] shape=torch.Size([1280]), dtype=torch.float16, device=cpu key['clip_prompt_embeds']: [Tensor] shape=torch.Size([77, 1280]), dtype=torch.float16, device=cpu key['text_encoder_3-item2']: [Dict] with 1 keys key['t5_prompt_embeds']: [Tensor] shape=torch.Size([77, 4096]), dtype=torch.float16, device=cpu key['text_encoder-item3']: [Dict] with 2 keys key['clip_pooled_prompt_embeds']: [Tensor] shape=torch.Size([768]), dtype=torch.float16, device=cpu key['clip_prompt_embeds']: [Tensor] shape=torch.Size([77, 768]), dtype=torch.float16, device=cpu key['text_encoder_2-item4']: [Dict] with 2 keys key['clip_pooled_prompt_embeds']: [Tensor] shape=torch.Size([1280]), dtype=torch.float16, device=cpu key['clip_prompt_embeds']: [Tensor] shape=torch.Size([77, 1280]), dtype=torch.float16, device=cpu key['text_encoder_3-item5']: [Dict] with 1 keys key['t5_prompt_embeds']: [Tensor] shape=torch.Size([77, 4096]), dtype=torch.float16, device=cpu key['vae.encoder-item6']: [Dict] with 2 keys key['vae.encoder_f0']: [Tensor] shape=torch.Size([32, 128, 128]), dtype=torch.float16, device=cpu key['vae.encoder_f1']: [Tensor] shape=torch.Size([32, 128, 128]), dtype=torch.float16, device=cpu key['vae.decoder']: [Tensor] shape=torch.Size([16, 128, 128]), dtype=torch.float16, device=cpu ⌛️ [1/4] FRONTEND: Load time: 0.009s ------------------------------------------------------------ SD3.5 Features Summary ------------------------------------------------------------ Number of text encoders: 3 Encoder names: ['text_encoder', 'text_encoder_2', 'text_encoder_3'] Has VAE latents: True Has VAE encoder features: True Data type: torch.float16 text_encoder-item0: clip_pooled_prompt_embeds: torch.Size([768]), torch.float16 clip_prompt_embeds: torch.Size([77, 768]), torch.float16 text_encoder_2-item1: clip_pooled_prompt_embeds: torch.Size([1280]), torch.float16 clip_prompt_embeds: torch.Size([77, 1280]), torch.float16 text_encoder_3-item2: t5_prompt_embeds: torch.Size([77, 4096]), torch.float16 text_encoder-item3: clip_pooled_prompt_embeds: torch.Size([768]), torch.float16 clip_prompt_embeds: torch.Size([77, 768]), torch.float16 text_encoder_2-item4: clip_pooled_prompt_embeds: torch.Size([1280]), torch.float16 clip_prompt_embeds: torch.Size([77, 1280]), torch.float16 text_encoder_3-item5: t5_prompt_embeds: torch.Size([77, 4096]), torch.float16 vae.decoder latents: torch.Size([16, 128, 128]) vae.encoder features: vae.encoder_f0: torch.Size([32, 128, 128]), torch.float16 vae.encoder_f1: torch.Size([32, 128, 128]), torch.float16 ------------------------------------------------------------ [2/4] FRONTEND: Pack + Encode (strategy: individual)... IndividualPacker: text_encoder-item0.clip_pooled_prompt_embeds: torch.Size([768])->torch.Size([1, 1, 1, 768]) text_encoder-item0.clip_prompt_embeds: torch.Size([77, 768])->torch.Size([1, 1, 77, 768]) text_encoder_2-item1.clip_pooled_prompt_embeds: torch.Size([1280])->torch.Size([1, 1, 1, 1280]) text_encoder_2-item1.clip_prompt_embeds: torch.Size([77, 1280])->torch.Size([1, 1, 77, 1280]) text_encoder_3-item2.t5_prompt_embeds: torch.Size([77, 4096])->torch.Size([1, 1, 77, 4096]) text_encoder-item3.clip_pooled_prompt_embeds: torch.Size([768])->torch.Size([1, 1, 1, 768]) text_encoder-item3.clip_prompt_embeds: torch.Size([77, 768])->torch.Size([1, 1, 77, 768]) text_encoder_2-item4.clip_pooled_prompt_embeds: torch.Size([1280])->torch.Size([1, 1, 1, 1280]) text_encoder_2-item4.clip_prompt_embeds: torch.Size([77, 1280])->torch.Size([1, 1, 77, 1280]) text_encoder_3-item5.t5_prompt_embeds: torch.Size([77, 4096])->torch.Size([1, 1, 77, 4096]) vae.encoder_f0: torch.Size([32, 128, 128])->torch.Size([1, 1, 512, 1024]) vae.encoder_f1: torch.Size([32, 128, 128])->torch.Size([1, 1, 512, 1024]) vae.decoder: torch.Size([16, 128, 128])->torch.Size([1, 1, 512, 512]) Using per-key quantization points (text_encoder-item0.clip_pooled_prompt_embeds: torch.Size([256])) for text_encoder-item0.clip_pooled_prompt_embeds text_encoder-item0.clip_pooled_prompt_embeds: 736B, BPFP=7.6667 Using per-key quantization points (text_encoder-item0.clip_prompt_embeds: torch.Size([256])) for text_encoder-item0.clip_prompt_embeds text_encoder-item0.clip_prompt_embeds: 6,424B, BPFP=0.8690 Using per-key quantization points (text_encoder_2-item1.clip_pooled_prompt_embeds: torch.Size([256])) for text_encoder_2-item1.clip_pooled_prompt_embeds text_encoder_2-item1.clip_pooled_prompt_embeds: 1,208B, BPFP=7.5500 Using per-key quantization points (text_encoder_2-item1.clip_prompt_embeds: torch.Size([256])) for text_encoder_2-item1.clip_prompt_embeds text_encoder_2-item1.clip_prompt_embeds: 11,564B, BPFP=0.9386 Using per-key quantization points (text_encoder_3-item2.t5_prompt_embeds: torch.Size([256])) for text_encoder_3-item2.t5_prompt_embeds text_encoder_3-item2.t5_prompt_embeds: 27,828B, BPFP=0.7059 Using per-key quantization points (text_encoder-item3.clip_pooled_prompt_embeds: torch.Size([256])) for text_encoder-item3.clip_pooled_prompt_embeds text_encoder-item3.clip_pooled_prompt_embeds: 760B, BPFP=7.9167 Using per-key quantization points (text_encoder-item3.clip_prompt_embeds: torch.Size([256])) for text_encoder-item3.clip_prompt_embeds text_encoder-item3.clip_prompt_embeds: 4,024B, BPFP=0.5444 Using per-key quantization points (text_encoder_2-item4.clip_pooled: torch.Size([256])) for text_encoder_2-item4.clip_pooled_prompt_embeds text_encoder_2-item4.clip_pooled_prompt_embeds: 1,268B, BPFP=7.9250 Using per-key quantization points (text_encoder_2-item4.clip_prompt: torch.Size([256])) for text_encoder_2-item4.clip_prompt_embeds text_encoder_2-item4.clip_prompt_embeds: 6,996B, BPFP=0.5679 Using per-key quantization points (text_encoder_3-item5.t5_prompt_embeds: torch.Size([256])) for text_encoder_3-item5.t5_prompt_embeds text_encoder_3-item5.t5_prompt_embeds: 18,416B, BPFP=0.4671 Using per-key quantization points (vae.encoder_f0: torch.Size([256])) for vae.encoder_f0 vae.encoder_f0: 12,848B, BPFP=0.1960 Using per-key quantization points (vae.encoder_f1: torch.Size([256])) for vae.encoder_f1 vae.encoder_f1: 12,848B, BPFP=0.1960 Using per-key quantization points (vae.decoder: torch.Size([256])) for vae.decoder vae.decoder: 9,500B, BPFP=0.2899 ⌛️ [2/4] FRONTEND: Frontend time: 0.295s (Pack+Encode) [3/4] BACKEND: Decode + Unpack... Using per-key quantization points (text_encoder-item0.clip_pooled_prompt_embeds: torch.Size([256])) for text_encoder-item0.clip_pooled_prompt_embeds Using per-key quantization points (text_encoder-item0.clip_prompt_embeds: torch.Size([256])) for text_encoder-item0.clip_prompt_embeds Using per-key quantization points (text_encoder_2-item1.clip_pooled_prompt_embeds: torch.Size([256])) for text_encoder_2-item1.clip_pooled_prompt_embeds Using per-key quantization points (text_encoder_2-item1.clip_prompt_embeds: torch.Size([256])) for text_encoder_2-item1.clip_prompt_embeds Using per-key quantization points (text_encoder_3-item2.t5_prompt_embeds: torch.Size([256])) for text_encoder_3-item2.t5_prompt_embeds Using per-key quantization points (text_encoder-item3.clip_pooled_prompt_embeds: torch.Size([256])) for text_encoder-item3.clip_pooled_prompt_embeds Using per-key quantization points (text_encoder-item3.clip_prompt_embeds: torch.Size([256])) for text_encoder-item3.clip_prompt_embeds Using per-key quantization points (text_encoder_2-item4.clip_pooled: torch.Size([256])) for text_encoder_2-item4.clip_pooled_prompt_embeds Using per-key quantization points (text_encoder_2-item4.clip_prompt: torch.Size([256])) for text_encoder_2-item4.clip_prompt_embeds Using per-key quantization points (text_encoder_3-item5.t5_prompt_embeds: torch.Size([256])) for text_encoder_3-item5.t5_prompt_embeds Using per-key quantization points (vae.encoder_f0: torch.Size([256])) for vae.encoder_f0 Using per-key quantization points (vae.encoder_f1: torch.Size([256])) for vae.encoder_f1 Using per-key quantization points (vae.decoder: torch.Size([256])) for vae.decoder IndividualUnPacker: text_encoder-item0.clip_pooled_prompt_embeds: torch.Size([768]) text_encoder-item0.clip_prompt_embeds: torch.Size([77, 768]) text_encoder_2-item1.clip_pooled_prompt_embeds: torch.Size([1280]) text_encoder_2-item1.clip_prompt_embeds: torch.Size([77, 1280]) text_encoder_3-item2.t5_prompt_embeds: torch.Size([77, 4096]) text_encoder-item3.clip_pooled_prompt_embeds: torch.Size([768]) text_encoder-item3.clip_prompt_embeds: torch.Size([77, 768]) text_encoder_2-item4.clip_pooled_prompt_embeds: torch.Size([1280]) text_encoder_2-item4.clip_prompt_embeds: torch.Size([77, 1280]) text_encoder_3-item5.t5_prompt_embeds: torch.Size([77, 4096]) vae.decoder: torch.Size([16, 128, 128]) vae.encoder_f0: torch.Size([32, 128, 128]) vae.encoder_f1: torch.Size([32, 128, 128]) ⌛️ [3/4] BACKEND: Backend time: 0.452s [4/4] METRICS: Computing MSE Breakdown... Using per-key quantization points (text_encoder-item0.clip_pooled_prompt_embeds: torch.Size([256])) for text_encoder-item0.clip_pooled_prompt_embeds Using per-key quantization points (text_encoder-item0.clip_prompt_embeds: torch.Size([256])) for text_encoder-item0.clip_prompt_embeds Using per-key quantization points (text_encoder_2-item1.clip_pooled_prompt_embeds: torch.Size([256])) for text_encoder_2-item1.clip_pooled_prompt_embeds Using per-key quantization points (text_encoder_2-item1.clip_prompt_embeds: torch.Size([256])) for text_encoder_2-item1.clip_prompt_embeds Using per-key quantization points (text_encoder_3-item2.t5_prompt_embeds: torch.Size([256])) for text_encoder_3-item2.t5_prompt_embeds Using per-key quantization points (text_encoder-item3.clip_pooled_prompt_embeds: torch.Size([256])) for text_encoder-item3.clip_pooled_prompt_embeds Using per-key quantization points (text_encoder-item3.clip_prompt_embeds: torch.Size([256])) for text_encoder-item3.clip_prompt_embeds Using per-key quantization points (text_encoder_2-item4.clip_pooled: torch.Size([256])) for text_encoder_2-item4.clip_pooled_prompt_embeds Using per-key quantization points (text_encoder_2-item4.clip_prompt: torch.Size([256])) for text_encoder_2-item4.clip_prompt_embeds Using per-key quantization points (text_encoder_3-item5.t5_prompt_embeds: torch.Size([256])) for text_encoder_3-item5.t5_prompt_embeds Using per-key quantization points (vae.encoder_f0: torch.Size([256])) for vae.encoder_f0 Using per-key quantization points (vae.encoder_f1: torch.Size([256])) for vae.encoder_f1 Using per-key quantization points (vae.decoder: torch.Size([256])) for vae.decoder IndividualUnPacker: text_encoder-item0.clip_pooled_prompt_embeds: torch.Size([768]) text_encoder-item0.clip_prompt_embeds: torch.Size([77, 768]) text_encoder_2-item1.clip_pooled_prompt_embeds: torch.Size([1280]) text_encoder_2-item1.clip_prompt_embeds: torch.Size([77, 1280]) text_encoder_3-item2.t5_prompt_embeds: torch.Size([77, 4096]) text_encoder-item3.clip_pooled_prompt_embeds: torch.Size([768]) text_encoder-item3.clip_prompt_embeds: torch.Size([77, 768]) text_encoder_2-item4.clip_pooled_prompt_embeds: torch.Size([1280]) text_encoder_2-item4.clip_prompt_embeds: torch.Size([77, 1280]) text_encoder_3-item5.t5_prompt_embeds: torch.Size([77, 4096]) vae.decoder: torch.Size([16, 128, 128]) vae.encoder_f0: torch.Size([32, 128, 128]) vae.encoder_f1: torch.Size([32, 128, 128]) Per-key MSE Breakdown (Quant=Truncation+Quantization, Codec=Compression): Key Quant-MSE Total-MSE ------------------------------------------------------------------------------------- text_encoder-item0.clip_pooled_prompt_embeds 0.00029404 0.94159349 text_encoder-item0.clip_prompt_embeds 0.00022201 23.93505859 text_encoder_2-item1.clip_pooled_prompt_embeds 0.00030500 0.94038029 text_encoder_2-item1.clip_prompt_embeds 0.00020541 0.14154603 text_encoder_3-item2.t5_prompt_embeds 0.00000847 0.00323732 text_encoder-item3.clip_pooled_prompt_embeds 0.00841799 1.01420029 text_encoder-item3.clip_prompt_embeds 0.00023247 23.90570887 text_encoder_2-item4.clip_pooled_prompt_embeds 0.00016751 0.67051430 text_encoder_2-item4.clip_prompt_embeds 0.00009830 0.47049568 text_encoder_3-item5.t5_prompt_embeds 0.00001263 0.00227117 vae.encoder_f0 0.00831743 2.48620129 vae.encoder_f1 0.00831926 2.48616362 vae.decoder 0.00028593 0.06129293 ------------------------------------------------------------------------------------- TOTAL 0.00392223 2.44041315 (elements=2,260,992) ---------------------- -------------------------------------------------------- SAMPLE-WISE STATISTICS ---------------------- -------------------------------------------------------- Handler sd35 Strategy individual Architecture hyperprior-featurecoding ---------------------- -------------------------------------------------------- Total Elements 2260992 Total Bytes 114420 BPFP 0.4048 bits/point EBPFP 0.8097 equivalent bits/point MSE 2.440413 ---------------------- -------------------------------------------------------- Time: 0.755s Load: 0.009s, Pack+Encode: 0.295s, Decode+Unpack: 0.452s ---------------------- -------------------------------------------------------- Restored Feature Format: [Dict] with 8 keys key['text_encoder-item0']: [Dict] with 2 keys key['clip_pooled_prompt_embeds']: [Tensor] shape=torch.Size([768]), dtype=torch.float16, device=cpu key['clip_prompt_embeds']: [Tensor] shape=torch.Size([77, 768]), dtype=torch.float16, device=cpu key['text_encoder_2-item1']: [Dict] with 2 keys key['clip_pooled_prompt_embeds']: [Tensor] shape=torch.Size([1280]), dtype=torch.float16, device=cpu key['clip_prompt_embeds']: [Tensor] shape=torch.Size([77, 1280]), dtype=torch.float16, device=cpu key['text_encoder_3-item2']: [Dict] with 1 keys key['t5_prompt_embeds']: [Tensor] shape=torch.Size([77, 4096]), dtype=torch.float16, device=cpu key['text_encoder-item3']: [Dict] with 2 keys key['clip_pooled_prompt_embeds']: [Tensor] shape=torch.Size([768]), dtype=torch.float16, device=cpu key['clip_prompt_embeds']: [Tensor] shape=torch.Size([77, 768]), dtype=torch.float16, device=cpu key['text_encoder_2-item4']: [Dict] with 2 keys key['clip_pooled_prompt_embeds']: [Tensor] shape=torch.Size([1280]), dtype=torch.float16, device=cpu key['clip_prompt_embeds']: [Tensor] shape=torch.Size([77, 1280]), dtype=torch.float16, device=cpu key['text_encoder_3-item5']: [Dict] with 1 keys key['t5_prompt_embeds']: [Tensor] shape=torch.Size([77, 4096]), dtype=torch.float16, device=cpu key['vae.decoder']: [Tensor] shape=torch.Size([16, 128, 128]), dtype=torch.float16, device=cpu key['vae.encoder']: [Dict] with 2 keys key['vae.encoder_f0']: [Tensor] shape=torch.Size([32, 128, 128]), dtype=torch.float16, device=cpu key['vae.encoder_f1']: [Tensor] shape=torch.Size([32, 128, 128]), dtype=torch.float16, device=cpu 💾 Converting with 2.4404 MSE: from ../datasets/FeatureCoding-StableDiffusion3.5/100samples_features/000000239843.zst to output-fixed/sd35/lambda0.004/hyperprior-featurecoding-8bit-individual/sd35cond/000000239843.zst 💪 Processing: ../datasets/FeatureCoding-StableDiffusion3.5/100samples_features/000000240250.zst (39/100) [1/4] FRONTEND: Loading features from ../datasets/FeatureCoding-StableDiffusion3.5/100samples_features/000000240250.zst... Original data structure: root: [Dict] with 8 keys key['text_encoder-item0']: [Dict] with 2 keys key['clip_pooled_prompt_embeds']: [Tensor] shape=torch.Size([768]), dtype=torch.float16, device=cpu key['clip_prompt_embeds']: [Tensor] shape=torch.Size([77, 768]), dtype=torch.float16, device=cpu key['text_encoder_2-item1']: [Dict] with 2 keys key['clip_pooled_prompt_embeds']: [Tensor] shape=torch.Size([1280]), dtype=torch.float16, device=cpu key['clip_prompt_embeds']: [Tensor] shape=torch.Size([77, 1280]), dtype=torch.float16, device=cpu key['text_encoder_3-item2']: [Dict] with 1 keys key['t5_prompt_embeds']: [Tensor] shape=torch.Size([77, 4096]), dtype=torch.float16, device=cpu key['text_encoder-item3']: [Dict] with 2 keys key['clip_pooled_prompt_embeds']: [Tensor] shape=torch.Size([768]), dtype=torch.float16, device=cpu key['clip_prompt_embeds']: [Tensor] shape=torch.Size([77, 768]), dtype=torch.float16, device=cpu key['text_encoder_2-item4']: [Dict] with 2 keys key['clip_pooled_prompt_embeds']: [Tensor] shape=torch.Size([1280]), dtype=torch.float16, device=cpu key['clip_prompt_embeds']: [Tensor] shape=torch.Size([77, 1280]), dtype=torch.float16, device=cpu key['text_encoder_3-item5']: [Dict] with 1 keys key['t5_prompt_embeds']: [Tensor] shape=torch.Size([77, 4096]), dtype=torch.float16, device=cpu key['vae.encoder-item6']: [Dict] with 2 keys key['vae.encoder_f0']: [Tensor] shape=torch.Size([32, 128, 128]), dtype=torch.float16, device=cpu key['vae.encoder_f1']: [Tensor] shape=torch.Size([32, 128, 128]), dtype=torch.float16, device=cpu key['vae.decoder']: [Tensor] shape=torch.Size([16, 128, 128]), dtype=torch.float16, device=cpu ⌛️ [1/4] FRONTEND: Load time: 0.009s ------------------------------------------------------------ SD3.5 Features Summary ------------------------------------------------------------ Number of text encoders: 3 Encoder names: ['text_encoder', 'text_encoder_2', 'text_encoder_3'] Has VAE latents: True Has VAE encoder features: True Data type: torch.float16 text_encoder-item0: clip_pooled_prompt_embeds: torch.Size([768]), torch.float16 clip_prompt_embeds: torch.Size([77, 768]), torch.float16 text_encoder_2-item1: clip_pooled_prompt_embeds: torch.Size([1280]), torch.float16 clip_prompt_embeds: torch.Size([77, 1280]), torch.float16 text_encoder_3-item2: t5_prompt_embeds: torch.Size([77, 4096]), torch.float16 text_encoder-item3: clip_pooled_prompt_embeds: torch.Size([768]), torch.float16 clip_prompt_embeds: torch.Size([77, 768]), torch.float16 text_encoder_2-item4: clip_pooled_prompt_embeds: torch.Size([1280]), torch.float16 clip_prompt_embeds: torch.Size([77, 1280]), torch.float16 text_encoder_3-item5: t5_prompt_embeds: torch.Size([77, 4096]), torch.float16 vae.decoder latents: torch.Size([16, 128, 128]) vae.encoder features: vae.encoder_f0: torch.Size([32, 128, 128]), torch.float16 vae.encoder_f1: torch.Size([32, 128, 128]), torch.float16 ------------------------------------------------------------ [2/4] FRONTEND: Pack + Encode (strategy: individual)... IndividualPacker: text_encoder-item0.clip_pooled_prompt_embeds: torch.Size([768])->torch.Size([1, 1, 1, 768]) text_encoder-item0.clip_prompt_embeds: torch.Size([77, 768])->torch.Size([1, 1, 77, 768]) text_encoder_2-item1.clip_pooled_prompt_embeds: torch.Size([1280])->torch.Size([1, 1, 1, 1280]) text_encoder_2-item1.clip_prompt_embeds: torch.Size([77, 1280])->torch.Size([1, 1, 77, 1280]) text_encoder_3-item2.t5_prompt_embeds: torch.Size([77, 4096])->torch.Size([1, 1, 77, 4096]) text_encoder-item3.clip_pooled_prompt_embeds: torch.Size([768])->torch.Size([1, 1, 1, 768]) text_encoder-item3.clip_prompt_embeds: torch.Size([77, 768])->torch.Size([1, 1, 77, 768]) text_encoder_2-item4.clip_pooled_prompt_embeds: torch.Size([1280])->torch.Size([1, 1, 1, 1280]) text_encoder_2-item4.clip_prompt_embeds: torch.Size([77, 1280])->torch.Size([1, 1, 77, 1280]) text_encoder_3-item5.t5_prompt_embeds: torch.Size([77, 4096])->torch.Size([1, 1, 77, 4096]) vae.encoder_f0: torch.Size([32, 128, 128])->torch.Size([1, 1, 512, 1024]) vae.encoder_f1: torch.Size([32, 128, 128])->torch.Size([1, 1, 512, 1024]) vae.decoder: torch.Size([16, 128, 128])->torch.Size([1, 1, 512, 512]) Using per-key quantization points (text_encoder-item0.clip_pooled_prompt_embeds: torch.Size([256])) for text_encoder-item0.clip_pooled_prompt_embeds text_encoder-item0.clip_pooled_prompt_embeds: 732B, BPFP=7.6250 Using per-key quantization points (text_encoder-item0.clip_prompt_embeds: torch.Size([256])) for text_encoder-item0.clip_prompt_embeds text_encoder-item0.clip_prompt_embeds: 6,784B, BPFP=0.9177 Using per-key quantization points (text_encoder_2-item1.clip_pooled_prompt_embeds: torch.Size([256])) for text_encoder_2-item1.clip_pooled_prompt_embeds text_encoder_2-item1.clip_pooled_prompt_embeds: 1,204B, BPFP=7.5250 Using per-key quantization points (text_encoder_2-item1.clip_prompt_embeds: torch.Size([256])) for text_encoder_2-item1.clip_prompt_embeds text_encoder_2-item1.clip_prompt_embeds: 10,992B, BPFP=0.8922 Using per-key quantization points (text_encoder_3-item2.t5_prompt_embeds: torch.Size([256])) for text_encoder_3-item2.t5_prompt_embeds text_encoder_3-item2.t5_prompt_embeds: 29,908B, BPFP=0.7586 Using per-key quantization points (text_encoder-item3.clip_pooled_prompt_embeds: torch.Size([256])) for text_encoder-item3.clip_pooled_prompt_embeds text_encoder-item3.clip_pooled_prompt_embeds: 760B, BPFP=7.9167 Using per-key quantization points (text_encoder-item3.clip_prompt_embeds: torch.Size([256])) for text_encoder-item3.clip_prompt_embeds text_encoder-item3.clip_prompt_embeds: 4,024B, BPFP=0.5444 Using per-key quantization points (text_encoder_2-item4.clip_pooled: torch.Size([256])) for text_encoder_2-item4.clip_pooled_prompt_embeds text_encoder_2-item4.clip_pooled_prompt_embeds: 1,268B, BPFP=7.9250 Using per-key quantization points (text_encoder_2-item4.clip_prompt: torch.Size([256])) for text_encoder_2-item4.clip_prompt_embeds text_encoder_2-item4.clip_prompt_embeds: 6,996B, BPFP=0.5679 Using per-key quantization points (text_encoder_3-item5.t5_prompt_embeds: torch.Size([256])) for text_encoder_3-item5.t5_prompt_embeds text_encoder_3-item5.t5_prompt_embeds: 18,416B, BPFP=0.4671 Using per-key quantization points (vae.encoder_f0: torch.Size([256])) for vae.encoder_f0 vae.encoder_f0: 20,448B, BPFP=0.3120 Using per-key quantization points (vae.encoder_f1: torch.Size([256])) for vae.encoder_f1 vae.encoder_f1: 20,452B, BPFP=0.3121 Using per-key quantization points (vae.decoder: torch.Size([256])) for vae.decoder vae.decoder: 7,424B, BPFP=0.2266 ⌛️ [2/4] FRONTEND: Frontend time: 0.295s (Pack+Encode) [3/4] BACKEND: Decode + Unpack... Using per-key quantization points (text_encoder-item0.clip_pooled_prompt_embeds: torch.Size([256])) for text_encoder-item0.clip_pooled_prompt_embeds Using per-key quantization points (text_encoder-item0.clip_prompt_embeds: torch.Size([256])) for text_encoder-item0.clip_prompt_embeds Using per-key quantization points (text_encoder_2-item1.clip_pooled_prompt_embeds: torch.Size([256])) for text_encoder_2-item1.clip_pooled_prompt_embeds Using per-key quantization points (text_encoder_2-item1.clip_prompt_embeds: torch.Size([256])) for text_encoder_2-item1.clip_prompt_embeds Using per-key quantization points (text_encoder_3-item2.t5_prompt_embeds: torch.Size([256])) for text_encoder_3-item2.t5_prompt_embeds Using per-key quantization points (text_encoder-item3.clip_pooled_prompt_embeds: torch.Size([256])) for text_encoder-item3.clip_pooled_prompt_embeds Using per-key quantization points (text_encoder-item3.clip_prompt_embeds: torch.Size([256])) for text_encoder-item3.clip_prompt_embeds Using per-key quantization points (text_encoder_2-item4.clip_pooled: torch.Size([256])) for text_encoder_2-item4.clip_pooled_prompt_embeds Using per-key quantization points (text_encoder_2-item4.clip_prompt: torch.Size([256])) for text_encoder_2-item4.clip_prompt_embeds Using per-key quantization points (text_encoder_3-item5.t5_prompt_embeds: torch.Size([256])) for text_encoder_3-item5.t5_prompt_embeds Using per-key quantization points (vae.encoder_f0: torch.Size([256])) for vae.encoder_f0 Using per-key quantization points (vae.encoder_f1: torch.Size([256])) for vae.encoder_f1 Using per-key quantization points (vae.decoder: torch.Size([256])) for vae.decoder IndividualUnPacker: text_encoder-item0.clip_pooled_prompt_embeds: torch.Size([768]) text_encoder-item0.clip_prompt_embeds: torch.Size([77, 768]) text_encoder_2-item1.clip_pooled_prompt_embeds: torch.Size([1280]) text_encoder_2-item1.clip_prompt_embeds: torch.Size([77, 1280]) text_encoder_3-item2.t5_prompt_embeds: torch.Size([77, 4096]) text_encoder-item3.clip_pooled_prompt_embeds: torch.Size([768]) text_encoder-item3.clip_prompt_embeds: torch.Size([77, 768]) text_encoder_2-item4.clip_pooled_prompt_embeds: torch.Size([1280]) text_encoder_2-item4.clip_prompt_embeds: torch.Size([77, 1280]) text_encoder_3-item5.t5_prompt_embeds: torch.Size([77, 4096]) vae.decoder: torch.Size([16, 128, 128]) vae.encoder_f0: torch.Size([32, 128, 128]) vae.encoder_f1: torch.Size([32, 128, 128]) ⌛️ [3/4] BACKEND: Backend time: 0.453s [4/4] METRICS: Computing MSE Breakdown... Using per-key quantization points (text_encoder-item0.clip_pooled_prompt_embeds: torch.Size([256])) for text_encoder-item0.clip_pooled_prompt_embeds Using per-key quantization points (text_encoder-item0.clip_prompt_embeds: torch.Size([256])) for text_encoder-item0.clip_prompt_embeds Using per-key quantization points (text_encoder_2-item1.clip_pooled_prompt_embeds: torch.Size([256])) for text_encoder_2-item1.clip_pooled_prompt_embeds Using per-key quantization points (text_encoder_2-item1.clip_prompt_embeds: torch.Size([256])) for text_encoder_2-item1.clip_prompt_embeds Using per-key quantization points (text_encoder_3-item2.t5_prompt_embeds: torch.Size([256])) for text_encoder_3-item2.t5_prompt_embeds Using per-key quantization points (text_encoder-item3.clip_pooled_prompt_embeds: torch.Size([256])) for text_encoder-item3.clip_pooled_prompt_embeds Using per-key quantization points (text_encoder-item3.clip_prompt_embeds: torch.Size([256])) for text_encoder-item3.clip_prompt_embeds Using per-key quantization points (text_encoder_2-item4.clip_pooled: torch.Size([256])) for text_encoder_2-item4.clip_pooled_prompt_embeds Using per-key quantization points (text_encoder_2-item4.clip_prompt: torch.Size([256])) for text_encoder_2-item4.clip_prompt_embeds Using per-key quantization points (text_encoder_3-item5.t5_prompt_embeds: torch.Size([256])) for text_encoder_3-item5.t5_prompt_embeds Using per-key quantization points (vae.encoder_f0: torch.Size([256])) for vae.encoder_f0 Using per-key quantization points (vae.encoder_f1: torch.Size([256])) for vae.encoder_f1 Using per-key quantization points (vae.decoder: torch.Size([256])) for vae.decoder IndividualUnPacker: text_encoder-item0.clip_pooled_prompt_embeds: torch.Size([768]) text_encoder-item0.clip_prompt_embeds: torch.Size([77, 768]) text_encoder_2-item1.clip_pooled_prompt_embeds: torch.Size([1280]) text_encoder_2-item1.clip_prompt_embeds: torch.Size([77, 1280]) text_encoder_3-item2.t5_prompt_embeds: torch.Size([77, 4096]) text_encoder-item3.clip_pooled_prompt_embeds: torch.Size([768]) text_encoder-item3.clip_prompt_embeds: torch.Size([77, 768]) text_encoder_2-item4.clip_pooled_prompt_embeds: torch.Size([1280]) text_encoder_2-item4.clip_prompt_embeds: torch.Size([77, 1280]) text_encoder_3-item5.t5_prompt_embeds: torch.Size([77, 4096]) vae.decoder: torch.Size([16, 128, 128]) vae.encoder_f0: torch.Size([32, 128, 128]) vae.encoder_f1: torch.Size([32, 128, 128]) Per-key MSE Breakdown (Quant=Truncation+Quantization, Codec=Compression): Key Quant-MSE Total-MSE ------------------------------------------------------------------------------------- text_encoder-item0.clip_pooled_prompt_embeds 0.00025874 0.93228912 text_encoder-item0.clip_prompt_embeds 0.00026808 23.93481974 text_encoder_2-item1.clip_pooled_prompt_embeds 0.00033998 0.96259098 text_encoder_2-item1.clip_prompt_embeds 0.00021475 0.14612143 text_encoder_3-item2.t5_prompt_embeds 0.00000768 0.00398927 text_encoder-item3.clip_pooled_prompt_embeds 0.00841799 1.01420029 text_encoder-item3.clip_prompt_embeds 0.00023247 23.90570887 text_encoder_2-item4.clip_pooled_prompt_embeds 0.00016751 0.67051430 text_encoder_2-item4.clip_prompt_embeds 0.00009830 0.47049568 text_encoder_3-item5.t5_prompt_embeds 0.00001263 0.00227117 vae.encoder_f0 0.00606586 3.10035968 vae.encoder_f1 0.00607066 3.09903717 vae.decoder 0.00019664 0.05825450 ------------------------------------------------------------------------------------- TOTAL 0.00286987 2.72489752 (elements=2,260,992) ---------------------- -------------------------------------------------------- SAMPLE-WISE STATISTICS ---------------------- -------------------------------------------------------- Handler sd35 Strategy individual Architecture hyperprior-featurecoding ---------------------- -------------------------------------------------------- Total Elements 2260992 Total Bytes 129408 BPFP 0.4579 bits/point EBPFP 0.9158 equivalent bits/point MSE 2.724898 ---------------------- -------------------------------------------------------- Time: 0.757s Load: 0.009s, Pack+Encode: 0.295s, Decode+Unpack: 0.453s ---------------------- -------------------------------------------------------- Restored Feature Format: [Dict] with 8 keys key['text_encoder-item0']: [Dict] with 2 keys key['clip_pooled_prompt_embeds']: [Tensor] shape=torch.Size([768]), dtype=torch.float16, device=cpu key['clip_prompt_embeds']: [Tensor] shape=torch.Size([77, 768]), dtype=torch.float16, device=cpu key['text_encoder_2-item1']: [Dict] with 2 keys key['clip_pooled_prompt_embeds']: [Tensor] shape=torch.Size([1280]), dtype=torch.float16, device=cpu key['clip_prompt_embeds']: [Tensor] shape=torch.Size([77, 1280]), dtype=torch.float16, device=cpu key['text_encoder_3-item2']: [Dict] with 1 keys key['t5_prompt_embeds']: [Tensor] shape=torch.Size([77, 4096]), dtype=torch.float16, device=cpu key['text_encoder-item3']: [Dict] with 2 keys key['clip_pooled_prompt_embeds']: [Tensor] shape=torch.Size([768]), dtype=torch.float16, device=cpu key['clip_prompt_embeds']: [Tensor] shape=torch.Size([77, 768]), dtype=torch.float16, device=cpu key['text_encoder_2-item4']: [Dict] with 2 keys key['clip_pooled_prompt_embeds']: [Tensor] shape=torch.Size([1280]), dtype=torch.float16, device=cpu key['clip_prompt_embeds']: [Tensor] shape=torch.Size([77, 1280]), dtype=torch.float16, device=cpu key['text_encoder_3-item5']: [Dict] with 1 keys key['t5_prompt_embeds']: [Tensor] shape=torch.Size([77, 4096]), dtype=torch.float16, device=cpu key['vae.decoder']: [Tensor] shape=torch.Size([16, 128, 128]), dtype=torch.float16, device=cpu key['vae.encoder']: [Dict] with 2 keys key['vae.encoder_f0']: [Tensor] shape=torch.Size([32, 128, 128]), dtype=torch.float16, device=cpu key['vae.encoder_f1']: [Tensor] shape=torch.Size([32, 128, 128]), dtype=torch.float16, device=cpu 💾 Converting with 2.7249 MSE: from ../datasets/FeatureCoding-StableDiffusion3.5/100samples_features/000000240250.zst to output-fixed/sd35/lambda0.004/hyperprior-featurecoding-8bit-individual/sd35cond/000000240250.zst 💪 Processing: ../datasets/FeatureCoding-StableDiffusion3.5/100samples_features/000000258793.zst (40/100) [1/4] FRONTEND: Loading features from ../datasets/FeatureCoding-StableDiffusion3.5/100samples_features/000000258793.zst... Original data structure: root: [Dict] with 8 keys key['text_encoder-item0']: [Dict] with 2 keys key['clip_pooled_prompt_embeds']: [Tensor] shape=torch.Size([768]), dtype=torch.float16, device=cpu key['clip_prompt_embeds']: [Tensor] shape=torch.Size([77, 768]), dtype=torch.float16, device=cpu key['text_encoder_2-item1']: [Dict] with 2 keys key['clip_pooled_prompt_embeds']: [Tensor] shape=torch.Size([1280]), dtype=torch.float16, device=cpu key['clip_prompt_embeds']: [Tensor] shape=torch.Size([77, 1280]), dtype=torch.float16, device=cpu key['text_encoder_3-item2']: [Dict] with 1 keys key['t5_prompt_embeds']: [Tensor] shape=torch.Size([77, 4096]), dtype=torch.float16, device=cpu key['text_encoder-item3']: [Dict] with 2 keys key['clip_pooled_prompt_embeds']: [Tensor] shape=torch.Size([768]), dtype=torch.float16, device=cpu key['clip_prompt_embeds']: [Tensor] shape=torch.Size([77, 768]), dtype=torch.float16, device=cpu key['text_encoder_2-item4']: [Dict] with 2 keys key['clip_pooled_prompt_embeds']: [Tensor] shape=torch.Size([1280]), dtype=torch.float16, device=cpu key['clip_prompt_embeds']: [Tensor] shape=torch.Size([77, 1280]), dtype=torch.float16, device=cpu key['text_encoder_3-item5']: [Dict] with 1 keys key['t5_prompt_embeds']: [Tensor] shape=torch.Size([77, 4096]), dtype=torch.float16, device=cpu key['vae.encoder-item6']: [Dict] with 2 keys key['vae.encoder_f0']: [Tensor] shape=torch.Size([32, 128, 128]), dtype=torch.float16, device=cpu key['vae.encoder_f1']: [Tensor] shape=torch.Size([32, 128, 128]), dtype=torch.float16, device=cpu key['vae.decoder']: [Tensor] shape=torch.Size([16, 128, 128]), dtype=torch.float16, device=cpu ⌛️ [1/4] FRONTEND: Load time: 0.009s ------------------------------------------------------------ SD3.5 Features Summary ------------------------------------------------------------ Number of text encoders: 3 Encoder names: ['text_encoder', 'text_encoder_2', 'text_encoder_3'] Has VAE latents: True Has VAE encoder features: True Data type: torch.float16 text_encoder-item0: clip_pooled_prompt_embeds: torch.Size([768]), torch.float16 clip_prompt_embeds: torch.Size([77, 768]), torch.float16 text_encoder_2-item1: clip_pooled_prompt_embeds: torch.Size([1280]), torch.float16 clip_prompt_embeds: torch.Size([77, 1280]), torch.float16 text_encoder_3-item2: t5_prompt_embeds: torch.Size([77, 4096]), torch.float16 text_encoder-item3: clip_pooled_prompt_embeds: torch.Size([768]), torch.float16 clip_prompt_embeds: torch.Size([77, 768]), torch.float16 text_encoder_2-item4: clip_pooled_prompt_embeds: torch.Size([1280]), torch.float16 clip_prompt_embeds: torch.Size([77, 1280]), torch.float16 text_encoder_3-item5: t5_prompt_embeds: torch.Size([77, 4096]), torch.float16 vae.decoder latents: torch.Size([16, 128, 128]) vae.encoder features: vae.encoder_f0: torch.Size([32, 128, 128]), torch.float16 vae.encoder_f1: torch.Size([32, 128, 128]), torch.float16 ------------------------------------------------------------ [2/4] FRONTEND: Pack + Encode (strategy: individual)... IndividualPacker: text_encoder-item0.clip_pooled_prompt_embeds: torch.Size([768])->torch.Size([1, 1, 1, 768]) text_encoder-item0.clip_prompt_embeds: torch.Size([77, 768])->torch.Size([1, 1, 77, 768]) text_encoder_2-item1.clip_pooled_prompt_embeds: torch.Size([1280])->torch.Size([1, 1, 1, 1280]) text_encoder_2-item1.clip_prompt_embeds: torch.Size([77, 1280])->torch.Size([1, 1, 77, 1280]) text_encoder_3-item2.t5_prompt_embeds: torch.Size([77, 4096])->torch.Size([1, 1, 77, 4096]) text_encoder-item3.clip_pooled_prompt_embeds: torch.Size([768])->torch.Size([1, 1, 1, 768]) text_encoder-item3.clip_prompt_embeds: torch.Size([77, 768])->torch.Size([1, 1, 77, 768]) text_encoder_2-item4.clip_pooled_prompt_embeds: torch.Size([1280])->torch.Size([1, 1, 1, 1280]) text_encoder_2-item4.clip_prompt_embeds: torch.Size([77, 1280])->torch.Size([1, 1, 77, 1280]) text_encoder_3-item5.t5_prompt_embeds: torch.Size([77, 4096])->torch.Size([1, 1, 77, 4096]) vae.encoder_f0: torch.Size([32, 128, 128])->torch.Size([1, 1, 512, 1024]) vae.encoder_f1: torch.Size([32, 128, 128])->torch.Size([1, 1, 512, 1024]) vae.decoder: torch.Size([16, 128, 128])->torch.Size([1, 1, 512, 512]) Using per-key quantization points (text_encoder-item0.clip_pooled_prompt_embeds: torch.Size([256])) for text_encoder-item0.clip_pooled_prompt_embeds text_encoder-item0.clip_pooled_prompt_embeds: 724B, BPFP=7.5417 Using per-key quantization points (text_encoder-item0.clip_prompt_embeds: torch.Size([256])) for text_encoder-item0.clip_prompt_embeds text_encoder-item0.clip_prompt_embeds: 6,620B, BPFP=0.8956 Using per-key quantization points (text_encoder_2-item1.clip_pooled_prompt_embeds: torch.Size([256])) for text_encoder_2-item1.clip_pooled_prompt_embeds text_encoder_2-item1.clip_pooled_prompt_embeds: 1,200B, BPFP=7.5000 Using per-key quantization points (text_encoder_2-item1.clip_prompt_embeds: torch.Size([256])) for text_encoder_2-item1.clip_prompt_embeds text_encoder_2-item1.clip_prompt_embeds: 10,776B, BPFP=0.8747 Using per-key quantization points (text_encoder_3-item2.t5_prompt_embeds: torch.Size([256])) for text_encoder_3-item2.t5_prompt_embeds text_encoder_3-item2.t5_prompt_embeds: 29,292B, BPFP=0.7430 Using per-key quantization points (text_encoder-item3.clip_pooled_prompt_embeds: torch.Size([256])) for text_encoder-item3.clip_pooled_prompt_embeds text_encoder-item3.clip_pooled_prompt_embeds: 760B, BPFP=7.9167 Using per-key quantization points (text_encoder-item3.clip_prompt_embeds: torch.Size([256])) for text_encoder-item3.clip_prompt_embeds text_encoder-item3.clip_prompt_embeds: 4,024B, BPFP=0.5444 Using per-key quantization points (text_encoder_2-item4.clip_pooled: torch.Size([256])) for text_encoder_2-item4.clip_pooled_prompt_embeds text_encoder_2-item4.clip_pooled_prompt_embeds: 1,268B, BPFP=7.9250 Using per-key quantization points (text_encoder_2-item4.clip_prompt: torch.Size([256])) for text_encoder_2-item4.clip_prompt_embeds text_encoder_2-item4.clip_prompt_embeds: 6,996B, BPFP=0.5679 Using per-key quantization points (text_encoder_3-item5.t5_prompt_embeds: torch.Size([256])) for text_encoder_3-item5.t5_prompt_embeds text_encoder_3-item5.t5_prompt_embeds: 18,416B, BPFP=0.4671 Using per-key quantization points (vae.encoder_f0: torch.Size([256])) for vae.encoder_f0 vae.encoder_f0: 16,024B, BPFP=0.2445 Using per-key quantization points (vae.encoder_f1: torch.Size([256])) for vae.encoder_f1 vae.encoder_f1: 16,024B, BPFP=0.2445 Using per-key quantization points (vae.decoder: torch.Size([256])) for vae.decoder vae.decoder: 6,568B, BPFP=0.2004 ⌛️ [2/4] FRONTEND: Frontend time: 0.294s (Pack+Encode) [3/4] BACKEND: Decode + Unpack... Using per-key quantization points (text_encoder-item0.clip_pooled_prompt_embeds: torch.Size([256])) for text_encoder-item0.clip_pooled_prompt_embeds Using per-key quantization points (text_encoder-item0.clip_prompt_embeds: torch.Size([256])) for text_encoder-item0.clip_prompt_embeds Using per-key quantization points (text_encoder_2-item1.clip_pooled_prompt_embeds: torch.Size([256])) for text_encoder_2-item1.clip_pooled_prompt_embeds Using per-key quantization points (text_encoder_2-item1.clip_prompt_embeds: torch.Size([256])) for text_encoder_2-item1.clip_prompt_embeds Using per-key quantization points (text_encoder_3-item2.t5_prompt_embeds: torch.Size([256])) for text_encoder_3-item2.t5_prompt_embeds Using per-key quantization points (text_encoder-item3.clip_pooled_prompt_embeds: torch.Size([256])) for text_encoder-item3.clip_pooled_prompt_embeds Using per-key quantization points (text_encoder-item3.clip_prompt_embeds: torch.Size([256])) for text_encoder-item3.clip_prompt_embeds Using per-key quantization points (text_encoder_2-item4.clip_pooled: torch.Size([256])) for text_encoder_2-item4.clip_pooled_prompt_embeds Using per-key quantization points (text_encoder_2-item4.clip_prompt: torch.Size([256])) for text_encoder_2-item4.clip_prompt_embeds Using per-key quantization points (text_encoder_3-item5.t5_prompt_embeds: torch.Size([256])) for text_encoder_3-item5.t5_prompt_embeds Using per-key quantization points (vae.encoder_f0: torch.Size([256])) for vae.encoder_f0 Using per-key quantization points (vae.encoder_f1: torch.Size([256])) for vae.encoder_f1 Using per-key quantization points (vae.decoder: torch.Size([256])) for vae.decoder IndividualUnPacker: text_encoder-item0.clip_pooled_prompt_embeds: torch.Size([768]) text_encoder-item0.clip_prompt_embeds: torch.Size([77, 768]) text_encoder_2-item1.clip_pooled_prompt_embeds: torch.Size([1280]) text_encoder_2-item1.clip_prompt_embeds: torch.Size([77, 1280]) text_encoder_3-item2.t5_prompt_embeds: torch.Size([77, 4096]) text_encoder-item3.clip_pooled_prompt_embeds: torch.Size([768]) text_encoder-item3.clip_prompt_embeds: torch.Size([77, 768]) text_encoder_2-item4.clip_pooled_prompt_embeds: torch.Size([1280]) text_encoder_2-item4.clip_prompt_embeds: torch.Size([77, 1280]) text_encoder_3-item5.t5_prompt_embeds: torch.Size([77, 4096]) vae.decoder: torch.Size([16, 128, 128]) vae.encoder_f0: torch.Size([32, 128, 128]) vae.encoder_f1: torch.Size([32, 128, 128]) ⌛️ [3/4] BACKEND: Backend time: 0.446s [4/4] METRICS: Computing MSE Breakdown... Using per-key quantization points (text_encoder-item0.clip_pooled_prompt_embeds: torch.Size([256])) for text_encoder-item0.clip_pooled_prompt_embeds Using per-key quantization points (text_encoder-item0.clip_prompt_embeds: torch.Size([256])) for text_encoder-item0.clip_prompt_embeds Using per-key quantization points (text_encoder_2-item1.clip_pooled_prompt_embeds: torch.Size([256])) for text_encoder_2-item1.clip_pooled_prompt_embeds Using per-key quantization points (text_encoder_2-item1.clip_prompt_embeds: torch.Size([256])) for text_encoder_2-item1.clip_prompt_embeds Using per-key quantization points (text_encoder_3-item2.t5_prompt_embeds: torch.Size([256])) for text_encoder_3-item2.t5_prompt_embeds Using per-key quantization points (text_encoder-item3.clip_pooled_prompt_embeds: torch.Size([256])) for text_encoder-item3.clip_pooled_prompt_embeds Using per-key quantization points (text_encoder-item3.clip_prompt_embeds: torch.Size([256])) for text_encoder-item3.clip_prompt_embeds Using per-key quantization points (text_encoder_2-item4.clip_pooled: torch.Size([256])) for text_encoder_2-item4.clip_pooled_prompt_embeds Using per-key quantization points (text_encoder_2-item4.clip_prompt: torch.Size([256])) for text_encoder_2-item4.clip_prompt_embeds Using per-key quantization points (text_encoder_3-item5.t5_prompt_embeds: torch.Size([256])) for text_encoder_3-item5.t5_prompt_embeds Using per-key quantization points (vae.encoder_f0: torch.Size([256])) for vae.encoder_f0 Using per-key quantization points (vae.encoder_f1: torch.Size([256])) for vae.encoder_f1 Using per-key quantization points (vae.decoder: torch.Size([256])) for vae.decoder IndividualUnPacker: text_encoder-item0.clip_pooled_prompt_embeds: torch.Size([768]) text_encoder-item0.clip_prompt_embeds: torch.Size([77, 768]) text_encoder_2-item1.clip_pooled_prompt_embeds: torch.Size([1280]) text_encoder_2-item1.clip_prompt_embeds: torch.Size([77, 1280]) text_encoder_3-item2.t5_prompt_embeds: torch.Size([77, 4096]) text_encoder-item3.clip_pooled_prompt_embeds: torch.Size([768]) text_encoder-item3.clip_prompt_embeds: torch.Size([77, 768]) text_encoder_2-item4.clip_pooled_prompt_embeds: torch.Size([1280]) text_encoder_2-item4.clip_prompt_embeds: torch.Size([77, 1280]) text_encoder_3-item5.t5_prompt_embeds: torch.Size([77, 4096]) vae.decoder: torch.Size([16, 128, 128]) vae.encoder_f0: torch.Size([32, 128, 128]) vae.encoder_f1: torch.Size([32, 128, 128]) Per-key MSE Breakdown (Quant=Truncation+Quantization, Codec=Compression): Key Quant-MSE Total-MSE ------------------------------------------------------------------------------------- text_encoder-item0.clip_pooled_prompt_embeds 0.00028013 0.94207589 text_encoder-item0.clip_prompt_embeds 0.00023198 34.93996254 text_encoder_2-item1.clip_pooled_prompt_embeds 0.00035192 0.98193111 text_encoder_2-item1.clip_prompt_embeds 0.00017676 0.14449975 text_encoder_3-item2.t5_prompt_embeds 0.00000830 0.00277646 text_encoder-item3.clip_pooled_prompt_embeds 0.00841799 1.01420029 text_encoder-item3.clip_prompt_embeds 0.00023247 23.90570887 text_encoder_2-item4.clip_pooled_prompt_embeds 0.00016751 0.67051430 text_encoder_2-item4.clip_prompt_embeds 0.00009830 0.47049568 text_encoder_3-item5.t5_prompt_embeds 0.00001263 0.00227117 vae.encoder_f0 0.05216765 3.80797935 vae.encoder_f1 0.05216896 3.80751586 vae.decoder 0.00017960 0.04968261 ------------------------------------------------------------------------------------- TOTAL 0.02424513 3.33988703 (elements=2,260,992) ---------------------- -------------------------------------------------------- SAMPLE-WISE STATISTICS ---------------------- -------------------------------------------------------- Handler sd35 Strategy individual Architecture hyperprior-featurecoding ---------------------- -------------------------------------------------------- Total Elements 2260992 Total Bytes 118692 BPFP 0.4200 bits/point EBPFP 0.8399 equivalent bits/point MSE 3.339887 ---------------------- -------------------------------------------------------- Time: 0.749s Load: 0.009s, Pack+Encode: 0.294s, Decode+Unpack: 0.446s ---------------------- -------------------------------------------------------- Restored Feature Format: [Dict] with 8 keys key['text_encoder-item0']: [Dict] with 2 keys key['clip_pooled_prompt_embeds']: [Tensor] shape=torch.Size([768]), dtype=torch.float16, device=cpu key['clip_prompt_embeds']: [Tensor] shape=torch.Size([77, 768]), dtype=torch.float16, device=cpu key['text_encoder_2-item1']: [Dict] with 2 keys key['clip_pooled_prompt_embeds']: [Tensor] shape=torch.Size([1280]), dtype=torch.float16, device=cpu key['clip_prompt_embeds']: [Tensor] shape=torch.Size([77, 1280]), dtype=torch.float16, device=cpu key['text_encoder_3-item2']: [Dict] with 1 keys key['t5_prompt_embeds']: [Tensor] shape=torch.Size([77, 4096]), dtype=torch.float16, device=cpu key['text_encoder-item3']: [Dict] with 2 keys key['clip_pooled_prompt_embeds']: [Tensor] shape=torch.Size([768]), dtype=torch.float16, device=cpu key['clip_prompt_embeds']: [Tensor] shape=torch.Size([77, 768]), dtype=torch.float16, device=cpu key['text_encoder_2-item4']: [Dict] with 2 keys key['clip_pooled_prompt_embeds']: [Tensor] shape=torch.Size([1280]), dtype=torch.float16, device=cpu key['clip_prompt_embeds']: [Tensor] shape=torch.Size([77, 1280]), dtype=torch.float16, device=cpu key['text_encoder_3-item5']: [Dict] with 1 keys key['t5_prompt_embeds']: [Tensor] shape=torch.Size([77, 4096]), dtype=torch.float16, device=cpu key['vae.decoder']: [Tensor] shape=torch.Size([16, 128, 128]), dtype=torch.float16, device=cpu key['vae.encoder']: [Dict] with 2 keys key['vae.encoder_f0']: [Tensor] shape=torch.Size([32, 128, 128]), dtype=torch.float16, device=cpu key['vae.encoder_f1']: [Tensor] shape=torch.Size([32, 128, 128]), dtype=torch.float16, device=cpu 💾 Converting with 3.3399 MSE: from ../datasets/FeatureCoding-StableDiffusion3.5/100samples_features/000000258793.zst to output-fixed/sd35/lambda0.004/hyperprior-featurecoding-8bit-individual/sd35cond/000000258793.zst 💪 Processing: ../datasets/FeatureCoding-StableDiffusion3.5/100samples_features/000000270402.zst (41/100) [1/4] FRONTEND: Loading features from ../datasets/FeatureCoding-StableDiffusion3.5/100samples_features/000000270402.zst... Original data structure: root: [Dict] with 8 keys key['text_encoder-item0']: [Dict] with 2 keys key['clip_pooled_prompt_embeds']: [Tensor] shape=torch.Size([768]), dtype=torch.float16, device=cpu key['clip_prompt_embeds']: [Tensor] shape=torch.Size([77, 768]), dtype=torch.float16, device=cpu key['text_encoder_2-item1']: [Dict] with 2 keys key['clip_pooled_prompt_embeds']: [Tensor] shape=torch.Size([1280]), dtype=torch.float16, device=cpu key['clip_prompt_embeds']: [Tensor] shape=torch.Size([77, 1280]), dtype=torch.float16, device=cpu key['text_encoder_3-item2']: [Dict] with 1 keys key['t5_prompt_embeds']: [Tensor] shape=torch.Size([77, 4096]), dtype=torch.float16, device=cpu key['text_encoder-item3']: [Dict] with 2 keys key['clip_pooled_prompt_embeds']: [Tensor] shape=torch.Size([768]), dtype=torch.float16, device=cpu key['clip_prompt_embeds']: [Tensor] shape=torch.Size([77, 768]), dtype=torch.float16, device=cpu key['text_encoder_2-item4']: [Dict] with 2 keys key['clip_pooled_prompt_embeds']: [Tensor] shape=torch.Size([1280]), dtype=torch.float16, device=cpu key['clip_prompt_embeds']: [Tensor] shape=torch.Size([77, 1280]), dtype=torch.float16, device=cpu key['text_encoder_3-item5']: [Dict] with 1 keys key['t5_prompt_embeds']: [Tensor] shape=torch.Size([77, 4096]), dtype=torch.float16, device=cpu key['vae.encoder-item6']: [Dict] with 2 keys key['vae.encoder_f0']: [Tensor] shape=torch.Size([32, 128, 128]), dtype=torch.float16, device=cpu key['vae.encoder_f1']: [Tensor] shape=torch.Size([32, 128, 128]), dtype=torch.float16, device=cpu key['vae.decoder']: [Tensor] shape=torch.Size([16, 128, 128]), dtype=torch.float16, device=cpu ⌛️ [1/4] FRONTEND: Load time: 0.009s ------------------------------------------------------------ SD3.5 Features Summary ------------------------------------------------------------ Number of text encoders: 3 Encoder names: ['text_encoder', 'text_encoder_2', 'text_encoder_3'] Has VAE latents: True Has VAE encoder features: True Data type: torch.float16 text_encoder-item0: clip_pooled_prompt_embeds: torch.Size([768]), torch.float16 clip_prompt_embeds: torch.Size([77, 768]), torch.float16 text_encoder_2-item1: clip_pooled_prompt_embeds: torch.Size([1280]), torch.float16 clip_prompt_embeds: torch.Size([77, 1280]), torch.float16 text_encoder_3-item2: t5_prompt_embeds: torch.Size([77, 4096]), torch.float16 text_encoder-item3: clip_pooled_prompt_embeds: torch.Size([768]), torch.float16 clip_prompt_embeds: torch.Size([77, 768]), torch.float16 text_encoder_2-item4: clip_pooled_prompt_embeds: torch.Size([1280]), torch.float16 clip_prompt_embeds: torch.Size([77, 1280]), torch.float16 text_encoder_3-item5: t5_prompt_embeds: torch.Size([77, 4096]), torch.float16 vae.decoder latents: torch.Size([16, 128, 128]) vae.encoder features: vae.encoder_f0: torch.Size([32, 128, 128]), torch.float16 vae.encoder_f1: torch.Size([32, 128, 128]), torch.float16 ------------------------------------------------------------ [2/4] FRONTEND: Pack + Encode (strategy: individual)... IndividualPacker: text_encoder-item0.clip_pooled_prompt_embeds: torch.Size([768])->torch.Size([1, 1, 1, 768]) text_encoder-item0.clip_prompt_embeds: torch.Size([77, 768])->torch.Size([1, 1, 77, 768]) text_encoder_2-item1.clip_pooled_prompt_embeds: torch.Size([1280])->torch.Size([1, 1, 1, 1280]) text_encoder_2-item1.clip_prompt_embeds: torch.Size([77, 1280])->torch.Size([1, 1, 77, 1280]) text_encoder_3-item2.t5_prompt_embeds: torch.Size([77, 4096])->torch.Size([1, 1, 77, 4096]) text_encoder-item3.clip_pooled_prompt_embeds: torch.Size([768])->torch.Size([1, 1, 1, 768]) text_encoder-item3.clip_prompt_embeds: torch.Size([77, 768])->torch.Size([1, 1, 77, 768]) text_encoder_2-item4.clip_pooled_prompt_embeds: torch.Size([1280])->torch.Size([1, 1, 1, 1280]) text_encoder_2-item4.clip_prompt_embeds: torch.Size([77, 1280])->torch.Size([1, 1, 77, 1280]) text_encoder_3-item5.t5_prompt_embeds: torch.Size([77, 4096])->torch.Size([1, 1, 77, 4096]) vae.encoder_f0: torch.Size([32, 128, 128])->torch.Size([1, 1, 512, 1024]) vae.encoder_f1: torch.Size([32, 128, 128])->torch.Size([1, 1, 512, 1024]) vae.decoder: torch.Size([16, 128, 128])->torch.Size([1, 1, 512, 512]) Using per-key quantization points (text_encoder-item0.clip_pooled_prompt_embeds: torch.Size([256])) for text_encoder-item0.clip_pooled_prompt_embeds text_encoder-item0.clip_pooled_prompt_embeds: 732B, BPFP=7.6250 Using per-key quantization points (text_encoder-item0.clip_prompt_embeds: torch.Size([256])) for text_encoder-item0.clip_prompt_embeds text_encoder-item0.clip_prompt_embeds: 6,000B, BPFP=0.8117 Using per-key quantization points (text_encoder_2-item1.clip_pooled_prompt_embeds: torch.Size([256])) for text_encoder_2-item1.clip_pooled_prompt_embeds text_encoder_2-item1.clip_pooled_prompt_embeds: 1,196B, BPFP=7.4750 Using per-key quantization points (text_encoder_2-item1.clip_prompt_embeds: torch.Size([256])) for text_encoder_2-item1.clip_prompt_embeds text_encoder_2-item1.clip_prompt_embeds: 11,056B, BPFP=0.8974 Using per-key quantization points (text_encoder_3-item2.t5_prompt_embeds: torch.Size([256])) for text_encoder_3-item2.t5_prompt_embeds text_encoder_3-item2.t5_prompt_embeds: 25,324B, BPFP=0.6423 Using per-key quantization points (text_encoder-item3.clip_pooled_prompt_embeds: torch.Size([256])) for text_encoder-item3.clip_pooled_prompt_embeds text_encoder-item3.clip_pooled_prompt_embeds: 760B, BPFP=7.9167 Using per-key quantization points (text_encoder-item3.clip_prompt_embeds: torch.Size([256])) for text_encoder-item3.clip_prompt_embeds text_encoder-item3.clip_prompt_embeds: 4,024B, BPFP=0.5444 Using per-key quantization points (text_encoder_2-item4.clip_pooled: torch.Size([256])) for text_encoder_2-item4.clip_pooled_prompt_embeds text_encoder_2-item4.clip_pooled_prompt_embeds: 1,268B, BPFP=7.9250 Using per-key quantization points (text_encoder_2-item4.clip_prompt: torch.Size([256])) for text_encoder_2-item4.clip_prompt_embeds text_encoder_2-item4.clip_prompt_embeds: 6,996B, BPFP=0.5679 Using per-key quantization points (text_encoder_3-item5.t5_prompt_embeds: torch.Size([256])) for text_encoder_3-item5.t5_prompt_embeds text_encoder_3-item5.t5_prompt_embeds: 18,416B, BPFP=0.4671 Using per-key quantization points (vae.encoder_f0: torch.Size([256])) for vae.encoder_f0 vae.encoder_f0: 20,488B, BPFP=0.3126 Using per-key quantization points (vae.encoder_f1: torch.Size([256])) for vae.encoder_f1 vae.encoder_f1: 20,488B, BPFP=0.3126 Using per-key quantization points (vae.decoder: torch.Size([256])) for vae.decoder vae.decoder: 6,824B, BPFP=0.2083 ⌛️ [2/4] FRONTEND: Frontend time: 0.295s (Pack+Encode) [3/4] BACKEND: Decode + Unpack... Using per-key quantization points (text_encoder-item0.clip_pooled_prompt_embeds: torch.Size([256])) for text_encoder-item0.clip_pooled_prompt_embeds Using per-key quantization points (text_encoder-item0.clip_prompt_embeds: torch.Size([256])) for text_encoder-item0.clip_prompt_embeds Using per-key quantization points (text_encoder_2-item1.clip_pooled_prompt_embeds: torch.Size([256])) for text_encoder_2-item1.clip_pooled_prompt_embeds Using per-key quantization points (text_encoder_2-item1.clip_prompt_embeds: torch.Size([256])) for text_encoder_2-item1.clip_prompt_embeds Using per-key quantization points (text_encoder_3-item2.t5_prompt_embeds: torch.Size([256])) for text_encoder_3-item2.t5_prompt_embeds Using per-key quantization points (text_encoder-item3.clip_pooled_prompt_embeds: torch.Size([256])) for text_encoder-item3.clip_pooled_prompt_embeds Using per-key quantization points (text_encoder-item3.clip_prompt_embeds: torch.Size([256])) for text_encoder-item3.clip_prompt_embeds Using per-key quantization points (text_encoder_2-item4.clip_pooled: torch.Size([256])) for text_encoder_2-item4.clip_pooled_prompt_embeds Using per-key quantization points (text_encoder_2-item4.clip_prompt: torch.Size([256])) for text_encoder_2-item4.clip_prompt_embeds Using per-key quantization points (text_encoder_3-item5.t5_prompt_embeds: torch.Size([256])) for text_encoder_3-item5.t5_prompt_embeds Using per-key quantization points (vae.encoder_f0: torch.Size([256])) for vae.encoder_f0 Using per-key quantization points (vae.encoder_f1: torch.Size([256])) for vae.encoder_f1 Using per-key quantization points (vae.decoder: torch.Size([256])) for vae.decoder IndividualUnPacker: text_encoder-item0.clip_pooled_prompt_embeds: torch.Size([768]) text_encoder-item0.clip_prompt_embeds: torch.Size([77, 768]) text_encoder_2-item1.clip_pooled_prompt_embeds: torch.Size([1280]) text_encoder_2-item1.clip_prompt_embeds: torch.Size([77, 1280]) text_encoder_3-item2.t5_prompt_embeds: torch.Size([77, 4096]) text_encoder-item3.clip_pooled_prompt_embeds: torch.Size([768]) text_encoder-item3.clip_prompt_embeds: torch.Size([77, 768]) text_encoder_2-item4.clip_pooled_prompt_embeds: torch.Size([1280]) text_encoder_2-item4.clip_prompt_embeds: torch.Size([77, 1280]) text_encoder_3-item5.t5_prompt_embeds: torch.Size([77, 4096]) vae.decoder: torch.Size([16, 128, 128]) vae.encoder_f0: torch.Size([32, 128, 128]) vae.encoder_f1: torch.Size([32, 128, 128]) ⌛️ [3/4] BACKEND: Backend time: 0.446s [4/4] METRICS: Computing MSE Breakdown... Using per-key quantization points (text_encoder-item0.clip_pooled_prompt_embeds: torch.Size([256])) for text_encoder-item0.clip_pooled_prompt_embeds Using per-key quantization points (text_encoder-item0.clip_prompt_embeds: torch.Size([256])) for text_encoder-item0.clip_prompt_embeds Using per-key quantization points (text_encoder_2-item1.clip_pooled_prompt_embeds: torch.Size([256])) for text_encoder_2-item1.clip_pooled_prompt_embeds Using per-key quantization points (text_encoder_2-item1.clip_prompt_embeds: torch.Size([256])) for text_encoder_2-item1.clip_prompt_embeds Using per-key quantization points (text_encoder_3-item2.t5_prompt_embeds: torch.Size([256])) for text_encoder_3-item2.t5_prompt_embeds Using per-key quantization points (text_encoder-item3.clip_pooled_prompt_embeds: torch.Size([256])) for text_encoder-item3.clip_pooled_prompt_embeds Using per-key quantization points (text_encoder-item3.clip_prompt_embeds: torch.Size([256])) for text_encoder-item3.clip_prompt_embeds Using per-key quantization points (text_encoder_2-item4.clip_pooled: torch.Size([256])) for text_encoder_2-item4.clip_pooled_prompt_embeds Using per-key quantization points (text_encoder_2-item4.clip_prompt: torch.Size([256])) for text_encoder_2-item4.clip_prompt_embeds Using per-key quantization points (text_encoder_3-item5.t5_prompt_embeds: torch.Size([256])) for text_encoder_3-item5.t5_prompt_embeds Using per-key quantization points (vae.encoder_f0: torch.Size([256])) for vae.encoder_f0 Using per-key quantization points (vae.encoder_f1: torch.Size([256])) for vae.encoder_f1 Using per-key quantization points (vae.decoder: torch.Size([256])) for vae.decoder IndividualUnPacker: text_encoder-item0.clip_pooled_prompt_embeds: torch.Size([768]) text_encoder-item0.clip_prompt_embeds: torch.Size([77, 768]) text_encoder_2-item1.clip_pooled_prompt_embeds: torch.Size([1280]) text_encoder_2-item1.clip_prompt_embeds: torch.Size([77, 1280]) text_encoder_3-item2.t5_prompt_embeds: torch.Size([77, 4096]) text_encoder-item3.clip_pooled_prompt_embeds: torch.Size([768]) text_encoder-item3.clip_prompt_embeds: torch.Size([77, 768]) text_encoder_2-item4.clip_pooled_prompt_embeds: torch.Size([1280]) text_encoder_2-item4.clip_prompt_embeds: torch.Size([77, 1280]) text_encoder_3-item5.t5_prompt_embeds: torch.Size([77, 4096]) vae.decoder: torch.Size([16, 128, 128]) vae.encoder_f0: torch.Size([32, 128, 128]) vae.encoder_f1: torch.Size([32, 128, 128]) Per-key MSE Breakdown (Quant=Truncation+Quantization, Codec=Compression): Key Quant-MSE Total-MSE ------------------------------------------------------------------------------------- text_encoder-item0.clip_pooled_prompt_embeds 0.00048242 0.94063735 text_encoder-item0.clip_prompt_embeds 0.00023125 23.95665077 text_encoder_2-item1.clip_pooled_prompt_embeds 0.00024484 0.97316809 text_encoder_2-item1.clip_prompt_embeds 0.00020589 0.16126856 text_encoder_3-item2.t5_prompt_embeds 0.00000799 0.00284531 text_encoder-item3.clip_pooled_prompt_embeds 0.00841799 1.01420029 text_encoder-item3.clip_prompt_embeds 0.00023247 23.90570887 text_encoder_2-item4.clip_pooled_prompt_embeds 0.00016751 0.67051430 text_encoder_2-item4.clip_prompt_embeds 0.00009830 0.47049568 text_encoder_3-item5.t5_prompt_embeds 0.00001263 0.00227117 vae.encoder_f0 0.00620361 2.58321953 vae.encoder_f1 0.00620966 2.58307409 vae.decoder 0.00020748 0.05751245 ------------------------------------------------------------------------------------- TOTAL 0.00293402 2.48633184 (elements=2,260,992) ---------------------- -------------------------------------------------------- SAMPLE-WISE STATISTICS ---------------------- -------------------------------------------------------- Handler sd35 Strategy individual Architecture hyperprior-featurecoding ---------------------- -------------------------------------------------------- Total Elements 2260992 Total Bytes 123572 BPFP 0.4372 bits/point EBPFP 0.8745 equivalent bits/point MSE 2.486332 ---------------------- -------------------------------------------------------- Time: 0.750s Load: 0.009s, Pack+Encode: 0.295s, Decode+Unpack: 0.446s ---------------------- -------------------------------------------------------- Restored Feature Format: [Dict] with 8 keys key['text_encoder-item0']: [Dict] with 2 keys key['clip_pooled_prompt_embeds']: [Tensor] shape=torch.Size([768]), dtype=torch.float16, device=cpu key['clip_prompt_embeds']: [Tensor] shape=torch.Size([77, 768]), dtype=torch.float16, device=cpu key['text_encoder_2-item1']: [Dict] with 2 keys key['clip_pooled_prompt_embeds']: [Tensor] shape=torch.Size([1280]), dtype=torch.float16, device=cpu key['clip_prompt_embeds']: [Tensor] shape=torch.Size([77, 1280]), dtype=torch.float16, device=cpu key['text_encoder_3-item2']: [Dict] with 1 keys key['t5_prompt_embeds']: [Tensor] shape=torch.Size([77, 4096]), dtype=torch.float16, device=cpu key['text_encoder-item3']: [Dict] with 2 keys key['clip_pooled_prompt_embeds']: [Tensor] shape=torch.Size([768]), dtype=torch.float16, device=cpu key['clip_prompt_embeds']: [Tensor] shape=torch.Size([77, 768]), dtype=torch.float16, device=cpu key['text_encoder_2-item4']: [Dict] with 2 keys key['clip_pooled_prompt_embeds']: [Tensor] shape=torch.Size([1280]), dtype=torch.float16, device=cpu key['clip_prompt_embeds']: [Tensor] shape=torch.Size([77, 1280]), dtype=torch.float16, device=cpu key['text_encoder_3-item5']: [Dict] with 1 keys key['t5_prompt_embeds']: [Tensor] shape=torch.Size([77, 4096]), dtype=torch.float16, device=cpu key['vae.decoder']: [Tensor] shape=torch.Size([16, 128, 128]), dtype=torch.float16, device=cpu key['vae.encoder']: [Dict] with 2 keys key['vae.encoder_f0']: [Tensor] shape=torch.Size([32, 128, 128]), dtype=torch.float16, device=cpu key['vae.encoder_f1']: [Tensor] shape=torch.Size([32, 128, 128]), dtype=torch.float16, device=cpu 💾 Converting with 2.4863 MSE: from ../datasets/FeatureCoding-StableDiffusion3.5/100samples_features/000000270402.zst to output-fixed/sd35/lambda0.004/hyperprior-featurecoding-8bit-individual/sd35cond/000000270402.zst 💪 Processing: ../datasets/FeatureCoding-StableDiffusion3.5/100samples_features/000000274272.zst (42/100) [1/4] FRONTEND: Loading features from ../datasets/FeatureCoding-StableDiffusion3.5/100samples_features/000000274272.zst... Original data structure: root: [Dict] with 8 keys key['text_encoder-item0']: [Dict] with 2 keys key['clip_pooled_prompt_embeds']: [Tensor] shape=torch.Size([768]), dtype=torch.float16, device=cpu key['clip_prompt_embeds']: [Tensor] shape=torch.Size([77, 768]), dtype=torch.float16, device=cpu key['text_encoder_2-item1']: [Dict] with 2 keys key['clip_pooled_prompt_embeds']: [Tensor] shape=torch.Size([1280]), dtype=torch.float16, device=cpu key['clip_prompt_embeds']: [Tensor] shape=torch.Size([77, 1280]), dtype=torch.float16, device=cpu key['text_encoder_3-item2']: [Dict] with 1 keys key['t5_prompt_embeds']: [Tensor] shape=torch.Size([77, 4096]), dtype=torch.float16, device=cpu key['text_encoder-item3']: [Dict] with 2 keys key['clip_pooled_prompt_embeds']: [Tensor] shape=torch.Size([768]), dtype=torch.float16, device=cpu key['clip_prompt_embeds']: [Tensor] shape=torch.Size([77, 768]), dtype=torch.float16, device=cpu key['text_encoder_2-item4']: [Dict] with 2 keys key['clip_pooled_prompt_embeds']: [Tensor] shape=torch.Size([1280]), dtype=torch.float16, device=cpu key['clip_prompt_embeds']: [Tensor] shape=torch.Size([77, 1280]), dtype=torch.float16, device=cpu key['text_encoder_3-item5']: [Dict] with 1 keys key['t5_prompt_embeds']: [Tensor] shape=torch.Size([77, 4096]), dtype=torch.float16, device=cpu key['vae.encoder-item6']: [Dict] with 2 keys key['vae.encoder_f0']: [Tensor] shape=torch.Size([32, 128, 128]), dtype=torch.float16, device=cpu key['vae.encoder_f1']: [Tensor] shape=torch.Size([32, 128, 128]), dtype=torch.float16, device=cpu key['vae.decoder']: [Tensor] shape=torch.Size([16, 128, 128]), dtype=torch.float16, device=cpu ⌛️ [1/4] FRONTEND: Load time: 0.009s ------------------------------------------------------------ SD3.5 Features Summary ------------------------------------------------------------ Number of text encoders: 3 Encoder names: ['text_encoder', 'text_encoder_2', 'text_encoder_3'] Has VAE latents: True Has VAE encoder features: True Data type: torch.float16 text_encoder-item0: clip_pooled_prompt_embeds: torch.Size([768]), torch.float16 clip_prompt_embeds: torch.Size([77, 768]), torch.float16 text_encoder_2-item1: clip_pooled_prompt_embeds: torch.Size([1280]), torch.float16 clip_prompt_embeds: torch.Size([77, 1280]), torch.float16 text_encoder_3-item2: t5_prompt_embeds: torch.Size([77, 4096]), torch.float16 text_encoder-item3: clip_pooled_prompt_embeds: torch.Size([768]), torch.float16 clip_prompt_embeds: torch.Size([77, 768]), torch.float16 text_encoder_2-item4: clip_pooled_prompt_embeds: torch.Size([1280]), torch.float16 clip_prompt_embeds: torch.Size([77, 1280]), torch.float16 text_encoder_3-item5: t5_prompt_embeds: torch.Size([77, 4096]), torch.float16 vae.decoder latents: torch.Size([16, 128, 128]) vae.encoder features: vae.encoder_f0: torch.Size([32, 128, 128]), torch.float16 vae.encoder_f1: torch.Size([32, 128, 128]), torch.float16 ------------------------------------------------------------ [2/4] FRONTEND: Pack + Encode (strategy: individual)... IndividualPacker: text_encoder-item0.clip_pooled_prompt_embeds: torch.Size([768])->torch.Size([1, 1, 1, 768]) text_encoder-item0.clip_prompt_embeds: torch.Size([77, 768])->torch.Size([1, 1, 77, 768]) text_encoder_2-item1.clip_pooled_prompt_embeds: torch.Size([1280])->torch.Size([1, 1, 1, 1280]) text_encoder_2-item1.clip_prompt_embeds: torch.Size([77, 1280])->torch.Size([1, 1, 77, 1280]) text_encoder_3-item2.t5_prompt_embeds: torch.Size([77, 4096])->torch.Size([1, 1, 77, 4096]) text_encoder-item3.clip_pooled_prompt_embeds: torch.Size([768])->torch.Size([1, 1, 1, 768]) text_encoder-item3.clip_prompt_embeds: torch.Size([77, 768])->torch.Size([1, 1, 77, 768]) text_encoder_2-item4.clip_pooled_prompt_embeds: torch.Size([1280])->torch.Size([1, 1, 1, 1280]) text_encoder_2-item4.clip_prompt_embeds: torch.Size([77, 1280])->torch.Size([1, 1, 77, 1280]) text_encoder_3-item5.t5_prompt_embeds: torch.Size([77, 4096])->torch.Size([1, 1, 77, 4096]) vae.encoder_f0: torch.Size([32, 128, 128])->torch.Size([1, 1, 512, 1024]) vae.encoder_f1: torch.Size([32, 128, 128])->torch.Size([1, 1, 512, 1024]) vae.decoder: torch.Size([16, 128, 128])->torch.Size([1, 1, 512, 512]) Using per-key quantization points (text_encoder-item0.clip_pooled_prompt_embeds: torch.Size([256])) for text_encoder-item0.clip_pooled_prompt_embeds text_encoder-item0.clip_pooled_prompt_embeds: 748B, BPFP=7.7917 Using per-key quantization points (text_encoder-item0.clip_prompt_embeds: torch.Size([256])) for text_encoder-item0.clip_prompt_embeds text_encoder-item0.clip_prompt_embeds: 5,944B, BPFP=0.8041 Using per-key quantization points (text_encoder_2-item1.clip_pooled_prompt_embeds: torch.Size([256])) for text_encoder_2-item1.clip_pooled_prompt_embeds text_encoder_2-item1.clip_pooled_prompt_embeds: 1,184B, BPFP=7.4000 Using per-key quantization points (text_encoder_2-item1.clip_prompt_embeds: torch.Size([256])) for text_encoder_2-item1.clip_prompt_embeds text_encoder_2-item1.clip_prompt_embeds: 10,788B, BPFP=0.8756 Using per-key quantization points (text_encoder_3-item2.t5_prompt_embeds: torch.Size([256])) for text_encoder_3-item2.t5_prompt_embeds text_encoder_3-item2.t5_prompt_embeds: 26,728B, BPFP=0.6780 Using per-key quantization points (text_encoder-item3.clip_pooled_prompt_embeds: torch.Size([256])) for text_encoder-item3.clip_pooled_prompt_embeds text_encoder-item3.clip_pooled_prompt_embeds: 760B, BPFP=7.9167 Using per-key quantization points (text_encoder-item3.clip_prompt_embeds: torch.Size([256])) for text_encoder-item3.clip_prompt_embeds text_encoder-item3.clip_prompt_embeds: 4,024B, BPFP=0.5444 Using per-key quantization points (text_encoder_2-item4.clip_pooled: torch.Size([256])) for text_encoder_2-item4.clip_pooled_prompt_embeds text_encoder_2-item4.clip_pooled_prompt_embeds: 1,268B, BPFP=7.9250 Using per-key quantization points (text_encoder_2-item4.clip_prompt: torch.Size([256])) for text_encoder_2-item4.clip_prompt_embeds text_encoder_2-item4.clip_prompt_embeds: 6,996B, BPFP=0.5679 Using per-key quantization points (text_encoder_3-item5.t5_prompt_embeds: torch.Size([256])) for text_encoder_3-item5.t5_prompt_embeds text_encoder_3-item5.t5_prompt_embeds: 18,416B, BPFP=0.4671 Using per-key quantization points (vae.encoder_f0: torch.Size([256])) for vae.encoder_f0 vae.encoder_f0: 18,380B, BPFP=0.2805 Using per-key quantization points (vae.encoder_f1: torch.Size([256])) for vae.encoder_f1 vae.encoder_f1: 18,380B, BPFP=0.2805 Using per-key quantization points (vae.decoder: torch.Size([256])) for vae.decoder vae.decoder: 8,364B, BPFP=0.2552 ⌛️ [2/4] FRONTEND: Frontend time: 0.294s (Pack+Encode) [3/4] BACKEND: Decode + Unpack... Using per-key quantization points (text_encoder-item0.clip_pooled_prompt_embeds: torch.Size([256])) for text_encoder-item0.clip_pooled_prompt_embeds Using per-key quantization points (text_encoder-item0.clip_prompt_embeds: torch.Size([256])) for text_encoder-item0.clip_prompt_embeds Using per-key quantization points (text_encoder_2-item1.clip_pooled_prompt_embeds: torch.Size([256])) for text_encoder_2-item1.clip_pooled_prompt_embeds Using per-key quantization points (text_encoder_2-item1.clip_prompt_embeds: torch.Size([256])) for text_encoder_2-item1.clip_prompt_embeds Using per-key quantization points (text_encoder_3-item2.t5_prompt_embeds: torch.Size([256])) for text_encoder_3-item2.t5_prompt_embeds Using per-key quantization points (text_encoder-item3.clip_pooled_prompt_embeds: torch.Size([256])) for text_encoder-item3.clip_pooled_prompt_embeds Using per-key quantization points (text_encoder-item3.clip_prompt_embeds: torch.Size([256])) for text_encoder-item3.clip_prompt_embeds Using per-key quantization points (text_encoder_2-item4.clip_pooled: torch.Size([256])) for text_encoder_2-item4.clip_pooled_prompt_embeds Using per-key quantization points (text_encoder_2-item4.clip_prompt: torch.Size([256])) for text_encoder_2-item4.clip_prompt_embeds Using per-key quantization points (text_encoder_3-item5.t5_prompt_embeds: torch.Size([256])) for text_encoder_3-item5.t5_prompt_embeds Using per-key quantization points (vae.encoder_f0: torch.Size([256])) for vae.encoder_f0 Using per-key quantization points (vae.encoder_f1: torch.Size([256])) for vae.encoder_f1 Using per-key quantization points (vae.decoder: torch.Size([256])) for vae.decoder IndividualUnPacker: text_encoder-item0.clip_pooled_prompt_embeds: torch.Size([768]) text_encoder-item0.clip_prompt_embeds: torch.Size([77, 768]) text_encoder_2-item1.clip_pooled_prompt_embeds: torch.Size([1280]) text_encoder_2-item1.clip_prompt_embeds: torch.Size([77, 1280]) text_encoder_3-item2.t5_prompt_embeds: torch.Size([77, 4096]) text_encoder-item3.clip_pooled_prompt_embeds: torch.Size([768]) text_encoder-item3.clip_prompt_embeds: torch.Size([77, 768]) text_encoder_2-item4.clip_pooled_prompt_embeds: torch.Size([1280]) text_encoder_2-item4.clip_prompt_embeds: torch.Size([77, 1280]) text_encoder_3-item5.t5_prompt_embeds: torch.Size([77, 4096]) vae.decoder: torch.Size([16, 128, 128]) vae.encoder_f0: torch.Size([32, 128, 128]) vae.encoder_f1: torch.Size([32, 128, 128]) ⌛️ [3/4] BACKEND: Backend time: 0.447s [4/4] METRICS: Computing MSE Breakdown... Using per-key quantization points (text_encoder-item0.clip_pooled_prompt_embeds: torch.Size([256])) for text_encoder-item0.clip_pooled_prompt_embeds Using per-key quantization points (text_encoder-item0.clip_prompt_embeds: torch.Size([256])) for text_encoder-item0.clip_prompt_embeds Using per-key quantization points (text_encoder_2-item1.clip_pooled_prompt_embeds: torch.Size([256])) for text_encoder_2-item1.clip_pooled_prompt_embeds Using per-key quantization points (text_encoder_2-item1.clip_prompt_embeds: torch.Size([256])) for text_encoder_2-item1.clip_prompt_embeds Using per-key quantization points (text_encoder_3-item2.t5_prompt_embeds: torch.Size([256])) for text_encoder_3-item2.t5_prompt_embeds Using per-key quantization points (text_encoder-item3.clip_pooled_prompt_embeds: torch.Size([256])) for text_encoder-item3.clip_pooled_prompt_embeds Using per-key quantization points (text_encoder-item3.clip_prompt_embeds: torch.Size([256])) for text_encoder-item3.clip_prompt_embeds Using per-key quantization points (text_encoder_2-item4.clip_pooled: torch.Size([256])) for text_encoder_2-item4.clip_pooled_prompt_embeds Using per-key quantization points (text_encoder_2-item4.clip_prompt: torch.Size([256])) for text_encoder_2-item4.clip_prompt_embeds Using per-key quantization points (text_encoder_3-item5.t5_prompt_embeds: torch.Size([256])) for text_encoder_3-item5.t5_prompt_embeds Using per-key quantization points (vae.encoder_f0: torch.Size([256])) for vae.encoder_f0 Using per-key quantization points (vae.encoder_f1: torch.Size([256])) for vae.encoder_f1 Using per-key quantization points (vae.decoder: torch.Size([256])) for vae.decoder IndividualUnPacker: text_encoder-item0.clip_pooled_prompt_embeds: torch.Size([768]) text_encoder-item0.clip_prompt_embeds: torch.Size([77, 768]) text_encoder_2-item1.clip_pooled_prompt_embeds: torch.Size([1280]) text_encoder_2-item1.clip_prompt_embeds: torch.Size([77, 1280]) text_encoder_3-item2.t5_prompt_embeds: torch.Size([77, 4096]) text_encoder-item3.clip_pooled_prompt_embeds: torch.Size([768]) text_encoder-item3.clip_prompt_embeds: torch.Size([77, 768]) text_encoder_2-item4.clip_pooled_prompt_embeds: torch.Size([1280]) text_encoder_2-item4.clip_prompt_embeds: torch.Size([77, 1280]) text_encoder_3-item5.t5_prompt_embeds: torch.Size([77, 4096]) vae.decoder: torch.Size([16, 128, 128]) vae.encoder_f0: torch.Size([32, 128, 128]) vae.encoder_f1: torch.Size([32, 128, 128]) Per-key MSE Breakdown (Quant=Truncation+Quantization, Codec=Compression): Key Quant-MSE Total-MSE ------------------------------------------------------------------------------------- text_encoder-item0.clip_pooled_prompt_embeds 0.00022540 0.91874607 text_encoder-item0.clip_prompt_embeds 0.00023066 23.95234713 text_encoder_2-item1.clip_pooled_prompt_embeds 0.00044687 0.85953379 text_encoder_2-item1.clip_prompt_embeds 0.00018171 0.13225843 text_encoder_3-item2.t5_prompt_embeds 0.00000793 0.00375941 text_encoder-item3.clip_pooled_prompt_embeds 0.00841799 1.01420029 text_encoder-item3.clip_prompt_embeds 0.00023247 23.90570887 text_encoder_2-item4.clip_pooled_prompt_embeds 0.00016751 0.67051430 text_encoder_2-item4.clip_prompt_embeds 0.00009830 0.47049568 text_encoder_3-item5.t5_prompt_embeds 0.00001263 0.00227117 vae.encoder_f0 0.03159856 2.55363798 vae.encoder_f1 0.03160188 2.55290556 vae.decoder 0.00018417 0.05672223 ------------------------------------------------------------------------------------- TOTAL 0.01470700 2.47106372 (elements=2,260,992) ---------------------- -------------------------------------------------------- SAMPLE-WISE STATISTICS ---------------------- -------------------------------------------------------- Handler sd35 Strategy individual Architecture hyperprior-featurecoding ---------------------- -------------------------------------------------------- Total Elements 2260992 Total Bytes 121980 BPFP 0.4316 bits/point EBPFP 0.8632 equivalent bits/point MSE 2.471064 ---------------------- -------------------------------------------------------- Time: 0.750s Load: 0.009s, Pack+Encode: 0.294s, Decode+Unpack: 0.447s ---------------------- -------------------------------------------------------- Restored Feature Format: [Dict] with 8 keys key['text_encoder-item0']: [Dict] with 2 keys key['clip_pooled_prompt_embeds']: [Tensor] shape=torch.Size([768]), dtype=torch.float16, device=cpu key['clip_prompt_embeds']: [Tensor] shape=torch.Size([77, 768]), dtype=torch.float16, device=cpu key['text_encoder_2-item1']: [Dict] with 2 keys key['clip_pooled_prompt_embeds']: [Tensor] shape=torch.Size([1280]), dtype=torch.float16, device=cpu key['clip_prompt_embeds']: [Tensor] shape=torch.Size([77, 1280]), dtype=torch.float16, device=cpu key['text_encoder_3-item2']: [Dict] with 1 keys key['t5_prompt_embeds']: [Tensor] shape=torch.Size([77, 4096]), dtype=torch.float16, device=cpu key['text_encoder-item3']: [Dict] with 2 keys key['clip_pooled_prompt_embeds']: [Tensor] shape=torch.Size([768]), dtype=torch.float16, device=cpu key['clip_prompt_embeds']: [Tensor] shape=torch.Size([77, 768]), dtype=torch.float16, device=cpu key['text_encoder_2-item4']: [Dict] with 2 keys key['clip_pooled_prompt_embeds']: [Tensor] shape=torch.Size([1280]), dtype=torch.float16, device=cpu key['clip_prompt_embeds']: [Tensor] shape=torch.Size([77, 1280]), dtype=torch.float16, device=cpu key['text_encoder_3-item5']: [Dict] with 1 keys key['t5_prompt_embeds']: [Tensor] shape=torch.Size([77, 4096]), dtype=torch.float16, device=cpu key['vae.decoder']: [Tensor] shape=torch.Size([16, 128, 128]), dtype=torch.float16, device=cpu key['vae.encoder']: [Dict] with 2 keys key['vae.encoder_f0']: [Tensor] shape=torch.Size([32, 128, 128]), dtype=torch.float16, device=cpu key['vae.encoder_f1']: [Tensor] shape=torch.Size([32, 128, 128]), dtype=torch.float16, device=cpu 💾 Converting with 2.4711 MSE: from ../datasets/FeatureCoding-StableDiffusion3.5/100samples_features/000000274272.zst to output-fixed/sd35/lambda0.004/hyperprior-featurecoding-8bit-individual/sd35cond/000000274272.zst 💪 Processing: ../datasets/FeatureCoding-StableDiffusion3.5/100samples_features/000000280891.zst (43/100) [1/4] FRONTEND: Loading features from ../datasets/FeatureCoding-StableDiffusion3.5/100samples_features/000000280891.zst... Original data structure: root: [Dict] with 8 keys key['text_encoder-item0']: [Dict] with 2 keys key['clip_pooled_prompt_embeds']: [Tensor] shape=torch.Size([768]), dtype=torch.float16, device=cpu key['clip_prompt_embeds']: [Tensor] shape=torch.Size([77, 768]), dtype=torch.float16, device=cpu key['text_encoder_2-item1']: [Dict] with 2 keys key['clip_pooled_prompt_embeds']: [Tensor] shape=torch.Size([1280]), dtype=torch.float16, device=cpu key['clip_prompt_embeds']: [Tensor] shape=torch.Size([77, 1280]), dtype=torch.float16, device=cpu key['text_encoder_3-item2']: [Dict] with 1 keys key['t5_prompt_embeds']: [Tensor] shape=torch.Size([77, 4096]), dtype=torch.float16, device=cpu key['text_encoder-item3']: [Dict] with 2 keys key['clip_pooled_prompt_embeds']: [Tensor] shape=torch.Size([768]), dtype=torch.float16, device=cpu key['clip_prompt_embeds']: [Tensor] shape=torch.Size([77, 768]), dtype=torch.float16, device=cpu key['text_encoder_2-item4']: [Dict] with 2 keys key['clip_pooled_prompt_embeds']: [Tensor] shape=torch.Size([1280]), dtype=torch.float16, device=cpu key['clip_prompt_embeds']: [Tensor] shape=torch.Size([77, 1280]), dtype=torch.float16, device=cpu key['text_encoder_3-item5']: [Dict] with 1 keys key['t5_prompt_embeds']: [Tensor] shape=torch.Size([77, 4096]), dtype=torch.float16, device=cpu key['vae.encoder-item6']: [Dict] with 2 keys key['vae.encoder_f0']: [Tensor] shape=torch.Size([32, 128, 128]), dtype=torch.float16, device=cpu key['vae.encoder_f1']: [Tensor] shape=torch.Size([32, 128, 128]), dtype=torch.float16, device=cpu key['vae.decoder']: [Tensor] shape=torch.Size([16, 128, 128]), dtype=torch.float16, device=cpu ⌛️ [1/4] FRONTEND: Load time: 0.008s ------------------------------------------------------------ SD3.5 Features Summary ------------------------------------------------------------ Number of text encoders: 3 Encoder names: ['text_encoder', 'text_encoder_2', 'text_encoder_3'] Has VAE latents: True Has VAE encoder features: True Data type: torch.float16 text_encoder-item0: clip_pooled_prompt_embeds: torch.Size([768]), torch.float16 clip_prompt_embeds: torch.Size([77, 768]), torch.float16 text_encoder_2-item1: clip_pooled_prompt_embeds: torch.Size([1280]), torch.float16 clip_prompt_embeds: torch.Size([77, 1280]), torch.float16 text_encoder_3-item2: t5_prompt_embeds: torch.Size([77, 4096]), torch.float16 text_encoder-item3: clip_pooled_prompt_embeds: torch.Size([768]), torch.float16 clip_prompt_embeds: torch.Size([77, 768]), torch.float16 text_encoder_2-item4: clip_pooled_prompt_embeds: torch.Size([1280]), torch.float16 clip_prompt_embeds: torch.Size([77, 1280]), torch.float16 text_encoder_3-item5: t5_prompt_embeds: torch.Size([77, 4096]), torch.float16 vae.decoder latents: torch.Size([16, 128, 128]) vae.encoder features: vae.encoder_f0: torch.Size([32, 128, 128]), torch.float16 vae.encoder_f1: torch.Size([32, 128, 128]), torch.float16 ------------------------------------------------------------ [2/4] FRONTEND: Pack + Encode (strategy: individual)... IndividualPacker: text_encoder-item0.clip_pooled_prompt_embeds: torch.Size([768])->torch.Size([1, 1, 1, 768]) text_encoder-item0.clip_prompt_embeds: torch.Size([77, 768])->torch.Size([1, 1, 77, 768]) text_encoder_2-item1.clip_pooled_prompt_embeds: torch.Size([1280])->torch.Size([1, 1, 1, 1280]) text_encoder_2-item1.clip_prompt_embeds: torch.Size([77, 1280])->torch.Size([1, 1, 77, 1280]) text_encoder_3-item2.t5_prompt_embeds: torch.Size([77, 4096])->torch.Size([1, 1, 77, 4096]) text_encoder-item3.clip_pooled_prompt_embeds: torch.Size([768])->torch.Size([1, 1, 1, 768]) text_encoder-item3.clip_prompt_embeds: torch.Size([77, 768])->torch.Size([1, 1, 77, 768]) text_encoder_2-item4.clip_pooled_prompt_embeds: torch.Size([1280])->torch.Size([1, 1, 1, 1280]) text_encoder_2-item4.clip_prompt_embeds: torch.Size([77, 1280])->torch.Size([1, 1, 77, 1280]) text_encoder_3-item5.t5_prompt_embeds: torch.Size([77, 4096])->torch.Size([1, 1, 77, 4096]) vae.encoder_f0: torch.Size([32, 128, 128])->torch.Size([1, 1, 512, 1024]) vae.encoder_f1: torch.Size([32, 128, 128])->torch.Size([1, 1, 512, 1024]) vae.decoder: torch.Size([16, 128, 128])->torch.Size([1, 1, 512, 512]) Using per-key quantization points (text_encoder-item0.clip_pooled_prompt_embeds: torch.Size([256])) for text_encoder-item0.clip_pooled_prompt_embeds text_encoder-item0.clip_pooled_prompt_embeds: 732B, BPFP=7.6250 Using per-key quantization points (text_encoder-item0.clip_prompt_embeds: torch.Size([256])) for text_encoder-item0.clip_prompt_embeds text_encoder-item0.clip_prompt_embeds: 6,364B, BPFP=0.8609 Using per-key quantization points (text_encoder_2-item1.clip_pooled_prompt_embeds: torch.Size([256])) for text_encoder_2-item1.clip_pooled_prompt_embeds text_encoder_2-item1.clip_pooled_prompt_embeds: 1,200B, BPFP=7.5000 Using per-key quantization points (text_encoder_2-item1.clip_prompt_embeds: torch.Size([256])) for text_encoder_2-item1.clip_prompt_embeds text_encoder_2-item1.clip_prompt_embeds: 10,832B, BPFP=0.8792 Using per-key quantization points (text_encoder_3-item2.t5_prompt_embeds: torch.Size([256])) for text_encoder_3-item2.t5_prompt_embeds text_encoder_3-item2.t5_prompt_embeds: 27,536B, BPFP=0.6985 Using per-key quantization points (text_encoder-item3.clip_pooled_prompt_embeds: torch.Size([256])) for text_encoder-item3.clip_pooled_prompt_embeds text_encoder-item3.clip_pooled_prompt_embeds: 760B, BPFP=7.9167 Using per-key quantization points (text_encoder-item3.clip_prompt_embeds: torch.Size([256])) for text_encoder-item3.clip_prompt_embeds text_encoder-item3.clip_prompt_embeds: 4,024B, BPFP=0.5444 Using per-key quantization points (text_encoder_2-item4.clip_pooled: torch.Size([256])) for text_encoder_2-item4.clip_pooled_prompt_embeds text_encoder_2-item4.clip_pooled_prompt_embeds: 1,268B, BPFP=7.9250 Using per-key quantization points (text_encoder_2-item4.clip_prompt: torch.Size([256])) for text_encoder_2-item4.clip_prompt_embeds text_encoder_2-item4.clip_prompt_embeds: 6,996B, BPFP=0.5679 Using per-key quantization points (text_encoder_3-item5.t5_prompt_embeds: torch.Size([256])) for text_encoder_3-item5.t5_prompt_embeds text_encoder_3-item5.t5_prompt_embeds: 18,416B, BPFP=0.4671 Using per-key quantization points (vae.encoder_f0: torch.Size([256])) for vae.encoder_f0 vae.encoder_f0: 23,176B, BPFP=0.3536 Using per-key quantization points (vae.encoder_f1: torch.Size([256])) for vae.encoder_f1 vae.encoder_f1: 23,172B, BPFP=0.3536 Using per-key quantization points (vae.decoder: torch.Size([256])) for vae.decoder vae.decoder: 6,484B, BPFP=0.1979 ⌛️ [2/4] FRONTEND: Frontend time: 0.295s (Pack+Encode) [3/4] BACKEND: Decode + Unpack... Using per-key quantization points (text_encoder-item0.clip_pooled_prompt_embeds: torch.Size([256])) for text_encoder-item0.clip_pooled_prompt_embeds Using per-key quantization points (text_encoder-item0.clip_prompt_embeds: torch.Size([256])) for text_encoder-item0.clip_prompt_embeds Using per-key quantization points (text_encoder_2-item1.clip_pooled_prompt_embeds: torch.Size([256])) for text_encoder_2-item1.clip_pooled_prompt_embeds Using per-key quantization points (text_encoder_2-item1.clip_prompt_embeds: torch.Size([256])) for text_encoder_2-item1.clip_prompt_embeds Using per-key quantization points (text_encoder_3-item2.t5_prompt_embeds: torch.Size([256])) for text_encoder_3-item2.t5_prompt_embeds Using per-key quantization points (text_encoder-item3.clip_pooled_prompt_embeds: torch.Size([256])) for text_encoder-item3.clip_pooled_prompt_embeds Using per-key quantization points (text_encoder-item3.clip_prompt_embeds: torch.Size([256])) for text_encoder-item3.clip_prompt_embeds Using per-key quantization points (text_encoder_2-item4.clip_pooled: torch.Size([256])) for text_encoder_2-item4.clip_pooled_prompt_embeds Using per-key quantization points (text_encoder_2-item4.clip_prompt: torch.Size([256])) for text_encoder_2-item4.clip_prompt_embeds Using per-key quantization points (text_encoder_3-item5.t5_prompt_embeds: torch.Size([256])) for text_encoder_3-item5.t5_prompt_embeds Using per-key quantization points (vae.encoder_f0: torch.Size([256])) for vae.encoder_f0 Using per-key quantization points (vae.encoder_f1: torch.Size([256])) for vae.encoder_f1 Using per-key quantization points (vae.decoder: torch.Size([256])) for vae.decoder IndividualUnPacker: text_encoder-item0.clip_pooled_prompt_embeds: torch.Size([768]) text_encoder-item0.clip_prompt_embeds: torch.Size([77, 768]) text_encoder_2-item1.clip_pooled_prompt_embeds: torch.Size([1280]) text_encoder_2-item1.clip_prompt_embeds: torch.Size([77, 1280]) text_encoder_3-item2.t5_prompt_embeds: torch.Size([77, 4096]) text_encoder-item3.clip_pooled_prompt_embeds: torch.Size([768]) text_encoder-item3.clip_prompt_embeds: torch.Size([77, 768]) text_encoder_2-item4.clip_pooled_prompt_embeds: torch.Size([1280]) text_encoder_2-item4.clip_prompt_embeds: torch.Size([77, 1280]) text_encoder_3-item5.t5_prompt_embeds: torch.Size([77, 4096]) vae.decoder: torch.Size([16, 128, 128]) vae.encoder_f0: torch.Size([32, 128, 128]) vae.encoder_f1: torch.Size([32, 128, 128]) ⌛️ [3/4] BACKEND: Backend time: 0.447s [4/4] METRICS: Computing MSE Breakdown... Using per-key quantization points (text_encoder-item0.clip_pooled_prompt_embeds: torch.Size([256])) for text_encoder-item0.clip_pooled_prompt_embeds Using per-key quantization points (text_encoder-item0.clip_prompt_embeds: torch.Size([256])) for text_encoder-item0.clip_prompt_embeds Using per-key quantization points (text_encoder_2-item1.clip_pooled_prompt_embeds: torch.Size([256])) for text_encoder_2-item1.clip_pooled_prompt_embeds Using per-key quantization points (text_encoder_2-item1.clip_prompt_embeds: torch.Size([256])) for text_encoder_2-item1.clip_prompt_embeds Using per-key quantization points (text_encoder_3-item2.t5_prompt_embeds: torch.Size([256])) for text_encoder_3-item2.t5_prompt_embeds Using per-key quantization points (text_encoder-item3.clip_pooled_prompt_embeds: torch.Size([256])) for text_encoder-item3.clip_pooled_prompt_embeds Using per-key quantization points (text_encoder-item3.clip_prompt_embeds: torch.Size([256])) for text_encoder-item3.clip_prompt_embeds Using per-key quantization points (text_encoder_2-item4.clip_pooled: torch.Size([256])) for text_encoder_2-item4.clip_pooled_prompt_embeds Using per-key quantization points (text_encoder_2-item4.clip_prompt: torch.Size([256])) for text_encoder_2-item4.clip_prompt_embeds Using per-key quantization points (text_encoder_3-item5.t5_prompt_embeds: torch.Size([256])) for text_encoder_3-item5.t5_prompt_embeds Using per-key quantization points (vae.encoder_f0: torch.Size([256])) for vae.encoder_f0 Using per-key quantization points (vae.encoder_f1: torch.Size([256])) for vae.encoder_f1 Using per-key quantization points (vae.decoder: torch.Size([256])) for vae.decoder IndividualUnPacker: text_encoder-item0.clip_pooled_prompt_embeds: torch.Size([768]) text_encoder-item0.clip_prompt_embeds: torch.Size([77, 768]) text_encoder_2-item1.clip_pooled_prompt_embeds: torch.Size([1280]) text_encoder_2-item1.clip_prompt_embeds: torch.Size([77, 1280]) text_encoder_3-item2.t5_prompt_embeds: torch.Size([77, 4096]) text_encoder-item3.clip_pooled_prompt_embeds: torch.Size([768]) text_encoder-item3.clip_prompt_embeds: torch.Size([77, 768]) text_encoder_2-item4.clip_pooled_prompt_embeds: torch.Size([1280]) text_encoder_2-item4.clip_prompt_embeds: torch.Size([77, 1280]) text_encoder_3-item5.t5_prompt_embeds: torch.Size([77, 4096]) vae.decoder: torch.Size([16, 128, 128]) vae.encoder_f0: torch.Size([32, 128, 128]) vae.encoder_f1: torch.Size([32, 128, 128]) Per-key MSE Breakdown (Quant=Truncation+Quantization, Codec=Compression): Key Quant-MSE Total-MSE ------------------------------------------------------------------------------------- text_encoder-item0.clip_pooled_prompt_embeds 0.00017642 0.91375510 text_encoder-item0.clip_prompt_embeds 0.00024948 23.94296199 text_encoder_2-item1.clip_pooled_prompt_embeds 0.00032352 0.86925554 text_encoder_2-item1.clip_prompt_embeds 0.00019749 0.12651515 text_encoder_3-item2.t5_prompt_embeds 0.00000781 0.00391921 text_encoder-item3.clip_pooled_prompt_embeds 0.00841799 1.01420029 text_encoder-item3.clip_prompt_embeds 0.00023247 23.90570887 text_encoder_2-item4.clip_pooled_prompt_embeds 0.00016751 0.67051430 text_encoder_2-item4.clip_prompt_embeds 0.00009830 0.47049568 text_encoder_3-item5.t5_prompt_embeds 0.00001263 0.00227117 vae.encoder_f0 0.03490865 4.28346348 vae.encoder_f1 0.03491008 4.28428364 vae.decoder 0.00028462 0.05988157 ------------------------------------------------------------------------------------- TOTAL 0.01625440 3.27355822 (elements=2,260,992) ---------------------- -------------------------------------------------------- SAMPLE-WISE STATISTICS ---------------------- -------------------------------------------------------- Handler sd35 Strategy individual Architecture hyperprior-featurecoding ---------------------- -------------------------------------------------------- Total Elements 2260992 Total Bytes 130960 BPFP 0.4634 bits/point EBPFP 0.9267 equivalent bits/point MSE 3.273558 ---------------------- -------------------------------------------------------- Time: 0.750s Load: 0.008s, Pack+Encode: 0.295s, Decode+Unpack: 0.447s ---------------------- -------------------------------------------------------- Restored Feature Format: [Dict] with 8 keys key['text_encoder-item0']: [Dict] with 2 keys key['clip_pooled_prompt_embeds']: [Tensor] shape=torch.Size([768]), dtype=torch.float16, device=cpu key['clip_prompt_embeds']: [Tensor] shape=torch.Size([77, 768]), dtype=torch.float16, device=cpu key['text_encoder_2-item1']: [Dict] with 2 keys key['clip_pooled_prompt_embeds']: [Tensor] shape=torch.Size([1280]), dtype=torch.float16, device=cpu key['clip_prompt_embeds']: [Tensor] shape=torch.Size([77, 1280]), dtype=torch.float16, device=cpu key['text_encoder_3-item2']: [Dict] with 1 keys key['t5_prompt_embeds']: [Tensor] shape=torch.Size([77, 4096]), dtype=torch.float16, device=cpu key['text_encoder-item3']: [Dict] with 2 keys key['clip_pooled_prompt_embeds']: [Tensor] shape=torch.Size([768]), dtype=torch.float16, device=cpu key['clip_prompt_embeds']: [Tensor] shape=torch.Size([77, 768]), dtype=torch.float16, device=cpu key['text_encoder_2-item4']: [Dict] with 2 keys key['clip_pooled_prompt_embeds']: [Tensor] shape=torch.Size([1280]), dtype=torch.float16, device=cpu key['clip_prompt_embeds']: [Tensor] shape=torch.Size([77, 1280]), dtype=torch.float16, device=cpu key['text_encoder_3-item5']: [Dict] with 1 keys key['t5_prompt_embeds']: [Tensor] shape=torch.Size([77, 4096]), dtype=torch.float16, device=cpu key['vae.decoder']: [Tensor] shape=torch.Size([16, 128, 128]), dtype=torch.float16, device=cpu key['vae.encoder']: [Dict] with 2 keys key['vae.encoder_f0']: [Tensor] shape=torch.Size([32, 128, 128]), dtype=torch.float16, device=cpu key['vae.encoder_f1']: [Tensor] shape=torch.Size([32, 128, 128]), dtype=torch.float16, device=cpu 💾 Converting with 3.2736 MSE: from ../datasets/FeatureCoding-StableDiffusion3.5/100samples_features/000000280891.zst to output-fixed/sd35/lambda0.004/hyperprior-featurecoding-8bit-individual/sd35cond/000000280891.zst 💪 Processing: ../datasets/FeatureCoding-StableDiffusion3.5/100samples_features/000000285788.zst (44/100) [1/4] FRONTEND: Loading features from ../datasets/FeatureCoding-StableDiffusion3.5/100samples_features/000000285788.zst... Original data structure: root: [Dict] with 8 keys key['text_encoder-item0']: [Dict] with 2 keys key['clip_pooled_prompt_embeds']: [Tensor] shape=torch.Size([768]), dtype=torch.float16, device=cpu key['clip_prompt_embeds']: [Tensor] shape=torch.Size([77, 768]), dtype=torch.float16, device=cpu key['text_encoder_2-item1']: [Dict] with 2 keys key['clip_pooled_prompt_embeds']: [Tensor] shape=torch.Size([1280]), dtype=torch.float16, device=cpu key['clip_prompt_embeds']: [Tensor] shape=torch.Size([77, 1280]), dtype=torch.float16, device=cpu key['text_encoder_3-item2']: [Dict] with 1 keys key['t5_prompt_embeds']: [Tensor] shape=torch.Size([77, 4096]), dtype=torch.float16, device=cpu key['text_encoder-item3']: [Dict] with 2 keys key['clip_pooled_prompt_embeds']: [Tensor] shape=torch.Size([768]), dtype=torch.float16, device=cpu key['clip_prompt_embeds']: [Tensor] shape=torch.Size([77, 768]), dtype=torch.float16, device=cpu key['text_encoder_2-item4']: [Dict] with 2 keys key['clip_pooled_prompt_embeds']: [Tensor] shape=torch.Size([1280]), dtype=torch.float16, device=cpu key['clip_prompt_embeds']: [Tensor] shape=torch.Size([77, 1280]), dtype=torch.float16, device=cpu key['text_encoder_3-item5']: [Dict] with 1 keys key['t5_prompt_embeds']: [Tensor] shape=torch.Size([77, 4096]), dtype=torch.float16, device=cpu key['vae.encoder-item6']: [Dict] with 2 keys key['vae.encoder_f0']: [Tensor] shape=torch.Size([32, 128, 128]), dtype=torch.float16, device=cpu key['vae.encoder_f1']: [Tensor] shape=torch.Size([32, 128, 128]), dtype=torch.float16, device=cpu key['vae.decoder']: [Tensor] shape=torch.Size([16, 128, 128]), dtype=torch.float16, device=cpu ⌛️ [1/4] FRONTEND: Load time: 0.008s ------------------------------------------------------------ SD3.5 Features Summary ------------------------------------------------------------ Number of text encoders: 3 Encoder names: ['text_encoder', 'text_encoder_2', 'text_encoder_3'] Has VAE latents: True Has VAE encoder features: True Data type: torch.float16 text_encoder-item0: clip_pooled_prompt_embeds: torch.Size([768]), torch.float16 clip_prompt_embeds: torch.Size([77, 768]), torch.float16 text_encoder_2-item1: clip_pooled_prompt_embeds: torch.Size([1280]), torch.float16 clip_prompt_embeds: torch.Size([77, 1280]), torch.float16 text_encoder_3-item2: t5_prompt_embeds: torch.Size([77, 4096]), torch.float16 text_encoder-item3: clip_pooled_prompt_embeds: torch.Size([768]), torch.float16 clip_prompt_embeds: torch.Size([77, 768]), torch.float16 text_encoder_2-item4: clip_pooled_prompt_embeds: torch.Size([1280]), torch.float16 clip_prompt_embeds: torch.Size([77, 1280]), torch.float16 text_encoder_3-item5: t5_prompt_embeds: torch.Size([77, 4096]), torch.float16 vae.decoder latents: torch.Size([16, 128, 128]) vae.encoder features: vae.encoder_f0: torch.Size([32, 128, 128]), torch.float16 vae.encoder_f1: torch.Size([32, 128, 128]), torch.float16 ------------------------------------------------------------ [2/4] FRONTEND: Pack + Encode (strategy: individual)... IndividualPacker: text_encoder-item0.clip_pooled_prompt_embeds: torch.Size([768])->torch.Size([1, 1, 1, 768]) text_encoder-item0.clip_prompt_embeds: torch.Size([77, 768])->torch.Size([1, 1, 77, 768]) text_encoder_2-item1.clip_pooled_prompt_embeds: torch.Size([1280])->torch.Size([1, 1, 1, 1280]) text_encoder_2-item1.clip_prompt_embeds: torch.Size([77, 1280])->torch.Size([1, 1, 77, 1280]) text_encoder_3-item2.t5_prompt_embeds: torch.Size([77, 4096])->torch.Size([1, 1, 77, 4096]) text_encoder-item3.clip_pooled_prompt_embeds: torch.Size([768])->torch.Size([1, 1, 1, 768]) text_encoder-item3.clip_prompt_embeds: torch.Size([77, 768])->torch.Size([1, 1, 77, 768]) text_encoder_2-item4.clip_pooled_prompt_embeds: torch.Size([1280])->torch.Size([1, 1, 1, 1280]) text_encoder_2-item4.clip_prompt_embeds: torch.Size([77, 1280])->torch.Size([1, 1, 77, 1280]) text_encoder_3-item5.t5_prompt_embeds: torch.Size([77, 4096])->torch.Size([1, 1, 77, 4096]) vae.encoder_f0: torch.Size([32, 128, 128])->torch.Size([1, 1, 512, 1024]) vae.encoder_f1: torch.Size([32, 128, 128])->torch.Size([1, 1, 512, 1024]) vae.decoder: torch.Size([16, 128, 128])->torch.Size([1, 1, 512, 512]) Using per-key quantization points (text_encoder-item0.clip_pooled_prompt_embeds: torch.Size([256])) for text_encoder-item0.clip_pooled_prompt_embeds text_encoder-item0.clip_pooled_prompt_embeds: 740B, BPFP=7.7083 Using per-key quantization points (text_encoder-item0.clip_prompt_embeds: torch.Size([256])) for text_encoder-item0.clip_prompt_embeds text_encoder-item0.clip_prompt_embeds: 5,768B, BPFP=0.7803 Using per-key quantization points (text_encoder_2-item1.clip_pooled_prompt_embeds: torch.Size([256])) for text_encoder_2-item1.clip_pooled_prompt_embeds text_encoder_2-item1.clip_pooled_prompt_embeds: 1,200B, BPFP=7.5000 Using per-key quantization points (text_encoder_2-item1.clip_prompt_embeds: torch.Size([256])) for text_encoder_2-item1.clip_prompt_embeds text_encoder_2-item1.clip_prompt_embeds: 11,008B, BPFP=0.8935 Using per-key quantization points (text_encoder_3-item2.t5_prompt_embeds: torch.Size([256])) for text_encoder_3-item2.t5_prompt_embeds text_encoder_3-item2.t5_prompt_embeds: 29,536B, BPFP=0.7492 Using per-key quantization points (text_encoder-item3.clip_pooled_prompt_embeds: torch.Size([256])) for text_encoder-item3.clip_pooled_prompt_embeds text_encoder-item3.clip_pooled_prompt_embeds: 760B, BPFP=7.9167 Using per-key quantization points (text_encoder-item3.clip_prompt_embeds: torch.Size([256])) for text_encoder-item3.clip_prompt_embeds text_encoder-item3.clip_prompt_embeds: 4,024B, BPFP=0.5444 Using per-key quantization points (text_encoder_2-item4.clip_pooled: torch.Size([256])) for text_encoder_2-item4.clip_pooled_prompt_embeds text_encoder_2-item4.clip_pooled_prompt_embeds: 1,268B, BPFP=7.9250 Using per-key quantization points (text_encoder_2-item4.clip_prompt: torch.Size([256])) for text_encoder_2-item4.clip_prompt_embeds text_encoder_2-item4.clip_prompt_embeds: 6,996B, BPFP=0.5679 Using per-key quantization points (text_encoder_3-item5.t5_prompt_embeds: torch.Size([256])) for text_encoder_3-item5.t5_prompt_embeds text_encoder_3-item5.t5_prompt_embeds: 18,416B, BPFP=0.4671 Using per-key quantization points (vae.encoder_f0: torch.Size([256])) for vae.encoder_f0 vae.encoder_f0: 9,800B, BPFP=0.1495 Using per-key quantization points (vae.encoder_f1: torch.Size([256])) for vae.encoder_f1 vae.encoder_f1: 9,800B, BPFP=0.1495 Using per-key quantization points (vae.decoder: torch.Size([256])) for vae.decoder vae.decoder: 8,448B, BPFP=0.2578 ⌛️ [2/4] FRONTEND: Frontend time: 0.295s (Pack+Encode) [3/4] BACKEND: Decode + Unpack... Using per-key quantization points (text_encoder-item0.clip_pooled_prompt_embeds: torch.Size([256])) for text_encoder-item0.clip_pooled_prompt_embeds Using per-key quantization points (text_encoder-item0.clip_prompt_embeds: torch.Size([256])) for text_encoder-item0.clip_prompt_embeds Using per-key quantization points (text_encoder_2-item1.clip_pooled_prompt_embeds: torch.Size([256])) for text_encoder_2-item1.clip_pooled_prompt_embeds Using per-key quantization points (text_encoder_2-item1.clip_prompt_embeds: torch.Size([256])) for text_encoder_2-item1.clip_prompt_embeds Using per-key quantization points (text_encoder_3-item2.t5_prompt_embeds: torch.Size([256])) for text_encoder_3-item2.t5_prompt_embeds Using per-key quantization points (text_encoder-item3.clip_pooled_prompt_embeds: torch.Size([256])) for text_encoder-item3.clip_pooled_prompt_embeds Using per-key quantization points (text_encoder-item3.clip_prompt_embeds: torch.Size([256])) for text_encoder-item3.clip_prompt_embeds Using per-key quantization points (text_encoder_2-item4.clip_pooled: torch.Size([256])) for text_encoder_2-item4.clip_pooled_prompt_embeds Using per-key quantization points (text_encoder_2-item4.clip_prompt: torch.Size([256])) for text_encoder_2-item4.clip_prompt_embeds Using per-key quantization points (text_encoder_3-item5.t5_prompt_embeds: torch.Size([256])) for text_encoder_3-item5.t5_prompt_embeds Using per-key quantization points (vae.encoder_f0: torch.Size([256])) for vae.encoder_f0 Using per-key quantization points (vae.encoder_f1: torch.Size([256])) for vae.encoder_f1 Using per-key quantization points (vae.decoder: torch.Size([256])) for vae.decoder IndividualUnPacker: text_encoder-item0.clip_pooled_prompt_embeds: torch.Size([768]) text_encoder-item0.clip_prompt_embeds: torch.Size([77, 768]) text_encoder_2-item1.clip_pooled_prompt_embeds: torch.Size([1280]) text_encoder_2-item1.clip_prompt_embeds: torch.Size([77, 1280]) text_encoder_3-item2.t5_prompt_embeds: torch.Size([77, 4096]) text_encoder-item3.clip_pooled_prompt_embeds: torch.Size([768]) text_encoder-item3.clip_prompt_embeds: torch.Size([77, 768]) text_encoder_2-item4.clip_pooled_prompt_embeds: torch.Size([1280]) text_encoder_2-item4.clip_prompt_embeds: torch.Size([77, 1280]) text_encoder_3-item5.t5_prompt_embeds: torch.Size([77, 4096]) vae.decoder: torch.Size([16, 128, 128]) vae.encoder_f0: torch.Size([32, 128, 128]) vae.encoder_f1: torch.Size([32, 128, 128]) ⌛️ [3/4] BACKEND: Backend time: 0.450s [4/4] METRICS: Computing MSE Breakdown... Using per-key quantization points (text_encoder-item0.clip_pooled_prompt_embeds: torch.Size([256])) for text_encoder-item0.clip_pooled_prompt_embeds Using per-key quantization points (text_encoder-item0.clip_prompt_embeds: torch.Size([256])) for text_encoder-item0.clip_prompt_embeds Using per-key quantization points (text_encoder_2-item1.clip_pooled_prompt_embeds: torch.Size([256])) for text_encoder_2-item1.clip_pooled_prompt_embeds Using per-key quantization points (text_encoder_2-item1.clip_prompt_embeds: torch.Size([256])) for text_encoder_2-item1.clip_prompt_embeds Using per-key quantization points (text_encoder_3-item2.t5_prompt_embeds: torch.Size([256])) for text_encoder_3-item2.t5_prompt_embeds Using per-key quantization points (text_encoder-item3.clip_pooled_prompt_embeds: torch.Size([256])) for text_encoder-item3.clip_pooled_prompt_embeds Using per-key quantization points (text_encoder-item3.clip_prompt_embeds: torch.Size([256])) for text_encoder-item3.clip_prompt_embeds Using per-key quantization points (text_encoder_2-item4.clip_pooled: torch.Size([256])) for text_encoder_2-item4.clip_pooled_prompt_embeds Using per-key quantization points (text_encoder_2-item4.clip_prompt: torch.Size([256])) for text_encoder_2-item4.clip_prompt_embeds Using per-key quantization points (text_encoder_3-item5.t5_prompt_embeds: torch.Size([256])) for text_encoder_3-item5.t5_prompt_embeds Using per-key quantization points (vae.encoder_f0: torch.Size([256])) for vae.encoder_f0 Using per-key quantization points (vae.encoder_f1: torch.Size([256])) for vae.encoder_f1 Using per-key quantization points (vae.decoder: torch.Size([256])) for vae.decoder IndividualUnPacker: text_encoder-item0.clip_pooled_prompt_embeds: torch.Size([768]) text_encoder-item0.clip_prompt_embeds: torch.Size([77, 768]) text_encoder_2-item1.clip_pooled_prompt_embeds: torch.Size([1280]) text_encoder_2-item1.clip_prompt_embeds: torch.Size([77, 1280]) text_encoder_3-item2.t5_prompt_embeds: torch.Size([77, 4096]) text_encoder-item3.clip_pooled_prompt_embeds: torch.Size([768]) text_encoder-item3.clip_prompt_embeds: torch.Size([77, 768]) text_encoder_2-item4.clip_pooled_prompt_embeds: torch.Size([1280]) text_encoder_2-item4.clip_prompt_embeds: torch.Size([77, 1280]) text_encoder_3-item5.t5_prompt_embeds: torch.Size([77, 4096]) vae.decoder: torch.Size([16, 128, 128]) vae.encoder_f0: torch.Size([32, 128, 128]) vae.encoder_f1: torch.Size([32, 128, 128]) Per-key MSE Breakdown (Quant=Truncation+Quantization, Codec=Compression): Key Quant-MSE Total-MSE ------------------------------------------------------------------------------------- text_encoder-item0.clip_pooled_prompt_embeds 0.00017474 0.92312511 text_encoder-item0.clip_prompt_embeds 0.00021560 23.92873419 text_encoder_2-item1.clip_pooled_prompt_embeds 0.00023980 0.85583429 text_encoder_2-item1.clip_prompt_embeds 0.00021108 0.12873979 text_encoder_3-item2.t5_prompt_embeds 0.00000804 0.00350815 text_encoder-item3.clip_pooled_prompt_embeds 0.00841799 1.01420029 text_encoder-item3.clip_prompt_embeds 0.00023247 23.90570887 text_encoder_2-item4.clip_pooled_prompt_embeds 0.00016751 0.67051430 text_encoder_2-item4.clip_prompt_embeds 0.00009830 0.47049568 text_encoder_3-item5.t5_prompt_embeds 0.00001263 0.00227117 vae.encoder_f0 0.00544735 1.08977914 vae.encoder_f1 0.00544843 1.08977556 vae.decoder 0.00018632 0.07804158 ------------------------------------------------------------------------------------- TOTAL 0.00257940 1.79400684 (elements=2,260,992) ---------------------- -------------------------------------------------------- SAMPLE-WISE STATISTICS ---------------------- -------------------------------------------------------- Handler sd35 Strategy individual Architecture hyperprior-featurecoding ---------------------- -------------------------------------------------------- Total Elements 2260992 Total Bytes 107764 BPFP 0.3813 bits/point EBPFP 0.7626 equivalent bits/point MSE 1.794007 ---------------------- -------------------------------------------------------- Time: 0.753s Load: 0.008s, Pack+Encode: 0.295s, Decode+Unpack: 0.450s ---------------------- -------------------------------------------------------- Restored Feature Format: [Dict] with 8 keys key['text_encoder-item0']: [Dict] with 2 keys key['clip_pooled_prompt_embeds']: [Tensor] shape=torch.Size([768]), dtype=torch.float16, device=cpu key['clip_prompt_embeds']: [Tensor] shape=torch.Size([77, 768]), dtype=torch.float16, device=cpu key['text_encoder_2-item1']: [Dict] with 2 keys key['clip_pooled_prompt_embeds']: [Tensor] shape=torch.Size([1280]), dtype=torch.float16, device=cpu key['clip_prompt_embeds']: [Tensor] shape=torch.Size([77, 1280]), dtype=torch.float16, device=cpu key['text_encoder_3-item2']: [Dict] with 1 keys key['t5_prompt_embeds']: [Tensor] shape=torch.Size([77, 4096]), dtype=torch.float16, device=cpu key['text_encoder-item3']: [Dict] with 2 keys key['clip_pooled_prompt_embeds']: [Tensor] shape=torch.Size([768]), dtype=torch.float16, device=cpu key['clip_prompt_embeds']: [Tensor] shape=torch.Size([77, 768]), dtype=torch.float16, device=cpu key['text_encoder_2-item4']: [Dict] with 2 keys key['clip_pooled_prompt_embeds']: [Tensor] shape=torch.Size([1280]), dtype=torch.float16, device=cpu key['clip_prompt_embeds']: [Tensor] shape=torch.Size([77, 1280]), dtype=torch.float16, device=cpu key['text_encoder_3-item5']: [Dict] with 1 keys key['t5_prompt_embeds']: [Tensor] shape=torch.Size([77, 4096]), dtype=torch.float16, device=cpu key['vae.decoder']: [Tensor] shape=torch.Size([16, 128, 128]), dtype=torch.float16, device=cpu key['vae.encoder']: [Dict] with 2 keys key['vae.encoder_f0']: [Tensor] shape=torch.Size([32, 128, 128]), dtype=torch.float16, device=cpu key['vae.encoder_f1']: [Tensor] shape=torch.Size([32, 128, 128]), dtype=torch.float16, device=cpu 💾 Converting with 1.7940 MSE: from ../datasets/FeatureCoding-StableDiffusion3.5/100samples_features/000000285788.zst to output-fixed/sd35/lambda0.004/hyperprior-featurecoding-8bit-individual/sd35cond/000000285788.zst 💪 Processing: ../datasets/FeatureCoding-StableDiffusion3.5/100samples_features/000000287291.zst (45/100) [1/4] FRONTEND: Loading features from ../datasets/FeatureCoding-StableDiffusion3.5/100samples_features/000000287291.zst... Original data structure: root: [Dict] with 8 keys key['text_encoder-item0']: [Dict] with 2 keys key['clip_pooled_prompt_embeds']: [Tensor] shape=torch.Size([768]), dtype=torch.float16, device=cpu key['clip_prompt_embeds']: [Tensor] shape=torch.Size([77, 768]), dtype=torch.float16, device=cpu key['text_encoder_2-item1']: [Dict] with 2 keys key['clip_pooled_prompt_embeds']: [Tensor] shape=torch.Size([1280]), dtype=torch.float16, device=cpu key['clip_prompt_embeds']: [Tensor] shape=torch.Size([77, 1280]), dtype=torch.float16, device=cpu key['text_encoder_3-item2']: [Dict] with 1 keys key['t5_prompt_embeds']: [Tensor] shape=torch.Size([77, 4096]), dtype=torch.float16, device=cpu key['text_encoder-item3']: [Dict] with 2 keys key['clip_pooled_prompt_embeds']: [Tensor] shape=torch.Size([768]), dtype=torch.float16, device=cpu key['clip_prompt_embeds']: [Tensor] shape=torch.Size([77, 768]), dtype=torch.float16, device=cpu key['text_encoder_2-item4']: [Dict] with 2 keys key['clip_pooled_prompt_embeds']: [Tensor] shape=torch.Size([1280]), dtype=torch.float16, device=cpu key['clip_prompt_embeds']: [Tensor] shape=torch.Size([77, 1280]), dtype=torch.float16, device=cpu key['text_encoder_3-item5']: [Dict] with 1 keys key['t5_prompt_embeds']: [Tensor] shape=torch.Size([77, 4096]), dtype=torch.float16, device=cpu key['vae.encoder-item6']: [Dict] with 2 keys key['vae.encoder_f0']: [Tensor] shape=torch.Size([32, 128, 128]), dtype=torch.float16, device=cpu key['vae.encoder_f1']: [Tensor] shape=torch.Size([32, 128, 128]), dtype=torch.float16, device=cpu key['vae.decoder']: [Tensor] shape=torch.Size([16, 128, 128]), dtype=torch.float16, device=cpu ⌛️ [1/4] FRONTEND: Load time: 0.008s ------------------------------------------------------------ SD3.5 Features Summary ------------------------------------------------------------ Number of text encoders: 3 Encoder names: ['text_encoder', 'text_encoder_2', 'text_encoder_3'] Has VAE latents: True Has VAE encoder features: True Data type: torch.float16 text_encoder-item0: clip_pooled_prompt_embeds: torch.Size([768]), torch.float16 clip_prompt_embeds: torch.Size([77, 768]), torch.float16 text_encoder_2-item1: clip_pooled_prompt_embeds: torch.Size([1280]), torch.float16 clip_prompt_embeds: torch.Size([77, 1280]), torch.float16 text_encoder_3-item2: t5_prompt_embeds: torch.Size([77, 4096]), torch.float16 text_encoder-item3: clip_pooled_prompt_embeds: torch.Size([768]), torch.float16 clip_prompt_embeds: torch.Size([77, 768]), torch.float16 text_encoder_2-item4: clip_pooled_prompt_embeds: torch.Size([1280]), torch.float16 clip_prompt_embeds: torch.Size([77, 1280]), torch.float16 text_encoder_3-item5: t5_prompt_embeds: torch.Size([77, 4096]), torch.float16 vae.decoder latents: torch.Size([16, 128, 128]) vae.encoder features: vae.encoder_f0: torch.Size([32, 128, 128]), torch.float16 vae.encoder_f1: torch.Size([32, 128, 128]), torch.float16 ------------------------------------------------------------ [2/4] FRONTEND: Pack + Encode (strategy: individual)... IndividualPacker: text_encoder-item0.clip_pooled_prompt_embeds: torch.Size([768])->torch.Size([1, 1, 1, 768]) text_encoder-item0.clip_prompt_embeds: torch.Size([77, 768])->torch.Size([1, 1, 77, 768]) text_encoder_2-item1.clip_pooled_prompt_embeds: torch.Size([1280])->torch.Size([1, 1, 1, 1280]) text_encoder_2-item1.clip_prompt_embeds: torch.Size([77, 1280])->torch.Size([1, 1, 77, 1280]) text_encoder_3-item2.t5_prompt_embeds: torch.Size([77, 4096])->torch.Size([1, 1, 77, 4096]) text_encoder-item3.clip_pooled_prompt_embeds: torch.Size([768])->torch.Size([1, 1, 1, 768]) text_encoder-item3.clip_prompt_embeds: torch.Size([77, 768])->torch.Size([1, 1, 77, 768]) text_encoder_2-item4.clip_pooled_prompt_embeds: torch.Size([1280])->torch.Size([1, 1, 1, 1280]) text_encoder_2-item4.clip_prompt_embeds: torch.Size([77, 1280])->torch.Size([1, 1, 77, 1280]) text_encoder_3-item5.t5_prompt_embeds: torch.Size([77, 4096])->torch.Size([1, 1, 77, 4096]) vae.encoder_f0: torch.Size([32, 128, 128])->torch.Size([1, 1, 512, 1024]) vae.encoder_f1: torch.Size([32, 128, 128])->torch.Size([1, 1, 512, 1024]) vae.decoder: torch.Size([16, 128, 128])->torch.Size([1, 1, 512, 512]) Using per-key quantization points (text_encoder-item0.clip_pooled_prompt_embeds: torch.Size([256])) for text_encoder-item0.clip_pooled_prompt_embeds text_encoder-item0.clip_pooled_prompt_embeds: 736B, BPFP=7.6667 Using per-key quantization points (text_encoder-item0.clip_prompt_embeds: torch.Size([256])) for text_encoder-item0.clip_prompt_embeds text_encoder-item0.clip_prompt_embeds: 5,988B, BPFP=0.8101 Using per-key quantization points (text_encoder_2-item1.clip_pooled_prompt_embeds: torch.Size([256])) for text_encoder_2-item1.clip_pooled_prompt_embeds text_encoder_2-item1.clip_pooled_prompt_embeds: 1,196B, BPFP=7.4750 Using per-key quantization points (text_encoder_2-item1.clip_prompt_embeds: torch.Size([256])) for text_encoder_2-item1.clip_prompt_embeds text_encoder_2-item1.clip_prompt_embeds: 11,256B, BPFP=0.9136 Using per-key quantization points (text_encoder_3-item2.t5_prompt_embeds: torch.Size([256])) for text_encoder_3-item2.t5_prompt_embeds text_encoder_3-item2.t5_prompt_embeds: 25,300B, BPFP=0.6417 Using per-key quantization points (text_encoder-item3.clip_pooled_prompt_embeds: torch.Size([256])) for text_encoder-item3.clip_pooled_prompt_embeds text_encoder-item3.clip_pooled_prompt_embeds: 760B, BPFP=7.9167 Using per-key quantization points (text_encoder-item3.clip_prompt_embeds: torch.Size([256])) for text_encoder-item3.clip_prompt_embeds text_encoder-item3.clip_prompt_embeds: 4,024B, BPFP=0.5444 Using per-key quantization points (text_encoder_2-item4.clip_pooled: torch.Size([256])) for text_encoder_2-item4.clip_pooled_prompt_embeds text_encoder_2-item4.clip_pooled_prompt_embeds: 1,268B, BPFP=7.9250 Using per-key quantization points (text_encoder_2-item4.clip_prompt: torch.Size([256])) for text_encoder_2-item4.clip_prompt_embeds text_encoder_2-item4.clip_prompt_embeds: 6,996B, BPFP=0.5679 Using per-key quantization points (text_encoder_3-item5.t5_prompt_embeds: torch.Size([256])) for text_encoder_3-item5.t5_prompt_embeds text_encoder_3-item5.t5_prompt_embeds: 18,416B, BPFP=0.4671 Using per-key quantization points (vae.encoder_f0: torch.Size([256])) for vae.encoder_f0 vae.encoder_f0: 19,316B, BPFP=0.2947 Using per-key quantization points (vae.encoder_f1: torch.Size([256])) for vae.encoder_f1 vae.encoder_f1: 19,316B, BPFP=0.2947 Using per-key quantization points (vae.decoder: torch.Size([256])) for vae.decoder vae.decoder: 8,424B, BPFP=0.2571 ⌛️ [2/4] FRONTEND: Frontend time: 0.295s (Pack+Encode) [3/4] BACKEND: Decode + Unpack... Using per-key quantization points (text_encoder-item0.clip_pooled_prompt_embeds: torch.Size([256])) for text_encoder-item0.clip_pooled_prompt_embeds Using per-key quantization points (text_encoder-item0.clip_prompt_embeds: torch.Size([256])) for text_encoder-item0.clip_prompt_embeds Using per-key quantization points (text_encoder_2-item1.clip_pooled_prompt_embeds: torch.Size([256])) for text_encoder_2-item1.clip_pooled_prompt_embeds Using per-key quantization points (text_encoder_2-item1.clip_prompt_embeds: torch.Size([256])) for text_encoder_2-item1.clip_prompt_embeds Using per-key quantization points (text_encoder_3-item2.t5_prompt_embeds: torch.Size([256])) for text_encoder_3-item2.t5_prompt_embeds Using per-key quantization points (text_encoder-item3.clip_pooled_prompt_embeds: torch.Size([256])) for text_encoder-item3.clip_pooled_prompt_embeds Using per-key quantization points (text_encoder-item3.clip_prompt_embeds: torch.Size([256])) for text_encoder-item3.clip_prompt_embeds Using per-key quantization points (text_encoder_2-item4.clip_pooled: torch.Size([256])) for text_encoder_2-item4.clip_pooled_prompt_embeds Using per-key quantization points (text_encoder_2-item4.clip_prompt: torch.Size([256])) for text_encoder_2-item4.clip_prompt_embeds Using per-key quantization points (text_encoder_3-item5.t5_prompt_embeds: torch.Size([256])) for text_encoder_3-item5.t5_prompt_embeds Using per-key quantization points (vae.encoder_f0: torch.Size([256])) for vae.encoder_f0 Using per-key quantization points (vae.encoder_f1: torch.Size([256])) for vae.encoder_f1 Using per-key quantization points (vae.decoder: torch.Size([256])) for vae.decoder IndividualUnPacker: text_encoder-item0.clip_pooled_prompt_embeds: torch.Size([768]) text_encoder-item0.clip_prompt_embeds: torch.Size([77, 768]) text_encoder_2-item1.clip_pooled_prompt_embeds: torch.Size([1280]) text_encoder_2-item1.clip_prompt_embeds: torch.Size([77, 1280]) text_encoder_3-item2.t5_prompt_embeds: torch.Size([77, 4096]) text_encoder-item3.clip_pooled_prompt_embeds: torch.Size([768]) text_encoder-item3.clip_prompt_embeds: torch.Size([77, 768]) text_encoder_2-item4.clip_pooled_prompt_embeds: torch.Size([1280]) text_encoder_2-item4.clip_prompt_embeds: torch.Size([77, 1280]) text_encoder_3-item5.t5_prompt_embeds: torch.Size([77, 4096]) vae.decoder: torch.Size([16, 128, 128]) vae.encoder_f0: torch.Size([32, 128, 128]) vae.encoder_f1: torch.Size([32, 128, 128]) ⌛️ [3/4] BACKEND: Backend time: 0.447s [4/4] METRICS: Computing MSE Breakdown... Using per-key quantization points (text_encoder-item0.clip_pooled_prompt_embeds: torch.Size([256])) for text_encoder-item0.clip_pooled_prompt_embeds Using per-key quantization points (text_encoder-item0.clip_prompt_embeds: torch.Size([256])) for text_encoder-item0.clip_prompt_embeds Using per-key quantization points (text_encoder_2-item1.clip_pooled_prompt_embeds: torch.Size([256])) for text_encoder_2-item1.clip_pooled_prompt_embeds Using per-key quantization points (text_encoder_2-item1.clip_prompt_embeds: torch.Size([256])) for text_encoder_2-item1.clip_prompt_embeds Using per-key quantization points (text_encoder_3-item2.t5_prompt_embeds: torch.Size([256])) for text_encoder_3-item2.t5_prompt_embeds Using per-key quantization points (text_encoder-item3.clip_pooled_prompt_embeds: torch.Size([256])) for text_encoder-item3.clip_pooled_prompt_embeds Using per-key quantization points (text_encoder-item3.clip_prompt_embeds: torch.Size([256])) for text_encoder-item3.clip_prompt_embeds Using per-key quantization points (text_encoder_2-item4.clip_pooled: torch.Size([256])) for text_encoder_2-item4.clip_pooled_prompt_embeds Using per-key quantization points (text_encoder_2-item4.clip_prompt: torch.Size([256])) for text_encoder_2-item4.clip_prompt_embeds Using per-key quantization points (text_encoder_3-item5.t5_prompt_embeds: torch.Size([256])) for text_encoder_3-item5.t5_prompt_embeds Using per-key quantization points (vae.encoder_f0: torch.Size([256])) for vae.encoder_f0 Using per-key quantization points (vae.encoder_f1: torch.Size([256])) for vae.encoder_f1 Using per-key quantization points (vae.decoder: torch.Size([256])) for vae.decoder IndividualUnPacker: text_encoder-item0.clip_pooled_prompt_embeds: torch.Size([768]) text_encoder-item0.clip_prompt_embeds: torch.Size([77, 768]) text_encoder_2-item1.clip_pooled_prompt_embeds: torch.Size([1280]) text_encoder_2-item1.clip_prompt_embeds: torch.Size([77, 1280]) text_encoder_3-item2.t5_prompt_embeds: torch.Size([77, 4096]) text_encoder-item3.clip_pooled_prompt_embeds: torch.Size([768]) text_encoder-item3.clip_prompt_embeds: torch.Size([77, 768]) text_encoder_2-item4.clip_pooled_prompt_embeds: torch.Size([1280]) text_encoder_2-item4.clip_prompt_embeds: torch.Size([77, 1280]) text_encoder_3-item5.t5_prompt_embeds: torch.Size([77, 4096]) vae.decoder: torch.Size([16, 128, 128]) vae.encoder_f0: torch.Size([32, 128, 128]) vae.encoder_f1: torch.Size([32, 128, 128]) Per-key MSE Breakdown (Quant=Truncation+Quantization, Codec=Compression): Key Quant-MSE Total-MSE ------------------------------------------------------------------------------------- text_encoder-item0.clip_pooled_prompt_embeds 0.00241107 0.91010276 text_encoder-item0.clip_prompt_embeds 0.00022698 143.97444873 text_encoder_2-item1.clip_pooled_prompt_embeds 0.00024914 0.96758766 text_encoder_2-item1.clip_prompt_embeds 0.00021102 0.12269690 text_encoder_3-item2.t5_prompt_embeds 0.00000794 0.00311084 text_encoder-item3.clip_pooled_prompt_embeds 0.00841799 1.01420029 text_encoder-item3.clip_prompt_embeds 0.00023247 23.90570887 text_encoder_2-item4.clip_pooled_prompt_embeds 0.00016751 0.67051430 text_encoder_2-item4.clip_prompt_embeds 0.00009830 0.47049568 text_encoder_3-item5.t5_prompt_embeds 0.00001263 0.00227117 vae.encoder_f0 0.00630479 2.25577331 vae.encoder_f1 0.00631430 2.25579500 vae.decoder 0.00018596 0.05676587 ------------------------------------------------------------------------------------- TOTAL 0.00298001 5.47181949 (elements=2,260,992) ---------------------- -------------------------------------------------------- SAMPLE-WISE STATISTICS ---------------------- -------------------------------------------------------- Handler sd35 Strategy individual Architecture hyperprior-featurecoding ---------------------- -------------------------------------------------------- Total Elements 2260992 Total Bytes 122996 BPFP 0.4352 bits/point EBPFP 0.8704 equivalent bits/point MSE 5.471819 ---------------------- -------------------------------------------------------- Time: 0.750s Load: 0.008s, Pack+Encode: 0.295s, Decode+Unpack: 0.447s ---------------------- -------------------------------------------------------- Restored Feature Format: [Dict] with 8 keys key['text_encoder-item0']: [Dict] with 2 keys key['clip_pooled_prompt_embeds']: [Tensor] shape=torch.Size([768]), dtype=torch.float16, device=cpu key['clip_prompt_embeds']: [Tensor] shape=torch.Size([77, 768]), dtype=torch.float16, device=cpu key['text_encoder_2-item1']: [Dict] with 2 keys key['clip_pooled_prompt_embeds']: [Tensor] shape=torch.Size([1280]), dtype=torch.float16, device=cpu key['clip_prompt_embeds']: [Tensor] shape=torch.Size([77, 1280]), dtype=torch.float16, device=cpu key['text_encoder_3-item2']: [Dict] with 1 keys key['t5_prompt_embeds']: [Tensor] shape=torch.Size([77, 4096]), dtype=torch.float16, device=cpu key['text_encoder-item3']: [Dict] with 2 keys key['clip_pooled_prompt_embeds']: [Tensor] shape=torch.Size([768]), dtype=torch.float16, device=cpu key['clip_prompt_embeds']: [Tensor] shape=torch.Size([77, 768]), dtype=torch.float16, device=cpu key['text_encoder_2-item4']: [Dict] with 2 keys key['clip_pooled_prompt_embeds']: [Tensor] shape=torch.Size([1280]), dtype=torch.float16, device=cpu key['clip_prompt_embeds']: [Tensor] shape=torch.Size([77, 1280]), dtype=torch.float16, device=cpu key['text_encoder_3-item5']: [Dict] with 1 keys key['t5_prompt_embeds']: [Tensor] shape=torch.Size([77, 4096]), dtype=torch.float16, device=cpu key['vae.decoder']: [Tensor] shape=torch.Size([16, 128, 128]), dtype=torch.float16, device=cpu key['vae.encoder']: [Dict] with 2 keys key['vae.encoder_f0']: [Tensor] shape=torch.Size([32, 128, 128]), dtype=torch.float16, device=cpu key['vae.encoder_f1']: [Tensor] shape=torch.Size([32, 128, 128]), dtype=torch.float16, device=cpu 💾 Converting with 5.4718 MSE: from ../datasets/FeatureCoding-StableDiffusion3.5/100samples_features/000000287291.zst to output-fixed/sd35/lambda0.004/hyperprior-featurecoding-8bit-individual/sd35cond/000000287291.zst 💪 Processing: ../datasets/FeatureCoding-StableDiffusion3.5/100samples_features/000000289343.zst (46/100) [1/4] FRONTEND: Loading features from ../datasets/FeatureCoding-StableDiffusion3.5/100samples_features/000000289343.zst... Original data structure: root: [Dict] with 8 keys key['text_encoder-item0']: [Dict] with 2 keys key['clip_pooled_prompt_embeds']: [Tensor] shape=torch.Size([768]), dtype=torch.float16, device=cpu key['clip_prompt_embeds']: [Tensor] shape=torch.Size([77, 768]), dtype=torch.float16, device=cpu key['text_encoder_2-item1']: [Dict] with 2 keys key['clip_pooled_prompt_embeds']: [Tensor] shape=torch.Size([1280]), dtype=torch.float16, device=cpu key['clip_prompt_embeds']: [Tensor] shape=torch.Size([77, 1280]), dtype=torch.float16, device=cpu key['text_encoder_3-item2']: [Dict] with 1 keys key['t5_prompt_embeds']: [Tensor] shape=torch.Size([77, 4096]), dtype=torch.float16, device=cpu key['text_encoder-item3']: [Dict] with 2 keys key['clip_pooled_prompt_embeds']: [Tensor] shape=torch.Size([768]), dtype=torch.float16, device=cpu key['clip_prompt_embeds']: [Tensor] shape=torch.Size([77, 768]), dtype=torch.float16, device=cpu key['text_encoder_2-item4']: [Dict] with 2 keys key['clip_pooled_prompt_embeds']: [Tensor] shape=torch.Size([1280]), dtype=torch.float16, device=cpu key['clip_prompt_embeds']: [Tensor] shape=torch.Size([77, 1280]), dtype=torch.float16, device=cpu key['text_encoder_3-item5']: [Dict] with 1 keys key['t5_prompt_embeds']: [Tensor] shape=torch.Size([77, 4096]), dtype=torch.float16, device=cpu key['vae.encoder-item6']: [Dict] with 2 keys key['vae.encoder_f0']: [Tensor] shape=torch.Size([32, 128, 128]), dtype=torch.float16, device=cpu key['vae.encoder_f1']: [Tensor] shape=torch.Size([32, 128, 128]), dtype=torch.float16, device=cpu key['vae.decoder']: [Tensor] shape=torch.Size([16, 128, 128]), dtype=torch.float16, device=cpu ⌛️ [1/4] FRONTEND: Load time: 0.009s ------------------------------------------------------------ SD3.5 Features Summary ------------------------------------------------------------ Number of text encoders: 3 Encoder names: ['text_encoder', 'text_encoder_2', 'text_encoder_3'] Has VAE latents: True Has VAE encoder features: True Data type: torch.float16 text_encoder-item0: clip_pooled_prompt_embeds: torch.Size([768]), torch.float16 clip_prompt_embeds: torch.Size([77, 768]), torch.float16 text_encoder_2-item1: clip_pooled_prompt_embeds: torch.Size([1280]), torch.float16 clip_prompt_embeds: torch.Size([77, 1280]), torch.float16 text_encoder_3-item2: t5_prompt_embeds: torch.Size([77, 4096]), torch.float16 text_encoder-item3: clip_pooled_prompt_embeds: torch.Size([768]), torch.float16 clip_prompt_embeds: torch.Size([77, 768]), torch.float16 text_encoder_2-item4: clip_pooled_prompt_embeds: torch.Size([1280]), torch.float16 clip_prompt_embeds: torch.Size([77, 1280]), torch.float16 text_encoder_3-item5: t5_prompt_embeds: torch.Size([77, 4096]), torch.float16 vae.decoder latents: torch.Size([16, 128, 128]) vae.encoder features: vae.encoder_f0: torch.Size([32, 128, 128]), torch.float16 vae.encoder_f1: torch.Size([32, 128, 128]), torch.float16 ------------------------------------------------------------ [2/4] FRONTEND: Pack + Encode (strategy: individual)... IndividualPacker: text_encoder-item0.clip_pooled_prompt_embeds: torch.Size([768])->torch.Size([1, 1, 1, 768]) text_encoder-item0.clip_prompt_embeds: torch.Size([77, 768])->torch.Size([1, 1, 77, 768]) text_encoder_2-item1.clip_pooled_prompt_embeds: torch.Size([1280])->torch.Size([1, 1, 1, 1280]) text_encoder_2-item1.clip_prompt_embeds: torch.Size([77, 1280])->torch.Size([1, 1, 77, 1280]) text_encoder_3-item2.t5_prompt_embeds: torch.Size([77, 4096])->torch.Size([1, 1, 77, 4096]) text_encoder-item3.clip_pooled_prompt_embeds: torch.Size([768])->torch.Size([1, 1, 1, 768]) text_encoder-item3.clip_prompt_embeds: torch.Size([77, 768])->torch.Size([1, 1, 77, 768]) text_encoder_2-item4.clip_pooled_prompt_embeds: torch.Size([1280])->torch.Size([1, 1, 1, 1280]) text_encoder_2-item4.clip_prompt_embeds: torch.Size([77, 1280])->torch.Size([1, 1, 77, 1280]) text_encoder_3-item5.t5_prompt_embeds: torch.Size([77, 4096])->torch.Size([1, 1, 77, 4096]) vae.encoder_f0: torch.Size([32, 128, 128])->torch.Size([1, 1, 512, 1024]) vae.encoder_f1: torch.Size([32, 128, 128])->torch.Size([1, 1, 512, 1024]) vae.decoder: torch.Size([16, 128, 128])->torch.Size([1, 1, 512, 512]) Using per-key quantization points (text_encoder-item0.clip_pooled_prompt_embeds: torch.Size([256])) for text_encoder-item0.clip_pooled_prompt_embeds text_encoder-item0.clip_pooled_prompt_embeds: 724B, BPFP=7.5417 Using per-key quantization points (text_encoder-item0.clip_prompt_embeds: torch.Size([256])) for text_encoder-item0.clip_prompt_embeds text_encoder-item0.clip_prompt_embeds: 6,008B, BPFP=0.8128 Using per-key quantization points (text_encoder_2-item1.clip_pooled_prompt_embeds: torch.Size([256])) for text_encoder_2-item1.clip_pooled_prompt_embeds text_encoder_2-item1.clip_pooled_prompt_embeds: 1,204B, BPFP=7.5250 Using per-key quantization points (text_encoder_2-item1.clip_prompt_embeds: torch.Size([256])) for text_encoder_2-item1.clip_prompt_embeds text_encoder_2-item1.clip_prompt_embeds: 10,952B, BPFP=0.8890 Using per-key quantization points (text_encoder_3-item2.t5_prompt_embeds: torch.Size([256])) for text_encoder_3-item2.t5_prompt_embeds text_encoder_3-item2.t5_prompt_embeds: 23,284B, BPFP=0.5906 Using per-key quantization points (text_encoder-item3.clip_pooled_prompt_embeds: torch.Size([256])) for text_encoder-item3.clip_pooled_prompt_embeds text_encoder-item3.clip_pooled_prompt_embeds: 760B, BPFP=7.9167 Using per-key quantization points (text_encoder-item3.clip_prompt_embeds: torch.Size([256])) for text_encoder-item3.clip_prompt_embeds text_encoder-item3.clip_prompt_embeds: 4,024B, BPFP=0.5444 Using per-key quantization points (text_encoder_2-item4.clip_pooled: torch.Size([256])) for text_encoder_2-item4.clip_pooled_prompt_embeds text_encoder_2-item4.clip_pooled_prompt_embeds: 1,268B, BPFP=7.9250 Using per-key quantization points (text_encoder_2-item4.clip_prompt: torch.Size([256])) for text_encoder_2-item4.clip_prompt_embeds text_encoder_2-item4.clip_prompt_embeds: 6,996B, BPFP=0.5679 Using per-key quantization points (text_encoder_3-item5.t5_prompt_embeds: torch.Size([256])) for text_encoder_3-item5.t5_prompt_embeds text_encoder_3-item5.t5_prompt_embeds: 18,416B, BPFP=0.4671 Using per-key quantization points (vae.encoder_f0: torch.Size([256])) for vae.encoder_f0 vae.encoder_f0: 18,160B, BPFP=0.2771 Using per-key quantization points (vae.encoder_f1: torch.Size([256])) for vae.encoder_f1 vae.encoder_f1: 18,160B, BPFP=0.2771 Using per-key quantization points (vae.decoder: torch.Size([256])) for vae.decoder vae.decoder: 9,160B, BPFP=0.2795 ⌛️ [2/4] FRONTEND: Frontend time: 0.291s (Pack+Encode) [3/4] BACKEND: Decode + Unpack... Using per-key quantization points (text_encoder-item0.clip_pooled_prompt_embeds: torch.Size([256])) for text_encoder-item0.clip_pooled_prompt_embeds Using per-key quantization points (text_encoder-item0.clip_prompt_embeds: torch.Size([256])) for text_encoder-item0.clip_prompt_embeds Using per-key quantization points (text_encoder_2-item1.clip_pooled_prompt_embeds: torch.Size([256])) for text_encoder_2-item1.clip_pooled_prompt_embeds Using per-key quantization points (text_encoder_2-item1.clip_prompt_embeds: torch.Size([256])) for text_encoder_2-item1.clip_prompt_embeds Using per-key quantization points (text_encoder_3-item2.t5_prompt_embeds: torch.Size([256])) for text_encoder_3-item2.t5_prompt_embeds Using per-key quantization points (text_encoder-item3.clip_pooled_prompt_embeds: torch.Size([256])) for text_encoder-item3.clip_pooled_prompt_embeds Using per-key quantization points (text_encoder-item3.clip_prompt_embeds: torch.Size([256])) for text_encoder-item3.clip_prompt_embeds Using per-key quantization points (text_encoder_2-item4.clip_pooled: torch.Size([256])) for text_encoder_2-item4.clip_pooled_prompt_embeds Using per-key quantization points (text_encoder_2-item4.clip_prompt: torch.Size([256])) for text_encoder_2-item4.clip_prompt_embeds Using per-key quantization points (text_encoder_3-item5.t5_prompt_embeds: torch.Size([256])) for text_encoder_3-item5.t5_prompt_embeds Using per-key quantization points (vae.encoder_f0: torch.Size([256])) for vae.encoder_f0 Using per-key quantization points (vae.encoder_f1: torch.Size([256])) for vae.encoder_f1 Using per-key quantization points (vae.decoder: torch.Size([256])) for vae.decoder IndividualUnPacker: text_encoder-item0.clip_pooled_prompt_embeds: torch.Size([768]) text_encoder-item0.clip_prompt_embeds: torch.Size([77, 768]) text_encoder_2-item1.clip_pooled_prompt_embeds: torch.Size([1280]) text_encoder_2-item1.clip_prompt_embeds: torch.Size([77, 1280]) text_encoder_3-item2.t5_prompt_embeds: torch.Size([77, 4096]) text_encoder-item3.clip_pooled_prompt_embeds: torch.Size([768]) text_encoder-item3.clip_prompt_embeds: torch.Size([77, 768]) text_encoder_2-item4.clip_pooled_prompt_embeds: torch.Size([1280]) text_encoder_2-item4.clip_prompt_embeds: torch.Size([77, 1280]) text_encoder_3-item5.t5_prompt_embeds: torch.Size([77, 4096]) vae.decoder: torch.Size([16, 128, 128]) vae.encoder_f0: torch.Size([32, 128, 128]) vae.encoder_f1: torch.Size([32, 128, 128]) ⌛️ [3/4] BACKEND: Backend time: 0.444s [4/4] METRICS: Computing MSE Breakdown... Using per-key quantization points (text_encoder-item0.clip_pooled_prompt_embeds: torch.Size([256])) for text_encoder-item0.clip_pooled_prompt_embeds Using per-key quantization points (text_encoder-item0.clip_prompt_embeds: torch.Size([256])) for text_encoder-item0.clip_prompt_embeds Using per-key quantization points (text_encoder_2-item1.clip_pooled_prompt_embeds: torch.Size([256])) for text_encoder_2-item1.clip_pooled_prompt_embeds Using per-key quantization points (text_encoder_2-item1.clip_prompt_embeds: torch.Size([256])) for text_encoder_2-item1.clip_prompt_embeds Using per-key quantization points (text_encoder_3-item2.t5_prompt_embeds: torch.Size([256])) for text_encoder_3-item2.t5_prompt_embeds Using per-key quantization points (text_encoder-item3.clip_pooled_prompt_embeds: torch.Size([256])) for text_encoder-item3.clip_pooled_prompt_embeds Using per-key quantization points (text_encoder-item3.clip_prompt_embeds: torch.Size([256])) for text_encoder-item3.clip_prompt_embeds Using per-key quantization points (text_encoder_2-item4.clip_pooled: torch.Size([256])) for text_encoder_2-item4.clip_pooled_prompt_embeds Using per-key quantization points (text_encoder_2-item4.clip_prompt: torch.Size([256])) for text_encoder_2-item4.clip_prompt_embeds Using per-key quantization points (text_encoder_3-item5.t5_prompt_embeds: torch.Size([256])) for text_encoder_3-item5.t5_prompt_embeds Using per-key quantization points (vae.encoder_f0: torch.Size([256])) for vae.encoder_f0 Using per-key quantization points (vae.encoder_f1: torch.Size([256])) for vae.encoder_f1 Using per-key quantization points (vae.decoder: torch.Size([256])) for vae.decoder IndividualUnPacker: text_encoder-item0.clip_pooled_prompt_embeds: torch.Size([768]) text_encoder-item0.clip_prompt_embeds: torch.Size([77, 768]) text_encoder_2-item1.clip_pooled_prompt_embeds: torch.Size([1280]) text_encoder_2-item1.clip_prompt_embeds: torch.Size([77, 1280]) text_encoder_3-item2.t5_prompt_embeds: torch.Size([77, 4096]) text_encoder-item3.clip_pooled_prompt_embeds: torch.Size([768]) text_encoder-item3.clip_prompt_embeds: torch.Size([77, 768]) text_encoder_2-item4.clip_pooled_prompt_embeds: torch.Size([1280]) text_encoder_2-item4.clip_prompt_embeds: torch.Size([77, 1280]) text_encoder_3-item5.t5_prompt_embeds: torch.Size([77, 4096]) vae.decoder: torch.Size([16, 128, 128]) vae.encoder_f0: torch.Size([32, 128, 128]) vae.encoder_f1: torch.Size([32, 128, 128]) Per-key MSE Breakdown (Quant=Truncation+Quantization, Codec=Compression): Key Quant-MSE Total-MSE ------------------------------------------------------------------------------------- text_encoder-item0.clip_pooled_prompt_embeds 0.00074171 0.98713930 text_encoder-item0.clip_prompt_embeds 0.00024643 23.96090157 text_encoder_2-item1.clip_pooled_prompt_embeds 0.00022451 0.99402981 text_encoder_2-item1.clip_prompt_embeds 0.00018967 0.12742973 text_encoder_3-item2.t5_prompt_embeds 0.00000778 0.00265886 text_encoder-item3.clip_pooled_prompt_embeds 0.00841799 1.01420029 text_encoder-item3.clip_prompt_embeds 0.00023247 23.90570887 text_encoder_2-item4.clip_pooled_prompt_embeds 0.00016751 0.67051430 text_encoder_2-item4.clip_prompt_embeds 0.00009830 0.47049568 text_encoder_3-item5.t5_prompt_embeds 0.00001263 0.00227117 vae.encoder_f0 0.00612578 2.08261251 vae.encoder_f1 0.00613243 2.08300090 vae.decoder 0.00018179 0.06517903 ------------------------------------------------------------------------------------- TOTAL 0.00289482 2.25381663 (elements=2,260,992) ---------------------- -------------------------------------------------------- SAMPLE-WISE STATISTICS ---------------------- -------------------------------------------------------- Handler sd35 Strategy individual Architecture hyperprior-featurecoding ---------------------- -------------------------------------------------------- Total Elements 2260992 Total Bytes 119116 BPFP 0.4215 bits/point EBPFP 0.8429 equivalent bits/point MSE 2.253817 ---------------------- -------------------------------------------------------- Time: 0.743s Load: 0.009s, Pack+Encode: 0.291s, Decode+Unpack: 0.444s ---------------------- -------------------------------------------------------- Restored Feature Format: [Dict] with 8 keys key['text_encoder-item0']: [Dict] with 2 keys key['clip_pooled_prompt_embeds']: [Tensor] shape=torch.Size([768]), dtype=torch.float16, device=cpu key['clip_prompt_embeds']: [Tensor] shape=torch.Size([77, 768]), dtype=torch.float16, device=cpu key['text_encoder_2-item1']: [Dict] with 2 keys key['clip_pooled_prompt_embeds']: [Tensor] shape=torch.Size([1280]), dtype=torch.float16, device=cpu key['clip_prompt_embeds']: [Tensor] shape=torch.Size([77, 1280]), dtype=torch.float16, device=cpu key['text_encoder_3-item2']: [Dict] with 1 keys key['t5_prompt_embeds']: [Tensor] shape=torch.Size([77, 4096]), dtype=torch.float16, device=cpu key['text_encoder-item3']: [Dict] with 2 keys key['clip_pooled_prompt_embeds']: [Tensor] shape=torch.Size([768]), dtype=torch.float16, device=cpu key['clip_prompt_embeds']: [Tensor] shape=torch.Size([77, 768]), dtype=torch.float16, device=cpu key['text_encoder_2-item4']: [Dict] with 2 keys key['clip_pooled_prompt_embeds']: [Tensor] shape=torch.Size([1280]), dtype=torch.float16, device=cpu key['clip_prompt_embeds']: [Tensor] shape=torch.Size([77, 1280]), dtype=torch.float16, device=cpu key['text_encoder_3-item5']: [Dict] with 1 keys key['t5_prompt_embeds']: [Tensor] shape=torch.Size([77, 4096]), dtype=torch.float16, device=cpu key['vae.decoder']: [Tensor] shape=torch.Size([16, 128, 128]), dtype=torch.float16, device=cpu key['vae.encoder']: [Dict] with 2 keys key['vae.encoder_f0']: [Tensor] shape=torch.Size([32, 128, 128]), dtype=torch.float16, device=cpu key['vae.encoder_f1']: [Tensor] shape=torch.Size([32, 128, 128]), dtype=torch.float16, device=cpu 💾 Converting with 2.2538 MSE: from ../datasets/FeatureCoding-StableDiffusion3.5/100samples_features/000000289343.zst to output-fixed/sd35/lambda0.004/hyperprior-featurecoding-8bit-individual/sd35cond/000000289343.zst 💪 Processing: ../datasets/FeatureCoding-StableDiffusion3.5/100samples_features/000000304545.zst (47/100) [1/4] FRONTEND: Loading features from ../datasets/FeatureCoding-StableDiffusion3.5/100samples_features/000000304545.zst... Original data structure: root: [Dict] with 8 keys key['text_encoder-item0']: [Dict] with 2 keys key['clip_pooled_prompt_embeds']: [Tensor] shape=torch.Size([768]), dtype=torch.float16, device=cpu key['clip_prompt_embeds']: [Tensor] shape=torch.Size([77, 768]), dtype=torch.float16, device=cpu key['text_encoder_2-item1']: [Dict] with 2 keys key['clip_pooled_prompt_embeds']: [Tensor] shape=torch.Size([1280]), dtype=torch.float16, device=cpu key['clip_prompt_embeds']: [Tensor] shape=torch.Size([77, 1280]), dtype=torch.float16, device=cpu key['text_encoder_3-item2']: [Dict] with 1 keys key['t5_prompt_embeds']: [Tensor] shape=torch.Size([77, 4096]), dtype=torch.float16, device=cpu key['text_encoder-item3']: [Dict] with 2 keys key['clip_pooled_prompt_embeds']: [Tensor] shape=torch.Size([768]), dtype=torch.float16, device=cpu key['clip_prompt_embeds']: [Tensor] shape=torch.Size([77, 768]), dtype=torch.float16, device=cpu key['text_encoder_2-item4']: [Dict] with 2 keys key['clip_pooled_prompt_embeds']: [Tensor] shape=torch.Size([1280]), dtype=torch.float16, device=cpu key['clip_prompt_embeds']: [Tensor] shape=torch.Size([77, 1280]), dtype=torch.float16, device=cpu key['text_encoder_3-item5']: [Dict] with 1 keys key['t5_prompt_embeds']: [Tensor] shape=torch.Size([77, 4096]), dtype=torch.float16, device=cpu key['vae.encoder-item6']: [Dict] with 2 keys key['vae.encoder_f0']: [Tensor] shape=torch.Size([32, 128, 128]), dtype=torch.float16, device=cpu key['vae.encoder_f1']: [Tensor] shape=torch.Size([32, 128, 128]), dtype=torch.float16, device=cpu key['vae.decoder']: [Tensor] shape=torch.Size([16, 128, 128]), dtype=torch.float16, device=cpu ⌛️ [1/4] FRONTEND: Load time: 0.009s ------------------------------------------------------------ SD3.5 Features Summary ------------------------------------------------------------ Number of text encoders: 3 Encoder names: ['text_encoder', 'text_encoder_2', 'text_encoder_3'] Has VAE latents: True Has VAE encoder features: True Data type: torch.float16 text_encoder-item0: clip_pooled_prompt_embeds: torch.Size([768]), torch.float16 clip_prompt_embeds: torch.Size([77, 768]), torch.float16 text_encoder_2-item1: clip_pooled_prompt_embeds: torch.Size([1280]), torch.float16 clip_prompt_embeds: torch.Size([77, 1280]), torch.float16 text_encoder_3-item2: t5_prompt_embeds: torch.Size([77, 4096]), torch.float16 text_encoder-item3: clip_pooled_prompt_embeds: torch.Size([768]), torch.float16 clip_prompt_embeds: torch.Size([77, 768]), torch.float16 text_encoder_2-item4: clip_pooled_prompt_embeds: torch.Size([1280]), torch.float16 clip_prompt_embeds: torch.Size([77, 1280]), torch.float16 text_encoder_3-item5: t5_prompt_embeds: torch.Size([77, 4096]), torch.float16 vae.decoder latents: torch.Size([16, 128, 128]) vae.encoder features: vae.encoder_f0: torch.Size([32, 128, 128]), torch.float16 vae.encoder_f1: torch.Size([32, 128, 128]), torch.float16 ------------------------------------------------------------ [2/4] FRONTEND: Pack + Encode (strategy: individual)... IndividualPacker: text_encoder-item0.clip_pooled_prompt_embeds: torch.Size([768])->torch.Size([1, 1, 1, 768]) text_encoder-item0.clip_prompt_embeds: torch.Size([77, 768])->torch.Size([1, 1, 77, 768]) text_encoder_2-item1.clip_pooled_prompt_embeds: torch.Size([1280])->torch.Size([1, 1, 1, 1280]) text_encoder_2-item1.clip_prompt_embeds: torch.Size([77, 1280])->torch.Size([1, 1, 77, 1280]) text_encoder_3-item2.t5_prompt_embeds: torch.Size([77, 4096])->torch.Size([1, 1, 77, 4096]) text_encoder-item3.clip_pooled_prompt_embeds: torch.Size([768])->torch.Size([1, 1, 1, 768]) text_encoder-item3.clip_prompt_embeds: torch.Size([77, 768])->torch.Size([1, 1, 77, 768]) text_encoder_2-item4.clip_pooled_prompt_embeds: torch.Size([1280])->torch.Size([1, 1, 1, 1280]) text_encoder_2-item4.clip_prompt_embeds: torch.Size([77, 1280])->torch.Size([1, 1, 77, 1280]) text_encoder_3-item5.t5_prompt_embeds: torch.Size([77, 4096])->torch.Size([1, 1, 77, 4096]) vae.encoder_f0: torch.Size([32, 128, 128])->torch.Size([1, 1, 512, 1024]) vae.encoder_f1: torch.Size([32, 128, 128])->torch.Size([1, 1, 512, 1024]) vae.decoder: torch.Size([16, 128, 128])->torch.Size([1, 1, 512, 512]) Using per-key quantization points (text_encoder-item0.clip_pooled_prompt_embeds: torch.Size([256])) for text_encoder-item0.clip_pooled_prompt_embeds text_encoder-item0.clip_pooled_prompt_embeds: 728B, BPFP=7.5833 Using per-key quantization points (text_encoder-item0.clip_prompt_embeds: torch.Size([256])) for text_encoder-item0.clip_prompt_embeds text_encoder-item0.clip_prompt_embeds: 7,288B, BPFP=0.9859 Using per-key quantization points (text_encoder_2-item1.clip_pooled_prompt_embeds: torch.Size([256])) for text_encoder_2-item1.clip_pooled_prompt_embeds text_encoder_2-item1.clip_pooled_prompt_embeds: 1,192B, BPFP=7.4500 Using per-key quantization points (text_encoder_2-item1.clip_prompt_embeds: torch.Size([256])) for text_encoder_2-item1.clip_prompt_embeds text_encoder_2-item1.clip_prompt_embeds: 11,508B, BPFP=0.9341 Using per-key quantization points (text_encoder_3-item2.t5_prompt_embeds: torch.Size([256])) for text_encoder_3-item2.t5_prompt_embeds text_encoder_3-item2.t5_prompt_embeds: 30,484B, BPFP=0.7732 Using per-key quantization points (text_encoder-item3.clip_pooled_prompt_embeds: torch.Size([256])) for text_encoder-item3.clip_pooled_prompt_embeds text_encoder-item3.clip_pooled_prompt_embeds: 760B, BPFP=7.9167 Using per-key quantization points (text_encoder-item3.clip_prompt_embeds: torch.Size([256])) for text_encoder-item3.clip_prompt_embeds text_encoder-item3.clip_prompt_embeds: 4,024B, BPFP=0.5444 Using per-key quantization points (text_encoder_2-item4.clip_pooled: torch.Size([256])) for text_encoder_2-item4.clip_pooled_prompt_embeds text_encoder_2-item4.clip_pooled_prompt_embeds: 1,268B, BPFP=7.9250 Using per-key quantization points (text_encoder_2-item4.clip_prompt: torch.Size([256])) for text_encoder_2-item4.clip_prompt_embeds text_encoder_2-item4.clip_prompt_embeds: 6,996B, BPFP=0.5679 Using per-key quantization points (text_encoder_3-item5.t5_prompt_embeds: torch.Size([256])) for text_encoder_3-item5.t5_prompt_embeds text_encoder_3-item5.t5_prompt_embeds: 18,416B, BPFP=0.4671 Using per-key quantization points (vae.encoder_f0: torch.Size([256])) for vae.encoder_f0 vae.encoder_f0: 6,772B, BPFP=0.1033 Using per-key quantization points (vae.encoder_f1: torch.Size([256])) for vae.encoder_f1 vae.encoder_f1: 6,772B, BPFP=0.1033 Using per-key quantization points (vae.decoder: torch.Size([256])) for vae.decoder vae.decoder: 9,864B, BPFP=0.3010 ⌛️ [2/4] FRONTEND: Frontend time: 0.287s (Pack+Encode) [3/4] BACKEND: Decode + Unpack... Using per-key quantization points (text_encoder-item0.clip_pooled_prompt_embeds: torch.Size([256])) for text_encoder-item0.clip_pooled_prompt_embeds Using per-key quantization points (text_encoder-item0.clip_prompt_embeds: torch.Size([256])) for text_encoder-item0.clip_prompt_embeds Using per-key quantization points (text_encoder_2-item1.clip_pooled_prompt_embeds: torch.Size([256])) for text_encoder_2-item1.clip_pooled_prompt_embeds Using per-key quantization points (text_encoder_2-item1.clip_prompt_embeds: torch.Size([256])) for text_encoder_2-item1.clip_prompt_embeds Using per-key quantization points (text_encoder_3-item2.t5_prompt_embeds: torch.Size([256])) for text_encoder_3-item2.t5_prompt_embeds Using per-key quantization points (text_encoder-item3.clip_pooled_prompt_embeds: torch.Size([256])) for text_encoder-item3.clip_pooled_prompt_embeds Using per-key quantization points (text_encoder-item3.clip_prompt_embeds: torch.Size([256])) for text_encoder-item3.clip_prompt_embeds Using per-key quantization points (text_encoder_2-item4.clip_pooled: torch.Size([256])) for text_encoder_2-item4.clip_pooled_prompt_embeds Using per-key quantization points (text_encoder_2-item4.clip_prompt: torch.Size([256])) for text_encoder_2-item4.clip_prompt_embeds Using per-key quantization points (text_encoder_3-item5.t5_prompt_embeds: torch.Size([256])) for text_encoder_3-item5.t5_prompt_embeds Using per-key quantization points (vae.encoder_f0: torch.Size([256])) for vae.encoder_f0 Using per-key quantization points (vae.encoder_f1: torch.Size([256])) for vae.encoder_f1 Using per-key quantization points (vae.decoder: torch.Size([256])) for vae.decoder IndividualUnPacker: text_encoder-item0.clip_pooled_prompt_embeds: torch.Size([768]) text_encoder-item0.clip_prompt_embeds: torch.Size([77, 768]) text_encoder_2-item1.clip_pooled_prompt_embeds: torch.Size([1280]) text_encoder_2-item1.clip_prompt_embeds: torch.Size([77, 1280]) text_encoder_3-item2.t5_prompt_embeds: torch.Size([77, 4096]) text_encoder-item3.clip_pooled_prompt_embeds: torch.Size([768]) text_encoder-item3.clip_prompt_embeds: torch.Size([77, 768]) text_encoder_2-item4.clip_pooled_prompt_embeds: torch.Size([1280]) text_encoder_2-item4.clip_prompt_embeds: torch.Size([77, 1280]) text_encoder_3-item5.t5_prompt_embeds: torch.Size([77, 4096]) vae.decoder: torch.Size([16, 128, 128]) vae.encoder_f0: torch.Size([32, 128, 128]) vae.encoder_f1: torch.Size([32, 128, 128]) ⌛️ [3/4] BACKEND: Backend time: 0.452s [4/4] METRICS: Computing MSE Breakdown... Using per-key quantization points (text_encoder-item0.clip_pooled_prompt_embeds: torch.Size([256])) for text_encoder-item0.clip_pooled_prompt_embeds Using per-key quantization points (text_encoder-item0.clip_prompt_embeds: torch.Size([256])) for text_encoder-item0.clip_prompt_embeds Using per-key quantization points (text_encoder_2-item1.clip_pooled_prompt_embeds: torch.Size([256])) for text_encoder_2-item1.clip_pooled_prompt_embeds Using per-key quantization points (text_encoder_2-item1.clip_prompt_embeds: torch.Size([256])) for text_encoder_2-item1.clip_prompt_embeds Using per-key quantization points (text_encoder_3-item2.t5_prompt_embeds: torch.Size([256])) for text_encoder_3-item2.t5_prompt_embeds Using per-key quantization points (text_encoder-item3.clip_pooled_prompt_embeds: torch.Size([256])) for text_encoder-item3.clip_pooled_prompt_embeds Using per-key quantization points (text_encoder-item3.clip_prompt_embeds: torch.Size([256])) for text_encoder-item3.clip_prompt_embeds Using per-key quantization points (text_encoder_2-item4.clip_pooled: torch.Size([256])) for text_encoder_2-item4.clip_pooled_prompt_embeds Using per-key quantization points (text_encoder_2-item4.clip_prompt: torch.Size([256])) for text_encoder_2-item4.clip_prompt_embeds Using per-key quantization points (text_encoder_3-item5.t5_prompt_embeds: torch.Size([256])) for text_encoder_3-item5.t5_prompt_embeds Using per-key quantization points (vae.encoder_f0: torch.Size([256])) for vae.encoder_f0 Using per-key quantization points (vae.encoder_f1: torch.Size([256])) for vae.encoder_f1 Using per-key quantization points (vae.decoder: torch.Size([256])) for vae.decoder IndividualUnPacker: text_encoder-item0.clip_pooled_prompt_embeds: torch.Size([768]) text_encoder-item0.clip_prompt_embeds: torch.Size([77, 768]) text_encoder_2-item1.clip_pooled_prompt_embeds: torch.Size([1280]) text_encoder_2-item1.clip_prompt_embeds: torch.Size([77, 1280]) text_encoder_3-item2.t5_prompt_embeds: torch.Size([77, 4096]) text_encoder-item3.clip_pooled_prompt_embeds: torch.Size([768]) text_encoder-item3.clip_prompt_embeds: torch.Size([77, 768]) text_encoder_2-item4.clip_pooled_prompt_embeds: torch.Size([1280]) text_encoder_2-item4.clip_prompt_embeds: torch.Size([77, 1280]) text_encoder_3-item5.t5_prompt_embeds: torch.Size([77, 4096]) vae.decoder: torch.Size([16, 128, 128]) vae.encoder_f0: torch.Size([32, 128, 128]) vae.encoder_f1: torch.Size([32, 128, 128]) Per-key MSE Breakdown (Quant=Truncation+Quantization, Codec=Compression): Key Quant-MSE Total-MSE ------------------------------------------------------------------------------------- text_encoder-item0.clip_pooled_prompt_embeds 0.00018845 0.93068663 text_encoder-item0.clip_prompt_embeds 0.00024049 34.89450588 text_encoder_2-item1.clip_pooled_prompt_embeds 0.00023104 0.95069761 text_encoder_2-item1.clip_prompt_embeds 0.00016878 0.13729683 text_encoder_3-item2.t5_prompt_embeds 0.00000794 0.00370935 text_encoder-item3.clip_pooled_prompt_embeds 0.00841799 1.01420029 text_encoder-item3.clip_prompt_embeds 0.00023247 23.90570887 text_encoder_2-item4.clip_pooled_prompt_embeds 0.00016751 0.67051430 text_encoder_2-item4.clip_prompt_embeds 0.00009830 0.47049568 text_encoder_3-item5.t5_prompt_embeds 0.00001263 0.00227117 vae.encoder_f0 0.00526071 0.45857000 vae.encoder_f1 0.00526072 0.45856228 vae.decoder 0.00016981 0.07236104 ------------------------------------------------------------------------------------- TOTAL 0.00248947 1.78787852 (elements=2,260,992) ---------------------- -------------------------------------------------------- SAMPLE-WISE STATISTICS ---------------------- -------------------------------------------------------- Handler sd35 Strategy individual Architecture hyperprior-featurecoding ---------------------- -------------------------------------------------------- Total Elements 2260992 Total Bytes 106072 BPFP 0.3753 bits/point EBPFP 0.7506 equivalent bits/point MSE 1.787879 ---------------------- -------------------------------------------------------- Time: 0.747s Load: 0.009s, Pack+Encode: 0.287s, Decode+Unpack: 0.452s ---------------------- -------------------------------------------------------- Restored Feature Format: [Dict] with 8 keys key['text_encoder-item0']: [Dict] with 2 keys key['clip_pooled_prompt_embeds']: [Tensor] shape=torch.Size([768]), dtype=torch.float16, device=cpu key['clip_prompt_embeds']: [Tensor] shape=torch.Size([77, 768]), dtype=torch.float16, device=cpu key['text_encoder_2-item1']: [Dict] with 2 keys key['clip_pooled_prompt_embeds']: [Tensor] shape=torch.Size([1280]), dtype=torch.float16, device=cpu key['clip_prompt_embeds']: [Tensor] shape=torch.Size([77, 1280]), dtype=torch.float16, device=cpu key['text_encoder_3-item2']: [Dict] with 1 keys key['t5_prompt_embeds']: [Tensor] shape=torch.Size([77, 4096]), dtype=torch.float16, device=cpu key['text_encoder-item3']: [Dict] with 2 keys key['clip_pooled_prompt_embeds']: [Tensor] shape=torch.Size([768]), dtype=torch.float16, device=cpu key['clip_prompt_embeds']: [Tensor] shape=torch.Size([77, 768]), dtype=torch.float16, device=cpu key['text_encoder_2-item4']: [Dict] with 2 keys key['clip_pooled_prompt_embeds']: [Tensor] shape=torch.Size([1280]), dtype=torch.float16, device=cpu key['clip_prompt_embeds']: [Tensor] shape=torch.Size([77, 1280]), dtype=torch.float16, device=cpu key['text_encoder_3-item5']: [Dict] with 1 keys key['t5_prompt_embeds']: [Tensor] shape=torch.Size([77, 4096]), dtype=torch.float16, device=cpu key['vae.decoder']: [Tensor] shape=torch.Size([16, 128, 128]), dtype=torch.float16, device=cpu key['vae.encoder']: [Dict] with 2 keys key['vae.encoder_f0']: [Tensor] shape=torch.Size([32, 128, 128]), dtype=torch.float16, device=cpu key['vae.encoder_f1']: [Tensor] shape=torch.Size([32, 128, 128]), dtype=torch.float16, device=cpu 💾 Converting with 1.7879 MSE: from ../datasets/FeatureCoding-StableDiffusion3.5/100samples_features/000000304545.zst to output-fixed/sd35/lambda0.004/hyperprior-featurecoding-8bit-individual/sd35cond/000000304545.zst 💪 Processing: ../datasets/FeatureCoding-StableDiffusion3.5/100samples_features/000000310622.zst (48/100) [1/4] FRONTEND: Loading features from ../datasets/FeatureCoding-StableDiffusion3.5/100samples_features/000000310622.zst... Original data structure: root: [Dict] with 8 keys key['text_encoder-item0']: [Dict] with 2 keys key['clip_pooled_prompt_embeds']: [Tensor] shape=torch.Size([768]), dtype=torch.float16, device=cpu key['clip_prompt_embeds']: [Tensor] shape=torch.Size([77, 768]), dtype=torch.float16, device=cpu key['text_encoder_2-item1']: [Dict] with 2 keys key['clip_pooled_prompt_embeds']: [Tensor] shape=torch.Size([1280]), dtype=torch.float16, device=cpu key['clip_prompt_embeds']: [Tensor] shape=torch.Size([77, 1280]), dtype=torch.float16, device=cpu key['text_encoder_3-item2']: [Dict] with 1 keys key['t5_prompt_embeds']: [Tensor] shape=torch.Size([77, 4096]), dtype=torch.float16, device=cpu key['text_encoder-item3']: [Dict] with 2 keys key['clip_pooled_prompt_embeds']: [Tensor] shape=torch.Size([768]), dtype=torch.float16, device=cpu key['clip_prompt_embeds']: [Tensor] shape=torch.Size([77, 768]), dtype=torch.float16, device=cpu key['text_encoder_2-item4']: [Dict] with 2 keys key['clip_pooled_prompt_embeds']: [Tensor] shape=torch.Size([1280]), dtype=torch.float16, device=cpu key['clip_prompt_embeds']: [Tensor] shape=torch.Size([77, 1280]), dtype=torch.float16, device=cpu key['text_encoder_3-item5']: [Dict] with 1 keys key['t5_prompt_embeds']: [Tensor] shape=torch.Size([77, 4096]), dtype=torch.float16, device=cpu key['vae.encoder-item6']: [Dict] with 2 keys key['vae.encoder_f0']: [Tensor] shape=torch.Size([32, 128, 128]), dtype=torch.float16, device=cpu key['vae.encoder_f1']: [Tensor] shape=torch.Size([32, 128, 128]), dtype=torch.float16, device=cpu key['vae.decoder']: [Tensor] shape=torch.Size([16, 128, 128]), dtype=torch.float16, device=cpu ⌛️ [1/4] FRONTEND: Load time: 0.009s ------------------------------------------------------------ SD3.5 Features Summary ------------------------------------------------------------ Number of text encoders: 3 Encoder names: ['text_encoder', 'text_encoder_2', 'text_encoder_3'] Has VAE latents: True Has VAE encoder features: True Data type: torch.float16 text_encoder-item0: clip_pooled_prompt_embeds: torch.Size([768]), torch.float16 clip_prompt_embeds: torch.Size([77, 768]), torch.float16 text_encoder_2-item1: clip_pooled_prompt_embeds: torch.Size([1280]), torch.float16 clip_prompt_embeds: torch.Size([77, 1280]), torch.float16 text_encoder_3-item2: t5_prompt_embeds: torch.Size([77, 4096]), torch.float16 text_encoder-item3: clip_pooled_prompt_embeds: torch.Size([768]), torch.float16 clip_prompt_embeds: torch.Size([77, 768]), torch.float16 text_encoder_2-item4: clip_pooled_prompt_embeds: torch.Size([1280]), torch.float16 clip_prompt_embeds: torch.Size([77, 1280]), torch.float16 text_encoder_3-item5: t5_prompt_embeds: torch.Size([77, 4096]), torch.float16 vae.decoder latents: torch.Size([16, 128, 128]) vae.encoder features: vae.encoder_f0: torch.Size([32, 128, 128]), torch.float16 vae.encoder_f1: torch.Size([32, 128, 128]), torch.float16 ------------------------------------------------------------ [2/4] FRONTEND: Pack + Encode (strategy: individual)... IndividualPacker: text_encoder-item0.clip_pooled_prompt_embeds: torch.Size([768])->torch.Size([1, 1, 1, 768]) text_encoder-item0.clip_prompt_embeds: torch.Size([77, 768])->torch.Size([1, 1, 77, 768]) text_encoder_2-item1.clip_pooled_prompt_embeds: torch.Size([1280])->torch.Size([1, 1, 1, 1280]) text_encoder_2-item1.clip_prompt_embeds: torch.Size([77, 1280])->torch.Size([1, 1, 77, 1280]) text_encoder_3-item2.t5_prompt_embeds: torch.Size([77, 4096])->torch.Size([1, 1, 77, 4096]) text_encoder-item3.clip_pooled_prompt_embeds: torch.Size([768])->torch.Size([1, 1, 1, 768]) text_encoder-item3.clip_prompt_embeds: torch.Size([77, 768])->torch.Size([1, 1, 77, 768]) text_encoder_2-item4.clip_pooled_prompt_embeds: torch.Size([1280])->torch.Size([1, 1, 1, 1280]) text_encoder_2-item4.clip_prompt_embeds: torch.Size([77, 1280])->torch.Size([1, 1, 77, 1280]) text_encoder_3-item5.t5_prompt_embeds: torch.Size([77, 4096])->torch.Size([1, 1, 77, 4096]) vae.encoder_f0: torch.Size([32, 128, 128])->torch.Size([1, 1, 512, 1024]) vae.encoder_f1: torch.Size([32, 128, 128])->torch.Size([1, 1, 512, 1024]) vae.decoder: torch.Size([16, 128, 128])->torch.Size([1, 1, 512, 512]) Using per-key quantization points (text_encoder-item0.clip_pooled_prompt_embeds: torch.Size([256])) for text_encoder-item0.clip_pooled_prompt_embeds text_encoder-item0.clip_pooled_prompt_embeds: 744B, BPFP=7.7500 Using per-key quantization points (text_encoder-item0.clip_prompt_embeds: torch.Size([256])) for text_encoder-item0.clip_prompt_embeds text_encoder-item0.clip_prompt_embeds: 6,152B, BPFP=0.8323 Using per-key quantization points (text_encoder_2-item1.clip_pooled_prompt_embeds: torch.Size([256])) for text_encoder_2-item1.clip_pooled_prompt_embeds text_encoder_2-item1.clip_pooled_prompt_embeds: 1,200B, BPFP=7.5000 Using per-key quantization points (text_encoder_2-item1.clip_prompt_embeds: torch.Size([256])) for text_encoder_2-item1.clip_prompt_embeds text_encoder_2-item1.clip_prompt_embeds: 10,776B, BPFP=0.8747 Using per-key quantization points (text_encoder_3-item2.t5_prompt_embeds: torch.Size([256])) for text_encoder_3-item2.t5_prompt_embeds text_encoder_3-item2.t5_prompt_embeds: 26,296B, BPFP=0.6670 Using per-key quantization points (text_encoder-item3.clip_pooled_prompt_embeds: torch.Size([256])) for text_encoder-item3.clip_pooled_prompt_embeds text_encoder-item3.clip_pooled_prompt_embeds: 760B, BPFP=7.9167 Using per-key quantization points (text_encoder-item3.clip_prompt_embeds: torch.Size([256])) for text_encoder-item3.clip_prompt_embeds text_encoder-item3.clip_prompt_embeds: 4,024B, BPFP=0.5444 Using per-key quantization points (text_encoder_2-item4.clip_pooled: torch.Size([256])) for text_encoder_2-item4.clip_pooled_prompt_embeds text_encoder_2-item4.clip_pooled_prompt_embeds: 1,268B, BPFP=7.9250 Using per-key quantization points (text_encoder_2-item4.clip_prompt: torch.Size([256])) for text_encoder_2-item4.clip_prompt_embeds text_encoder_2-item4.clip_prompt_embeds: 6,996B, BPFP=0.5679 Using per-key quantization points (text_encoder_3-item5.t5_prompt_embeds: torch.Size([256])) for text_encoder_3-item5.t5_prompt_embeds text_encoder_3-item5.t5_prompt_embeds: 18,416B, BPFP=0.4671 Using per-key quantization points (vae.encoder_f0: torch.Size([256])) for vae.encoder_f0 vae.encoder_f0: 19,276B, BPFP=0.2941 Using per-key quantization points (vae.encoder_f1: torch.Size([256])) for vae.encoder_f1 vae.encoder_f1: 19,276B, BPFP=0.2941 Using per-key quantization points (vae.decoder: torch.Size([256])) for vae.decoder vae.decoder: 9,260B, BPFP=0.2826 ⌛️ [2/4] FRONTEND: Frontend time: 0.295s (Pack+Encode) [3/4] BACKEND: Decode + Unpack... Using per-key quantization points (text_encoder-item0.clip_pooled_prompt_embeds: torch.Size([256])) for text_encoder-item0.clip_pooled_prompt_embeds Using per-key quantization points (text_encoder-item0.clip_prompt_embeds: torch.Size([256])) for text_encoder-item0.clip_prompt_embeds Using per-key quantization points (text_encoder_2-item1.clip_pooled_prompt_embeds: torch.Size([256])) for text_encoder_2-item1.clip_pooled_prompt_embeds Using per-key quantization points (text_encoder_2-item1.clip_prompt_embeds: torch.Size([256])) for text_encoder_2-item1.clip_prompt_embeds Using per-key quantization points (text_encoder_3-item2.t5_prompt_embeds: torch.Size([256])) for text_encoder_3-item2.t5_prompt_embeds Using per-key quantization points (text_encoder-item3.clip_pooled_prompt_embeds: torch.Size([256])) for text_encoder-item3.clip_pooled_prompt_embeds Using per-key quantization points (text_encoder-item3.clip_prompt_embeds: torch.Size([256])) for text_encoder-item3.clip_prompt_embeds Using per-key quantization points (text_encoder_2-item4.clip_pooled: torch.Size([256])) for text_encoder_2-item4.clip_pooled_prompt_embeds Using per-key quantization points (text_encoder_2-item4.clip_prompt: torch.Size([256])) for text_encoder_2-item4.clip_prompt_embeds Using per-key quantization points (text_encoder_3-item5.t5_prompt_embeds: torch.Size([256])) for text_encoder_3-item5.t5_prompt_embeds Using per-key quantization points (vae.encoder_f0: torch.Size([256])) for vae.encoder_f0 Using per-key quantization points (vae.encoder_f1: torch.Size([256])) for vae.encoder_f1 Using per-key quantization points (vae.decoder: torch.Size([256])) for vae.decoder IndividualUnPacker: text_encoder-item0.clip_pooled_prompt_embeds: torch.Size([768]) text_encoder-item0.clip_prompt_embeds: torch.Size([77, 768]) text_encoder_2-item1.clip_pooled_prompt_embeds: torch.Size([1280]) text_encoder_2-item1.clip_prompt_embeds: torch.Size([77, 1280]) text_encoder_3-item2.t5_prompt_embeds: torch.Size([77, 4096]) text_encoder-item3.clip_pooled_prompt_embeds: torch.Size([768]) text_encoder-item3.clip_prompt_embeds: torch.Size([77, 768]) text_encoder_2-item4.clip_pooled_prompt_embeds: torch.Size([1280]) text_encoder_2-item4.clip_prompt_embeds: torch.Size([77, 1280]) text_encoder_3-item5.t5_prompt_embeds: torch.Size([77, 4096]) vae.decoder: torch.Size([16, 128, 128]) vae.encoder_f0: torch.Size([32, 128, 128]) vae.encoder_f1: torch.Size([32, 128, 128]) ⌛️ [3/4] BACKEND: Backend time: 0.450s [4/4] METRICS: Computing MSE Breakdown... Using per-key quantization points (text_encoder-item0.clip_pooled_prompt_embeds: torch.Size([256])) for text_encoder-item0.clip_pooled_prompt_embeds Using per-key quantization points (text_encoder-item0.clip_prompt_embeds: torch.Size([256])) for text_encoder-item0.clip_prompt_embeds Using per-key quantization points (text_encoder_2-item1.clip_pooled_prompt_embeds: torch.Size([256])) for text_encoder_2-item1.clip_pooled_prompt_embeds Using per-key quantization points (text_encoder_2-item1.clip_prompt_embeds: torch.Size([256])) for text_encoder_2-item1.clip_prompt_embeds Using per-key quantization points (text_encoder_3-item2.t5_prompt_embeds: torch.Size([256])) for text_encoder_3-item2.t5_prompt_embeds Using per-key quantization points (text_encoder-item3.clip_pooled_prompt_embeds: torch.Size([256])) for text_encoder-item3.clip_pooled_prompt_embeds Using per-key quantization points (text_encoder-item3.clip_prompt_embeds: torch.Size([256])) for text_encoder-item3.clip_prompt_embeds Using per-key quantization points (text_encoder_2-item4.clip_pooled: torch.Size([256])) for text_encoder_2-item4.clip_pooled_prompt_embeds Using per-key quantization points (text_encoder_2-item4.clip_prompt: torch.Size([256])) for text_encoder_2-item4.clip_prompt_embeds Using per-key quantization points (text_encoder_3-item5.t5_prompt_embeds: torch.Size([256])) for text_encoder_3-item5.t5_prompt_embeds Using per-key quantization points (vae.encoder_f0: torch.Size([256])) for vae.encoder_f0 Using per-key quantization points (vae.encoder_f1: torch.Size([256])) for vae.encoder_f1 Using per-key quantization points (vae.decoder: torch.Size([256])) for vae.decoder IndividualUnPacker: text_encoder-item0.clip_pooled_prompt_embeds: torch.Size([768]) text_encoder-item0.clip_prompt_embeds: torch.Size([77, 768]) text_encoder_2-item1.clip_pooled_prompt_embeds: torch.Size([1280]) text_encoder_2-item1.clip_prompt_embeds: torch.Size([77, 1280]) text_encoder_3-item2.t5_prompt_embeds: torch.Size([77, 4096]) text_encoder-item3.clip_pooled_prompt_embeds: torch.Size([768]) text_encoder-item3.clip_prompt_embeds: torch.Size([77, 768]) text_encoder_2-item4.clip_pooled_prompt_embeds: torch.Size([1280]) text_encoder_2-item4.clip_prompt_embeds: torch.Size([77, 1280]) text_encoder_3-item5.t5_prompt_embeds: torch.Size([77, 4096]) vae.decoder: torch.Size([16, 128, 128]) vae.encoder_f0: torch.Size([32, 128, 128]) vae.encoder_f1: torch.Size([32, 128, 128]) Per-key MSE Breakdown (Quant=Truncation+Quantization, Codec=Compression): Key Quant-MSE Total-MSE ------------------------------------------------------------------------------------- text_encoder-item0.clip_pooled_prompt_embeds 0.00063331 0.85254916 text_encoder-item0.clip_prompt_embeds 0.00022843 59.98590114 text_encoder_2-item1.clip_pooled_prompt_embeds 0.00086038 0.90360165 text_encoder_2-item1.clip_prompt_embeds 0.00016207 0.11642714 text_encoder_3-item2.t5_prompt_embeds 0.00000746 0.00298480 text_encoder-item3.clip_pooled_prompt_embeds 0.00841799 1.01420029 text_encoder-item3.clip_prompt_embeds 0.00023247 23.90570887 text_encoder_2-item4.clip_pooled_prompt_embeds 0.00016751 0.67051430 text_encoder_2-item4.clip_prompt_embeds 0.00009830 0.47049568 text_encoder_3-item5.t5_prompt_embeds 0.00001263 0.00227117 vae.encoder_f0 0.00622977 2.36609244 vae.encoder_f1 0.00623684 2.36605406 vae.decoder 0.00019755 0.06656589 ------------------------------------------------------------------------------------- TOTAL 0.00294358 3.32704630 (elements=2,260,992) ---------------------- -------------------------------------------------------- SAMPLE-WISE STATISTICS ---------------------- -------------------------------------------------------- Handler sd35 Strategy individual Architecture hyperprior-featurecoding ---------------------- -------------------------------------------------------- Total Elements 2260992 Total Bytes 124444 BPFP 0.4403 bits/point EBPFP 0.8806 equivalent bits/point MSE 3.327046 ---------------------- -------------------------------------------------------- Time: 0.754s Load: 0.009s, Pack+Encode: 0.295s, Decode+Unpack: 0.450s ---------------------- -------------------------------------------------------- Restored Feature Format: [Dict] with 8 keys key['text_encoder-item0']: [Dict] with 2 keys key['clip_pooled_prompt_embeds']: [Tensor] shape=torch.Size([768]), dtype=torch.float16, device=cpu key['clip_prompt_embeds']: [Tensor] shape=torch.Size([77, 768]), dtype=torch.float16, device=cpu key['text_encoder_2-item1']: [Dict] with 2 keys key['clip_pooled_prompt_embeds']: [Tensor] shape=torch.Size([1280]), dtype=torch.float16, device=cpu key['clip_prompt_embeds']: [Tensor] shape=torch.Size([77, 1280]), dtype=torch.float16, device=cpu key['text_encoder_3-item2']: [Dict] with 1 keys key['t5_prompt_embeds']: [Tensor] shape=torch.Size([77, 4096]), dtype=torch.float16, device=cpu key['text_encoder-item3']: [Dict] with 2 keys key['clip_pooled_prompt_embeds']: [Tensor] shape=torch.Size([768]), dtype=torch.float16, device=cpu key['clip_prompt_embeds']: [Tensor] shape=torch.Size([77, 768]), dtype=torch.float16, device=cpu key['text_encoder_2-item4']: [Dict] with 2 keys key['clip_pooled_prompt_embeds']: [Tensor] shape=torch.Size([1280]), dtype=torch.float16, device=cpu key['clip_prompt_embeds']: [Tensor] shape=torch.Size([77, 1280]), dtype=torch.float16, device=cpu key['text_encoder_3-item5']: [Dict] with 1 keys key['t5_prompt_embeds']: [Tensor] shape=torch.Size([77, 4096]), dtype=torch.float16, device=cpu key['vae.decoder']: [Tensor] shape=torch.Size([16, 128, 128]), dtype=torch.float16, device=cpu key['vae.encoder']: [Dict] with 2 keys key['vae.encoder_f0']: [Tensor] shape=torch.Size([32, 128, 128]), dtype=torch.float16, device=cpu key['vae.encoder_f1']: [Tensor] shape=torch.Size([32, 128, 128]), dtype=torch.float16, device=cpu 💾 Converting with 3.3270 MSE: from ../datasets/FeatureCoding-StableDiffusion3.5/100samples_features/000000310622.zst to output-fixed/sd35/lambda0.004/hyperprior-featurecoding-8bit-individual/sd35cond/000000310622.zst 💪 Processing: ../datasets/FeatureCoding-StableDiffusion3.5/100samples_features/000000311394.zst (49/100) [1/4] FRONTEND: Loading features from ../datasets/FeatureCoding-StableDiffusion3.5/100samples_features/000000311394.zst... Original data structure: root: [Dict] with 8 keys key['text_encoder-item0']: [Dict] with 2 keys key['clip_pooled_prompt_embeds']: [Tensor] shape=torch.Size([768]), dtype=torch.float16, device=cpu key['clip_prompt_embeds']: [Tensor] shape=torch.Size([77, 768]), dtype=torch.float16, device=cpu key['text_encoder_2-item1']: [Dict] with 2 keys key['clip_pooled_prompt_embeds']: [Tensor] shape=torch.Size([1280]), dtype=torch.float16, device=cpu key['clip_prompt_embeds']: [Tensor] shape=torch.Size([77, 1280]), dtype=torch.float16, device=cpu key['text_encoder_3-item2']: [Dict] with 1 keys key['t5_prompt_embeds']: [Tensor] shape=torch.Size([77, 4096]), dtype=torch.float16, device=cpu key['text_encoder-item3']: [Dict] with 2 keys key['clip_pooled_prompt_embeds']: [Tensor] shape=torch.Size([768]), dtype=torch.float16, device=cpu key['clip_prompt_embeds']: [Tensor] shape=torch.Size([77, 768]), dtype=torch.float16, device=cpu key['text_encoder_2-item4']: [Dict] with 2 keys key['clip_pooled_prompt_embeds']: [Tensor] shape=torch.Size([1280]), dtype=torch.float16, device=cpu key['clip_prompt_embeds']: [Tensor] shape=torch.Size([77, 1280]), dtype=torch.float16, device=cpu key['text_encoder_3-item5']: [Dict] with 1 keys key['t5_prompt_embeds']: [Tensor] shape=torch.Size([77, 4096]), dtype=torch.float16, device=cpu key['vae.encoder-item6']: [Dict] with 2 keys key['vae.encoder_f0']: [Tensor] shape=torch.Size([32, 128, 128]), dtype=torch.float16, device=cpu key['vae.encoder_f1']: [Tensor] shape=torch.Size([32, 128, 128]), dtype=torch.float16, device=cpu key['vae.decoder']: [Tensor] shape=torch.Size([16, 128, 128]), dtype=torch.float16, device=cpu ⌛️ [1/4] FRONTEND: Load time: 0.009s ------------------------------------------------------------ SD3.5 Features Summary ------------------------------------------------------------ Number of text encoders: 3 Encoder names: ['text_encoder', 'text_encoder_2', 'text_encoder_3'] Has VAE latents: True Has VAE encoder features: True Data type: torch.float16 text_encoder-item0: clip_pooled_prompt_embeds: torch.Size([768]), torch.float16 clip_prompt_embeds: torch.Size([77, 768]), torch.float16 text_encoder_2-item1: clip_pooled_prompt_embeds: torch.Size([1280]), torch.float16 clip_prompt_embeds: torch.Size([77, 1280]), torch.float16 text_encoder_3-item2: t5_prompt_embeds: torch.Size([77, 4096]), torch.float16 text_encoder-item3: clip_pooled_prompt_embeds: torch.Size([768]), torch.float16 clip_prompt_embeds: torch.Size([77, 768]), torch.float16 text_encoder_2-item4: clip_pooled_prompt_embeds: torch.Size([1280]), torch.float16 clip_prompt_embeds: torch.Size([77, 1280]), torch.float16 text_encoder_3-item5: t5_prompt_embeds: torch.Size([77, 4096]), torch.float16 vae.decoder latents: torch.Size([16, 128, 128]) vae.encoder features: vae.encoder_f0: torch.Size([32, 128, 128]), torch.float16 vae.encoder_f1: torch.Size([32, 128, 128]), torch.float16 ------------------------------------------------------------ [2/4] FRONTEND: Pack + Encode (strategy: individual)... IndividualPacker: text_encoder-item0.clip_pooled_prompt_embeds: torch.Size([768])->torch.Size([1, 1, 1, 768]) text_encoder-item0.clip_prompt_embeds: torch.Size([77, 768])->torch.Size([1, 1, 77, 768]) text_encoder_2-item1.clip_pooled_prompt_embeds: torch.Size([1280])->torch.Size([1, 1, 1, 1280]) text_encoder_2-item1.clip_prompt_embeds: torch.Size([77, 1280])->torch.Size([1, 1, 77, 1280]) text_encoder_3-item2.t5_prompt_embeds: torch.Size([77, 4096])->torch.Size([1, 1, 77, 4096]) text_encoder-item3.clip_pooled_prompt_embeds: torch.Size([768])->torch.Size([1, 1, 1, 768]) text_encoder-item3.clip_prompt_embeds: torch.Size([77, 768])->torch.Size([1, 1, 77, 768]) text_encoder_2-item4.clip_pooled_prompt_embeds: torch.Size([1280])->torch.Size([1, 1, 1, 1280]) text_encoder_2-item4.clip_prompt_embeds: torch.Size([77, 1280])->torch.Size([1, 1, 77, 1280]) text_encoder_3-item5.t5_prompt_embeds: torch.Size([77, 4096])->torch.Size([1, 1, 77, 4096]) vae.encoder_f0: torch.Size([32, 128, 128])->torch.Size([1, 1, 512, 1024]) vae.encoder_f1: torch.Size([32, 128, 128])->torch.Size([1, 1, 512, 1024]) vae.decoder: torch.Size([16, 128, 128])->torch.Size([1, 1, 512, 512]) Using per-key quantization points (text_encoder-item0.clip_pooled_prompt_embeds: torch.Size([256])) for text_encoder-item0.clip_pooled_prompt_embeds text_encoder-item0.clip_pooled_prompt_embeds: 728B, BPFP=7.5833 Using per-key quantization points (text_encoder-item0.clip_prompt_embeds: torch.Size([256])) for text_encoder-item0.clip_prompt_embeds text_encoder-item0.clip_prompt_embeds: 6,372B, BPFP=0.8620 Using per-key quantization points (text_encoder_2-item1.clip_pooled_prompt_embeds: torch.Size([256])) for text_encoder_2-item1.clip_pooled_prompt_embeds text_encoder_2-item1.clip_pooled_prompt_embeds: 1,200B, BPFP=7.5000 Using per-key quantization points (text_encoder_2-item1.clip_prompt_embeds: torch.Size([256])) for text_encoder_2-item1.clip_prompt_embeds text_encoder_2-item1.clip_prompt_embeds: 11,068B, BPFP=0.8984 Using per-key quantization points (text_encoder_3-item2.t5_prompt_embeds: torch.Size([256])) for text_encoder_3-item2.t5_prompt_embeds text_encoder_3-item2.t5_prompt_embeds: 27,528B, BPFP=0.6983 Using per-key quantization points (text_encoder-item3.clip_pooled_prompt_embeds: torch.Size([256])) for text_encoder-item3.clip_pooled_prompt_embeds text_encoder-item3.clip_pooled_prompt_embeds: 760B, BPFP=7.9167 Using per-key quantization points (text_encoder-item3.clip_prompt_embeds: torch.Size([256])) for text_encoder-item3.clip_prompt_embeds text_encoder-item3.clip_prompt_embeds: 4,024B, BPFP=0.5444 Using per-key quantization points (text_encoder_2-item4.clip_pooled: torch.Size([256])) for text_encoder_2-item4.clip_pooled_prompt_embeds text_encoder_2-item4.clip_pooled_prompt_embeds: 1,268B, BPFP=7.9250 Using per-key quantization points (text_encoder_2-item4.clip_prompt: torch.Size([256])) for text_encoder_2-item4.clip_prompt_embeds text_encoder_2-item4.clip_prompt_embeds: 6,996B, BPFP=0.5679 Using per-key quantization points (text_encoder_3-item5.t5_prompt_embeds: torch.Size([256])) for text_encoder_3-item5.t5_prompt_embeds text_encoder_3-item5.t5_prompt_embeds: 18,416B, BPFP=0.4671 Using per-key quantization points (vae.encoder_f0: torch.Size([256])) for vae.encoder_f0 vae.encoder_f0: 14,468B, BPFP=0.2208 Using per-key quantization points (vae.encoder_f1: torch.Size([256])) for vae.encoder_f1 vae.encoder_f1: 14,468B, BPFP=0.2208 Using per-key quantization points (vae.decoder: torch.Size([256])) for vae.decoder vae.decoder: 6,464B, BPFP=0.1973 ⌛️ [2/4] FRONTEND: Frontend time: 0.296s (Pack+Encode) [3/4] BACKEND: Decode + Unpack... Using per-key quantization points (text_encoder-item0.clip_pooled_prompt_embeds: torch.Size([256])) for text_encoder-item0.clip_pooled_prompt_embeds Using per-key quantization points (text_encoder-item0.clip_prompt_embeds: torch.Size([256])) for text_encoder-item0.clip_prompt_embeds Using per-key quantization points (text_encoder_2-item1.clip_pooled_prompt_embeds: torch.Size([256])) for text_encoder_2-item1.clip_pooled_prompt_embeds Using per-key quantization points (text_encoder_2-item1.clip_prompt_embeds: torch.Size([256])) for text_encoder_2-item1.clip_prompt_embeds Using per-key quantization points (text_encoder_3-item2.t5_prompt_embeds: torch.Size([256])) for text_encoder_3-item2.t5_prompt_embeds Using per-key quantization points (text_encoder-item3.clip_pooled_prompt_embeds: torch.Size([256])) for text_encoder-item3.clip_pooled_prompt_embeds Using per-key quantization points (text_encoder-item3.clip_prompt_embeds: torch.Size([256])) for text_encoder-item3.clip_prompt_embeds Using per-key quantization points (text_encoder_2-item4.clip_pooled: torch.Size([256])) for text_encoder_2-item4.clip_pooled_prompt_embeds Using per-key quantization points (text_encoder_2-item4.clip_prompt: torch.Size([256])) for text_encoder_2-item4.clip_prompt_embeds Using per-key quantization points (text_encoder_3-item5.t5_prompt_embeds: torch.Size([256])) for text_encoder_3-item5.t5_prompt_embeds Using per-key quantization points (vae.encoder_f0: torch.Size([256])) for vae.encoder_f0 Using per-key quantization points (vae.encoder_f1: torch.Size([256])) for vae.encoder_f1 Using per-key quantization points (vae.decoder: torch.Size([256])) for vae.decoder IndividualUnPacker: text_encoder-item0.clip_pooled_prompt_embeds: torch.Size([768]) text_encoder-item0.clip_prompt_embeds: torch.Size([77, 768]) text_encoder_2-item1.clip_pooled_prompt_embeds: torch.Size([1280]) text_encoder_2-item1.clip_prompt_embeds: torch.Size([77, 1280]) text_encoder_3-item2.t5_prompt_embeds: torch.Size([77, 4096]) text_encoder-item3.clip_pooled_prompt_embeds: torch.Size([768]) text_encoder-item3.clip_prompt_embeds: torch.Size([77, 768]) text_encoder_2-item4.clip_pooled_prompt_embeds: torch.Size([1280]) text_encoder_2-item4.clip_prompt_embeds: torch.Size([77, 1280]) text_encoder_3-item5.t5_prompt_embeds: torch.Size([77, 4096]) vae.decoder: torch.Size([16, 128, 128]) vae.encoder_f0: torch.Size([32, 128, 128]) vae.encoder_f1: torch.Size([32, 128, 128]) ⌛️ [3/4] BACKEND: Backend time: 0.451s [4/4] METRICS: Computing MSE Breakdown... Using per-key quantization points (text_encoder-item0.clip_pooled_prompt_embeds: torch.Size([256])) for text_encoder-item0.clip_pooled_prompt_embeds Using per-key quantization points (text_encoder-item0.clip_prompt_embeds: torch.Size([256])) for text_encoder-item0.clip_prompt_embeds Using per-key quantization points (text_encoder_2-item1.clip_pooled_prompt_embeds: torch.Size([256])) for text_encoder_2-item1.clip_pooled_prompt_embeds Using per-key quantization points (text_encoder_2-item1.clip_prompt_embeds: torch.Size([256])) for text_encoder_2-item1.clip_prompt_embeds Using per-key quantization points (text_encoder_3-item2.t5_prompt_embeds: torch.Size([256])) for text_encoder_3-item2.t5_prompt_embeds Using per-key quantization points (text_encoder-item3.clip_pooled_prompt_embeds: torch.Size([256])) for text_encoder-item3.clip_pooled_prompt_embeds Using per-key quantization points (text_encoder-item3.clip_prompt_embeds: torch.Size([256])) for text_encoder-item3.clip_prompt_embeds Using per-key quantization points (text_encoder_2-item4.clip_pooled: torch.Size([256])) for text_encoder_2-item4.clip_pooled_prompt_embeds Using per-key quantization points (text_encoder_2-item4.clip_prompt: torch.Size([256])) for text_encoder_2-item4.clip_prompt_embeds Using per-key quantization points (text_encoder_3-item5.t5_prompt_embeds: torch.Size([256])) for text_encoder_3-item5.t5_prompt_embeds Using per-key quantization points (vae.encoder_f0: torch.Size([256])) for vae.encoder_f0 Using per-key quantization points (vae.encoder_f1: torch.Size([256])) for vae.encoder_f1 Using per-key quantization points (vae.decoder: torch.Size([256])) for vae.decoder IndividualUnPacker: text_encoder-item0.clip_pooled_prompt_embeds: torch.Size([768]) text_encoder-item0.clip_prompt_embeds: torch.Size([77, 768]) text_encoder_2-item1.clip_pooled_prompt_embeds: torch.Size([1280]) text_encoder_2-item1.clip_prompt_embeds: torch.Size([77, 1280]) text_encoder_3-item2.t5_prompt_embeds: torch.Size([77, 4096]) text_encoder-item3.clip_pooled_prompt_embeds: torch.Size([768]) text_encoder-item3.clip_prompt_embeds: torch.Size([77, 768]) text_encoder_2-item4.clip_pooled_prompt_embeds: torch.Size([1280]) text_encoder_2-item4.clip_prompt_embeds: torch.Size([77, 1280]) text_encoder_3-item5.t5_prompt_embeds: torch.Size([77, 4096]) vae.decoder: torch.Size([16, 128, 128]) vae.encoder_f0: torch.Size([32, 128, 128]) vae.encoder_f1: torch.Size([32, 128, 128]) Per-key MSE Breakdown (Quant=Truncation+Quantization, Codec=Compression): Key Quant-MSE Total-MSE ------------------------------------------------------------------------------------- text_encoder-item0.clip_pooled_prompt_embeds 0.00019653 0.94531051 text_encoder-item0.clip_prompt_embeds 0.00026004 24.03478423 text_encoder_2-item1.clip_pooled_prompt_embeds 0.00025016 1.01331501 text_encoder_2-item1.clip_prompt_embeds 0.00015074 0.11354867 text_encoder_3-item2.t5_prompt_embeds 0.00000873 0.00314615 text_encoder-item3.clip_pooled_prompt_embeds 0.00841799 1.01420029 text_encoder-item3.clip_prompt_embeds 0.00023247 23.90570887 text_encoder_2-item4.clip_pooled_prompt_embeds 0.00016751 0.67051430 text_encoder_2-item4.clip_prompt_embeds 0.00009830 0.47049568 text_encoder_3-item5.t5_prompt_embeds 0.00001263 0.00227117 vae.encoder_f0 0.00725303 1.98702812 vae.encoder_f1 0.00725507 1.98784900 vae.decoder 0.00017991 0.04503912 ------------------------------------------------------------------------------------- TOTAL 0.00341494 2.20864484 (elements=2,260,992) ---------------------- -------------------------------------------------------- SAMPLE-WISE STATISTICS ---------------------- -------------------------------------------------------- Handler sd35 Strategy individual Architecture hyperprior-featurecoding ---------------------- -------------------------------------------------------- Total Elements 2260992 Total Bytes 113760 BPFP 0.4025 bits/point EBPFP 0.8050 equivalent bits/point MSE 2.208645 ---------------------- -------------------------------------------------------- Time: 0.756s Load: 0.009s, Pack+Encode: 0.296s, Decode+Unpack: 0.451s ---------------------- -------------------------------------------------------- Restored Feature Format: [Dict] with 8 keys key['text_encoder-item0']: [Dict] with 2 keys key['clip_pooled_prompt_embeds']: [Tensor] shape=torch.Size([768]), dtype=torch.float16, device=cpu key['clip_prompt_embeds']: [Tensor] shape=torch.Size([77, 768]), dtype=torch.float16, device=cpu key['text_encoder_2-item1']: [Dict] with 2 keys key['clip_pooled_prompt_embeds']: [Tensor] shape=torch.Size([1280]), dtype=torch.float16, device=cpu key['clip_prompt_embeds']: [Tensor] shape=torch.Size([77, 1280]), dtype=torch.float16, device=cpu key['text_encoder_3-item2']: [Dict] with 1 keys key['t5_prompt_embeds']: [Tensor] shape=torch.Size([77, 4096]), dtype=torch.float16, device=cpu key['text_encoder-item3']: [Dict] with 2 keys key['clip_pooled_prompt_embeds']: [Tensor] shape=torch.Size([768]), dtype=torch.float16, device=cpu key['clip_prompt_embeds']: [Tensor] shape=torch.Size([77, 768]), dtype=torch.float16, device=cpu key['text_encoder_2-item4']: [Dict] with 2 keys key['clip_pooled_prompt_embeds']: [Tensor] shape=torch.Size([1280]), dtype=torch.float16, device=cpu key['clip_prompt_embeds']: [Tensor] shape=torch.Size([77, 1280]), dtype=torch.float16, device=cpu key['text_encoder_3-item5']: [Dict] with 1 keys key['t5_prompt_embeds']: [Tensor] shape=torch.Size([77, 4096]), dtype=torch.float16, device=cpu key['vae.decoder']: [Tensor] shape=torch.Size([16, 128, 128]), dtype=torch.float16, device=cpu key['vae.encoder']: [Dict] with 2 keys key['vae.encoder_f0']: [Tensor] shape=torch.Size([32, 128, 128]), dtype=torch.float16, device=cpu key['vae.encoder_f1']: [Tensor] shape=torch.Size([32, 128, 128]), dtype=torch.float16, device=cpu 💾 Converting with 2.2086 MSE: from ../datasets/FeatureCoding-StableDiffusion3.5/100samples_features/000000311394.zst to output-fixed/sd35/lambda0.004/hyperprior-featurecoding-8bit-individual/sd35cond/000000311394.zst 💪 Processing: ../datasets/FeatureCoding-StableDiffusion3.5/100samples_features/000000316015.zst (50/100) [1/4] FRONTEND: Loading features from ../datasets/FeatureCoding-StableDiffusion3.5/100samples_features/000000316015.zst... Original data structure: root: [Dict] with 8 keys key['text_encoder-item0']: [Dict] with 2 keys key['clip_pooled_prompt_embeds']: [Tensor] shape=torch.Size([768]), dtype=torch.float16, device=cpu key['clip_prompt_embeds']: [Tensor] shape=torch.Size([77, 768]), dtype=torch.float16, device=cpu key['text_encoder_2-item1']: [Dict] with 2 keys key['clip_pooled_prompt_embeds']: [Tensor] shape=torch.Size([1280]), dtype=torch.float16, device=cpu key['clip_prompt_embeds']: [Tensor] shape=torch.Size([77, 1280]), dtype=torch.float16, device=cpu key['text_encoder_3-item2']: [Dict] with 1 keys key['t5_prompt_embeds']: [Tensor] shape=torch.Size([77, 4096]), dtype=torch.float16, device=cpu key['text_encoder-item3']: [Dict] with 2 keys key['clip_pooled_prompt_embeds']: [Tensor] shape=torch.Size([768]), dtype=torch.float16, device=cpu key['clip_prompt_embeds']: [Tensor] shape=torch.Size([77, 768]), dtype=torch.float16, device=cpu key['text_encoder_2-item4']: [Dict] with 2 keys key['clip_pooled_prompt_embeds']: [Tensor] shape=torch.Size([1280]), dtype=torch.float16, device=cpu key['clip_prompt_embeds']: [Tensor] shape=torch.Size([77, 1280]), dtype=torch.float16, device=cpu key['text_encoder_3-item5']: [Dict] with 1 keys key['t5_prompt_embeds']: [Tensor] shape=torch.Size([77, 4096]), dtype=torch.float16, device=cpu key['vae.encoder-item6']: [Dict] with 2 keys key['vae.encoder_f0']: [Tensor] shape=torch.Size([32, 128, 128]), dtype=torch.float16, device=cpu key['vae.encoder_f1']: [Tensor] shape=torch.Size([32, 128, 128]), dtype=torch.float16, device=cpu key['vae.decoder']: [Tensor] shape=torch.Size([16, 128, 128]), dtype=torch.float16, device=cpu ⌛️ [1/4] FRONTEND: Load time: 0.009s ------------------------------------------------------------ SD3.5 Features Summary ------------------------------------------------------------ Number of text encoders: 3 Encoder names: ['text_encoder', 'text_encoder_2', 'text_encoder_3'] Has VAE latents: True Has VAE encoder features: True Data type: torch.float16 text_encoder-item0: clip_pooled_prompt_embeds: torch.Size([768]), torch.float16 clip_prompt_embeds: torch.Size([77, 768]), torch.float16 text_encoder_2-item1: clip_pooled_prompt_embeds: torch.Size([1280]), torch.float16 clip_prompt_embeds: torch.Size([77, 1280]), torch.float16 text_encoder_3-item2: t5_prompt_embeds: torch.Size([77, 4096]), torch.float16 text_encoder-item3: clip_pooled_prompt_embeds: torch.Size([768]), torch.float16 clip_prompt_embeds: torch.Size([77, 768]), torch.float16 text_encoder_2-item4: clip_pooled_prompt_embeds: torch.Size([1280]), torch.float16 clip_prompt_embeds: torch.Size([77, 1280]), torch.float16 text_encoder_3-item5: t5_prompt_embeds: torch.Size([77, 4096]), torch.float16 vae.decoder latents: torch.Size([16, 128, 128]) vae.encoder features: vae.encoder_f0: torch.Size([32, 128, 128]), torch.float16 vae.encoder_f1: torch.Size([32, 128, 128]), torch.float16 ------------------------------------------------------------ [2/4] FRONTEND: Pack + Encode (strategy: individual)... IndividualPacker: text_encoder-item0.clip_pooled_prompt_embeds: torch.Size([768])->torch.Size([1, 1, 1, 768]) text_encoder-item0.clip_prompt_embeds: torch.Size([77, 768])->torch.Size([1, 1, 77, 768]) text_encoder_2-item1.clip_pooled_prompt_embeds: torch.Size([1280])->torch.Size([1, 1, 1, 1280]) text_encoder_2-item1.clip_prompt_embeds: torch.Size([77, 1280])->torch.Size([1, 1, 77, 1280]) text_encoder_3-item2.t5_prompt_embeds: torch.Size([77, 4096])->torch.Size([1, 1, 77, 4096]) text_encoder-item3.clip_pooled_prompt_embeds: torch.Size([768])->torch.Size([1, 1, 1, 768]) text_encoder-item3.clip_prompt_embeds: torch.Size([77, 768])->torch.Size([1, 1, 77, 768]) text_encoder_2-item4.clip_pooled_prompt_embeds: torch.Size([1280])->torch.Size([1, 1, 1, 1280]) text_encoder_2-item4.clip_prompt_embeds: torch.Size([77, 1280])->torch.Size([1, 1, 77, 1280]) text_encoder_3-item5.t5_prompt_embeds: torch.Size([77, 4096])->torch.Size([1, 1, 77, 4096]) vae.encoder_f0: torch.Size([32, 128, 128])->torch.Size([1, 1, 512, 1024]) vae.encoder_f1: torch.Size([32, 128, 128])->torch.Size([1, 1, 512, 1024]) vae.decoder: torch.Size([16, 128, 128])->torch.Size([1, 1, 512, 512]) Using per-key quantization points (text_encoder-item0.clip_pooled_prompt_embeds: torch.Size([256])) for text_encoder-item0.clip_pooled_prompt_embeds text_encoder-item0.clip_pooled_prompt_embeds: 732B, BPFP=7.6250 Using per-key quantization points (text_encoder-item0.clip_prompt_embeds: torch.Size([256])) for text_encoder-item0.clip_prompt_embeds text_encoder-item0.clip_prompt_embeds: 6,100B, BPFP=0.8252 Using per-key quantization points (text_encoder_2-item1.clip_pooled_prompt_embeds: torch.Size([256])) for text_encoder_2-item1.clip_pooled_prompt_embeds text_encoder_2-item1.clip_pooled_prompt_embeds: 1,204B, BPFP=7.5250 Using per-key quantization points (text_encoder_2-item1.clip_prompt_embeds: torch.Size([256])) for text_encoder_2-item1.clip_prompt_embeds text_encoder_2-item1.clip_prompt_embeds: 10,704B, BPFP=0.8688 Using per-key quantization points (text_encoder_3-item2.t5_prompt_embeds: torch.Size([256])) for text_encoder_3-item2.t5_prompt_embeds text_encoder_3-item2.t5_prompt_embeds: 26,920B, BPFP=0.6828 Using per-key quantization points (text_encoder-item3.clip_pooled_prompt_embeds: torch.Size([256])) for text_encoder-item3.clip_pooled_prompt_embeds text_encoder-item3.clip_pooled_prompt_embeds: 760B, BPFP=7.9167 Using per-key quantization points (text_encoder-item3.clip_prompt_embeds: torch.Size([256])) for text_encoder-item3.clip_prompt_embeds text_encoder-item3.clip_prompt_embeds: 4,024B, BPFP=0.5444 Using per-key quantization points (text_encoder_2-item4.clip_pooled: torch.Size([256])) for text_encoder_2-item4.clip_pooled_prompt_embeds text_encoder_2-item4.clip_pooled_prompt_embeds: 1,268B, BPFP=7.9250 Using per-key quantization points (text_encoder_2-item4.clip_prompt: torch.Size([256])) for text_encoder_2-item4.clip_prompt_embeds text_encoder_2-item4.clip_prompt_embeds: 6,996B, BPFP=0.5679 Using per-key quantization points (text_encoder_3-item5.t5_prompt_embeds: torch.Size([256])) for text_encoder_3-item5.t5_prompt_embeds text_encoder_3-item5.t5_prompt_embeds: 18,416B, BPFP=0.4671 Using per-key quantization points (vae.encoder_f0: torch.Size([256])) for vae.encoder_f0 vae.encoder_f0: 15,100B, BPFP=0.2304 Using per-key quantization points (vae.encoder_f1: torch.Size([256])) for vae.encoder_f1 vae.encoder_f1: 15,100B, BPFP=0.2304 Using per-key quantization points (vae.decoder: torch.Size([256])) for vae.decoder vae.decoder: 5,268B, BPFP=0.1608 ⌛️ [2/4] FRONTEND: Frontend time: 0.294s (Pack+Encode) [3/4] BACKEND: Decode + Unpack... Using per-key quantization points (text_encoder-item0.clip_pooled_prompt_embeds: torch.Size([256])) for text_encoder-item0.clip_pooled_prompt_embeds Using per-key quantization points (text_encoder-item0.clip_prompt_embeds: torch.Size([256])) for text_encoder-item0.clip_prompt_embeds Using per-key quantization points (text_encoder_2-item1.clip_pooled_prompt_embeds: torch.Size([256])) for text_encoder_2-item1.clip_pooled_prompt_embeds Using per-key quantization points (text_encoder_2-item1.clip_prompt_embeds: torch.Size([256])) for text_encoder_2-item1.clip_prompt_embeds Using per-key quantization points (text_encoder_3-item2.t5_prompt_embeds: torch.Size([256])) for text_encoder_3-item2.t5_prompt_embeds Using per-key quantization points (text_encoder-item3.clip_pooled_prompt_embeds: torch.Size([256])) for text_encoder-item3.clip_pooled_prompt_embeds Using per-key quantization points (text_encoder-item3.clip_prompt_embeds: torch.Size([256])) for text_encoder-item3.clip_prompt_embeds Using per-key quantization points (text_encoder_2-item4.clip_pooled: torch.Size([256])) for text_encoder_2-item4.clip_pooled_prompt_embeds Using per-key quantization points (text_encoder_2-item4.clip_prompt: torch.Size([256])) for text_encoder_2-item4.clip_prompt_embeds Using per-key quantization points (text_encoder_3-item5.t5_prompt_embeds: torch.Size([256])) for text_encoder_3-item5.t5_prompt_embeds Using per-key quantization points (vae.encoder_f0: torch.Size([256])) for vae.encoder_f0 Using per-key quantization points (vae.encoder_f1: torch.Size([256])) for vae.encoder_f1 Using per-key quantization points (vae.decoder: torch.Size([256])) for vae.decoder IndividualUnPacker: text_encoder-item0.clip_pooled_prompt_embeds: torch.Size([768]) text_encoder-item0.clip_prompt_embeds: torch.Size([77, 768]) text_encoder_2-item1.clip_pooled_prompt_embeds: torch.Size([1280]) text_encoder_2-item1.clip_prompt_embeds: torch.Size([77, 1280]) text_encoder_3-item2.t5_prompt_embeds: torch.Size([77, 4096]) text_encoder-item3.clip_pooled_prompt_embeds: torch.Size([768]) text_encoder-item3.clip_prompt_embeds: torch.Size([77, 768]) text_encoder_2-item4.clip_pooled_prompt_embeds: torch.Size([1280]) text_encoder_2-item4.clip_prompt_embeds: torch.Size([77, 1280]) text_encoder_3-item5.t5_prompt_embeds: torch.Size([77, 4096]) vae.decoder: torch.Size([16, 128, 128]) vae.encoder_f0: torch.Size([32, 128, 128]) vae.encoder_f1: torch.Size([32, 128, 128]) ⌛️ [3/4] BACKEND: Backend time: 0.450s [4/4] METRICS: Computing MSE Breakdown... Using per-key quantization points (text_encoder-item0.clip_pooled_prompt_embeds: torch.Size([256])) for text_encoder-item0.clip_pooled_prompt_embeds Using per-key quantization points (text_encoder-item0.clip_prompt_embeds: torch.Size([256])) for text_encoder-item0.clip_prompt_embeds Using per-key quantization points (text_encoder_2-item1.clip_pooled_prompt_embeds: torch.Size([256])) for text_encoder_2-item1.clip_pooled_prompt_embeds Using per-key quantization points (text_encoder_2-item1.clip_prompt_embeds: torch.Size([256])) for text_encoder_2-item1.clip_prompt_embeds Using per-key quantization points (text_encoder_3-item2.t5_prompt_embeds: torch.Size([256])) for text_encoder_3-item2.t5_prompt_embeds Using per-key quantization points (text_encoder-item3.clip_pooled_prompt_embeds: torch.Size([256])) for text_encoder-item3.clip_pooled_prompt_embeds Using per-key quantization points (text_encoder-item3.clip_prompt_embeds: torch.Size([256])) for text_encoder-item3.clip_prompt_embeds Using per-key quantization points (text_encoder_2-item4.clip_pooled: torch.Size([256])) for text_encoder_2-item4.clip_pooled_prompt_embeds Using per-key quantization points (text_encoder_2-item4.clip_prompt: torch.Size([256])) for text_encoder_2-item4.clip_prompt_embeds Using per-key quantization points (text_encoder_3-item5.t5_prompt_embeds: torch.Size([256])) for text_encoder_3-item5.t5_prompt_embeds Using per-key quantization points (vae.encoder_f0: torch.Size([256])) for vae.encoder_f0 Using per-key quantization points (vae.encoder_f1: torch.Size([256])) for vae.encoder_f1 Using per-key quantization points (vae.decoder: torch.Size([256])) for vae.decoder IndividualUnPacker: text_encoder-item0.clip_pooled_prompt_embeds: torch.Size([768]) text_encoder-item0.clip_prompt_embeds: torch.Size([77, 768]) text_encoder_2-item1.clip_pooled_prompt_embeds: torch.Size([1280]) text_encoder_2-item1.clip_prompt_embeds: torch.Size([77, 1280]) text_encoder_3-item2.t5_prompt_embeds: torch.Size([77, 4096]) text_encoder-item3.clip_pooled_prompt_embeds: torch.Size([768]) text_encoder-item3.clip_prompt_embeds: torch.Size([77, 768]) text_encoder_2-item4.clip_pooled_prompt_embeds: torch.Size([1280]) text_encoder_2-item4.clip_prompt_embeds: torch.Size([77, 1280]) text_encoder_3-item5.t5_prompt_embeds: torch.Size([77, 4096]) vae.decoder: torch.Size([16, 128, 128]) vae.encoder_f0: torch.Size([32, 128, 128]) vae.encoder_f1: torch.Size([32, 128, 128]) Per-key MSE Breakdown (Quant=Truncation+Quantization, Codec=Compression): Key Quant-MSE Total-MSE ------------------------------------------------------------------------------------- text_encoder-item0.clip_pooled_prompt_embeds 0.00056072 0.98833617 text_encoder-item0.clip_prompt_embeds 0.00031748 23.93289198 text_encoder_2-item1.clip_pooled_prompt_embeds 0.00022063 1.00185385 text_encoder_2-item1.clip_prompt_embeds 0.00019717 0.12780132 text_encoder_3-item2.t5_prompt_embeds 0.00000812 0.00308513 text_encoder-item3.clip_pooled_prompt_embeds 0.00841799 1.01420029 text_encoder-item3.clip_prompt_embeds 0.00023247 23.90570887 text_encoder_2-item4.clip_pooled_prompt_embeds 0.00016751 0.67051430 text_encoder_2-item4.clip_prompt_embeds 0.00009830 0.47049568 text_encoder_3-item5.t5_prompt_embeds 0.00001263 0.00227117 vae.encoder_f0 0.42111695 4.88432121 vae.encoder_f1 0.42111716 4.88440990 vae.decoder 0.00019827 0.03703041 ------------------------------------------------------------------------------------- TOTAL 0.19535708 3.54917459 (elements=2,260,992) ---------------------- -------------------------------------------------------- SAMPLE-WISE STATISTICS ---------------------- -------------------------------------------------------- Handler sd35 Strategy individual Architecture hyperprior-featurecoding ---------------------- -------------------------------------------------------- Total Elements 2260992 Total Bytes 112592 BPFP 0.3984 bits/point EBPFP 0.7968 equivalent bits/point MSE 3.549175 ---------------------- -------------------------------------------------------- Time: 0.753s Load: 0.009s, Pack+Encode: 0.294s, Decode+Unpack: 0.450s ---------------------- -------------------------------------------------------- Restored Feature Format: [Dict] with 8 keys key['text_encoder-item0']: [Dict] with 2 keys key['clip_pooled_prompt_embeds']: [Tensor] shape=torch.Size([768]), dtype=torch.float16, device=cpu key['clip_prompt_embeds']: [Tensor] shape=torch.Size([77, 768]), dtype=torch.float16, device=cpu key['text_encoder_2-item1']: [Dict] with 2 keys key['clip_pooled_prompt_embeds']: [Tensor] shape=torch.Size([1280]), dtype=torch.float16, device=cpu key['clip_prompt_embeds']: [Tensor] shape=torch.Size([77, 1280]), dtype=torch.float16, device=cpu key['text_encoder_3-item2']: [Dict] with 1 keys key['t5_prompt_embeds']: [Tensor] shape=torch.Size([77, 4096]), dtype=torch.float16, device=cpu key['text_encoder-item3']: [Dict] with 2 keys key['clip_pooled_prompt_embeds']: [Tensor] shape=torch.Size([768]), dtype=torch.float16, device=cpu key['clip_prompt_embeds']: [Tensor] shape=torch.Size([77, 768]), dtype=torch.float16, device=cpu key['text_encoder_2-item4']: [Dict] with 2 keys key['clip_pooled_prompt_embeds']: [Tensor] shape=torch.Size([1280]), dtype=torch.float16, device=cpu key['clip_prompt_embeds']: [Tensor] shape=torch.Size([77, 1280]), dtype=torch.float16, device=cpu key['text_encoder_3-item5']: [Dict] with 1 keys key['t5_prompt_embeds']: [Tensor] shape=torch.Size([77, 4096]), dtype=torch.float16, device=cpu key['vae.decoder']: [Tensor] shape=torch.Size([16, 128, 128]), dtype=torch.float16, device=cpu key['vae.encoder']: [Dict] with 2 keys key['vae.encoder_f0']: [Tensor] shape=torch.Size([32, 128, 128]), dtype=torch.float16, device=cpu key['vae.encoder_f1']: [Tensor] shape=torch.Size([32, 128, 128]), dtype=torch.float16, device=cpu 💾 Converting with 3.5492 MSE: from ../datasets/FeatureCoding-StableDiffusion3.5/100samples_features/000000316015.zst to output-fixed/sd35/lambda0.004/hyperprior-featurecoding-8bit-individual/sd35cond/000000316015.zst 💪 Processing: ../datasets/FeatureCoding-StableDiffusion3.5/100samples_features/000000323571.zst (51/100) [1/4] FRONTEND: Loading features from ../datasets/FeatureCoding-StableDiffusion3.5/100samples_features/000000323571.zst... Original data structure: root: [Dict] with 8 keys key['text_encoder-item0']: [Dict] with 2 keys key['clip_pooled_prompt_embeds']: [Tensor] shape=torch.Size([768]), dtype=torch.float16, device=cpu key['clip_prompt_embeds']: [Tensor] shape=torch.Size([77, 768]), dtype=torch.float16, device=cpu key['text_encoder_2-item1']: [Dict] with 2 keys key['clip_pooled_prompt_embeds']: [Tensor] shape=torch.Size([1280]), dtype=torch.float16, device=cpu key['clip_prompt_embeds']: [Tensor] shape=torch.Size([77, 1280]), dtype=torch.float16, device=cpu key['text_encoder_3-item2']: [Dict] with 1 keys key['t5_prompt_embeds']: [Tensor] shape=torch.Size([77, 4096]), dtype=torch.float16, device=cpu key['text_encoder-item3']: [Dict] with 2 keys key['clip_pooled_prompt_embeds']: [Tensor] shape=torch.Size([768]), dtype=torch.float16, device=cpu key['clip_prompt_embeds']: [Tensor] shape=torch.Size([77, 768]), dtype=torch.float16, device=cpu key['text_encoder_2-item4']: [Dict] with 2 keys key['clip_pooled_prompt_embeds']: [Tensor] shape=torch.Size([1280]), dtype=torch.float16, device=cpu key['clip_prompt_embeds']: [Tensor] shape=torch.Size([77, 1280]), dtype=torch.float16, device=cpu key['text_encoder_3-item5']: [Dict] with 1 keys key['t5_prompt_embeds']: [Tensor] shape=torch.Size([77, 4096]), dtype=torch.float16, device=cpu key['vae.encoder-item6']: [Dict] with 2 keys key['vae.encoder_f0']: [Tensor] shape=torch.Size([32, 128, 128]), dtype=torch.float16, device=cpu key['vae.encoder_f1']: [Tensor] shape=torch.Size([32, 128, 128]), dtype=torch.float16, device=cpu key['vae.decoder']: [Tensor] shape=torch.Size([16, 128, 128]), dtype=torch.float16, device=cpu ⌛️ [1/4] FRONTEND: Load time: 0.008s ------------------------------------------------------------ SD3.5 Features Summary ------------------------------------------------------------ Number of text encoders: 3 Encoder names: ['text_encoder', 'text_encoder_2', 'text_encoder_3'] Has VAE latents: True Has VAE encoder features: True Data type: torch.float16 text_encoder-item0: clip_pooled_prompt_embeds: torch.Size([768]), torch.float16 clip_prompt_embeds: torch.Size([77, 768]), torch.float16 text_encoder_2-item1: clip_pooled_prompt_embeds: torch.Size([1280]), torch.float16 clip_prompt_embeds: torch.Size([77, 1280]), torch.float16 text_encoder_3-item2: t5_prompt_embeds: torch.Size([77, 4096]), torch.float16 text_encoder-item3: clip_pooled_prompt_embeds: torch.Size([768]), torch.float16 clip_prompt_embeds: torch.Size([77, 768]), torch.float16 text_encoder_2-item4: clip_pooled_prompt_embeds: torch.Size([1280]), torch.float16 clip_prompt_embeds: torch.Size([77, 1280]), torch.float16 text_encoder_3-item5: t5_prompt_embeds: torch.Size([77, 4096]), torch.float16 vae.decoder latents: torch.Size([16, 128, 128]) vae.encoder features: vae.encoder_f0: torch.Size([32, 128, 128]), torch.float16 vae.encoder_f1: torch.Size([32, 128, 128]), torch.float16 ------------------------------------------------------------ [2/4] FRONTEND: Pack + Encode (strategy: individual)... IndividualPacker: text_encoder-item0.clip_pooled_prompt_embeds: torch.Size([768])->torch.Size([1, 1, 1, 768]) text_encoder-item0.clip_prompt_embeds: torch.Size([77, 768])->torch.Size([1, 1, 77, 768]) text_encoder_2-item1.clip_pooled_prompt_embeds: torch.Size([1280])->torch.Size([1, 1, 1, 1280]) text_encoder_2-item1.clip_prompt_embeds: torch.Size([77, 1280])->torch.Size([1, 1, 77, 1280]) text_encoder_3-item2.t5_prompt_embeds: torch.Size([77, 4096])->torch.Size([1, 1, 77, 4096]) text_encoder-item3.clip_pooled_prompt_embeds: torch.Size([768])->torch.Size([1, 1, 1, 768]) text_encoder-item3.clip_prompt_embeds: torch.Size([77, 768])->torch.Size([1, 1, 77, 768]) text_encoder_2-item4.clip_pooled_prompt_embeds: torch.Size([1280])->torch.Size([1, 1, 1, 1280]) text_encoder_2-item4.clip_prompt_embeds: torch.Size([77, 1280])->torch.Size([1, 1, 77, 1280]) text_encoder_3-item5.t5_prompt_embeds: torch.Size([77, 4096])->torch.Size([1, 1, 77, 4096]) vae.encoder_f0: torch.Size([32, 128, 128])->torch.Size([1, 1, 512, 1024]) vae.encoder_f1: torch.Size([32, 128, 128])->torch.Size([1, 1, 512, 1024]) vae.decoder: torch.Size([16, 128, 128])->torch.Size([1, 1, 512, 512]) Using per-key quantization points (text_encoder-item0.clip_pooled_prompt_embeds: torch.Size([256])) for text_encoder-item0.clip_pooled_prompt_embeds text_encoder-item0.clip_pooled_prompt_embeds: 740B, BPFP=7.7083 Using per-key quantization points (text_encoder-item0.clip_prompt_embeds: torch.Size([256])) for text_encoder-item0.clip_prompt_embeds text_encoder-item0.clip_prompt_embeds: 6,512B, BPFP=0.8810 Using per-key quantization points (text_encoder_2-item1.clip_pooled_prompt_embeds: torch.Size([256])) for text_encoder_2-item1.clip_pooled_prompt_embeds text_encoder_2-item1.clip_pooled_prompt_embeds: 1,200B, BPFP=7.5000 Using per-key quantization points (text_encoder_2-item1.clip_prompt_embeds: torch.Size([256])) for text_encoder_2-item1.clip_prompt_embeds text_encoder_2-item1.clip_prompt_embeds: 10,836B, BPFP=0.8795 Using per-key quantization points (text_encoder_3-item2.t5_prompt_embeds: torch.Size([256])) for text_encoder_3-item2.t5_prompt_embeds text_encoder_3-item2.t5_prompt_embeds: 30,632B, BPFP=0.7770 Using per-key quantization points (text_encoder-item3.clip_pooled_prompt_embeds: torch.Size([256])) for text_encoder-item3.clip_pooled_prompt_embeds text_encoder-item3.clip_pooled_prompt_embeds: 760B, BPFP=7.9167 Using per-key quantization points (text_encoder-item3.clip_prompt_embeds: torch.Size([256])) for text_encoder-item3.clip_prompt_embeds text_encoder-item3.clip_prompt_embeds: 4,024B, BPFP=0.5444 Using per-key quantization points (text_encoder_2-item4.clip_pooled: torch.Size([256])) for text_encoder_2-item4.clip_pooled_prompt_embeds text_encoder_2-item4.clip_pooled_prompt_embeds: 1,268B, BPFP=7.9250 Using per-key quantization points (text_encoder_2-item4.clip_prompt: torch.Size([256])) for text_encoder_2-item4.clip_prompt_embeds text_encoder_2-item4.clip_prompt_embeds: 6,996B, BPFP=0.5679 Using per-key quantization points (text_encoder_3-item5.t5_prompt_embeds: torch.Size([256])) for text_encoder_3-item5.t5_prompt_embeds text_encoder_3-item5.t5_prompt_embeds: 18,416B, BPFP=0.4671 Using per-key quantization points (vae.encoder_f0: torch.Size([256])) for vae.encoder_f0 vae.encoder_f0: 17,304B, BPFP=0.2640 Using per-key quantization points (vae.encoder_f1: torch.Size([256])) for vae.encoder_f1 vae.encoder_f1: 17,304B, BPFP=0.2640 Using per-key quantization points (vae.decoder: torch.Size([256])) for vae.decoder vae.decoder: 6,272B, BPFP=0.1914 ⌛️ [2/4] FRONTEND: Frontend time: 0.297s (Pack+Encode) [3/4] BACKEND: Decode + Unpack... Using per-key quantization points (text_encoder-item0.clip_pooled_prompt_embeds: torch.Size([256])) for text_encoder-item0.clip_pooled_prompt_embeds Using per-key quantization points (text_encoder-item0.clip_prompt_embeds: torch.Size([256])) for text_encoder-item0.clip_prompt_embeds Using per-key quantization points (text_encoder_2-item1.clip_pooled_prompt_embeds: torch.Size([256])) for text_encoder_2-item1.clip_pooled_prompt_embeds Using per-key quantization points (text_encoder_2-item1.clip_prompt_embeds: torch.Size([256])) for text_encoder_2-item1.clip_prompt_embeds Using per-key quantization points (text_encoder_3-item2.t5_prompt_embeds: torch.Size([256])) for text_encoder_3-item2.t5_prompt_embeds Using per-key quantization points (text_encoder-item3.clip_pooled_prompt_embeds: torch.Size([256])) for text_encoder-item3.clip_pooled_prompt_embeds Using per-key quantization points (text_encoder-item3.clip_prompt_embeds: torch.Size([256])) for text_encoder-item3.clip_prompt_embeds Using per-key quantization points (text_encoder_2-item4.clip_pooled: torch.Size([256])) for text_encoder_2-item4.clip_pooled_prompt_embeds Using per-key quantization points (text_encoder_2-item4.clip_prompt: torch.Size([256])) for text_encoder_2-item4.clip_prompt_embeds Using per-key quantization points (text_encoder_3-item5.t5_prompt_embeds: torch.Size([256])) for text_encoder_3-item5.t5_prompt_embeds Using per-key quantization points (vae.encoder_f0: torch.Size([256])) for vae.encoder_f0 Using per-key quantization points (vae.encoder_f1: torch.Size([256])) for vae.encoder_f1 Using per-key quantization points (vae.decoder: torch.Size([256])) for vae.decoder IndividualUnPacker: text_encoder-item0.clip_pooled_prompt_embeds: torch.Size([768]) text_encoder-item0.clip_prompt_embeds: torch.Size([77, 768]) text_encoder_2-item1.clip_pooled_prompt_embeds: torch.Size([1280]) text_encoder_2-item1.clip_prompt_embeds: torch.Size([77, 1280]) text_encoder_3-item2.t5_prompt_embeds: torch.Size([77, 4096]) text_encoder-item3.clip_pooled_prompt_embeds: torch.Size([768]) text_encoder-item3.clip_prompt_embeds: torch.Size([77, 768]) text_encoder_2-item4.clip_pooled_prompt_embeds: torch.Size([1280]) text_encoder_2-item4.clip_prompt_embeds: torch.Size([77, 1280]) text_encoder_3-item5.t5_prompt_embeds: torch.Size([77, 4096]) vae.decoder: torch.Size([16, 128, 128]) vae.encoder_f0: torch.Size([32, 128, 128]) vae.encoder_f1: torch.Size([32, 128, 128]) ⌛️ [3/4] BACKEND: Backend time: 0.449s [4/4] METRICS: Computing MSE Breakdown... Using per-key quantization points (text_encoder-item0.clip_pooled_prompt_embeds: torch.Size([256])) for text_encoder-item0.clip_pooled_prompt_embeds Using per-key quantization points (text_encoder-item0.clip_prompt_embeds: torch.Size([256])) for text_encoder-item0.clip_prompt_embeds Using per-key quantization points (text_encoder_2-item1.clip_pooled_prompt_embeds: torch.Size([256])) for text_encoder_2-item1.clip_pooled_prompt_embeds Using per-key quantization points (text_encoder_2-item1.clip_prompt_embeds: torch.Size([256])) for text_encoder_2-item1.clip_prompt_embeds Using per-key quantization points (text_encoder_3-item2.t5_prompt_embeds: torch.Size([256])) for text_encoder_3-item2.t5_prompt_embeds Using per-key quantization points (text_encoder-item3.clip_pooled_prompt_embeds: torch.Size([256])) for text_encoder-item3.clip_pooled_prompt_embeds Using per-key quantization points (text_encoder-item3.clip_prompt_embeds: torch.Size([256])) for text_encoder-item3.clip_prompt_embeds Using per-key quantization points (text_encoder_2-item4.clip_pooled: torch.Size([256])) for text_encoder_2-item4.clip_pooled_prompt_embeds Using per-key quantization points (text_encoder_2-item4.clip_prompt: torch.Size([256])) for text_encoder_2-item4.clip_prompt_embeds Using per-key quantization points (text_encoder_3-item5.t5_prompt_embeds: torch.Size([256])) for text_encoder_3-item5.t5_prompt_embeds Using per-key quantization points (vae.encoder_f0: torch.Size([256])) for vae.encoder_f0 Using per-key quantization points (vae.encoder_f1: torch.Size([256])) for vae.encoder_f1 Using per-key quantization points (vae.decoder: torch.Size([256])) for vae.decoder IndividualUnPacker: text_encoder-item0.clip_pooled_prompt_embeds: torch.Size([768]) text_encoder-item0.clip_prompt_embeds: torch.Size([77, 768]) text_encoder_2-item1.clip_pooled_prompt_embeds: torch.Size([1280]) text_encoder_2-item1.clip_prompt_embeds: torch.Size([77, 1280]) text_encoder_3-item2.t5_prompt_embeds: torch.Size([77, 4096]) text_encoder-item3.clip_pooled_prompt_embeds: torch.Size([768]) text_encoder-item3.clip_prompt_embeds: torch.Size([77, 768]) text_encoder_2-item4.clip_pooled_prompt_embeds: torch.Size([1280]) text_encoder_2-item4.clip_prompt_embeds: torch.Size([77, 1280]) text_encoder_3-item5.t5_prompt_embeds: torch.Size([77, 4096]) vae.decoder: torch.Size([16, 128, 128]) vae.encoder_f0: torch.Size([32, 128, 128]) vae.encoder_f1: torch.Size([32, 128, 128]) Per-key MSE Breakdown (Quant=Truncation+Quantization, Codec=Compression): Key Quant-MSE Total-MSE ------------------------------------------------------------------------------------- text_encoder-item0.clip_pooled_prompt_embeds 0.00020408 0.90175700 text_encoder-item0.clip_prompt_embeds 0.00024951 58.90313007 text_encoder_2-item1.clip_pooled_prompt_embeds 0.00020437 0.86725597 text_encoder_2-item1.clip_prompt_embeds 0.00016387 0.12510220 text_encoder_3-item2.t5_prompt_embeds 0.00000772 0.00351340 text_encoder-item3.clip_pooled_prompt_embeds 0.00841799 1.01420029 text_encoder-item3.clip_prompt_embeds 0.00023247 23.90570887 text_encoder_2-item4.clip_pooled_prompt_embeds 0.00016751 0.67051430 text_encoder_2-item4.clip_prompt_embeds 0.00009830 0.47049568 text_encoder_3-item5.t5_prompt_embeds 0.00001263 0.00227117 vae.encoder_f0 0.10376993 5.10718250 vae.encoder_f1 0.10377157 5.10824490 vae.decoder 0.00019787 0.04295679 ------------------------------------------------------------------------------------- TOTAL 0.04817852 4.56792271 (elements=2,260,992) ---------------------- -------------------------------------------------------- SAMPLE-WISE STATISTICS ---------------------- -------------------------------------------------------- Handler sd35 Strategy individual Architecture hyperprior-featurecoding ---------------------- -------------------------------------------------------- Total Elements 2260992 Total Bytes 122264 BPFP 0.4326 bits/point EBPFP 0.8652 equivalent bits/point MSE 4.567923 ---------------------- -------------------------------------------------------- Time: 0.753s Load: 0.008s, Pack+Encode: 0.297s, Decode+Unpack: 0.449s ---------------------- -------------------------------------------------------- Restored Feature Format: [Dict] with 8 keys key['text_encoder-item0']: [Dict] with 2 keys key['clip_pooled_prompt_embeds']: [Tensor] shape=torch.Size([768]), dtype=torch.float16, device=cpu key['clip_prompt_embeds']: [Tensor] shape=torch.Size([77, 768]), dtype=torch.float16, device=cpu key['text_encoder_2-item1']: [Dict] with 2 keys key['clip_pooled_prompt_embeds']: [Tensor] shape=torch.Size([1280]), dtype=torch.float16, device=cpu key['clip_prompt_embeds']: [Tensor] shape=torch.Size([77, 1280]), dtype=torch.float16, device=cpu key['text_encoder_3-item2']: [Dict] with 1 keys key['t5_prompt_embeds']: [Tensor] shape=torch.Size([77, 4096]), dtype=torch.float16, device=cpu key['text_encoder-item3']: [Dict] with 2 keys key['clip_pooled_prompt_embeds']: [Tensor] shape=torch.Size([768]), dtype=torch.float16, device=cpu key['clip_prompt_embeds']: [Tensor] shape=torch.Size([77, 768]), dtype=torch.float16, device=cpu key['text_encoder_2-item4']: [Dict] with 2 keys key['clip_pooled_prompt_embeds']: [Tensor] shape=torch.Size([1280]), dtype=torch.float16, device=cpu key['clip_prompt_embeds']: [Tensor] shape=torch.Size([77, 1280]), dtype=torch.float16, device=cpu key['text_encoder_3-item5']: [Dict] with 1 keys key['t5_prompt_embeds']: [Tensor] shape=torch.Size([77, 4096]), dtype=torch.float16, device=cpu key['vae.decoder']: [Tensor] shape=torch.Size([16, 128, 128]), dtype=torch.float16, device=cpu key['vae.encoder']: [Dict] with 2 keys key['vae.encoder_f0']: [Tensor] shape=torch.Size([32, 128, 128]), dtype=torch.float16, device=cpu key['vae.encoder_f1']: [Tensor] shape=torch.Size([32, 128, 128]), dtype=torch.float16, device=cpu 💾 Converting with 4.5679 MSE: from ../datasets/FeatureCoding-StableDiffusion3.5/100samples_features/000000323571.zst to output-fixed/sd35/lambda0.004/hyperprior-featurecoding-8bit-individual/sd35cond/000000323571.zst 💪 Processing: ../datasets/FeatureCoding-StableDiffusion3.5/100samples_features/000000325483.zst (52/100) [1/4] FRONTEND: Loading features from ../datasets/FeatureCoding-StableDiffusion3.5/100samples_features/000000325483.zst... Original data structure: root: [Dict] with 8 keys key['text_encoder-item0']: [Dict] with 2 keys key['clip_pooled_prompt_embeds']: [Tensor] shape=torch.Size([768]), dtype=torch.float16, device=cpu key['clip_prompt_embeds']: [Tensor] shape=torch.Size([77, 768]), dtype=torch.float16, device=cpu key['text_encoder_2-item1']: [Dict] with 2 keys key['clip_pooled_prompt_embeds']: [Tensor] shape=torch.Size([1280]), dtype=torch.float16, device=cpu key['clip_prompt_embeds']: [Tensor] shape=torch.Size([77, 1280]), dtype=torch.float16, device=cpu key['text_encoder_3-item2']: [Dict] with 1 keys key['t5_prompt_embeds']: [Tensor] shape=torch.Size([77, 4096]), dtype=torch.float16, device=cpu key['text_encoder-item3']: [Dict] with 2 keys key['clip_pooled_prompt_embeds']: [Tensor] shape=torch.Size([768]), dtype=torch.float16, device=cpu key['clip_prompt_embeds']: [Tensor] shape=torch.Size([77, 768]), dtype=torch.float16, device=cpu key['text_encoder_2-item4']: [Dict] with 2 keys key['clip_pooled_prompt_embeds']: [Tensor] shape=torch.Size([1280]), dtype=torch.float16, device=cpu key['clip_prompt_embeds']: [Tensor] shape=torch.Size([77, 1280]), dtype=torch.float16, device=cpu key['text_encoder_3-item5']: [Dict] with 1 keys key['t5_prompt_embeds']: [Tensor] shape=torch.Size([77, 4096]), dtype=torch.float16, device=cpu key['vae.encoder-item6']: [Dict] with 2 keys key['vae.encoder_f0']: [Tensor] shape=torch.Size([32, 128, 128]), dtype=torch.float16, device=cpu key['vae.encoder_f1']: [Tensor] shape=torch.Size([32, 128, 128]), dtype=torch.float16, device=cpu key['vae.decoder']: [Tensor] shape=torch.Size([16, 128, 128]), dtype=torch.float16, device=cpu ⌛️ [1/4] FRONTEND: Load time: 0.009s ------------------------------------------------------------ SD3.5 Features Summary ------------------------------------------------------------ Number of text encoders: 3 Encoder names: ['text_encoder', 'text_encoder_2', 'text_encoder_3'] Has VAE latents: True Has VAE encoder features: True Data type: torch.float16 text_encoder-item0: clip_pooled_prompt_embeds: torch.Size([768]), torch.float16 clip_prompt_embeds: torch.Size([77, 768]), torch.float16 text_encoder_2-item1: clip_pooled_prompt_embeds: torch.Size([1280]), torch.float16 clip_prompt_embeds: torch.Size([77, 1280]), torch.float16 text_encoder_3-item2: t5_prompt_embeds: torch.Size([77, 4096]), torch.float16 text_encoder-item3: clip_pooled_prompt_embeds: torch.Size([768]), torch.float16 clip_prompt_embeds: torch.Size([77, 768]), torch.float16 text_encoder_2-item4: clip_pooled_prompt_embeds: torch.Size([1280]), torch.float16 clip_prompt_embeds: torch.Size([77, 1280]), torch.float16 text_encoder_3-item5: t5_prompt_embeds: torch.Size([77, 4096]), torch.float16 vae.decoder latents: torch.Size([16, 128, 128]) vae.encoder features: vae.encoder_f0: torch.Size([32, 128, 128]), torch.float16 vae.encoder_f1: torch.Size([32, 128, 128]), torch.float16 ------------------------------------------------------------ [2/4] FRONTEND: Pack + Encode (strategy: individual)... IndividualPacker: text_encoder-item0.clip_pooled_prompt_embeds: torch.Size([768])->torch.Size([1, 1, 1, 768]) text_encoder-item0.clip_prompt_embeds: torch.Size([77, 768])->torch.Size([1, 1, 77, 768]) text_encoder_2-item1.clip_pooled_prompt_embeds: torch.Size([1280])->torch.Size([1, 1, 1, 1280]) text_encoder_2-item1.clip_prompt_embeds: torch.Size([77, 1280])->torch.Size([1, 1, 77, 1280]) text_encoder_3-item2.t5_prompt_embeds: torch.Size([77, 4096])->torch.Size([1, 1, 77, 4096]) text_encoder-item3.clip_pooled_prompt_embeds: torch.Size([768])->torch.Size([1, 1, 1, 768]) text_encoder-item3.clip_prompt_embeds: torch.Size([77, 768])->torch.Size([1, 1, 77, 768]) text_encoder_2-item4.clip_pooled_prompt_embeds: torch.Size([1280])->torch.Size([1, 1, 1, 1280]) text_encoder_2-item4.clip_prompt_embeds: torch.Size([77, 1280])->torch.Size([1, 1, 77, 1280]) text_encoder_3-item5.t5_prompt_embeds: torch.Size([77, 4096])->torch.Size([1, 1, 77, 4096]) vae.encoder_f0: torch.Size([32, 128, 128])->torch.Size([1, 1, 512, 1024]) vae.encoder_f1: torch.Size([32, 128, 128])->torch.Size([1, 1, 512, 1024]) vae.decoder: torch.Size([16, 128, 128])->torch.Size([1, 1, 512, 512]) Using per-key quantization points (text_encoder-item0.clip_pooled_prompt_embeds: torch.Size([256])) for text_encoder-item0.clip_pooled_prompt_embeds text_encoder-item0.clip_pooled_prompt_embeds: 732B, BPFP=7.6250 Using per-key quantization points (text_encoder-item0.clip_prompt_embeds: torch.Size([256])) for text_encoder-item0.clip_prompt_embeds text_encoder-item0.clip_prompt_embeds: 6,128B, BPFP=0.8290 Using per-key quantization points (text_encoder_2-item1.clip_pooled_prompt_embeds: torch.Size([256])) for text_encoder_2-item1.clip_pooled_prompt_embeds text_encoder_2-item1.clip_pooled_prompt_embeds: 1,200B, BPFP=7.5000 Using per-key quantization points (text_encoder_2-item1.clip_prompt_embeds: torch.Size([256])) for text_encoder_2-item1.clip_prompt_embeds text_encoder_2-item1.clip_prompt_embeds: 11,256B, BPFP=0.9136 Using per-key quantization points (text_encoder_3-item2.t5_prompt_embeds: torch.Size([256])) for text_encoder_3-item2.t5_prompt_embeds text_encoder_3-item2.t5_prompt_embeds: 28,084B, BPFP=0.7124 Using per-key quantization points (text_encoder-item3.clip_pooled_prompt_embeds: torch.Size([256])) for text_encoder-item3.clip_pooled_prompt_embeds text_encoder-item3.clip_pooled_prompt_embeds: 760B, BPFP=7.9167 Using per-key quantization points (text_encoder-item3.clip_prompt_embeds: torch.Size([256])) for text_encoder-item3.clip_prompt_embeds text_encoder-item3.clip_prompt_embeds: 4,024B, BPFP=0.5444 Using per-key quantization points (text_encoder_2-item4.clip_pooled: torch.Size([256])) for text_encoder_2-item4.clip_pooled_prompt_embeds text_encoder_2-item4.clip_pooled_prompt_embeds: 1,268B, BPFP=7.9250 Using per-key quantization points (text_encoder_2-item4.clip_prompt: torch.Size([256])) for text_encoder_2-item4.clip_prompt_embeds text_encoder_2-item4.clip_prompt_embeds: 6,996B, BPFP=0.5679 Using per-key quantization points (text_encoder_3-item5.t5_prompt_embeds: torch.Size([256])) for text_encoder_3-item5.t5_prompt_embeds text_encoder_3-item5.t5_prompt_embeds: 18,416B, BPFP=0.4671 Using per-key quantization points (vae.encoder_f0: torch.Size([256])) for vae.encoder_f0 vae.encoder_f0: 17,792B, BPFP=0.2715 Using per-key quantization points (vae.encoder_f1: torch.Size([256])) for vae.encoder_f1 vae.encoder_f1: 17,788B, BPFP=0.2714 Using per-key quantization points (vae.decoder: torch.Size([256])) for vae.decoder vae.decoder: 5,672B, BPFP=0.1731 ⌛️ [2/4] FRONTEND: Frontend time: 0.295s (Pack+Encode) [3/4] BACKEND: Decode + Unpack... Using per-key quantization points (text_encoder-item0.clip_pooled_prompt_embeds: torch.Size([256])) for text_encoder-item0.clip_pooled_prompt_embeds Using per-key quantization points (text_encoder-item0.clip_prompt_embeds: torch.Size([256])) for text_encoder-item0.clip_prompt_embeds Using per-key quantization points (text_encoder_2-item1.clip_pooled_prompt_embeds: torch.Size([256])) for text_encoder_2-item1.clip_pooled_prompt_embeds Using per-key quantization points (text_encoder_2-item1.clip_prompt_embeds: torch.Size([256])) for text_encoder_2-item1.clip_prompt_embeds Using per-key quantization points (text_encoder_3-item2.t5_prompt_embeds: torch.Size([256])) for text_encoder_3-item2.t5_prompt_embeds Using per-key quantization points (text_encoder-item3.clip_pooled_prompt_embeds: torch.Size([256])) for text_encoder-item3.clip_pooled_prompt_embeds Using per-key quantization points (text_encoder-item3.clip_prompt_embeds: torch.Size([256])) for text_encoder-item3.clip_prompt_embeds Using per-key quantization points (text_encoder_2-item4.clip_pooled: torch.Size([256])) for text_encoder_2-item4.clip_pooled_prompt_embeds Using per-key quantization points (text_encoder_2-item4.clip_prompt: torch.Size([256])) for text_encoder_2-item4.clip_prompt_embeds Using per-key quantization points (text_encoder_3-item5.t5_prompt_embeds: torch.Size([256])) for text_encoder_3-item5.t5_prompt_embeds Using per-key quantization points (vae.encoder_f0: torch.Size([256])) for vae.encoder_f0 Using per-key quantization points (vae.encoder_f1: torch.Size([256])) for vae.encoder_f1 Using per-key quantization points (vae.decoder: torch.Size([256])) for vae.decoder IndividualUnPacker: text_encoder-item0.clip_pooled_prompt_embeds: torch.Size([768]) text_encoder-item0.clip_prompt_embeds: torch.Size([77, 768]) text_encoder_2-item1.clip_pooled_prompt_embeds: torch.Size([1280]) text_encoder_2-item1.clip_prompt_embeds: torch.Size([77, 1280]) text_encoder_3-item2.t5_prompt_embeds: torch.Size([77, 4096]) text_encoder-item3.clip_pooled_prompt_embeds: torch.Size([768]) text_encoder-item3.clip_prompt_embeds: torch.Size([77, 768]) text_encoder_2-item4.clip_pooled_prompt_embeds: torch.Size([1280]) text_encoder_2-item4.clip_prompt_embeds: torch.Size([77, 1280]) text_encoder_3-item5.t5_prompt_embeds: torch.Size([77, 4096]) vae.decoder: torch.Size([16, 128, 128]) vae.encoder_f0: torch.Size([32, 128, 128]) vae.encoder_f1: torch.Size([32, 128, 128]) ⌛️ [3/4] BACKEND: Backend time: 0.447s [4/4] METRICS: Computing MSE Breakdown... Using per-key quantization points (text_encoder-item0.clip_pooled_prompt_embeds: torch.Size([256])) for text_encoder-item0.clip_pooled_prompt_embeds Using per-key quantization points (text_encoder-item0.clip_prompt_embeds: torch.Size([256])) for text_encoder-item0.clip_prompt_embeds Using per-key quantization points (text_encoder_2-item1.clip_pooled_prompt_embeds: torch.Size([256])) for text_encoder_2-item1.clip_pooled_prompt_embeds Using per-key quantization points (text_encoder_2-item1.clip_prompt_embeds: torch.Size([256])) for text_encoder_2-item1.clip_prompt_embeds Using per-key quantization points (text_encoder_3-item2.t5_prompt_embeds: torch.Size([256])) for text_encoder_3-item2.t5_prompt_embeds Using per-key quantization points (text_encoder-item3.clip_pooled_prompt_embeds: torch.Size([256])) for text_encoder-item3.clip_pooled_prompt_embeds Using per-key quantization points (text_encoder-item3.clip_prompt_embeds: torch.Size([256])) for text_encoder-item3.clip_prompt_embeds Using per-key quantization points (text_encoder_2-item4.clip_pooled: torch.Size([256])) for text_encoder_2-item4.clip_pooled_prompt_embeds Using per-key quantization points (text_encoder_2-item4.clip_prompt: torch.Size([256])) for text_encoder_2-item4.clip_prompt_embeds Using per-key quantization points (text_encoder_3-item5.t5_prompt_embeds: torch.Size([256])) for text_encoder_3-item5.t5_prompt_embeds Using per-key quantization points (vae.encoder_f0: torch.Size([256])) for vae.encoder_f0 Using per-key quantization points (vae.encoder_f1: torch.Size([256])) for vae.encoder_f1 Using per-key quantization points (vae.decoder: torch.Size([256])) for vae.decoder IndividualUnPacker: text_encoder-item0.clip_pooled_prompt_embeds: torch.Size([768]) text_encoder-item0.clip_prompt_embeds: torch.Size([77, 768]) text_encoder_2-item1.clip_pooled_prompt_embeds: torch.Size([1280]) text_encoder_2-item1.clip_prompt_embeds: torch.Size([77, 1280]) text_encoder_3-item2.t5_prompt_embeds: torch.Size([77, 4096]) text_encoder-item3.clip_pooled_prompt_embeds: torch.Size([768]) text_encoder-item3.clip_prompt_embeds: torch.Size([77, 768]) text_encoder_2-item4.clip_pooled_prompt_embeds: torch.Size([1280]) text_encoder_2-item4.clip_prompt_embeds: torch.Size([77, 1280]) text_encoder_3-item5.t5_prompt_embeds: torch.Size([77, 4096]) vae.decoder: torch.Size([16, 128, 128]) vae.encoder_f0: torch.Size([32, 128, 128]) vae.encoder_f1: torch.Size([32, 128, 128]) Per-key MSE Breakdown (Quant=Truncation+Quantization, Codec=Compression): Key Quant-MSE Total-MSE ------------------------------------------------------------------------------------- text_encoder-item0.clip_pooled_prompt_embeds 0.00035723 0.93556595 text_encoder-item0.clip_prompt_embeds 0.00022350 96.25196158 text_encoder_2-item1.clip_pooled_prompt_embeds 0.00046887 0.87969551 text_encoder_2-item1.clip_prompt_embeds 0.00019271 0.12995502 text_encoder_3-item2.t5_prompt_embeds 0.00000799 0.00284950 text_encoder-item3.clip_pooled_prompt_embeds 0.00841799 1.01420029 text_encoder-item3.clip_prompt_embeds 0.00023247 23.90570887 text_encoder_2-item4.clip_pooled_prompt_embeds 0.00016751 0.67051430 text_encoder_2-item4.clip_prompt_embeds 0.00009830 0.47049568 text_encoder_3-item5.t5_prompt_embeds 0.00001263 0.00227117 vae.encoder_f0 0.01346414 3.28489757 vae.encoder_f1 0.01346933 3.28296041 vae.decoder 0.00019243 0.04102654 ------------------------------------------------------------------------------------- TOTAL 0.00629858 4.69887780 (elements=2,260,992) ---------------------- -------------------------------------------------------- SAMPLE-WISE STATISTICS ---------------------- -------------------------------------------------------- Handler sd35 Strategy individual Architecture hyperprior-featurecoding ---------------------- -------------------------------------------------------- Total Elements 2260992 Total Bytes 120116 BPFP 0.4250 bits/point EBPFP 0.8500 equivalent bits/point MSE 4.698878 ---------------------- -------------------------------------------------------- Time: 0.751s Load: 0.009s, Pack+Encode: 0.295s, Decode+Unpack: 0.447s ---------------------- -------------------------------------------------------- Restored Feature Format: [Dict] with 8 keys key['text_encoder-item0']: [Dict] with 2 keys key['clip_pooled_prompt_embeds']: [Tensor] shape=torch.Size([768]), dtype=torch.float16, device=cpu key['clip_prompt_embeds']: [Tensor] shape=torch.Size([77, 768]), dtype=torch.float16, device=cpu key['text_encoder_2-item1']: [Dict] with 2 keys key['clip_pooled_prompt_embeds']: [Tensor] shape=torch.Size([1280]), dtype=torch.float16, device=cpu key['clip_prompt_embeds']: [Tensor] shape=torch.Size([77, 1280]), dtype=torch.float16, device=cpu key['text_encoder_3-item2']: [Dict] with 1 keys key['t5_prompt_embeds']: [Tensor] shape=torch.Size([77, 4096]), dtype=torch.float16, device=cpu key['text_encoder-item3']: [Dict] with 2 keys key['clip_pooled_prompt_embeds']: [Tensor] shape=torch.Size([768]), dtype=torch.float16, device=cpu key['clip_prompt_embeds']: [Tensor] shape=torch.Size([77, 768]), dtype=torch.float16, device=cpu key['text_encoder_2-item4']: [Dict] with 2 keys key['clip_pooled_prompt_embeds']: [Tensor] shape=torch.Size([1280]), dtype=torch.float16, device=cpu key['clip_prompt_embeds']: [Tensor] shape=torch.Size([77, 1280]), dtype=torch.float16, device=cpu key['text_encoder_3-item5']: [Dict] with 1 keys key['t5_prompt_embeds']: [Tensor] shape=torch.Size([77, 4096]), dtype=torch.float16, device=cpu key['vae.decoder']: [Tensor] shape=torch.Size([16, 128, 128]), dtype=torch.float16, device=cpu key['vae.encoder']: [Dict] with 2 keys key['vae.encoder_f0']: [Tensor] shape=torch.Size([32, 128, 128]), dtype=torch.float16, device=cpu key['vae.encoder_f1']: [Tensor] shape=torch.Size([32, 128, 128]), dtype=torch.float16, device=cpu 💾 Converting with 4.6989 MSE: from ../datasets/FeatureCoding-StableDiffusion3.5/100samples_features/000000325483.zst to output-fixed/sd35/lambda0.004/hyperprior-featurecoding-8bit-individual/sd35cond/000000325483.zst 💪 Processing: ../datasets/FeatureCoding-StableDiffusion3.5/100samples_features/000000325991.zst (53/100) [1/4] FRONTEND: Loading features from ../datasets/FeatureCoding-StableDiffusion3.5/100samples_features/000000325991.zst... Original data structure: root: [Dict] with 8 keys key['text_encoder-item0']: [Dict] with 2 keys key['clip_pooled_prompt_embeds']: [Tensor] shape=torch.Size([768]), dtype=torch.float16, device=cpu key['clip_prompt_embeds']: [Tensor] shape=torch.Size([77, 768]), dtype=torch.float16, device=cpu key['text_encoder_2-item1']: [Dict] with 2 keys key['clip_pooled_prompt_embeds']: [Tensor] shape=torch.Size([1280]), dtype=torch.float16, device=cpu key['clip_prompt_embeds']: [Tensor] shape=torch.Size([77, 1280]), dtype=torch.float16, device=cpu key['text_encoder_3-item2']: [Dict] with 1 keys key['t5_prompt_embeds']: [Tensor] shape=torch.Size([77, 4096]), dtype=torch.float16, device=cpu key['text_encoder-item3']: [Dict] with 2 keys key['clip_pooled_prompt_embeds']: [Tensor] shape=torch.Size([768]), dtype=torch.float16, device=cpu key['clip_prompt_embeds']: [Tensor] shape=torch.Size([77, 768]), dtype=torch.float16, device=cpu key['text_encoder_2-item4']: [Dict] with 2 keys key['clip_pooled_prompt_embeds']: [Tensor] shape=torch.Size([1280]), dtype=torch.float16, device=cpu key['clip_prompt_embeds']: [Tensor] shape=torch.Size([77, 1280]), dtype=torch.float16, device=cpu key['text_encoder_3-item5']: [Dict] with 1 keys key['t5_prompt_embeds']: [Tensor] shape=torch.Size([77, 4096]), dtype=torch.float16, device=cpu key['vae.encoder-item6']: [Dict] with 2 keys key['vae.encoder_f0']: [Tensor] shape=torch.Size([32, 128, 128]), dtype=torch.float16, device=cpu key['vae.encoder_f1']: [Tensor] shape=torch.Size([32, 128, 128]), dtype=torch.float16, device=cpu key['vae.decoder']: [Tensor] shape=torch.Size([16, 128, 128]), dtype=torch.float16, device=cpu ⌛️ [1/4] FRONTEND: Load time: 0.008s ------------------------------------------------------------ SD3.5 Features Summary ------------------------------------------------------------ Number of text encoders: 3 Encoder names: ['text_encoder', 'text_encoder_2', 'text_encoder_3'] Has VAE latents: True Has VAE encoder features: True Data type: torch.float16 text_encoder-item0: clip_pooled_prompt_embeds: torch.Size([768]), torch.float16 clip_prompt_embeds: torch.Size([77, 768]), torch.float16 text_encoder_2-item1: clip_pooled_prompt_embeds: torch.Size([1280]), torch.float16 clip_prompt_embeds: torch.Size([77, 1280]), torch.float16 text_encoder_3-item2: t5_prompt_embeds: torch.Size([77, 4096]), torch.float16 text_encoder-item3: clip_pooled_prompt_embeds: torch.Size([768]), torch.float16 clip_prompt_embeds: torch.Size([77, 768]), torch.float16 text_encoder_2-item4: clip_pooled_prompt_embeds: torch.Size([1280]), torch.float16 clip_prompt_embeds: torch.Size([77, 1280]), torch.float16 text_encoder_3-item5: t5_prompt_embeds: torch.Size([77, 4096]), torch.float16 vae.decoder latents: torch.Size([16, 128, 128]) vae.encoder features: vae.encoder_f0: torch.Size([32, 128, 128]), torch.float16 vae.encoder_f1: torch.Size([32, 128, 128]), torch.float16 ------------------------------------------------------------ [2/4] FRONTEND: Pack + Encode (strategy: individual)... IndividualPacker: text_encoder-item0.clip_pooled_prompt_embeds: torch.Size([768])->torch.Size([1, 1, 1, 768]) text_encoder-item0.clip_prompt_embeds: torch.Size([77, 768])->torch.Size([1, 1, 77, 768]) text_encoder_2-item1.clip_pooled_prompt_embeds: torch.Size([1280])->torch.Size([1, 1, 1, 1280]) text_encoder_2-item1.clip_prompt_embeds: torch.Size([77, 1280])->torch.Size([1, 1, 77, 1280]) text_encoder_3-item2.t5_prompt_embeds: torch.Size([77, 4096])->torch.Size([1, 1, 77, 4096]) text_encoder-item3.clip_pooled_prompt_embeds: torch.Size([768])->torch.Size([1, 1, 1, 768]) text_encoder-item3.clip_prompt_embeds: torch.Size([77, 768])->torch.Size([1, 1, 77, 768]) text_encoder_2-item4.clip_pooled_prompt_embeds: torch.Size([1280])->torch.Size([1, 1, 1, 1280]) text_encoder_2-item4.clip_prompt_embeds: torch.Size([77, 1280])->torch.Size([1, 1, 77, 1280]) text_encoder_3-item5.t5_prompt_embeds: torch.Size([77, 4096])->torch.Size([1, 1, 77, 4096]) vae.encoder_f0: torch.Size([32, 128, 128])->torch.Size([1, 1, 512, 1024]) vae.encoder_f1: torch.Size([32, 128, 128])->torch.Size([1, 1, 512, 1024]) vae.decoder: torch.Size([16, 128, 128])->torch.Size([1, 1, 512, 512]) Using per-key quantization points (text_encoder-item0.clip_pooled_prompt_embeds: torch.Size([256])) for text_encoder-item0.clip_pooled_prompt_embeds text_encoder-item0.clip_pooled_prompt_embeds: 740B, BPFP=7.7083 Using per-key quantization points (text_encoder-item0.clip_prompt_embeds: torch.Size([256])) for text_encoder-item0.clip_prompt_embeds text_encoder-item0.clip_prompt_embeds: 5,888B, BPFP=0.7965 Using per-key quantization points (text_encoder_2-item1.clip_pooled_prompt_embeds: torch.Size([256])) for text_encoder_2-item1.clip_pooled_prompt_embeds text_encoder_2-item1.clip_pooled_prompt_embeds: 1,212B, BPFP=7.5750 Using per-key quantization points (text_encoder_2-item1.clip_prompt_embeds: torch.Size([256])) for text_encoder_2-item1.clip_prompt_embeds text_encoder_2-item1.clip_prompt_embeds: 10,032B, BPFP=0.8143 Using per-key quantization points (text_encoder_3-item2.t5_prompt_embeds: torch.Size([256])) for text_encoder_3-item2.t5_prompt_embeds text_encoder_3-item2.t5_prompt_embeds: 29,168B, BPFP=0.7399 Using per-key quantization points (text_encoder-item3.clip_pooled_prompt_embeds: torch.Size([256])) for text_encoder-item3.clip_pooled_prompt_embeds text_encoder-item3.clip_pooled_prompt_embeds: 760B, BPFP=7.9167 Using per-key quantization points (text_encoder-item3.clip_prompt_embeds: torch.Size([256])) for text_encoder-item3.clip_prompt_embeds text_encoder-item3.clip_prompt_embeds: 4,024B, BPFP=0.5444 Using per-key quantization points (text_encoder_2-item4.clip_pooled: torch.Size([256])) for text_encoder_2-item4.clip_pooled_prompt_embeds text_encoder_2-item4.clip_pooled_prompt_embeds: 1,268B, BPFP=7.9250 Using per-key quantization points (text_encoder_2-item4.clip_prompt: torch.Size([256])) for text_encoder_2-item4.clip_prompt_embeds text_encoder_2-item4.clip_prompt_embeds: 6,996B, BPFP=0.5679 Using per-key quantization points (text_encoder_3-item5.t5_prompt_embeds: torch.Size([256])) for text_encoder_3-item5.t5_prompt_embeds text_encoder_3-item5.t5_prompt_embeds: 18,416B, BPFP=0.4671 Using per-key quantization points (vae.encoder_f0: torch.Size([256])) for vae.encoder_f0 vae.encoder_f0: 15,536B, BPFP=0.2371 Using per-key quantization points (vae.encoder_f1: torch.Size([256])) for vae.encoder_f1 vae.encoder_f1: 15,536B, BPFP=0.2371 Using per-key quantization points (vae.decoder: torch.Size([256])) for vae.decoder vae.decoder: 5,416B, BPFP=0.1653 ⌛️ [2/4] FRONTEND: Frontend time: 0.298s (Pack+Encode) [3/4] BACKEND: Decode + Unpack... Using per-key quantization points (text_encoder-item0.clip_pooled_prompt_embeds: torch.Size([256])) for text_encoder-item0.clip_pooled_prompt_embeds Using per-key quantization points (text_encoder-item0.clip_prompt_embeds: torch.Size([256])) for text_encoder-item0.clip_prompt_embeds Using per-key quantization points (text_encoder_2-item1.clip_pooled_prompt_embeds: torch.Size([256])) for text_encoder_2-item1.clip_pooled_prompt_embeds Using per-key quantization points (text_encoder_2-item1.clip_prompt_embeds: torch.Size([256])) for text_encoder_2-item1.clip_prompt_embeds Using per-key quantization points (text_encoder_3-item2.t5_prompt_embeds: torch.Size([256])) for text_encoder_3-item2.t5_prompt_embeds Using per-key quantization points (text_encoder-item3.clip_pooled_prompt_embeds: torch.Size([256])) for text_encoder-item3.clip_pooled_prompt_embeds Using per-key quantization points (text_encoder-item3.clip_prompt_embeds: torch.Size([256])) for text_encoder-item3.clip_prompt_embeds Using per-key quantization points (text_encoder_2-item4.clip_pooled: torch.Size([256])) for text_encoder_2-item4.clip_pooled_prompt_embeds Using per-key quantization points (text_encoder_2-item4.clip_prompt: torch.Size([256])) for text_encoder_2-item4.clip_prompt_embeds Using per-key quantization points (text_encoder_3-item5.t5_prompt_embeds: torch.Size([256])) for text_encoder_3-item5.t5_prompt_embeds Using per-key quantization points (vae.encoder_f0: torch.Size([256])) for vae.encoder_f0 Using per-key quantization points (vae.encoder_f1: torch.Size([256])) for vae.encoder_f1 Using per-key quantization points (vae.decoder: torch.Size([256])) for vae.decoder IndividualUnPacker: text_encoder-item0.clip_pooled_prompt_embeds: torch.Size([768]) text_encoder-item0.clip_prompt_embeds: torch.Size([77, 768]) text_encoder_2-item1.clip_pooled_prompt_embeds: torch.Size([1280]) text_encoder_2-item1.clip_prompt_embeds: torch.Size([77, 1280]) text_encoder_3-item2.t5_prompt_embeds: torch.Size([77, 4096]) text_encoder-item3.clip_pooled_prompt_embeds: torch.Size([768]) text_encoder-item3.clip_prompt_embeds: torch.Size([77, 768]) text_encoder_2-item4.clip_pooled_prompt_embeds: torch.Size([1280]) text_encoder_2-item4.clip_prompt_embeds: torch.Size([77, 1280]) text_encoder_3-item5.t5_prompt_embeds: torch.Size([77, 4096]) vae.decoder: torch.Size([16, 128, 128]) vae.encoder_f0: torch.Size([32, 128, 128]) vae.encoder_f1: torch.Size([32, 128, 128]) ⌛️ [3/4] BACKEND: Backend time: 0.460s [4/4] METRICS: Computing MSE Breakdown... Using per-key quantization points (text_encoder-item0.clip_pooled_prompt_embeds: torch.Size([256])) for text_encoder-item0.clip_pooled_prompt_embeds Using per-key quantization points (text_encoder-item0.clip_prompt_embeds: torch.Size([256])) for text_encoder-item0.clip_prompt_embeds Using per-key quantization points (text_encoder_2-item1.clip_pooled_prompt_embeds: torch.Size([256])) for text_encoder_2-item1.clip_pooled_prompt_embeds Using per-key quantization points (text_encoder_2-item1.clip_prompt_embeds: torch.Size([256])) for text_encoder_2-item1.clip_prompt_embeds Using per-key quantization points (text_encoder_3-item2.t5_prompt_embeds: torch.Size([256])) for text_encoder_3-item2.t5_prompt_embeds Using per-key quantization points (text_encoder-item3.clip_pooled_prompt_embeds: torch.Size([256])) for text_encoder-item3.clip_pooled_prompt_embeds Using per-key quantization points (text_encoder-item3.clip_prompt_embeds: torch.Size([256])) for text_encoder-item3.clip_prompt_embeds Using per-key quantization points (text_encoder_2-item4.clip_pooled: torch.Size([256])) for text_encoder_2-item4.clip_pooled_prompt_embeds Using per-key quantization points (text_encoder_2-item4.clip_prompt: torch.Size([256])) for text_encoder_2-item4.clip_prompt_embeds Using per-key quantization points (text_encoder_3-item5.t5_prompt_embeds: torch.Size([256])) for text_encoder_3-item5.t5_prompt_embeds Using per-key quantization points (vae.encoder_f0: torch.Size([256])) for vae.encoder_f0 Using per-key quantization points (vae.encoder_f1: torch.Size([256])) for vae.encoder_f1 Using per-key quantization points (vae.decoder: torch.Size([256])) for vae.decoder IndividualUnPacker: text_encoder-item0.clip_pooled_prompt_embeds: torch.Size([768]) text_encoder-item0.clip_prompt_embeds: torch.Size([77, 768]) text_encoder_2-item1.clip_pooled_prompt_embeds: torch.Size([1280]) text_encoder_2-item1.clip_prompt_embeds: torch.Size([77, 1280]) text_encoder_3-item2.t5_prompt_embeds: torch.Size([77, 4096]) text_encoder-item3.clip_pooled_prompt_embeds: torch.Size([768]) text_encoder-item3.clip_prompt_embeds: torch.Size([77, 768]) text_encoder_2-item4.clip_pooled_prompt_embeds: torch.Size([1280]) text_encoder_2-item4.clip_prompt_embeds: torch.Size([77, 1280]) text_encoder_3-item5.t5_prompt_embeds: torch.Size([77, 4096]) vae.decoder: torch.Size([16, 128, 128]) vae.encoder_f0: torch.Size([32, 128, 128]) vae.encoder_f1: torch.Size([32, 128, 128]) Per-key MSE Breakdown (Quant=Truncation+Quantization, Codec=Compression): Key Quant-MSE Total-MSE ------------------------------------------------------------------------------------- text_encoder-item0.clip_pooled_prompt_embeds 0.00021921 0.91926599 text_encoder-item0.clip_prompt_embeds 0.00024958 23.93213102 text_encoder_2-item1.clip_pooled_prompt_embeds 0.00135081 1.03085384 text_encoder_2-item1.clip_prompt_embeds 0.00018030 0.12885589 text_encoder_3-item2.t5_prompt_embeds 0.00000823 0.00411961 text_encoder-item3.clip_pooled_prompt_embeds 0.00841799 1.01420029 text_encoder-item3.clip_prompt_embeds 0.00023247 23.90570887 text_encoder_2-item4.clip_pooled_prompt_embeds 0.00016751 0.67051430 text_encoder_2-item4.clip_prompt_embeds 0.00009830 0.47049568 text_encoder_3-item5.t5_prompt_embeds 0.00001263 0.00227117 vae.encoder_f0 0.11196710 4.44764996 vae.encoder_f1 0.11196851 4.44640064 vae.decoder 0.00023459 0.04331188 ------------------------------------------------------------------------------------- TOTAL 0.05198575 3.34724174 (elements=2,260,992) ---------------------- -------------------------------------------------------- SAMPLE-WISE STATISTICS ---------------------- -------------------------------------------------------- Handler sd35 Strategy individual Architecture hyperprior-featurecoding ---------------------- -------------------------------------------------------- Total Elements 2260992 Total Bytes 114992 BPFP 0.4069 bits/point EBPFP 0.8137 equivalent bits/point MSE 3.347242 ---------------------- -------------------------------------------------------- Time: 0.766s Load: 0.008s, Pack+Encode: 0.298s, Decode+Unpack: 0.460s ---------------------- -------------------------------------------------------- Restored Feature Format: [Dict] with 8 keys key['text_encoder-item0']: [Dict] with 2 keys key['clip_pooled_prompt_embeds']: [Tensor] shape=torch.Size([768]), dtype=torch.float16, device=cpu key['clip_prompt_embeds']: [Tensor] shape=torch.Size([77, 768]), dtype=torch.float16, device=cpu key['text_encoder_2-item1']: [Dict] with 2 keys key['clip_pooled_prompt_embeds']: [Tensor] shape=torch.Size([1280]), dtype=torch.float16, device=cpu key['clip_prompt_embeds']: [Tensor] shape=torch.Size([77, 1280]), dtype=torch.float16, device=cpu key['text_encoder_3-item2']: [Dict] with 1 keys key['t5_prompt_embeds']: [Tensor] shape=torch.Size([77, 4096]), dtype=torch.float16, device=cpu key['text_encoder-item3']: [Dict] with 2 keys key['clip_pooled_prompt_embeds']: [Tensor] shape=torch.Size([768]), dtype=torch.float16, device=cpu key['clip_prompt_embeds']: [Tensor] shape=torch.Size([77, 768]), dtype=torch.float16, device=cpu key['text_encoder_2-item4']: [Dict] with 2 keys key['clip_pooled_prompt_embeds']: [Tensor] shape=torch.Size([1280]), dtype=torch.float16, device=cpu key['clip_prompt_embeds']: [Tensor] shape=torch.Size([77, 1280]), dtype=torch.float16, device=cpu key['text_encoder_3-item5']: [Dict] with 1 keys key['t5_prompt_embeds']: [Tensor] shape=torch.Size([77, 4096]), dtype=torch.float16, device=cpu key['vae.decoder']: [Tensor] shape=torch.Size([16, 128, 128]), dtype=torch.float16, device=cpu key['vae.encoder']: [Dict] with 2 keys key['vae.encoder_f0']: [Tensor] shape=torch.Size([32, 128, 128]), dtype=torch.float16, device=cpu key['vae.encoder_f1']: [Tensor] shape=torch.Size([32, 128, 128]), dtype=torch.float16, device=cpu 💾 Converting with 3.3472 MSE: from ../datasets/FeatureCoding-StableDiffusion3.5/100samples_features/000000325991.zst to output-fixed/sd35/lambda0.004/hyperprior-featurecoding-8bit-individual/sd35cond/000000325991.zst 💪 Processing: ../datasets/FeatureCoding-StableDiffusion3.5/100samples_features/000000329319.zst (54/100) [1/4] FRONTEND: Loading features from ../datasets/FeatureCoding-StableDiffusion3.5/100samples_features/000000329319.zst... Original data structure: root: [Dict] with 8 keys key['text_encoder-item0']: [Dict] with 2 keys key['clip_pooled_prompt_embeds']: [Tensor] shape=torch.Size([768]), dtype=torch.float16, device=cpu key['clip_prompt_embeds']: [Tensor] shape=torch.Size([77, 768]), dtype=torch.float16, device=cpu key['text_encoder_2-item1']: [Dict] with 2 keys key['clip_pooled_prompt_embeds']: [Tensor] shape=torch.Size([1280]), dtype=torch.float16, device=cpu key['clip_prompt_embeds']: [Tensor] shape=torch.Size([77, 1280]), dtype=torch.float16, device=cpu key['text_encoder_3-item2']: [Dict] with 1 keys key['t5_prompt_embeds']: [Tensor] shape=torch.Size([77, 4096]), dtype=torch.float16, device=cpu key['text_encoder-item3']: [Dict] with 2 keys key['clip_pooled_prompt_embeds']: [Tensor] shape=torch.Size([768]), dtype=torch.float16, device=cpu key['clip_prompt_embeds']: [Tensor] shape=torch.Size([77, 768]), dtype=torch.float16, device=cpu key['text_encoder_2-item4']: [Dict] with 2 keys key['clip_pooled_prompt_embeds']: [Tensor] shape=torch.Size([1280]), dtype=torch.float16, device=cpu key['clip_prompt_embeds']: [Tensor] shape=torch.Size([77, 1280]), dtype=torch.float16, device=cpu key['text_encoder_3-item5']: [Dict] with 1 keys key['t5_prompt_embeds']: [Tensor] shape=torch.Size([77, 4096]), dtype=torch.float16, device=cpu key['vae.encoder-item6']: [Dict] with 2 keys key['vae.encoder_f0']: [Tensor] shape=torch.Size([32, 128, 128]), dtype=torch.float16, device=cpu key['vae.encoder_f1']: [Tensor] shape=torch.Size([32, 128, 128]), dtype=torch.float16, device=cpu key['vae.decoder']: [Tensor] shape=torch.Size([16, 128, 128]), dtype=torch.float16, device=cpu ⌛️ [1/4] FRONTEND: Load time: 0.010s ------------------------------------------------------------ SD3.5 Features Summary ------------------------------------------------------------ Number of text encoders: 3 Encoder names: ['text_encoder', 'text_encoder_2', 'text_encoder_3'] Has VAE latents: True Has VAE encoder features: True Data type: torch.float16 text_encoder-item0: clip_pooled_prompt_embeds: torch.Size([768]), torch.float16 clip_prompt_embeds: torch.Size([77, 768]), torch.float16 text_encoder_2-item1: clip_pooled_prompt_embeds: torch.Size([1280]), torch.float16 clip_prompt_embeds: torch.Size([77, 1280]), torch.float16 text_encoder_3-item2: t5_prompt_embeds: torch.Size([77, 4096]), torch.float16 text_encoder-item3: clip_pooled_prompt_embeds: torch.Size([768]), torch.float16 clip_prompt_embeds: torch.Size([77, 768]), torch.float16 text_encoder_2-item4: clip_pooled_prompt_embeds: torch.Size([1280]), torch.float16 clip_prompt_embeds: torch.Size([77, 1280]), torch.float16 text_encoder_3-item5: t5_prompt_embeds: torch.Size([77, 4096]), torch.float16 vae.decoder latents: torch.Size([16, 128, 128]) vae.encoder features: vae.encoder_f0: torch.Size([32, 128, 128]), torch.float16 vae.encoder_f1: torch.Size([32, 128, 128]), torch.float16 ------------------------------------------------------------ [2/4] FRONTEND: Pack + Encode (strategy: individual)... IndividualPacker: text_encoder-item0.clip_pooled_prompt_embeds: torch.Size([768])->torch.Size([1, 1, 1, 768]) text_encoder-item0.clip_prompt_embeds: torch.Size([77, 768])->torch.Size([1, 1, 77, 768]) text_encoder_2-item1.clip_pooled_prompt_embeds: torch.Size([1280])->torch.Size([1, 1, 1, 1280]) text_encoder_2-item1.clip_prompt_embeds: torch.Size([77, 1280])->torch.Size([1, 1, 77, 1280]) text_encoder_3-item2.t5_prompt_embeds: torch.Size([77, 4096])->torch.Size([1, 1, 77, 4096]) text_encoder-item3.clip_pooled_prompt_embeds: torch.Size([768])->torch.Size([1, 1, 1, 768]) text_encoder-item3.clip_prompt_embeds: torch.Size([77, 768])->torch.Size([1, 1, 77, 768]) text_encoder_2-item4.clip_pooled_prompt_embeds: torch.Size([1280])->torch.Size([1, 1, 1, 1280]) text_encoder_2-item4.clip_prompt_embeds: torch.Size([77, 1280])->torch.Size([1, 1, 77, 1280]) text_encoder_3-item5.t5_prompt_embeds: torch.Size([77, 4096])->torch.Size([1, 1, 77, 4096]) vae.encoder_f0: torch.Size([32, 128, 128])->torch.Size([1, 1, 512, 1024]) vae.encoder_f1: torch.Size([32, 128, 128])->torch.Size([1, 1, 512, 1024]) vae.decoder: torch.Size([16, 128, 128])->torch.Size([1, 1, 512, 512]) Using per-key quantization points (text_encoder-item0.clip_pooled_prompt_embeds: torch.Size([256])) for text_encoder-item0.clip_pooled_prompt_embeds text_encoder-item0.clip_pooled_prompt_embeds: 732B, BPFP=7.6250 Using per-key quantization points (text_encoder-item0.clip_prompt_embeds: torch.Size([256])) for text_encoder-item0.clip_prompt_embeds text_encoder-item0.clip_prompt_embeds: 6,328B, BPFP=0.8561 Using per-key quantization points (text_encoder_2-item1.clip_pooled_prompt_embeds: torch.Size([256])) for text_encoder_2-item1.clip_pooled_prompt_embeds text_encoder_2-item1.clip_pooled_prompt_embeds: 1,200B, BPFP=7.5000 Using per-key quantization points (text_encoder_2-item1.clip_prompt_embeds: torch.Size([256])) for text_encoder_2-item1.clip_prompt_embeds text_encoder_2-item1.clip_prompt_embeds: 11,412B, BPFP=0.9263 Using per-key quantization points (text_encoder_3-item2.t5_prompt_embeds: torch.Size([256])) for text_encoder_3-item2.t5_prompt_embeds text_encoder_3-item2.t5_prompt_embeds: 25,956B, BPFP=0.6584 Using per-key quantization points (text_encoder-item3.clip_pooled_prompt_embeds: torch.Size([256])) for text_encoder-item3.clip_pooled_prompt_embeds text_encoder-item3.clip_pooled_prompt_embeds: 760B, BPFP=7.9167 Using per-key quantization points (text_encoder-item3.clip_prompt_embeds: torch.Size([256])) for text_encoder-item3.clip_prompt_embeds text_encoder-item3.clip_prompt_embeds: 4,024B, BPFP=0.5444 Using per-key quantization points (text_encoder_2-item4.clip_pooled: torch.Size([256])) for text_encoder_2-item4.clip_pooled_prompt_embeds text_encoder_2-item4.clip_pooled_prompt_embeds: 1,268B, BPFP=7.9250 Using per-key quantization points (text_encoder_2-item4.clip_prompt: torch.Size([256])) for text_encoder_2-item4.clip_prompt_embeds text_encoder_2-item4.clip_prompt_embeds: 6,996B, BPFP=0.5679 Using per-key quantization points (text_encoder_3-item5.t5_prompt_embeds: torch.Size([256])) for text_encoder_3-item5.t5_prompt_embeds text_encoder_3-item5.t5_prompt_embeds: 18,416B, BPFP=0.4671 Using per-key quantization points (vae.encoder_f0: torch.Size([256])) for vae.encoder_f0 vae.encoder_f0: 20,312B, BPFP=0.3099 Using per-key quantization points (vae.encoder_f1: torch.Size([256])) for vae.encoder_f1 vae.encoder_f1: 20,316B, BPFP=0.3100 Using per-key quantization points (vae.decoder: torch.Size([256])) for vae.decoder vae.decoder: 6,040B, BPFP=0.1843 ⌛️ [2/4] FRONTEND: Frontend time: 0.294s (Pack+Encode) [3/4] BACKEND: Decode + Unpack... Using per-key quantization points (text_encoder-item0.clip_pooled_prompt_embeds: torch.Size([256])) for text_encoder-item0.clip_pooled_prompt_embeds Using per-key quantization points (text_encoder-item0.clip_prompt_embeds: torch.Size([256])) for text_encoder-item0.clip_prompt_embeds Using per-key quantization points (text_encoder_2-item1.clip_pooled_prompt_embeds: torch.Size([256])) for text_encoder_2-item1.clip_pooled_prompt_embeds Using per-key quantization points (text_encoder_2-item1.clip_prompt_embeds: torch.Size([256])) for text_encoder_2-item1.clip_prompt_embeds Using per-key quantization points (text_encoder_3-item2.t5_prompt_embeds: torch.Size([256])) for text_encoder_3-item2.t5_prompt_embeds Using per-key quantization points (text_encoder-item3.clip_pooled_prompt_embeds: torch.Size([256])) for text_encoder-item3.clip_pooled_prompt_embeds Using per-key quantization points (text_encoder-item3.clip_prompt_embeds: torch.Size([256])) for text_encoder-item3.clip_prompt_embeds Using per-key quantization points (text_encoder_2-item4.clip_pooled: torch.Size([256])) for text_encoder_2-item4.clip_pooled_prompt_embeds Using per-key quantization points (text_encoder_2-item4.clip_prompt: torch.Size([256])) for text_encoder_2-item4.clip_prompt_embeds Using per-key quantization points (text_encoder_3-item5.t5_prompt_embeds: torch.Size([256])) for text_encoder_3-item5.t5_prompt_embeds Using per-key quantization points (vae.encoder_f0: torch.Size([256])) for vae.encoder_f0 Using per-key quantization points (vae.encoder_f1: torch.Size([256])) for vae.encoder_f1 Using per-key quantization points (vae.decoder: torch.Size([256])) for vae.decoder IndividualUnPacker: text_encoder-item0.clip_pooled_prompt_embeds: torch.Size([768]) text_encoder-item0.clip_prompt_embeds: torch.Size([77, 768]) text_encoder_2-item1.clip_pooled_prompt_embeds: torch.Size([1280]) text_encoder_2-item1.clip_prompt_embeds: torch.Size([77, 1280]) text_encoder_3-item2.t5_prompt_embeds: torch.Size([77, 4096]) text_encoder-item3.clip_pooled_prompt_embeds: torch.Size([768]) text_encoder-item3.clip_prompt_embeds: torch.Size([77, 768]) text_encoder_2-item4.clip_pooled_prompt_embeds: torch.Size([1280]) text_encoder_2-item4.clip_prompt_embeds: torch.Size([77, 1280]) text_encoder_3-item5.t5_prompt_embeds: torch.Size([77, 4096]) vae.decoder: torch.Size([16, 128, 128]) vae.encoder_f0: torch.Size([32, 128, 128]) vae.encoder_f1: torch.Size([32, 128, 128]) ⌛️ [3/4] BACKEND: Backend time: 0.451s [4/4] METRICS: Computing MSE Breakdown... Using per-key quantization points (text_encoder-item0.clip_pooled_prompt_embeds: torch.Size([256])) for text_encoder-item0.clip_pooled_prompt_embeds Using per-key quantization points (text_encoder-item0.clip_prompt_embeds: torch.Size([256])) for text_encoder-item0.clip_prompt_embeds Using per-key quantization points (text_encoder_2-item1.clip_pooled_prompt_embeds: torch.Size([256])) for text_encoder_2-item1.clip_pooled_prompt_embeds Using per-key quantization points (text_encoder_2-item1.clip_prompt_embeds: torch.Size([256])) for text_encoder_2-item1.clip_prompt_embeds Using per-key quantization points (text_encoder_3-item2.t5_prompt_embeds: torch.Size([256])) for text_encoder_3-item2.t5_prompt_embeds Using per-key quantization points (text_encoder-item3.clip_pooled_prompt_embeds: torch.Size([256])) for text_encoder-item3.clip_pooled_prompt_embeds Using per-key quantization points (text_encoder-item3.clip_prompt_embeds: torch.Size([256])) for text_encoder-item3.clip_prompt_embeds Using per-key quantization points (text_encoder_2-item4.clip_pooled: torch.Size([256])) for text_encoder_2-item4.clip_pooled_prompt_embeds Using per-key quantization points (text_encoder_2-item4.clip_prompt: torch.Size([256])) for text_encoder_2-item4.clip_prompt_embeds Using per-key quantization points (text_encoder_3-item5.t5_prompt_embeds: torch.Size([256])) for text_encoder_3-item5.t5_prompt_embeds Using per-key quantization points (vae.encoder_f0: torch.Size([256])) for vae.encoder_f0 Using per-key quantization points (vae.encoder_f1: torch.Size([256])) for vae.encoder_f1 Using per-key quantization points (vae.decoder: torch.Size([256])) for vae.decoder IndividualUnPacker: text_encoder-item0.clip_pooled_prompt_embeds: torch.Size([768]) text_encoder-item0.clip_prompt_embeds: torch.Size([77, 768]) text_encoder_2-item1.clip_pooled_prompt_embeds: torch.Size([1280]) text_encoder_2-item1.clip_prompt_embeds: torch.Size([77, 1280]) text_encoder_3-item2.t5_prompt_embeds: torch.Size([77, 4096]) text_encoder-item3.clip_pooled_prompt_embeds: torch.Size([768]) text_encoder-item3.clip_prompt_embeds: torch.Size([77, 768]) text_encoder_2-item4.clip_pooled_prompt_embeds: torch.Size([1280]) text_encoder_2-item4.clip_prompt_embeds: torch.Size([77, 1280]) text_encoder_3-item5.t5_prompt_embeds: torch.Size([77, 4096]) vae.decoder: torch.Size([16, 128, 128]) vae.encoder_f0: torch.Size([32, 128, 128]) vae.encoder_f1: torch.Size([32, 128, 128]) Per-key MSE Breakdown (Quant=Truncation+Quantization, Codec=Compression): Key Quant-MSE Total-MSE ------------------------------------------------------------------------------------- text_encoder-item0.clip_pooled_prompt_embeds 0.00021756 0.94755220 text_encoder-item0.clip_prompt_embeds 0.00025929 23.94684076 text_encoder_2-item1.clip_pooled_prompt_embeds 0.00021916 0.94049606 text_encoder_2-item1.clip_prompt_embeds 0.00042246 0.14851719 text_encoder_3-item2.t5_prompt_embeds 0.00000781 0.00252624 text_encoder-item3.clip_pooled_prompt_embeds 0.00841799 1.01420029 text_encoder-item3.clip_prompt_embeds 0.00023247 23.90570887 text_encoder_2-item4.clip_pooled_prompt_embeds 0.00016751 0.67051430 text_encoder_2-item4.clip_prompt_embeds 0.00009830 0.47049568 text_encoder_3-item5.t5_prompt_embeds 0.00001263 0.00227117 vae.encoder_f0 0.00675017 3.14154410 vae.encoder_f1 0.00675421 3.14164591 vae.decoder 0.00023635 0.05547103 ------------------------------------------------------------------------------------- TOTAL 0.00320042 2.74421254 (elements=2,260,992) ---------------------- -------------------------------------------------------- SAMPLE-WISE STATISTICS ---------------------- -------------------------------------------------------- Handler sd35 Strategy individual Architecture hyperprior-featurecoding ---------------------- -------------------------------------------------------- Total Elements 2260992 Total Bytes 123760 BPFP 0.4379 bits/point EBPFP 0.8758 equivalent bits/point MSE 2.744213 ---------------------- -------------------------------------------------------- Time: 0.755s Load: 0.010s, Pack+Encode: 0.294s, Decode+Unpack: 0.451s ---------------------- -------------------------------------------------------- Restored Feature Format: [Dict] with 8 keys key['text_encoder-item0']: [Dict] with 2 keys key['clip_pooled_prompt_embeds']: [Tensor] shape=torch.Size([768]), dtype=torch.float16, device=cpu key['clip_prompt_embeds']: [Tensor] shape=torch.Size([77, 768]), dtype=torch.float16, device=cpu key['text_encoder_2-item1']: [Dict] with 2 keys key['clip_pooled_prompt_embeds']: [Tensor] shape=torch.Size([1280]), dtype=torch.float16, device=cpu key['clip_prompt_embeds']: [Tensor] shape=torch.Size([77, 1280]), dtype=torch.float16, device=cpu key['text_encoder_3-item2']: [Dict] with 1 keys key['t5_prompt_embeds']: [Tensor] shape=torch.Size([77, 4096]), dtype=torch.float16, device=cpu key['text_encoder-item3']: [Dict] with 2 keys key['clip_pooled_prompt_embeds']: [Tensor] shape=torch.Size([768]), dtype=torch.float16, device=cpu key['clip_prompt_embeds']: [Tensor] shape=torch.Size([77, 768]), dtype=torch.float16, device=cpu key['text_encoder_2-item4']: [Dict] with 2 keys key['clip_pooled_prompt_embeds']: [Tensor] shape=torch.Size([1280]), dtype=torch.float16, device=cpu key['clip_prompt_embeds']: [Tensor] shape=torch.Size([77, 1280]), dtype=torch.float16, device=cpu key['text_encoder_3-item5']: [Dict] with 1 keys key['t5_prompt_embeds']: [Tensor] shape=torch.Size([77, 4096]), dtype=torch.float16, device=cpu key['vae.decoder']: [Tensor] shape=torch.Size([16, 128, 128]), dtype=torch.float16, device=cpu key['vae.encoder']: [Dict] with 2 keys key['vae.encoder_f0']: [Tensor] shape=torch.Size([32, 128, 128]), dtype=torch.float16, device=cpu key['vae.encoder_f1']: [Tensor] shape=torch.Size([32, 128, 128]), dtype=torch.float16, device=cpu 💾 Converting with 2.7442 MSE: from ../datasets/FeatureCoding-StableDiffusion3.5/100samples_features/000000329319.zst to output-fixed/sd35/lambda0.004/hyperprior-featurecoding-8bit-individual/sd35cond/000000329319.zst 💪 Processing: ../datasets/FeatureCoding-StableDiffusion3.5/100samples_features/000000335081.zst (55/100) [1/4] FRONTEND: Loading features from ../datasets/FeatureCoding-StableDiffusion3.5/100samples_features/000000335081.zst... Original data structure: root: [Dict] with 8 keys key['text_encoder-item0']: [Dict] with 2 keys key['clip_pooled_prompt_embeds']: [Tensor] shape=torch.Size([768]), dtype=torch.float16, device=cpu key['clip_prompt_embeds']: [Tensor] shape=torch.Size([77, 768]), dtype=torch.float16, device=cpu key['text_encoder_2-item1']: [Dict] with 2 keys key['clip_pooled_prompt_embeds']: [Tensor] shape=torch.Size([1280]), dtype=torch.float16, device=cpu key['clip_prompt_embeds']: [Tensor] shape=torch.Size([77, 1280]), dtype=torch.float16, device=cpu key['text_encoder_3-item2']: [Dict] with 1 keys key['t5_prompt_embeds']: [Tensor] shape=torch.Size([77, 4096]), dtype=torch.float16, device=cpu key['text_encoder-item3']: [Dict] with 2 keys key['clip_pooled_prompt_embeds']: [Tensor] shape=torch.Size([768]), dtype=torch.float16, device=cpu key['clip_prompt_embeds']: [Tensor] shape=torch.Size([77, 768]), dtype=torch.float16, device=cpu key['text_encoder_2-item4']: [Dict] with 2 keys key['clip_pooled_prompt_embeds']: [Tensor] shape=torch.Size([1280]), dtype=torch.float16, device=cpu key['clip_prompt_embeds']: [Tensor] shape=torch.Size([77, 1280]), dtype=torch.float16, device=cpu key['text_encoder_3-item5']: [Dict] with 1 keys key['t5_prompt_embeds']: [Tensor] shape=torch.Size([77, 4096]), dtype=torch.float16, device=cpu key['vae.encoder-item6']: [Dict] with 2 keys key['vae.encoder_f0']: [Tensor] shape=torch.Size([32, 128, 128]), dtype=torch.float16, device=cpu key['vae.encoder_f1']: [Tensor] shape=torch.Size([32, 128, 128]), dtype=torch.float16, device=cpu key['vae.decoder']: [Tensor] shape=torch.Size([16, 128, 128]), dtype=torch.float16, device=cpu ⌛️ [1/4] FRONTEND: Load time: 0.009s ------------------------------------------------------------ SD3.5 Features Summary ------------------------------------------------------------ Number of text encoders: 3 Encoder names: ['text_encoder', 'text_encoder_2', 'text_encoder_3'] Has VAE latents: True Has VAE encoder features: True Data type: torch.float16 text_encoder-item0: clip_pooled_prompt_embeds: torch.Size([768]), torch.float16 clip_prompt_embeds: torch.Size([77, 768]), torch.float16 text_encoder_2-item1: clip_pooled_prompt_embeds: torch.Size([1280]), torch.float16 clip_prompt_embeds: torch.Size([77, 1280]), torch.float16 text_encoder_3-item2: t5_prompt_embeds: torch.Size([77, 4096]), torch.float16 text_encoder-item3: clip_pooled_prompt_embeds: torch.Size([768]), torch.float16 clip_prompt_embeds: torch.Size([77, 768]), torch.float16 text_encoder_2-item4: clip_pooled_prompt_embeds: torch.Size([1280]), torch.float16 clip_prompt_embeds: torch.Size([77, 1280]), torch.float16 text_encoder_3-item5: t5_prompt_embeds: torch.Size([77, 4096]), torch.float16 vae.decoder latents: torch.Size([16, 128, 128]) vae.encoder features: vae.encoder_f0: torch.Size([32, 128, 128]), torch.float16 vae.encoder_f1: torch.Size([32, 128, 128]), torch.float16 ------------------------------------------------------------ [2/4] FRONTEND: Pack + Encode (strategy: individual)... IndividualPacker: text_encoder-item0.clip_pooled_prompt_embeds: torch.Size([768])->torch.Size([1, 1, 1, 768]) text_encoder-item0.clip_prompt_embeds: torch.Size([77, 768])->torch.Size([1, 1, 77, 768]) text_encoder_2-item1.clip_pooled_prompt_embeds: torch.Size([1280])->torch.Size([1, 1, 1, 1280]) text_encoder_2-item1.clip_prompt_embeds: torch.Size([77, 1280])->torch.Size([1, 1, 77, 1280]) text_encoder_3-item2.t5_prompt_embeds: torch.Size([77, 4096])->torch.Size([1, 1, 77, 4096]) text_encoder-item3.clip_pooled_prompt_embeds: torch.Size([768])->torch.Size([1, 1, 1, 768]) text_encoder-item3.clip_prompt_embeds: torch.Size([77, 768])->torch.Size([1, 1, 77, 768]) text_encoder_2-item4.clip_pooled_prompt_embeds: torch.Size([1280])->torch.Size([1, 1, 1, 1280]) text_encoder_2-item4.clip_prompt_embeds: torch.Size([77, 1280])->torch.Size([1, 1, 77, 1280]) text_encoder_3-item5.t5_prompt_embeds: torch.Size([77, 4096])->torch.Size([1, 1, 77, 4096]) vae.encoder_f0: torch.Size([32, 128, 128])->torch.Size([1, 1, 512, 1024]) vae.encoder_f1: torch.Size([32, 128, 128])->torch.Size([1, 1, 512, 1024]) vae.decoder: torch.Size([16, 128, 128])->torch.Size([1, 1, 512, 512]) Using per-key quantization points (text_encoder-item0.clip_pooled_prompt_embeds: torch.Size([256])) for text_encoder-item0.clip_pooled_prompt_embeds text_encoder-item0.clip_pooled_prompt_embeds: 736B, BPFP=7.6667 Using per-key quantization points (text_encoder-item0.clip_prompt_embeds: torch.Size([256])) for text_encoder-item0.clip_prompt_embeds text_encoder-item0.clip_prompt_embeds: 5,768B, BPFP=0.7803 Using per-key quantization points (text_encoder_2-item1.clip_pooled_prompt_embeds: torch.Size([256])) for text_encoder_2-item1.clip_pooled_prompt_embeds text_encoder_2-item1.clip_pooled_prompt_embeds: 1,188B, BPFP=7.4250 Using per-key quantization points (text_encoder_2-item1.clip_prompt_embeds: torch.Size([256])) for text_encoder_2-item1.clip_prompt_embeds text_encoder_2-item1.clip_prompt_embeds: 10,972B, BPFP=0.8906 Using per-key quantization points (text_encoder_3-item2.t5_prompt_embeds: torch.Size([256])) for text_encoder_3-item2.t5_prompt_embeds text_encoder_3-item2.t5_prompt_embeds: 27,880B, BPFP=0.7072 Using per-key quantization points (text_encoder-item3.clip_pooled_prompt_embeds: torch.Size([256])) for text_encoder-item3.clip_pooled_prompt_embeds text_encoder-item3.clip_pooled_prompt_embeds: 760B, BPFP=7.9167 Using per-key quantization points (text_encoder-item3.clip_prompt_embeds: torch.Size([256])) for text_encoder-item3.clip_prompt_embeds text_encoder-item3.clip_prompt_embeds: 4,024B, BPFP=0.5444 Using per-key quantization points (text_encoder_2-item4.clip_pooled: torch.Size([256])) for text_encoder_2-item4.clip_pooled_prompt_embeds text_encoder_2-item4.clip_pooled_prompt_embeds: 1,268B, BPFP=7.9250 Using per-key quantization points (text_encoder_2-item4.clip_prompt: torch.Size([256])) for text_encoder_2-item4.clip_prompt_embeds text_encoder_2-item4.clip_prompt_embeds: 6,996B, BPFP=0.5679 Using per-key quantization points (text_encoder_3-item5.t5_prompt_embeds: torch.Size([256])) for text_encoder_3-item5.t5_prompt_embeds text_encoder_3-item5.t5_prompt_embeds: 18,416B, BPFP=0.4671 Using per-key quantization points (vae.encoder_f0: torch.Size([256])) for vae.encoder_f0 vae.encoder_f0: 24,528B, BPFP=0.3743 Using per-key quantization points (vae.encoder_f1: torch.Size([256])) for vae.encoder_f1 vae.encoder_f1: 24,528B, BPFP=0.3743 Using per-key quantization points (vae.decoder: torch.Size([256])) for vae.decoder vae.decoder: 6,100B, BPFP=0.1862 ⌛️ [2/4] FRONTEND: Frontend time: 0.295s (Pack+Encode) [3/4] BACKEND: Decode + Unpack... Using per-key quantization points (text_encoder-item0.clip_pooled_prompt_embeds: torch.Size([256])) for text_encoder-item0.clip_pooled_prompt_embeds Using per-key quantization points (text_encoder-item0.clip_prompt_embeds: torch.Size([256])) for text_encoder-item0.clip_prompt_embeds Using per-key quantization points (text_encoder_2-item1.clip_pooled_prompt_embeds: torch.Size([256])) for text_encoder_2-item1.clip_pooled_prompt_embeds Using per-key quantization points (text_encoder_2-item1.clip_prompt_embeds: torch.Size([256])) for text_encoder_2-item1.clip_prompt_embeds Using per-key quantization points (text_encoder_3-item2.t5_prompt_embeds: torch.Size([256])) for text_encoder_3-item2.t5_prompt_embeds Using per-key quantization points (text_encoder-item3.clip_pooled_prompt_embeds: torch.Size([256])) for text_encoder-item3.clip_pooled_prompt_embeds Using per-key quantization points (text_encoder-item3.clip_prompt_embeds: torch.Size([256])) for text_encoder-item3.clip_prompt_embeds Using per-key quantization points (text_encoder_2-item4.clip_pooled: torch.Size([256])) for text_encoder_2-item4.clip_pooled_prompt_embeds Using per-key quantization points (text_encoder_2-item4.clip_prompt: torch.Size([256])) for text_encoder_2-item4.clip_prompt_embeds Using per-key quantization points (text_encoder_3-item5.t5_prompt_embeds: torch.Size([256])) for text_encoder_3-item5.t5_prompt_embeds Using per-key quantization points (vae.encoder_f0: torch.Size([256])) for vae.encoder_f0 Using per-key quantization points (vae.encoder_f1: torch.Size([256])) for vae.encoder_f1 Using per-key quantization points (vae.decoder: torch.Size([256])) for vae.decoder IndividualUnPacker: text_encoder-item0.clip_pooled_prompt_embeds: torch.Size([768]) text_encoder-item0.clip_prompt_embeds: torch.Size([77, 768]) text_encoder_2-item1.clip_pooled_prompt_embeds: torch.Size([1280]) text_encoder_2-item1.clip_prompt_embeds: torch.Size([77, 1280]) text_encoder_3-item2.t5_prompt_embeds: torch.Size([77, 4096]) text_encoder-item3.clip_pooled_prompt_embeds: torch.Size([768]) text_encoder-item3.clip_prompt_embeds: torch.Size([77, 768]) text_encoder_2-item4.clip_pooled_prompt_embeds: torch.Size([1280]) text_encoder_2-item4.clip_prompt_embeds: torch.Size([77, 1280]) text_encoder_3-item5.t5_prompt_embeds: torch.Size([77, 4096]) vae.decoder: torch.Size([16, 128, 128]) vae.encoder_f0: torch.Size([32, 128, 128]) vae.encoder_f1: torch.Size([32, 128, 128]) ⌛️ [3/4] BACKEND: Backend time: 0.448s [4/4] METRICS: Computing MSE Breakdown... Using per-key quantization points (text_encoder-item0.clip_pooled_prompt_embeds: torch.Size([256])) for text_encoder-item0.clip_pooled_prompt_embeds Using per-key quantization points (text_encoder-item0.clip_prompt_embeds: torch.Size([256])) for text_encoder-item0.clip_prompt_embeds Using per-key quantization points (text_encoder_2-item1.clip_pooled_prompt_embeds: torch.Size([256])) for text_encoder_2-item1.clip_pooled_prompt_embeds Using per-key quantization points (text_encoder_2-item1.clip_prompt_embeds: torch.Size([256])) for text_encoder_2-item1.clip_prompt_embeds Using per-key quantization points (text_encoder_3-item2.t5_prompt_embeds: torch.Size([256])) for text_encoder_3-item2.t5_prompt_embeds Using per-key quantization points (text_encoder-item3.clip_pooled_prompt_embeds: torch.Size([256])) for text_encoder-item3.clip_pooled_prompt_embeds Using per-key quantization points (text_encoder-item3.clip_prompt_embeds: torch.Size([256])) for text_encoder-item3.clip_prompt_embeds Using per-key quantization points (text_encoder_2-item4.clip_pooled: torch.Size([256])) for text_encoder_2-item4.clip_pooled_prompt_embeds Using per-key quantization points (text_encoder_2-item4.clip_prompt: torch.Size([256])) for text_encoder_2-item4.clip_prompt_embeds Using per-key quantization points (text_encoder_3-item5.t5_prompt_embeds: torch.Size([256])) for text_encoder_3-item5.t5_prompt_embeds Using per-key quantization points (vae.encoder_f0: torch.Size([256])) for vae.encoder_f0 Using per-key quantization points (vae.encoder_f1: torch.Size([256])) for vae.encoder_f1 Using per-key quantization points (vae.decoder: torch.Size([256])) for vae.decoder IndividualUnPacker: text_encoder-item0.clip_pooled_prompt_embeds: torch.Size([768]) text_encoder-item0.clip_prompt_embeds: torch.Size([77, 768]) text_encoder_2-item1.clip_pooled_prompt_embeds: torch.Size([1280]) text_encoder_2-item1.clip_prompt_embeds: torch.Size([77, 1280]) text_encoder_3-item2.t5_prompt_embeds: torch.Size([77, 4096]) text_encoder-item3.clip_pooled_prompt_embeds: torch.Size([768]) text_encoder-item3.clip_prompt_embeds: torch.Size([77, 768]) text_encoder_2-item4.clip_pooled_prompt_embeds: torch.Size([1280]) text_encoder_2-item4.clip_prompt_embeds: torch.Size([77, 1280]) text_encoder_3-item5.t5_prompt_embeds: torch.Size([77, 4096]) vae.decoder: torch.Size([16, 128, 128]) vae.encoder_f0: torch.Size([32, 128, 128]) vae.encoder_f1: torch.Size([32, 128, 128]) Per-key MSE Breakdown (Quant=Truncation+Quantization, Codec=Compression): Key Quant-MSE Total-MSE ------------------------------------------------------------------------------------- text_encoder-item0.clip_pooled_prompt_embeds 0.00017133 0.89779695 text_encoder-item0.clip_prompt_embeds 0.00064775 157.63193317 text_encoder_2-item1.clip_pooled_prompt_embeds 0.00128483 0.92495089 text_encoder_2-item1.clip_prompt_embeds 0.00019620 0.11822055 text_encoder_3-item2.t5_prompt_embeds 0.00000792 0.00337716 text_encoder-item3.clip_pooled_prompt_embeds 0.00841799 1.01420029 text_encoder-item3.clip_prompt_embeds 0.00023247 23.90570887 text_encoder_2-item4.clip_pooled_prompt_embeds 0.00016751 0.67051430 text_encoder_2-item4.clip_prompt_embeds 0.00009830 0.47049568 text_encoder_3-item5.t5_prompt_embeds 0.00001263 0.00227117 vae.encoder_f0 0.00728993 3.35865593 vae.encoder_f1 0.00729572 3.35963917 vae.decoder 0.00026488 0.05387092 ------------------------------------------------------------------------------------- TOTAL 0.00345536 6.34021233 (elements=2,260,992) ---------------------- -------------------------------------------------------- SAMPLE-WISE STATISTICS ---------------------- -------------------------------------------------------- Handler sd35 Strategy individual Architecture hyperprior-featurecoding ---------------------- -------------------------------------------------------- Total Elements 2260992 Total Bytes 133164 BPFP 0.4712 bits/point EBPFP 0.9423 equivalent bits/point MSE 6.340212 ---------------------- -------------------------------------------------------- Time: 0.752s Load: 0.009s, Pack+Encode: 0.295s, Decode+Unpack: 0.448s ---------------------- -------------------------------------------------------- Restored Feature Format: [Dict] with 8 keys key['text_encoder-item0']: [Dict] with 2 keys key['clip_pooled_prompt_embeds']: [Tensor] shape=torch.Size([768]), dtype=torch.float16, device=cpu key['clip_prompt_embeds']: [Tensor] shape=torch.Size([77, 768]), dtype=torch.float16, device=cpu key['text_encoder_2-item1']: [Dict] with 2 keys key['clip_pooled_prompt_embeds']: [Tensor] shape=torch.Size([1280]), dtype=torch.float16, device=cpu key['clip_prompt_embeds']: [Tensor] shape=torch.Size([77, 1280]), dtype=torch.float16, device=cpu key['text_encoder_3-item2']: [Dict] with 1 keys key['t5_prompt_embeds']: [Tensor] shape=torch.Size([77, 4096]), dtype=torch.float16, device=cpu key['text_encoder-item3']: [Dict] with 2 keys key['clip_pooled_prompt_embeds']: [Tensor] shape=torch.Size([768]), dtype=torch.float16, device=cpu key['clip_prompt_embeds']: [Tensor] shape=torch.Size([77, 768]), dtype=torch.float16, device=cpu key['text_encoder_2-item4']: [Dict] with 2 keys key['clip_pooled_prompt_embeds']: [Tensor] shape=torch.Size([1280]), dtype=torch.float16, device=cpu key['clip_prompt_embeds']: [Tensor] shape=torch.Size([77, 1280]), dtype=torch.float16, device=cpu key['text_encoder_3-item5']: [Dict] with 1 keys key['t5_prompt_embeds']: [Tensor] shape=torch.Size([77, 4096]), dtype=torch.float16, device=cpu key['vae.decoder']: [Tensor] shape=torch.Size([16, 128, 128]), dtype=torch.float16, device=cpu key['vae.encoder']: [Dict] with 2 keys key['vae.encoder_f0']: [Tensor] shape=torch.Size([32, 128, 128]), dtype=torch.float16, device=cpu key['vae.encoder_f1']: [Tensor] shape=torch.Size([32, 128, 128]), dtype=torch.float16, device=cpu 💾 Converting with 6.3402 MSE: from ../datasets/FeatureCoding-StableDiffusion3.5/100samples_features/000000335081.zst to output-fixed/sd35/lambda0.004/hyperprior-featurecoding-8bit-individual/sd35cond/000000335081.zst 💪 Processing: ../datasets/FeatureCoding-StableDiffusion3.5/100samples_features/000000342186.zst (56/100) [1/4] FRONTEND: Loading features from ../datasets/FeatureCoding-StableDiffusion3.5/100samples_features/000000342186.zst... Original data structure: root: [Dict] with 8 keys key['text_encoder-item0']: [Dict] with 2 keys key['clip_pooled_prompt_embeds']: [Tensor] shape=torch.Size([768]), dtype=torch.float16, device=cpu key['clip_prompt_embeds']: [Tensor] shape=torch.Size([77, 768]), dtype=torch.float16, device=cpu key['text_encoder_2-item1']: [Dict] with 2 keys key['clip_pooled_prompt_embeds']: [Tensor] shape=torch.Size([1280]), dtype=torch.float16, device=cpu key['clip_prompt_embeds']: [Tensor] shape=torch.Size([77, 1280]), dtype=torch.float16, device=cpu key['text_encoder_3-item2']: [Dict] with 1 keys key['t5_prompt_embeds']: [Tensor] shape=torch.Size([77, 4096]), dtype=torch.float16, device=cpu key['text_encoder-item3']: [Dict] with 2 keys key['clip_pooled_prompt_embeds']: [Tensor] shape=torch.Size([768]), dtype=torch.float16, device=cpu key['clip_prompt_embeds']: [Tensor] shape=torch.Size([77, 768]), dtype=torch.float16, device=cpu key['text_encoder_2-item4']: [Dict] with 2 keys key['clip_pooled_prompt_embeds']: [Tensor] shape=torch.Size([1280]), dtype=torch.float16, device=cpu key['clip_prompt_embeds']: [Tensor] shape=torch.Size([77, 1280]), dtype=torch.float16, device=cpu key['text_encoder_3-item5']: [Dict] with 1 keys key['t5_prompt_embeds']: [Tensor] shape=torch.Size([77, 4096]), dtype=torch.float16, device=cpu key['vae.encoder-item6']: [Dict] with 2 keys key['vae.encoder_f0']: [Tensor] shape=torch.Size([32, 128, 128]), dtype=torch.float16, device=cpu key['vae.encoder_f1']: [Tensor] shape=torch.Size([32, 128, 128]), dtype=torch.float16, device=cpu key['vae.decoder']: [Tensor] shape=torch.Size([16, 128, 128]), dtype=torch.float16, device=cpu ⌛️ [1/4] FRONTEND: Load time: 0.009s ------------------------------------------------------------ SD3.5 Features Summary ------------------------------------------------------------ Number of text encoders: 3 Encoder names: ['text_encoder', 'text_encoder_2', 'text_encoder_3'] Has VAE latents: True Has VAE encoder features: True Data type: torch.float16 text_encoder-item0: clip_pooled_prompt_embeds: torch.Size([768]), torch.float16 clip_prompt_embeds: torch.Size([77, 768]), torch.float16 text_encoder_2-item1: clip_pooled_prompt_embeds: torch.Size([1280]), torch.float16 clip_prompt_embeds: torch.Size([77, 1280]), torch.float16 text_encoder_3-item2: t5_prompt_embeds: torch.Size([77, 4096]), torch.float16 text_encoder-item3: clip_pooled_prompt_embeds: torch.Size([768]), torch.float16 clip_prompt_embeds: torch.Size([77, 768]), torch.float16 text_encoder_2-item4: clip_pooled_prompt_embeds: torch.Size([1280]), torch.float16 clip_prompt_embeds: torch.Size([77, 1280]), torch.float16 text_encoder_3-item5: t5_prompt_embeds: torch.Size([77, 4096]), torch.float16 vae.decoder latents: torch.Size([16, 128, 128]) vae.encoder features: vae.encoder_f0: torch.Size([32, 128, 128]), torch.float16 vae.encoder_f1: torch.Size([32, 128, 128]), torch.float16 ------------------------------------------------------------ [2/4] FRONTEND: Pack + Encode (strategy: individual)... IndividualPacker: text_encoder-item0.clip_pooled_prompt_embeds: torch.Size([768])->torch.Size([1, 1, 1, 768]) text_encoder-item0.clip_prompt_embeds: torch.Size([77, 768])->torch.Size([1, 1, 77, 768]) text_encoder_2-item1.clip_pooled_prompt_embeds: torch.Size([1280])->torch.Size([1, 1, 1, 1280]) text_encoder_2-item1.clip_prompt_embeds: torch.Size([77, 1280])->torch.Size([1, 1, 77, 1280]) text_encoder_3-item2.t5_prompt_embeds: torch.Size([77, 4096])->torch.Size([1, 1, 77, 4096]) text_encoder-item3.clip_pooled_prompt_embeds: torch.Size([768])->torch.Size([1, 1, 1, 768]) text_encoder-item3.clip_prompt_embeds: torch.Size([77, 768])->torch.Size([1, 1, 77, 768]) text_encoder_2-item4.clip_pooled_prompt_embeds: torch.Size([1280])->torch.Size([1, 1, 1, 1280]) text_encoder_2-item4.clip_prompt_embeds: torch.Size([77, 1280])->torch.Size([1, 1, 77, 1280]) text_encoder_3-item5.t5_prompt_embeds: torch.Size([77, 4096])->torch.Size([1, 1, 77, 4096]) vae.encoder_f0: torch.Size([32, 128, 128])->torch.Size([1, 1, 512, 1024]) vae.encoder_f1: torch.Size([32, 128, 128])->torch.Size([1, 1, 512, 1024]) vae.decoder: torch.Size([16, 128, 128])->torch.Size([1, 1, 512, 512]) Using per-key quantization points (text_encoder-item0.clip_pooled_prompt_embeds: torch.Size([256])) for text_encoder-item0.clip_pooled_prompt_embeds text_encoder-item0.clip_pooled_prompt_embeds: 724B, BPFP=7.5417 Using per-key quantization points (text_encoder-item0.clip_prompt_embeds: torch.Size([256])) for text_encoder-item0.clip_prompt_embeds text_encoder-item0.clip_prompt_embeds: 6,012B, BPFP=0.8133 Using per-key quantization points (text_encoder_2-item1.clip_pooled_prompt_embeds: torch.Size([256])) for text_encoder_2-item1.clip_pooled_prompt_embeds text_encoder_2-item1.clip_pooled_prompt_embeds: 1,200B, BPFP=7.5000 Using per-key quantization points (text_encoder_2-item1.clip_prompt_embeds: torch.Size([256])) for text_encoder_2-item1.clip_prompt_embeds text_encoder_2-item1.clip_prompt_embeds: 11,508B, BPFP=0.9341 Using per-key quantization points (text_encoder_3-item2.t5_prompt_embeds: torch.Size([256])) for text_encoder_3-item2.t5_prompt_embeds text_encoder_3-item2.t5_prompt_embeds: 28,260B, BPFP=0.7168 Using per-key quantization points (text_encoder-item3.clip_pooled_prompt_embeds: torch.Size([256])) for text_encoder-item3.clip_pooled_prompt_embeds text_encoder-item3.clip_pooled_prompt_embeds: 760B, BPFP=7.9167 Using per-key quantization points (text_encoder-item3.clip_prompt_embeds: torch.Size([256])) for text_encoder-item3.clip_prompt_embeds text_encoder-item3.clip_prompt_embeds: 4,024B, BPFP=0.5444 Using per-key quantization points (text_encoder_2-item4.clip_pooled: torch.Size([256])) for text_encoder_2-item4.clip_pooled_prompt_embeds text_encoder_2-item4.clip_pooled_prompt_embeds: 1,268B, BPFP=7.9250 Using per-key quantization points (text_encoder_2-item4.clip_prompt: torch.Size([256])) for text_encoder_2-item4.clip_prompt_embeds text_encoder_2-item4.clip_prompt_embeds: 6,996B, BPFP=0.5679 Using per-key quantization points (text_encoder_3-item5.t5_prompt_embeds: torch.Size([256])) for text_encoder_3-item5.t5_prompt_embeds text_encoder_3-item5.t5_prompt_embeds: 18,416B, BPFP=0.4671 Using per-key quantization points (vae.encoder_f0: torch.Size([256])) for vae.encoder_f0 vae.encoder_f0: 19,768B, BPFP=0.3016 Using per-key quantization points (vae.encoder_f1: torch.Size([256])) for vae.encoder_f1 vae.encoder_f1: 19,768B, BPFP=0.3016 Using per-key quantization points (vae.decoder: torch.Size([256])) for vae.decoder vae.decoder: 6,420B, BPFP=0.1959 ⌛️ [2/4] FRONTEND: Frontend time: 0.299s (Pack+Encode) [3/4] BACKEND: Decode + Unpack... Using per-key quantization points (text_encoder-item0.clip_pooled_prompt_embeds: torch.Size([256])) for text_encoder-item0.clip_pooled_prompt_embeds Using per-key quantization points (text_encoder-item0.clip_prompt_embeds: torch.Size([256])) for text_encoder-item0.clip_prompt_embeds Using per-key quantization points (text_encoder_2-item1.clip_pooled_prompt_embeds: torch.Size([256])) for text_encoder_2-item1.clip_pooled_prompt_embeds Using per-key quantization points (text_encoder_2-item1.clip_prompt_embeds: torch.Size([256])) for text_encoder_2-item1.clip_prompt_embeds Using per-key quantization points (text_encoder_3-item2.t5_prompt_embeds: torch.Size([256])) for text_encoder_3-item2.t5_prompt_embeds Using per-key quantization points (text_encoder-item3.clip_pooled_prompt_embeds: torch.Size([256])) for text_encoder-item3.clip_pooled_prompt_embeds Using per-key quantization points (text_encoder-item3.clip_prompt_embeds: torch.Size([256])) for text_encoder-item3.clip_prompt_embeds Using per-key quantization points (text_encoder_2-item4.clip_pooled: torch.Size([256])) for text_encoder_2-item4.clip_pooled_prompt_embeds Using per-key quantization points (text_encoder_2-item4.clip_prompt: torch.Size([256])) for text_encoder_2-item4.clip_prompt_embeds Using per-key quantization points (text_encoder_3-item5.t5_prompt_embeds: torch.Size([256])) for text_encoder_3-item5.t5_prompt_embeds Using per-key quantization points (vae.encoder_f0: torch.Size([256])) for vae.encoder_f0 Using per-key quantization points (vae.encoder_f1: torch.Size([256])) for vae.encoder_f1 Using per-key quantization points (vae.decoder: torch.Size([256])) for vae.decoder IndividualUnPacker: text_encoder-item0.clip_pooled_prompt_embeds: torch.Size([768]) text_encoder-item0.clip_prompt_embeds: torch.Size([77, 768]) text_encoder_2-item1.clip_pooled_prompt_embeds: torch.Size([1280]) text_encoder_2-item1.clip_prompt_embeds: torch.Size([77, 1280]) text_encoder_3-item2.t5_prompt_embeds: torch.Size([77, 4096]) text_encoder-item3.clip_pooled_prompt_embeds: torch.Size([768]) text_encoder-item3.clip_prompt_embeds: torch.Size([77, 768]) text_encoder_2-item4.clip_pooled_prompt_embeds: torch.Size([1280]) text_encoder_2-item4.clip_prompt_embeds: torch.Size([77, 1280]) text_encoder_3-item5.t5_prompt_embeds: torch.Size([77, 4096]) vae.decoder: torch.Size([16, 128, 128]) vae.encoder_f0: torch.Size([32, 128, 128]) vae.encoder_f1: torch.Size([32, 128, 128]) ⌛️ [3/4] BACKEND: Backend time: 0.447s [4/4] METRICS: Computing MSE Breakdown... Using per-key quantization points (text_encoder-item0.clip_pooled_prompt_embeds: torch.Size([256])) for text_encoder-item0.clip_pooled_prompt_embeds Using per-key quantization points (text_encoder-item0.clip_prompt_embeds: torch.Size([256])) for text_encoder-item0.clip_prompt_embeds Using per-key quantization points (text_encoder_2-item1.clip_pooled_prompt_embeds: torch.Size([256])) for text_encoder_2-item1.clip_pooled_prompt_embeds Using per-key quantization points (text_encoder_2-item1.clip_prompt_embeds: torch.Size([256])) for text_encoder_2-item1.clip_prompt_embeds Using per-key quantization points (text_encoder_3-item2.t5_prompt_embeds: torch.Size([256])) for text_encoder_3-item2.t5_prompt_embeds Using per-key quantization points (text_encoder-item3.clip_pooled_prompt_embeds: torch.Size([256])) for text_encoder-item3.clip_pooled_prompt_embeds Using per-key quantization points (text_encoder-item3.clip_prompt_embeds: torch.Size([256])) for text_encoder-item3.clip_prompt_embeds Using per-key quantization points (text_encoder_2-item4.clip_pooled: torch.Size([256])) for text_encoder_2-item4.clip_pooled_prompt_embeds Using per-key quantization points (text_encoder_2-item4.clip_prompt: torch.Size([256])) for text_encoder_2-item4.clip_prompt_embeds Using per-key quantization points (text_encoder_3-item5.t5_prompt_embeds: torch.Size([256])) for text_encoder_3-item5.t5_prompt_embeds Using per-key quantization points (vae.encoder_f0: torch.Size([256])) for vae.encoder_f0 Using per-key quantization points (vae.encoder_f1: torch.Size([256])) for vae.encoder_f1 Using per-key quantization points (vae.decoder: torch.Size([256])) for vae.decoder IndividualUnPacker: text_encoder-item0.clip_pooled_prompt_embeds: torch.Size([768]) text_encoder-item0.clip_prompt_embeds: torch.Size([77, 768]) text_encoder_2-item1.clip_pooled_prompt_embeds: torch.Size([1280]) text_encoder_2-item1.clip_prompt_embeds: torch.Size([77, 1280]) text_encoder_3-item2.t5_prompt_embeds: torch.Size([77, 4096]) text_encoder-item3.clip_pooled_prompt_embeds: torch.Size([768]) text_encoder-item3.clip_prompt_embeds: torch.Size([77, 768]) text_encoder_2-item4.clip_pooled_prompt_embeds: torch.Size([1280]) text_encoder_2-item4.clip_prompt_embeds: torch.Size([77, 1280]) text_encoder_3-item5.t5_prompt_embeds: torch.Size([77, 4096]) vae.decoder: torch.Size([16, 128, 128]) vae.encoder_f0: torch.Size([32, 128, 128]) vae.encoder_f1: torch.Size([32, 128, 128]) Per-key MSE Breakdown (Quant=Truncation+Quantization, Codec=Compression): Key Quant-MSE Total-MSE ------------------------------------------------------------------------------------- text_encoder-item0.clip_pooled_prompt_embeds 0.00042462 0.96080939 text_encoder-item0.clip_prompt_embeds 0.00023188 23.94390050 text_encoder_2-item1.clip_pooled_prompt_embeds 0.00022679 0.91358891 text_encoder_2-item1.clip_prompt_embeds 0.00015622 0.12621648 text_encoder_3-item2.t5_prompt_embeds 0.00000782 0.00333941 text_encoder-item3.clip_pooled_prompt_embeds 0.00841799 1.01420029 text_encoder-item3.clip_prompt_embeds 0.00023247 23.90570887 text_encoder_2-item4.clip_pooled_prompt_embeds 0.00016751 0.67051430 text_encoder_2-item4.clip_prompt_embeds 0.00009830 0.47049568 text_encoder_3-item5.t5_prompt_embeds 0.00001263 0.00227117 vae.encoder_f0 0.00613207 2.56686640 vae.encoder_f1 0.00613899 2.56699157 vae.decoder 0.00023812 0.05780491 ------------------------------------------------------------------------------------- TOTAL 0.00290239 2.47702503 (elements=2,260,992) ---------------------- -------------------------------------------------------- SAMPLE-WISE STATISTICS ---------------------- -------------------------------------------------------- Handler sd35 Strategy individual Architecture hyperprior-featurecoding ---------------------- -------------------------------------------------------- Total Elements 2260992 Total Bytes 125124 BPFP 0.4427 bits/point EBPFP 0.8854 equivalent bits/point MSE 2.477025 ---------------------- -------------------------------------------------------- Time: 0.755s Load: 0.009s, Pack+Encode: 0.299s, Decode+Unpack: 0.447s ---------------------- -------------------------------------------------------- Restored Feature Format: [Dict] with 8 keys key['text_encoder-item0']: [Dict] with 2 keys key['clip_pooled_prompt_embeds']: [Tensor] shape=torch.Size([768]), dtype=torch.float16, device=cpu key['clip_prompt_embeds']: [Tensor] shape=torch.Size([77, 768]), dtype=torch.float16, device=cpu key['text_encoder_2-item1']: [Dict] with 2 keys key['clip_pooled_prompt_embeds']: [Tensor] shape=torch.Size([1280]), dtype=torch.float16, device=cpu key['clip_prompt_embeds']: [Tensor] shape=torch.Size([77, 1280]), dtype=torch.float16, device=cpu key['text_encoder_3-item2']: [Dict] with 1 keys key['t5_prompt_embeds']: [Tensor] shape=torch.Size([77, 4096]), dtype=torch.float16, device=cpu key['text_encoder-item3']: [Dict] with 2 keys key['clip_pooled_prompt_embeds']: [Tensor] shape=torch.Size([768]), dtype=torch.float16, device=cpu key['clip_prompt_embeds']: [Tensor] shape=torch.Size([77, 768]), dtype=torch.float16, device=cpu key['text_encoder_2-item4']: [Dict] with 2 keys key['clip_pooled_prompt_embeds']: [Tensor] shape=torch.Size([1280]), dtype=torch.float16, device=cpu key['clip_prompt_embeds']: [Tensor] shape=torch.Size([77, 1280]), dtype=torch.float16, device=cpu key['text_encoder_3-item5']: [Dict] with 1 keys key['t5_prompt_embeds']: [Tensor] shape=torch.Size([77, 4096]), dtype=torch.float16, device=cpu key['vae.decoder']: [Tensor] shape=torch.Size([16, 128, 128]), dtype=torch.float16, device=cpu key['vae.encoder']: [Dict] with 2 keys key['vae.encoder_f0']: [Tensor] shape=torch.Size([32, 128, 128]), dtype=torch.float16, device=cpu key['vae.encoder_f1']: [Tensor] shape=torch.Size([32, 128, 128]), dtype=torch.float16, device=cpu 💾 Converting with 2.4770 MSE: from ../datasets/FeatureCoding-StableDiffusion3.5/100samples_features/000000342186.zst to output-fixed/sd35/lambda0.004/hyperprior-featurecoding-8bit-individual/sd35cond/000000342186.zst 💪 Processing: ../datasets/FeatureCoding-StableDiffusion3.5/100samples_features/000000343976.zst (57/100) [1/4] FRONTEND: Loading features from ../datasets/FeatureCoding-StableDiffusion3.5/100samples_features/000000343976.zst... Original data structure: root: [Dict] with 8 keys key['text_encoder-item0']: [Dict] with 2 keys key['clip_pooled_prompt_embeds']: [Tensor] shape=torch.Size([768]), dtype=torch.float16, device=cpu key['clip_prompt_embeds']: [Tensor] shape=torch.Size([77, 768]), dtype=torch.float16, device=cpu key['text_encoder_2-item1']: [Dict] with 2 keys key['clip_pooled_prompt_embeds']: [Tensor] shape=torch.Size([1280]), dtype=torch.float16, device=cpu key['clip_prompt_embeds']: [Tensor] shape=torch.Size([77, 1280]), dtype=torch.float16, device=cpu key['text_encoder_3-item2']: [Dict] with 1 keys key['t5_prompt_embeds']: [Tensor] shape=torch.Size([77, 4096]), dtype=torch.float16, device=cpu key['text_encoder-item3']: [Dict] with 2 keys key['clip_pooled_prompt_embeds']: [Tensor] shape=torch.Size([768]), dtype=torch.float16, device=cpu key['clip_prompt_embeds']: [Tensor] shape=torch.Size([77, 768]), dtype=torch.float16, device=cpu key['text_encoder_2-item4']: [Dict] with 2 keys key['clip_pooled_prompt_embeds']: [Tensor] shape=torch.Size([1280]), dtype=torch.float16, device=cpu key['clip_prompt_embeds']: [Tensor] shape=torch.Size([77, 1280]), dtype=torch.float16, device=cpu key['text_encoder_3-item5']: [Dict] with 1 keys key['t5_prompt_embeds']: [Tensor] shape=torch.Size([77, 4096]), dtype=torch.float16, device=cpu key['vae.encoder-item6']: [Dict] with 2 keys key['vae.encoder_f0']: [Tensor] shape=torch.Size([32, 128, 128]), dtype=torch.float16, device=cpu key['vae.encoder_f1']: [Tensor] shape=torch.Size([32, 128, 128]), dtype=torch.float16, device=cpu key['vae.decoder']: [Tensor] shape=torch.Size([16, 128, 128]), dtype=torch.float16, device=cpu ⌛️ [1/4] FRONTEND: Load time: 0.009s ------------------------------------------------------------ SD3.5 Features Summary ------------------------------------------------------------ Number of text encoders: 3 Encoder names: ['text_encoder', 'text_encoder_2', 'text_encoder_3'] Has VAE latents: True Has VAE encoder features: True Data type: torch.float16 text_encoder-item0: clip_pooled_prompt_embeds: torch.Size([768]), torch.float16 clip_prompt_embeds: torch.Size([77, 768]), torch.float16 text_encoder_2-item1: clip_pooled_prompt_embeds: torch.Size([1280]), torch.float16 clip_prompt_embeds: torch.Size([77, 1280]), torch.float16 text_encoder_3-item2: t5_prompt_embeds: torch.Size([77, 4096]), torch.float16 text_encoder-item3: clip_pooled_prompt_embeds: torch.Size([768]), torch.float16 clip_prompt_embeds: torch.Size([77, 768]), torch.float16 text_encoder_2-item4: clip_pooled_prompt_embeds: torch.Size([1280]), torch.float16 clip_prompt_embeds: torch.Size([77, 1280]), torch.float16 text_encoder_3-item5: t5_prompt_embeds: torch.Size([77, 4096]), torch.float16 vae.decoder latents: torch.Size([16, 128, 128]) vae.encoder features: vae.encoder_f0: torch.Size([32, 128, 128]), torch.float16 vae.encoder_f1: torch.Size([32, 128, 128]), torch.float16 ------------------------------------------------------------ [2/4] FRONTEND: Pack + Encode (strategy: individual)... IndividualPacker: text_encoder-item0.clip_pooled_prompt_embeds: torch.Size([768])->torch.Size([1, 1, 1, 768]) text_encoder-item0.clip_prompt_embeds: torch.Size([77, 768])->torch.Size([1, 1, 77, 768]) text_encoder_2-item1.clip_pooled_prompt_embeds: torch.Size([1280])->torch.Size([1, 1, 1, 1280]) text_encoder_2-item1.clip_prompt_embeds: torch.Size([77, 1280])->torch.Size([1, 1, 77, 1280]) text_encoder_3-item2.t5_prompt_embeds: torch.Size([77, 4096])->torch.Size([1, 1, 77, 4096]) text_encoder-item3.clip_pooled_prompt_embeds: torch.Size([768])->torch.Size([1, 1, 1, 768]) text_encoder-item3.clip_prompt_embeds: torch.Size([77, 768])->torch.Size([1, 1, 77, 768]) text_encoder_2-item4.clip_pooled_prompt_embeds: torch.Size([1280])->torch.Size([1, 1, 1, 1280]) text_encoder_2-item4.clip_prompt_embeds: torch.Size([77, 1280])->torch.Size([1, 1, 77, 1280]) text_encoder_3-item5.t5_prompt_embeds: torch.Size([77, 4096])->torch.Size([1, 1, 77, 4096]) vae.encoder_f0: torch.Size([32, 128, 128])->torch.Size([1, 1, 512, 1024]) vae.encoder_f1: torch.Size([32, 128, 128])->torch.Size([1, 1, 512, 1024]) vae.decoder: torch.Size([16, 128, 128])->torch.Size([1, 1, 512, 512]) Using per-key quantization points (text_encoder-item0.clip_pooled_prompt_embeds: torch.Size([256])) for text_encoder-item0.clip_pooled_prompt_embeds text_encoder-item0.clip_pooled_prompt_embeds: 732B, BPFP=7.6250 Using per-key quantization points (text_encoder-item0.clip_prompt_embeds: torch.Size([256])) for text_encoder-item0.clip_prompt_embeds text_encoder-item0.clip_prompt_embeds: 6,360B, BPFP=0.8604 Using per-key quantization points (text_encoder_2-item1.clip_pooled_prompt_embeds: torch.Size([256])) for text_encoder_2-item1.clip_pooled_prompt_embeds text_encoder_2-item1.clip_pooled_prompt_embeds: 1,200B, BPFP=7.5000 Using per-key quantization points (text_encoder_2-item1.clip_prompt_embeds: torch.Size([256])) for text_encoder_2-item1.clip_prompt_embeds text_encoder_2-item1.clip_prompt_embeds: 11,864B, BPFP=0.9630 Using per-key quantization points (text_encoder_3-item2.t5_prompt_embeds: torch.Size([256])) for text_encoder_3-item2.t5_prompt_embeds text_encoder_3-item2.t5_prompt_embeds: 28,048B, BPFP=0.7114 Using per-key quantization points (text_encoder-item3.clip_pooled_prompt_embeds: torch.Size([256])) for text_encoder-item3.clip_pooled_prompt_embeds text_encoder-item3.clip_pooled_prompt_embeds: 760B, BPFP=7.9167 Using per-key quantization points (text_encoder-item3.clip_prompt_embeds: torch.Size([256])) for text_encoder-item3.clip_prompt_embeds text_encoder-item3.clip_prompt_embeds: 4,024B, BPFP=0.5444 Using per-key quantization points (text_encoder_2-item4.clip_pooled: torch.Size([256])) for text_encoder_2-item4.clip_pooled_prompt_embeds text_encoder_2-item4.clip_pooled_prompt_embeds: 1,268B, BPFP=7.9250 Using per-key quantization points (text_encoder_2-item4.clip_prompt: torch.Size([256])) for text_encoder_2-item4.clip_prompt_embeds text_encoder_2-item4.clip_prompt_embeds: 6,996B, BPFP=0.5679 Using per-key quantization points (text_encoder_3-item5.t5_prompt_embeds: torch.Size([256])) for text_encoder_3-item5.t5_prompt_embeds text_encoder_3-item5.t5_prompt_embeds: 18,416B, BPFP=0.4671 Using per-key quantization points (vae.encoder_f0: torch.Size([256])) for vae.encoder_f0 vae.encoder_f0: 17,936B, BPFP=0.2737 Using per-key quantization points (vae.encoder_f1: torch.Size([256])) for vae.encoder_f1 vae.encoder_f1: 17,936B, BPFP=0.2737 Using per-key quantization points (vae.decoder: torch.Size([256])) for vae.decoder vae.decoder: 7,252B, BPFP=0.2213 ⌛️ [2/4] FRONTEND: Frontend time: 0.296s (Pack+Encode) [3/4] BACKEND: Decode + Unpack... Using per-key quantization points (text_encoder-item0.clip_pooled_prompt_embeds: torch.Size([256])) for text_encoder-item0.clip_pooled_prompt_embeds Using per-key quantization points (text_encoder-item0.clip_prompt_embeds: torch.Size([256])) for text_encoder-item0.clip_prompt_embeds Using per-key quantization points (text_encoder_2-item1.clip_pooled_prompt_embeds: torch.Size([256])) for text_encoder_2-item1.clip_pooled_prompt_embeds Using per-key quantization points (text_encoder_2-item1.clip_prompt_embeds: torch.Size([256])) for text_encoder_2-item1.clip_prompt_embeds Using per-key quantization points (text_encoder_3-item2.t5_prompt_embeds: torch.Size([256])) for text_encoder_3-item2.t5_prompt_embeds Using per-key quantization points (text_encoder-item3.clip_pooled_prompt_embeds: torch.Size([256])) for text_encoder-item3.clip_pooled_prompt_embeds Using per-key quantization points (text_encoder-item3.clip_prompt_embeds: torch.Size([256])) for text_encoder-item3.clip_prompt_embeds Using per-key quantization points (text_encoder_2-item4.clip_pooled: torch.Size([256])) for text_encoder_2-item4.clip_pooled_prompt_embeds Using per-key quantization points (text_encoder_2-item4.clip_prompt: torch.Size([256])) for text_encoder_2-item4.clip_prompt_embeds Using per-key quantization points (text_encoder_3-item5.t5_prompt_embeds: torch.Size([256])) for text_encoder_3-item5.t5_prompt_embeds Using per-key quantization points (vae.encoder_f0: torch.Size([256])) for vae.encoder_f0 Using per-key quantization points (vae.encoder_f1: torch.Size([256])) for vae.encoder_f1 Using per-key quantization points (vae.decoder: torch.Size([256])) for vae.decoder IndividualUnPacker: text_encoder-item0.clip_pooled_prompt_embeds: torch.Size([768]) text_encoder-item0.clip_prompt_embeds: torch.Size([77, 768]) text_encoder_2-item1.clip_pooled_prompt_embeds: torch.Size([1280]) text_encoder_2-item1.clip_prompt_embeds: torch.Size([77, 1280]) text_encoder_3-item2.t5_prompt_embeds: torch.Size([77, 4096]) text_encoder-item3.clip_pooled_prompt_embeds: torch.Size([768]) text_encoder-item3.clip_prompt_embeds: torch.Size([77, 768]) text_encoder_2-item4.clip_pooled_prompt_embeds: torch.Size([1280]) text_encoder_2-item4.clip_prompt_embeds: torch.Size([77, 1280]) text_encoder_3-item5.t5_prompt_embeds: torch.Size([77, 4096]) vae.decoder: torch.Size([16, 128, 128]) vae.encoder_f0: torch.Size([32, 128, 128]) vae.encoder_f1: torch.Size([32, 128, 128]) ⌛️ [3/4] BACKEND: Backend time: 0.449s [4/4] METRICS: Computing MSE Breakdown... Using per-key quantization points (text_encoder-item0.clip_pooled_prompt_embeds: torch.Size([256])) for text_encoder-item0.clip_pooled_prompt_embeds Using per-key quantization points (text_encoder-item0.clip_prompt_embeds: torch.Size([256])) for text_encoder-item0.clip_prompt_embeds Using per-key quantization points (text_encoder_2-item1.clip_pooled_prompt_embeds: torch.Size([256])) for text_encoder_2-item1.clip_pooled_prompt_embeds Using per-key quantization points (text_encoder_2-item1.clip_prompt_embeds: torch.Size([256])) for text_encoder_2-item1.clip_prompt_embeds Using per-key quantization points (text_encoder_3-item2.t5_prompt_embeds: torch.Size([256])) for text_encoder_3-item2.t5_prompt_embeds Using per-key quantization points (text_encoder-item3.clip_pooled_prompt_embeds: torch.Size([256])) for text_encoder-item3.clip_pooled_prompt_embeds Using per-key quantization points (text_encoder-item3.clip_prompt_embeds: torch.Size([256])) for text_encoder-item3.clip_prompt_embeds Using per-key quantization points (text_encoder_2-item4.clip_pooled: torch.Size([256])) for text_encoder_2-item4.clip_pooled_prompt_embeds Using per-key quantization points (text_encoder_2-item4.clip_prompt: torch.Size([256])) for text_encoder_2-item4.clip_prompt_embeds Using per-key quantization points (text_encoder_3-item5.t5_prompt_embeds: torch.Size([256])) for text_encoder_3-item5.t5_prompt_embeds Using per-key quantization points (vae.encoder_f0: torch.Size([256])) for vae.encoder_f0 Using per-key quantization points (vae.encoder_f1: torch.Size([256])) for vae.encoder_f1 Using per-key quantization points (vae.decoder: torch.Size([256])) for vae.decoder IndividualUnPacker: text_encoder-item0.clip_pooled_prompt_embeds: torch.Size([768]) text_encoder-item0.clip_prompt_embeds: torch.Size([77, 768]) text_encoder_2-item1.clip_pooled_prompt_embeds: torch.Size([1280]) text_encoder_2-item1.clip_prompt_embeds: torch.Size([77, 1280]) text_encoder_3-item2.t5_prompt_embeds: torch.Size([77, 4096]) text_encoder-item3.clip_pooled_prompt_embeds: torch.Size([768]) text_encoder-item3.clip_prompt_embeds: torch.Size([77, 768]) text_encoder_2-item4.clip_pooled_prompt_embeds: torch.Size([1280]) text_encoder_2-item4.clip_prompt_embeds: torch.Size([77, 1280]) text_encoder_3-item5.t5_prompt_embeds: torch.Size([77, 4096]) vae.decoder: torch.Size([16, 128, 128]) vae.encoder_f0: torch.Size([32, 128, 128]) vae.encoder_f1: torch.Size([32, 128, 128]) Per-key MSE Breakdown (Quant=Truncation+Quantization, Codec=Compression): Key Quant-MSE Total-MSE ------------------------------------------------------------------------------------- text_encoder-item0.clip_pooled_prompt_embeds 0.00019246 0.90121889 text_encoder-item0.clip_prompt_embeds 0.00023678 23.94834788 text_encoder_2-item1.clip_pooled_prompt_embeds 0.00028948 0.94244337 text_encoder_2-item1.clip_prompt_embeds 0.00019061 0.14236449 text_encoder_3-item2.t5_prompt_embeds 0.00000767 0.00324382 text_encoder-item3.clip_pooled_prompt_embeds 0.00841799 1.01420029 text_encoder-item3.clip_prompt_embeds 0.00023247 23.90570887 text_encoder_2-item4.clip_pooled_prompt_embeds 0.00016751 0.67051430 text_encoder_2-item4.clip_prompt_embeds 0.00009830 0.47049568 text_encoder_3-item5.t5_prompt_embeds 0.00001263 0.00227117 vae.encoder_f0 0.00636537 2.79507971 vae.encoder_f1 0.00636991 2.79422116 vae.decoder 0.00025538 0.05639221 ------------------------------------------------------------------------------------- TOTAL 0.00301360 2.58327418 (elements=2,260,992) ---------------------- -------------------------------------------------------- SAMPLE-WISE STATISTICS ---------------------- -------------------------------------------------------- Handler sd35 Strategy individual Architecture hyperprior-featurecoding ---------------------- -------------------------------------------------------- Total Elements 2260992 Total Bytes 122792 BPFP 0.4345 bits/point EBPFP 0.8689 equivalent bits/point MSE 2.583274 ---------------------- -------------------------------------------------------- Time: 0.755s Load: 0.009s, Pack+Encode: 0.296s, Decode+Unpack: 0.449s ---------------------- -------------------------------------------------------- Restored Feature Format: [Dict] with 8 keys key['text_encoder-item0']: [Dict] with 2 keys key['clip_pooled_prompt_embeds']: [Tensor] shape=torch.Size([768]), dtype=torch.float16, device=cpu key['clip_prompt_embeds']: [Tensor] shape=torch.Size([77, 768]), dtype=torch.float16, device=cpu key['text_encoder_2-item1']: [Dict] with 2 keys key['clip_pooled_prompt_embeds']: [Tensor] shape=torch.Size([1280]), dtype=torch.float16, device=cpu key['clip_prompt_embeds']: [Tensor] shape=torch.Size([77, 1280]), dtype=torch.float16, device=cpu key['text_encoder_3-item2']: [Dict] with 1 keys key['t5_prompt_embeds']: [Tensor] shape=torch.Size([77, 4096]), dtype=torch.float16, device=cpu key['text_encoder-item3']: [Dict] with 2 keys key['clip_pooled_prompt_embeds']: [Tensor] shape=torch.Size([768]), dtype=torch.float16, device=cpu key['clip_prompt_embeds']: [Tensor] shape=torch.Size([77, 768]), dtype=torch.float16, device=cpu key['text_encoder_2-item4']: [Dict] with 2 keys key['clip_pooled_prompt_embeds']: [Tensor] shape=torch.Size([1280]), dtype=torch.float16, device=cpu key['clip_prompt_embeds']: [Tensor] shape=torch.Size([77, 1280]), dtype=torch.float16, device=cpu key['text_encoder_3-item5']: [Dict] with 1 keys key['t5_prompt_embeds']: [Tensor] shape=torch.Size([77, 4096]), dtype=torch.float16, device=cpu key['vae.decoder']: [Tensor] shape=torch.Size([16, 128, 128]), dtype=torch.float16, device=cpu key['vae.encoder']: [Dict] with 2 keys key['vae.encoder_f0']: [Tensor] shape=torch.Size([32, 128, 128]), dtype=torch.float16, device=cpu key['vae.encoder_f1']: [Tensor] shape=torch.Size([32, 128, 128]), dtype=torch.float16, device=cpu 💾 Converting with 2.5833 MSE: from ../datasets/FeatureCoding-StableDiffusion3.5/100samples_features/000000343976.zst to output-fixed/sd35/lambda0.004/hyperprior-featurecoding-8bit-individual/sd35cond/000000343976.zst 💪 Processing: ../datasets/FeatureCoding-StableDiffusion3.5/100samples_features/000000351362.zst (58/100) [1/4] FRONTEND: Loading features from ../datasets/FeatureCoding-StableDiffusion3.5/100samples_features/000000351362.zst... Original data structure: root: [Dict] with 8 keys key['text_encoder-item0']: [Dict] with 2 keys key['clip_pooled_prompt_embeds']: [Tensor] shape=torch.Size([768]), dtype=torch.float16, device=cpu key['clip_prompt_embeds']: [Tensor] shape=torch.Size([77, 768]), dtype=torch.float16, device=cpu key['text_encoder_2-item1']: [Dict] with 2 keys key['clip_pooled_prompt_embeds']: [Tensor] shape=torch.Size([1280]), dtype=torch.float16, device=cpu key['clip_prompt_embeds']: [Tensor] shape=torch.Size([77, 1280]), dtype=torch.float16, device=cpu key['text_encoder_3-item2']: [Dict] with 1 keys key['t5_prompt_embeds']: [Tensor] shape=torch.Size([77, 4096]), dtype=torch.float16, device=cpu key['text_encoder-item3']: [Dict] with 2 keys key['clip_pooled_prompt_embeds']: [Tensor] shape=torch.Size([768]), dtype=torch.float16, device=cpu key['clip_prompt_embeds']: [Tensor] shape=torch.Size([77, 768]), dtype=torch.float16, device=cpu key['text_encoder_2-item4']: [Dict] with 2 keys key['clip_pooled_prompt_embeds']: [Tensor] shape=torch.Size([1280]), dtype=torch.float16, device=cpu key['clip_prompt_embeds']: [Tensor] shape=torch.Size([77, 1280]), dtype=torch.float16, device=cpu key['text_encoder_3-item5']: [Dict] with 1 keys key['t5_prompt_embeds']: [Tensor] shape=torch.Size([77, 4096]), dtype=torch.float16, device=cpu key['vae.encoder-item6']: [Dict] with 2 keys key['vae.encoder_f0']: [Tensor] shape=torch.Size([32, 128, 128]), dtype=torch.float16, device=cpu key['vae.encoder_f1']: [Tensor] shape=torch.Size([32, 128, 128]), dtype=torch.float16, device=cpu key['vae.decoder']: [Tensor] shape=torch.Size([16, 128, 128]), dtype=torch.float16, device=cpu ⌛️ [1/4] FRONTEND: Load time: 0.008s ------------------------------------------------------------ SD3.5 Features Summary ------------------------------------------------------------ Number of text encoders: 3 Encoder names: ['text_encoder', 'text_encoder_2', 'text_encoder_3'] Has VAE latents: True Has VAE encoder features: True Data type: torch.float16 text_encoder-item0: clip_pooled_prompt_embeds: torch.Size([768]), torch.float16 clip_prompt_embeds: torch.Size([77, 768]), torch.float16 text_encoder_2-item1: clip_pooled_prompt_embeds: torch.Size([1280]), torch.float16 clip_prompt_embeds: torch.Size([77, 1280]), torch.float16 text_encoder_3-item2: t5_prompt_embeds: torch.Size([77, 4096]), torch.float16 text_encoder-item3: clip_pooled_prompt_embeds: torch.Size([768]), torch.float16 clip_prompt_embeds: torch.Size([77, 768]), torch.float16 text_encoder_2-item4: clip_pooled_prompt_embeds: torch.Size([1280]), torch.float16 clip_prompt_embeds: torch.Size([77, 1280]), torch.float16 text_encoder_3-item5: t5_prompt_embeds: torch.Size([77, 4096]), torch.float16 vae.decoder latents: torch.Size([16, 128, 128]) vae.encoder features: vae.encoder_f0: torch.Size([32, 128, 128]), torch.float16 vae.encoder_f1: torch.Size([32, 128, 128]), torch.float16 ------------------------------------------------------------ [2/4] FRONTEND: Pack + Encode (strategy: individual)... IndividualPacker: text_encoder-item0.clip_pooled_prompt_embeds: torch.Size([768])->torch.Size([1, 1, 1, 768]) text_encoder-item0.clip_prompt_embeds: torch.Size([77, 768])->torch.Size([1, 1, 77, 768]) text_encoder_2-item1.clip_pooled_prompt_embeds: torch.Size([1280])->torch.Size([1, 1, 1, 1280]) text_encoder_2-item1.clip_prompt_embeds: torch.Size([77, 1280])->torch.Size([1, 1, 77, 1280]) text_encoder_3-item2.t5_prompt_embeds: torch.Size([77, 4096])->torch.Size([1, 1, 77, 4096]) text_encoder-item3.clip_pooled_prompt_embeds: torch.Size([768])->torch.Size([1, 1, 1, 768]) text_encoder-item3.clip_prompt_embeds: torch.Size([77, 768])->torch.Size([1, 1, 77, 768]) text_encoder_2-item4.clip_pooled_prompt_embeds: torch.Size([1280])->torch.Size([1, 1, 1, 1280]) text_encoder_2-item4.clip_prompt_embeds: torch.Size([77, 1280])->torch.Size([1, 1, 77, 1280]) text_encoder_3-item5.t5_prompt_embeds: torch.Size([77, 4096])->torch.Size([1, 1, 77, 4096]) vae.encoder_f0: torch.Size([32, 128, 128])->torch.Size([1, 1, 512, 1024]) vae.encoder_f1: torch.Size([32, 128, 128])->torch.Size([1, 1, 512, 1024]) vae.decoder: torch.Size([16, 128, 128])->torch.Size([1, 1, 512, 512]) Using per-key quantization points (text_encoder-item0.clip_pooled_prompt_embeds: torch.Size([256])) for text_encoder-item0.clip_pooled_prompt_embeds text_encoder-item0.clip_pooled_prompt_embeds: 728B, BPFP=7.5833 Using per-key quantization points (text_encoder-item0.clip_prompt_embeds: torch.Size([256])) for text_encoder-item0.clip_prompt_embeds text_encoder-item0.clip_prompt_embeds: 5,352B, BPFP=0.7240 Using per-key quantization points (text_encoder_2-item1.clip_pooled_prompt_embeds: torch.Size([256])) for text_encoder_2-item1.clip_pooled_prompt_embeds text_encoder_2-item1.clip_pooled_prompt_embeds: 1,188B, BPFP=7.4250 Using per-key quantization points (text_encoder_2-item1.clip_prompt_embeds: torch.Size([256])) for text_encoder_2-item1.clip_prompt_embeds text_encoder_2-item1.clip_prompt_embeds: 10,456B, BPFP=0.8487 Using per-key quantization points (text_encoder_3-item2.t5_prompt_embeds: torch.Size([256])) for text_encoder_3-item2.t5_prompt_embeds text_encoder_3-item2.t5_prompt_embeds: 24,020B, BPFP=0.6093 Using per-key quantization points (text_encoder-item3.clip_pooled_prompt_embeds: torch.Size([256])) for text_encoder-item3.clip_pooled_prompt_embeds text_encoder-item3.clip_pooled_prompt_embeds: 760B, BPFP=7.9167 Using per-key quantization points (text_encoder-item3.clip_prompt_embeds: torch.Size([256])) for text_encoder-item3.clip_prompt_embeds text_encoder-item3.clip_prompt_embeds: 4,024B, BPFP=0.5444 Using per-key quantization points (text_encoder_2-item4.clip_pooled: torch.Size([256])) for text_encoder_2-item4.clip_pooled_prompt_embeds text_encoder_2-item4.clip_pooled_prompt_embeds: 1,268B, BPFP=7.9250 Using per-key quantization points (text_encoder_2-item4.clip_prompt: torch.Size([256])) for text_encoder_2-item4.clip_prompt_embeds text_encoder_2-item4.clip_prompt_embeds: 6,996B, BPFP=0.5679 Using per-key quantization points (text_encoder_3-item5.t5_prompt_embeds: torch.Size([256])) for text_encoder_3-item5.t5_prompt_embeds text_encoder_3-item5.t5_prompt_embeds: 18,416B, BPFP=0.4671 Using per-key quantization points (vae.encoder_f0: torch.Size([256])) for vae.encoder_f0 vae.encoder_f0: 18,496B, BPFP=0.2822 Using per-key quantization points (vae.encoder_f1: torch.Size([256])) for vae.encoder_f1 vae.encoder_f1: 18,496B, BPFP=0.2822 Using per-key quantization points (vae.decoder: torch.Size([256])) for vae.decoder vae.decoder: 5,212B, BPFP=0.1591 ⌛️ [2/4] FRONTEND: Frontend time: 0.297s (Pack+Encode) [3/4] BACKEND: Decode + Unpack... Using per-key quantization points (text_encoder-item0.clip_pooled_prompt_embeds: torch.Size([256])) for text_encoder-item0.clip_pooled_prompt_embeds Using per-key quantization points (text_encoder-item0.clip_prompt_embeds: torch.Size([256])) for text_encoder-item0.clip_prompt_embeds Using per-key quantization points (text_encoder_2-item1.clip_pooled_prompt_embeds: torch.Size([256])) for text_encoder_2-item1.clip_pooled_prompt_embeds Using per-key quantization points (text_encoder_2-item1.clip_prompt_embeds: torch.Size([256])) for text_encoder_2-item1.clip_prompt_embeds Using per-key quantization points (text_encoder_3-item2.t5_prompt_embeds: torch.Size([256])) for text_encoder_3-item2.t5_prompt_embeds Using per-key quantization points (text_encoder-item3.clip_pooled_prompt_embeds: torch.Size([256])) for text_encoder-item3.clip_pooled_prompt_embeds Using per-key quantization points (text_encoder-item3.clip_prompt_embeds: torch.Size([256])) for text_encoder-item3.clip_prompt_embeds Using per-key quantization points (text_encoder_2-item4.clip_pooled: torch.Size([256])) for text_encoder_2-item4.clip_pooled_prompt_embeds Using per-key quantization points (text_encoder_2-item4.clip_prompt: torch.Size([256])) for text_encoder_2-item4.clip_prompt_embeds Using per-key quantization points (text_encoder_3-item5.t5_prompt_embeds: torch.Size([256])) for text_encoder_3-item5.t5_prompt_embeds Using per-key quantization points (vae.encoder_f0: torch.Size([256])) for vae.encoder_f0 Using per-key quantization points (vae.encoder_f1: torch.Size([256])) for vae.encoder_f1 Using per-key quantization points (vae.decoder: torch.Size([256])) for vae.decoder IndividualUnPacker: text_encoder-item0.clip_pooled_prompt_embeds: torch.Size([768]) text_encoder-item0.clip_prompt_embeds: torch.Size([77, 768]) text_encoder_2-item1.clip_pooled_prompt_embeds: torch.Size([1280]) text_encoder_2-item1.clip_prompt_embeds: torch.Size([77, 1280]) text_encoder_3-item2.t5_prompt_embeds: torch.Size([77, 4096]) text_encoder-item3.clip_pooled_prompt_embeds: torch.Size([768]) text_encoder-item3.clip_prompt_embeds: torch.Size([77, 768]) text_encoder_2-item4.clip_pooled_prompt_embeds: torch.Size([1280]) text_encoder_2-item4.clip_prompt_embeds: torch.Size([77, 1280]) text_encoder_3-item5.t5_prompt_embeds: torch.Size([77, 4096]) vae.decoder: torch.Size([16, 128, 128]) vae.encoder_f0: torch.Size([32, 128, 128]) vae.encoder_f1: torch.Size([32, 128, 128]) ⌛️ [3/4] BACKEND: Backend time: 0.453s [4/4] METRICS: Computing MSE Breakdown... Using per-key quantization points (text_encoder-item0.clip_pooled_prompt_embeds: torch.Size([256])) for text_encoder-item0.clip_pooled_prompt_embeds Using per-key quantization points (text_encoder-item0.clip_prompt_embeds: torch.Size([256])) for text_encoder-item0.clip_prompt_embeds Using per-key quantization points (text_encoder_2-item1.clip_pooled_prompt_embeds: torch.Size([256])) for text_encoder_2-item1.clip_pooled_prompt_embeds Using per-key quantization points (text_encoder_2-item1.clip_prompt_embeds: torch.Size([256])) for text_encoder_2-item1.clip_prompt_embeds Using per-key quantization points (text_encoder_3-item2.t5_prompt_embeds: torch.Size([256])) for text_encoder_3-item2.t5_prompt_embeds Using per-key quantization points (text_encoder-item3.clip_pooled_prompt_embeds: torch.Size([256])) for text_encoder-item3.clip_pooled_prompt_embeds Using per-key quantization points (text_encoder-item3.clip_prompt_embeds: torch.Size([256])) for text_encoder-item3.clip_prompt_embeds Using per-key quantization points (text_encoder_2-item4.clip_pooled: torch.Size([256])) for text_encoder_2-item4.clip_pooled_prompt_embeds Using per-key quantization points (text_encoder_2-item4.clip_prompt: torch.Size([256])) for text_encoder_2-item4.clip_prompt_embeds Using per-key quantization points (text_encoder_3-item5.t5_prompt_embeds: torch.Size([256])) for text_encoder_3-item5.t5_prompt_embeds Using per-key quantization points (vae.encoder_f0: torch.Size([256])) for vae.encoder_f0 Using per-key quantization points (vae.encoder_f1: torch.Size([256])) for vae.encoder_f1 Using per-key quantization points (vae.decoder: torch.Size([256])) for vae.decoder IndividualUnPacker: text_encoder-item0.clip_pooled_prompt_embeds: torch.Size([768]) text_encoder-item0.clip_prompt_embeds: torch.Size([77, 768]) text_encoder_2-item1.clip_pooled_prompt_embeds: torch.Size([1280]) text_encoder_2-item1.clip_prompt_embeds: torch.Size([77, 1280]) text_encoder_3-item2.t5_prompt_embeds: torch.Size([77, 4096]) text_encoder-item3.clip_pooled_prompt_embeds: torch.Size([768]) text_encoder-item3.clip_prompt_embeds: torch.Size([77, 768]) text_encoder_2-item4.clip_pooled_prompt_embeds: torch.Size([1280]) text_encoder_2-item4.clip_prompt_embeds: torch.Size([77, 1280]) text_encoder_3-item5.t5_prompt_embeds: torch.Size([77, 4096]) vae.decoder: torch.Size([16, 128, 128]) vae.encoder_f0: torch.Size([32, 128, 128]) vae.encoder_f1: torch.Size([32, 128, 128]) Per-key MSE Breakdown (Quant=Truncation+Quantization, Codec=Compression): Key Quant-MSE Total-MSE ------------------------------------------------------------------------------------- text_encoder-item0.clip_pooled_prompt_embeds 0.00036983 0.94918998 text_encoder-item0.clip_prompt_embeds 0.00023432 23.94796951 text_encoder_2-item1.clip_pooled_prompt_embeds 0.00018703 0.81648350 text_encoder_2-item1.clip_prompt_embeds 0.00017889 0.11644047 text_encoder_3-item2.t5_prompt_embeds 0.00000811 0.00331256 text_encoder-item3.clip_pooled_prompt_embeds 0.00841799 1.01420029 text_encoder-item3.clip_prompt_embeds 0.00023247 23.90570887 text_encoder_2-item4.clip_pooled_prompt_embeds 0.00016751 0.67051430 text_encoder_2-item4.clip_prompt_embeds 0.00009830 0.47049568 text_encoder_3-item5.t5_prompt_embeds 0.00001263 0.00227117 vae.encoder_f0 0.23155926 5.12364292 vae.encoder_f1 0.23156048 5.12171412 vae.decoder 0.00018572 0.03711218 ------------------------------------------------------------------------------------- TOTAL 0.10744199 3.65951863 (elements=2,260,992) ---------------------- -------------------------------------------------------- SAMPLE-WISE STATISTICS ---------------------- -------------------------------------------------------- Handler sd35 Strategy individual Architecture hyperprior-featurecoding ---------------------- -------------------------------------------------------- Total Elements 2260992 Total Bytes 115412 BPFP 0.4084 bits/point EBPFP 0.8167 equivalent bits/point MSE 3.659519 ---------------------- -------------------------------------------------------- Time: 0.759s Load: 0.008s, Pack+Encode: 0.297s, Decode+Unpack: 0.453s ---------------------- -------------------------------------------------------- Restored Feature Format: [Dict] with 8 keys key['text_encoder-item0']: [Dict] with 2 keys key['clip_pooled_prompt_embeds']: [Tensor] shape=torch.Size([768]), dtype=torch.float16, device=cpu key['clip_prompt_embeds']: [Tensor] shape=torch.Size([77, 768]), dtype=torch.float16, device=cpu key['text_encoder_2-item1']: [Dict] with 2 keys key['clip_pooled_prompt_embeds']: [Tensor] shape=torch.Size([1280]), dtype=torch.float16, device=cpu key['clip_prompt_embeds']: [Tensor] shape=torch.Size([77, 1280]), dtype=torch.float16, device=cpu key['text_encoder_3-item2']: [Dict] with 1 keys key['t5_prompt_embeds']: [Tensor] shape=torch.Size([77, 4096]), dtype=torch.float16, device=cpu key['text_encoder-item3']: [Dict] with 2 keys key['clip_pooled_prompt_embeds']: [Tensor] shape=torch.Size([768]), dtype=torch.float16, device=cpu key['clip_prompt_embeds']: [Tensor] shape=torch.Size([77, 768]), dtype=torch.float16, device=cpu key['text_encoder_2-item4']: [Dict] with 2 keys key['clip_pooled_prompt_embeds']: [Tensor] shape=torch.Size([1280]), dtype=torch.float16, device=cpu key['clip_prompt_embeds']: [Tensor] shape=torch.Size([77, 1280]), dtype=torch.float16, device=cpu key['text_encoder_3-item5']: [Dict] with 1 keys key['t5_prompt_embeds']: [Tensor] shape=torch.Size([77, 4096]), dtype=torch.float16, device=cpu key['vae.decoder']: [Tensor] shape=torch.Size([16, 128, 128]), dtype=torch.float16, device=cpu key['vae.encoder']: [Dict] with 2 keys key['vae.encoder_f0']: [Tensor] shape=torch.Size([32, 128, 128]), dtype=torch.float16, device=cpu key['vae.encoder_f1']: [Tensor] shape=torch.Size([32, 128, 128]), dtype=torch.float16, device=cpu 💾 Converting with 3.6595 MSE: from ../datasets/FeatureCoding-StableDiffusion3.5/100samples_features/000000351362.zst to output-fixed/sd35/lambda0.004/hyperprior-featurecoding-8bit-individual/sd35cond/000000351362.zst 💪 Processing: ../datasets/FeatureCoding-StableDiffusion3.5/100samples_features/000000357816.zst (59/100) [1/4] FRONTEND: Loading features from ../datasets/FeatureCoding-StableDiffusion3.5/100samples_features/000000357816.zst... Original data structure: root: [Dict] with 8 keys key['text_encoder-item0']: [Dict] with 2 keys key['clip_pooled_prompt_embeds']: [Tensor] shape=torch.Size([768]), dtype=torch.float16, device=cpu key['clip_prompt_embeds']: [Tensor] shape=torch.Size([77, 768]), dtype=torch.float16, device=cpu key['text_encoder_2-item1']: [Dict] with 2 keys key['clip_pooled_prompt_embeds']: [Tensor] shape=torch.Size([1280]), dtype=torch.float16, device=cpu key['clip_prompt_embeds']: [Tensor] shape=torch.Size([77, 1280]), dtype=torch.float16, device=cpu key['text_encoder_3-item2']: [Dict] with 1 keys key['t5_prompt_embeds']: [Tensor] shape=torch.Size([77, 4096]), dtype=torch.float16, device=cpu key['text_encoder-item3']: [Dict] with 2 keys key['clip_pooled_prompt_embeds']: [Tensor] shape=torch.Size([768]), dtype=torch.float16, device=cpu key['clip_prompt_embeds']: [Tensor] shape=torch.Size([77, 768]), dtype=torch.float16, device=cpu key['text_encoder_2-item4']: [Dict] with 2 keys key['clip_pooled_prompt_embeds']: [Tensor] shape=torch.Size([1280]), dtype=torch.float16, device=cpu key['clip_prompt_embeds']: [Tensor] shape=torch.Size([77, 1280]), dtype=torch.float16, device=cpu key['text_encoder_3-item5']: [Dict] with 1 keys key['t5_prompt_embeds']: [Tensor] shape=torch.Size([77, 4096]), dtype=torch.float16, device=cpu key['vae.encoder-item6']: [Dict] with 2 keys key['vae.encoder_f0']: [Tensor] shape=torch.Size([32, 128, 128]), dtype=torch.float16, device=cpu key['vae.encoder_f1']: [Tensor] shape=torch.Size([32, 128, 128]), dtype=torch.float16, device=cpu key['vae.decoder']: [Tensor] shape=torch.Size([16, 128, 128]), dtype=torch.float16, device=cpu ⌛️ [1/4] FRONTEND: Load time: 0.008s ------------------------------------------------------------ SD3.5 Features Summary ------------------------------------------------------------ Number of text encoders: 3 Encoder names: ['text_encoder', 'text_encoder_2', 'text_encoder_3'] Has VAE latents: True Has VAE encoder features: True Data type: torch.float16 text_encoder-item0: clip_pooled_prompt_embeds: torch.Size([768]), torch.float16 clip_prompt_embeds: torch.Size([77, 768]), torch.float16 text_encoder_2-item1: clip_pooled_prompt_embeds: torch.Size([1280]), torch.float16 clip_prompt_embeds: torch.Size([77, 1280]), torch.float16 text_encoder_3-item2: t5_prompt_embeds: torch.Size([77, 4096]), torch.float16 text_encoder-item3: clip_pooled_prompt_embeds: torch.Size([768]), torch.float16 clip_prompt_embeds: torch.Size([77, 768]), torch.float16 text_encoder_2-item4: clip_pooled_prompt_embeds: torch.Size([1280]), torch.float16 clip_prompt_embeds: torch.Size([77, 1280]), torch.float16 text_encoder_3-item5: t5_prompt_embeds: torch.Size([77, 4096]), torch.float16 vae.decoder latents: torch.Size([16, 128, 128]) vae.encoder features: vae.encoder_f0: torch.Size([32, 128, 128]), torch.float16 vae.encoder_f1: torch.Size([32, 128, 128]), torch.float16 ------------------------------------------------------------ [2/4] FRONTEND: Pack + Encode (strategy: individual)... IndividualPacker: text_encoder-item0.clip_pooled_prompt_embeds: torch.Size([768])->torch.Size([1, 1, 1, 768]) text_encoder-item0.clip_prompt_embeds: torch.Size([77, 768])->torch.Size([1, 1, 77, 768]) text_encoder_2-item1.clip_pooled_prompt_embeds: torch.Size([1280])->torch.Size([1, 1, 1, 1280]) text_encoder_2-item1.clip_prompt_embeds: torch.Size([77, 1280])->torch.Size([1, 1, 77, 1280]) text_encoder_3-item2.t5_prompt_embeds: torch.Size([77, 4096])->torch.Size([1, 1, 77, 4096]) text_encoder-item3.clip_pooled_prompt_embeds: torch.Size([768])->torch.Size([1, 1, 1, 768]) text_encoder-item3.clip_prompt_embeds: torch.Size([77, 768])->torch.Size([1, 1, 77, 768]) text_encoder_2-item4.clip_pooled_prompt_embeds: torch.Size([1280])->torch.Size([1, 1, 1, 1280]) text_encoder_2-item4.clip_prompt_embeds: torch.Size([77, 1280])->torch.Size([1, 1, 77, 1280]) text_encoder_3-item5.t5_prompt_embeds: torch.Size([77, 4096])->torch.Size([1, 1, 77, 4096]) vae.encoder_f0: torch.Size([32, 128, 128])->torch.Size([1, 1, 512, 1024]) vae.encoder_f1: torch.Size([32, 128, 128])->torch.Size([1, 1, 512, 1024]) vae.decoder: torch.Size([16, 128, 128])->torch.Size([1, 1, 512, 512]) Using per-key quantization points (text_encoder-item0.clip_pooled_prompt_embeds: torch.Size([256])) for text_encoder-item0.clip_pooled_prompt_embeds text_encoder-item0.clip_pooled_prompt_embeds: 728B, BPFP=7.5833 Using per-key quantization points (text_encoder-item0.clip_prompt_embeds: torch.Size([256])) for text_encoder-item0.clip_prompt_embeds text_encoder-item0.clip_prompt_embeds: 6,152B, BPFP=0.8323 Using per-key quantization points (text_encoder_2-item1.clip_pooled_prompt_embeds: torch.Size([256])) for text_encoder_2-item1.clip_pooled_prompt_embeds text_encoder_2-item1.clip_pooled_prompt_embeds: 1,196B, BPFP=7.4750 Using per-key quantization points (text_encoder_2-item1.clip_prompt_embeds: torch.Size([256])) for text_encoder_2-item1.clip_prompt_embeds text_encoder_2-item1.clip_prompt_embeds: 10,860B, BPFP=0.8815 Using per-key quantization points (text_encoder_3-item2.t5_prompt_embeds: torch.Size([256])) for text_encoder_3-item2.t5_prompt_embeds text_encoder_3-item2.t5_prompt_embeds: 26,956B, BPFP=0.6837 Using per-key quantization points (text_encoder-item3.clip_pooled_prompt_embeds: torch.Size([256])) for text_encoder-item3.clip_pooled_prompt_embeds text_encoder-item3.clip_pooled_prompt_embeds: 760B, BPFP=7.9167 Using per-key quantization points (text_encoder-item3.clip_prompt_embeds: torch.Size([256])) for text_encoder-item3.clip_prompt_embeds text_encoder-item3.clip_prompt_embeds: 4,024B, BPFP=0.5444 Using per-key quantization points (text_encoder_2-item4.clip_pooled: torch.Size([256])) for text_encoder_2-item4.clip_pooled_prompt_embeds text_encoder_2-item4.clip_pooled_prompt_embeds: 1,268B, BPFP=7.9250 Using per-key quantization points (text_encoder_2-item4.clip_prompt: torch.Size([256])) for text_encoder_2-item4.clip_prompt_embeds text_encoder_2-item4.clip_prompt_embeds: 6,996B, BPFP=0.5679 Using per-key quantization points (text_encoder_3-item5.t5_prompt_embeds: torch.Size([256])) for text_encoder_3-item5.t5_prompt_embeds text_encoder_3-item5.t5_prompt_embeds: 18,416B, BPFP=0.4671 Using per-key quantization points (vae.encoder_f0: torch.Size([256])) for vae.encoder_f0 vae.encoder_f0: 20,840B, BPFP=0.3180 Using per-key quantization points (vae.encoder_f1: torch.Size([256])) for vae.encoder_f1 vae.encoder_f1: 20,840B, BPFP=0.3180 Using per-key quantization points (vae.decoder: torch.Size([256])) for vae.decoder vae.decoder: 7,556B, BPFP=0.2306 ⌛️ [2/4] FRONTEND: Frontend time: 0.297s (Pack+Encode) [3/4] BACKEND: Decode + Unpack... Using per-key quantization points (text_encoder-item0.clip_pooled_prompt_embeds: torch.Size([256])) for text_encoder-item0.clip_pooled_prompt_embeds Using per-key quantization points (text_encoder-item0.clip_prompt_embeds: torch.Size([256])) for text_encoder-item0.clip_prompt_embeds Using per-key quantization points (text_encoder_2-item1.clip_pooled_prompt_embeds: torch.Size([256])) for text_encoder_2-item1.clip_pooled_prompt_embeds Using per-key quantization points (text_encoder_2-item1.clip_prompt_embeds: torch.Size([256])) for text_encoder_2-item1.clip_prompt_embeds Using per-key quantization points (text_encoder_3-item2.t5_prompt_embeds: torch.Size([256])) for text_encoder_3-item2.t5_prompt_embeds Using per-key quantization points (text_encoder-item3.clip_pooled_prompt_embeds: torch.Size([256])) for text_encoder-item3.clip_pooled_prompt_embeds Using per-key quantization points (text_encoder-item3.clip_prompt_embeds: torch.Size([256])) for text_encoder-item3.clip_prompt_embeds Using per-key quantization points (text_encoder_2-item4.clip_pooled: torch.Size([256])) for text_encoder_2-item4.clip_pooled_prompt_embeds Using per-key quantization points (text_encoder_2-item4.clip_prompt: torch.Size([256])) for text_encoder_2-item4.clip_prompt_embeds Using per-key quantization points (text_encoder_3-item5.t5_prompt_embeds: torch.Size([256])) for text_encoder_3-item5.t5_prompt_embeds Using per-key quantization points (vae.encoder_f0: torch.Size([256])) for vae.encoder_f0 Using per-key quantization points (vae.encoder_f1: torch.Size([256])) for vae.encoder_f1 Using per-key quantization points (vae.decoder: torch.Size([256])) for vae.decoder IndividualUnPacker: text_encoder-item0.clip_pooled_prompt_embeds: torch.Size([768]) text_encoder-item0.clip_prompt_embeds: torch.Size([77, 768]) text_encoder_2-item1.clip_pooled_prompt_embeds: torch.Size([1280]) text_encoder_2-item1.clip_prompt_embeds: torch.Size([77, 1280]) text_encoder_3-item2.t5_prompt_embeds: torch.Size([77, 4096]) text_encoder-item3.clip_pooled_prompt_embeds: torch.Size([768]) text_encoder-item3.clip_prompt_embeds: torch.Size([77, 768]) text_encoder_2-item4.clip_pooled_prompt_embeds: torch.Size([1280]) text_encoder_2-item4.clip_prompt_embeds: torch.Size([77, 1280]) text_encoder_3-item5.t5_prompt_embeds: torch.Size([77, 4096]) vae.decoder: torch.Size([16, 128, 128]) vae.encoder_f0: torch.Size([32, 128, 128]) vae.encoder_f1: torch.Size([32, 128, 128]) ⌛️ [3/4] BACKEND: Backend time: 0.451s [4/4] METRICS: Computing MSE Breakdown... Using per-key quantization points (text_encoder-item0.clip_pooled_prompt_embeds: torch.Size([256])) for text_encoder-item0.clip_pooled_prompt_embeds Using per-key quantization points (text_encoder-item0.clip_prompt_embeds: torch.Size([256])) for text_encoder-item0.clip_prompt_embeds Using per-key quantization points (text_encoder_2-item1.clip_pooled_prompt_embeds: torch.Size([256])) for text_encoder_2-item1.clip_pooled_prompt_embeds Using per-key quantization points (text_encoder_2-item1.clip_prompt_embeds: torch.Size([256])) for text_encoder_2-item1.clip_prompt_embeds Using per-key quantization points (text_encoder_3-item2.t5_prompt_embeds: torch.Size([256])) for text_encoder_3-item2.t5_prompt_embeds Using per-key quantization points (text_encoder-item3.clip_pooled_prompt_embeds: torch.Size([256])) for text_encoder-item3.clip_pooled_prompt_embeds Using per-key quantization points (text_encoder-item3.clip_prompt_embeds: torch.Size([256])) for text_encoder-item3.clip_prompt_embeds Using per-key quantization points (text_encoder_2-item4.clip_pooled: torch.Size([256])) for text_encoder_2-item4.clip_pooled_prompt_embeds Using per-key quantization points (text_encoder_2-item4.clip_prompt: torch.Size([256])) for text_encoder_2-item4.clip_prompt_embeds Using per-key quantization points (text_encoder_3-item5.t5_prompt_embeds: torch.Size([256])) for text_encoder_3-item5.t5_prompt_embeds Using per-key quantization points (vae.encoder_f0: torch.Size([256])) for vae.encoder_f0 Using per-key quantization points (vae.encoder_f1: torch.Size([256])) for vae.encoder_f1 Using per-key quantization points (vae.decoder: torch.Size([256])) for vae.decoder IndividualUnPacker: text_encoder-item0.clip_pooled_prompt_embeds: torch.Size([768]) text_encoder-item0.clip_prompt_embeds: torch.Size([77, 768]) text_encoder_2-item1.clip_pooled_prompt_embeds: torch.Size([1280]) text_encoder_2-item1.clip_prompt_embeds: torch.Size([77, 1280]) text_encoder_3-item2.t5_prompt_embeds: torch.Size([77, 4096]) text_encoder-item3.clip_pooled_prompt_embeds: torch.Size([768]) text_encoder-item3.clip_prompt_embeds: torch.Size([77, 768]) text_encoder_2-item4.clip_pooled_prompt_embeds: torch.Size([1280]) text_encoder_2-item4.clip_prompt_embeds: torch.Size([77, 1280]) text_encoder_3-item5.t5_prompt_embeds: torch.Size([77, 4096]) vae.decoder: torch.Size([16, 128, 128]) vae.encoder_f0: torch.Size([32, 128, 128]) vae.encoder_f1: torch.Size([32, 128, 128]) Per-key MSE Breakdown (Quant=Truncation+Quantization, Codec=Compression): Key Quant-MSE Total-MSE ------------------------------------------------------------------------------------- text_encoder-item0.clip_pooled_prompt_embeds 0.00020740 0.92589521 text_encoder-item0.clip_prompt_embeds 0.00022528 23.92777242 text_encoder_2-item1.clip_pooled_prompt_embeds 0.00022839 0.93098631 text_encoder_2-item1.clip_prompt_embeds 0.00016484 0.12487290 text_encoder_3-item2.t5_prompt_embeds 0.00000786 0.00319853 text_encoder-item3.clip_pooled_prompt_embeds 0.00841799 1.01420029 text_encoder-item3.clip_prompt_embeds 0.00023247 23.90570887 text_encoder_2-item4.clip_pooled_prompt_embeds 0.00016751 0.67051430 text_encoder_2-item4.clip_prompt_embeds 0.00009830 0.47049568 text_encoder_3-item5.t5_prompt_embeds 0.00001263 0.00227117 vae.encoder_f0 0.00729824 3.08189273 vae.encoder_f1 0.00730369 3.08265924 vae.decoder 0.00019938 0.06681862 ------------------------------------------------------------------------------------- TOTAL 0.00343853 2.71656955 (elements=2,260,992) ---------------------- -------------------------------------------------------- SAMPLE-WISE STATISTICS ---------------------- -------------------------------------------------------- Handler sd35 Strategy individual Architecture hyperprior-featurecoding ---------------------- -------------------------------------------------------- Total Elements 2260992 Total Bytes 126592 BPFP 0.4479 bits/point EBPFP 0.8958 equivalent bits/point MSE 2.716570 ---------------------- -------------------------------------------------------- Time: 0.757s Load: 0.008s, Pack+Encode: 0.297s, Decode+Unpack: 0.451s ---------------------- -------------------------------------------------------- Restored Feature Format: [Dict] with 8 keys key['text_encoder-item0']: [Dict] with 2 keys key['clip_pooled_prompt_embeds']: [Tensor] shape=torch.Size([768]), dtype=torch.float16, device=cpu key['clip_prompt_embeds']: [Tensor] shape=torch.Size([77, 768]), dtype=torch.float16, device=cpu key['text_encoder_2-item1']: [Dict] with 2 keys key['clip_pooled_prompt_embeds']: [Tensor] shape=torch.Size([1280]), dtype=torch.float16, device=cpu key['clip_prompt_embeds']: [Tensor] shape=torch.Size([77, 1280]), dtype=torch.float16, device=cpu key['text_encoder_3-item2']: [Dict] with 1 keys key['t5_prompt_embeds']: [Tensor] shape=torch.Size([77, 4096]), dtype=torch.float16, device=cpu key['text_encoder-item3']: [Dict] with 2 keys key['clip_pooled_prompt_embeds']: [Tensor] shape=torch.Size([768]), dtype=torch.float16, device=cpu key['clip_prompt_embeds']: [Tensor] shape=torch.Size([77, 768]), dtype=torch.float16, device=cpu key['text_encoder_2-item4']: [Dict] with 2 keys key['clip_pooled_prompt_embeds']: [Tensor] shape=torch.Size([1280]), dtype=torch.float16, device=cpu key['clip_prompt_embeds']: [Tensor] shape=torch.Size([77, 1280]), dtype=torch.float16, device=cpu key['text_encoder_3-item5']: [Dict] with 1 keys key['t5_prompt_embeds']: [Tensor] shape=torch.Size([77, 4096]), dtype=torch.float16, device=cpu key['vae.decoder']: [Tensor] shape=torch.Size([16, 128, 128]), dtype=torch.float16, device=cpu key['vae.encoder']: [Dict] with 2 keys key['vae.encoder_f0']: [Tensor] shape=torch.Size([32, 128, 128]), dtype=torch.float16, device=cpu key['vae.encoder_f1']: [Tensor] shape=torch.Size([32, 128, 128]), dtype=torch.float16, device=cpu 💾 Converting with 2.7166 MSE: from ../datasets/FeatureCoding-StableDiffusion3.5/100samples_features/000000357816.zst to output-fixed/sd35/lambda0.004/hyperprior-featurecoding-8bit-individual/sd35cond/000000357816.zst 💪 Processing: ../datasets/FeatureCoding-StableDiffusion3.5/100samples_features/000000361180.zst (60/100) [1/4] FRONTEND: Loading features from ../datasets/FeatureCoding-StableDiffusion3.5/100samples_features/000000361180.zst... Original data structure: root: [Dict] with 8 keys key['text_encoder-item0']: [Dict] with 2 keys key['clip_pooled_prompt_embeds']: [Tensor] shape=torch.Size([768]), dtype=torch.float16, device=cpu key['clip_prompt_embeds']: [Tensor] shape=torch.Size([77, 768]), dtype=torch.float16, device=cpu key['text_encoder_2-item1']: [Dict] with 2 keys key['clip_pooled_prompt_embeds']: [Tensor] shape=torch.Size([1280]), dtype=torch.float16, device=cpu key['clip_prompt_embeds']: [Tensor] shape=torch.Size([77, 1280]), dtype=torch.float16, device=cpu key['text_encoder_3-item2']: [Dict] with 1 keys key['t5_prompt_embeds']: [Tensor] shape=torch.Size([77, 4096]), dtype=torch.float16, device=cpu key['text_encoder-item3']: [Dict] with 2 keys key['clip_pooled_prompt_embeds']: [Tensor] shape=torch.Size([768]), dtype=torch.float16, device=cpu key['clip_prompt_embeds']: [Tensor] shape=torch.Size([77, 768]), dtype=torch.float16, device=cpu key['text_encoder_2-item4']: [Dict] with 2 keys key['clip_pooled_prompt_embeds']: [Tensor] shape=torch.Size([1280]), dtype=torch.float16, device=cpu key['clip_prompt_embeds']: [Tensor] shape=torch.Size([77, 1280]), dtype=torch.float16, device=cpu key['text_encoder_3-item5']: [Dict] with 1 keys key['t5_prompt_embeds']: [Tensor] shape=torch.Size([77, 4096]), dtype=torch.float16, device=cpu key['vae.encoder-item6']: [Dict] with 2 keys key['vae.encoder_f0']: [Tensor] shape=torch.Size([32, 128, 128]), dtype=torch.float16, device=cpu key['vae.encoder_f1']: [Tensor] shape=torch.Size([32, 128, 128]), dtype=torch.float16, device=cpu key['vae.decoder']: [Tensor] shape=torch.Size([16, 128, 128]), dtype=torch.float16, device=cpu ⌛️ [1/4] FRONTEND: Load time: 0.009s ------------------------------------------------------------ SD3.5 Features Summary ------------------------------------------------------------ Number of text encoders: 3 Encoder names: ['text_encoder', 'text_encoder_2', 'text_encoder_3'] Has VAE latents: True Has VAE encoder features: True Data type: torch.float16 text_encoder-item0: clip_pooled_prompt_embeds: torch.Size([768]), torch.float16 clip_prompt_embeds: torch.Size([77, 768]), torch.float16 text_encoder_2-item1: clip_pooled_prompt_embeds: torch.Size([1280]), torch.float16 clip_prompt_embeds: torch.Size([77, 1280]), torch.float16 text_encoder_3-item2: t5_prompt_embeds: torch.Size([77, 4096]), torch.float16 text_encoder-item3: clip_pooled_prompt_embeds: torch.Size([768]), torch.float16 clip_prompt_embeds: torch.Size([77, 768]), torch.float16 text_encoder_2-item4: clip_pooled_prompt_embeds: torch.Size([1280]), torch.float16 clip_prompt_embeds: torch.Size([77, 1280]), torch.float16 text_encoder_3-item5: t5_prompt_embeds: torch.Size([77, 4096]), torch.float16 vae.decoder latents: torch.Size([16, 128, 128]) vae.encoder features: vae.encoder_f0: torch.Size([32, 128, 128]), torch.float16 vae.encoder_f1: torch.Size([32, 128, 128]), torch.float16 ------------------------------------------------------------ [2/4] FRONTEND: Pack + Encode (strategy: individual)... IndividualPacker: text_encoder-item0.clip_pooled_prompt_embeds: torch.Size([768])->torch.Size([1, 1, 1, 768]) text_encoder-item0.clip_prompt_embeds: torch.Size([77, 768])->torch.Size([1, 1, 77, 768]) text_encoder_2-item1.clip_pooled_prompt_embeds: torch.Size([1280])->torch.Size([1, 1, 1, 1280]) text_encoder_2-item1.clip_prompt_embeds: torch.Size([77, 1280])->torch.Size([1, 1, 77, 1280]) text_encoder_3-item2.t5_prompt_embeds: torch.Size([77, 4096])->torch.Size([1, 1, 77, 4096]) text_encoder-item3.clip_pooled_prompt_embeds: torch.Size([768])->torch.Size([1, 1, 1, 768]) text_encoder-item3.clip_prompt_embeds: torch.Size([77, 768])->torch.Size([1, 1, 77, 768]) text_encoder_2-item4.clip_pooled_prompt_embeds: torch.Size([1280])->torch.Size([1, 1, 1, 1280]) text_encoder_2-item4.clip_prompt_embeds: torch.Size([77, 1280])->torch.Size([1, 1, 77, 1280]) text_encoder_3-item5.t5_prompt_embeds: torch.Size([77, 4096])->torch.Size([1, 1, 77, 4096]) vae.encoder_f0: torch.Size([32, 128, 128])->torch.Size([1, 1, 512, 1024]) vae.encoder_f1: torch.Size([32, 128, 128])->torch.Size([1, 1, 512, 1024]) vae.decoder: torch.Size([16, 128, 128])->torch.Size([1, 1, 512, 512]) Using per-key quantization points (text_encoder-item0.clip_pooled_prompt_embeds: torch.Size([256])) for text_encoder-item0.clip_pooled_prompt_embeds text_encoder-item0.clip_pooled_prompt_embeds: 740B, BPFP=7.7083 Using per-key quantization points (text_encoder-item0.clip_prompt_embeds: torch.Size([256])) for text_encoder-item0.clip_prompt_embeds text_encoder-item0.clip_prompt_embeds: 5,696B, BPFP=0.7706 Using per-key quantization points (text_encoder_2-item1.clip_pooled_prompt_embeds: torch.Size([256])) for text_encoder_2-item1.clip_pooled_prompt_embeds text_encoder_2-item1.clip_pooled_prompt_embeds: 1,208B, BPFP=7.5500 Using per-key quantization points (text_encoder_2-item1.clip_prompt_embeds: torch.Size([256])) for text_encoder_2-item1.clip_prompt_embeds text_encoder_2-item1.clip_prompt_embeds: 11,012B, BPFP=0.8938 Using per-key quantization points (text_encoder_3-item2.t5_prompt_embeds: torch.Size([256])) for text_encoder_3-item2.t5_prompt_embeds text_encoder_3-item2.t5_prompt_embeds: 24,840B, BPFP=0.6301 Using per-key quantization points (text_encoder-item3.clip_pooled_prompt_embeds: torch.Size([256])) for text_encoder-item3.clip_pooled_prompt_embeds text_encoder-item3.clip_pooled_prompt_embeds: 760B, BPFP=7.9167 Using per-key quantization points (text_encoder-item3.clip_prompt_embeds: torch.Size([256])) for text_encoder-item3.clip_prompt_embeds text_encoder-item3.clip_prompt_embeds: 4,024B, BPFP=0.5444 Using per-key quantization points (text_encoder_2-item4.clip_pooled: torch.Size([256])) for text_encoder_2-item4.clip_pooled_prompt_embeds text_encoder_2-item4.clip_pooled_prompt_embeds: 1,268B, BPFP=7.9250 Using per-key quantization points (text_encoder_2-item4.clip_prompt: torch.Size([256])) for text_encoder_2-item4.clip_prompt_embeds text_encoder_2-item4.clip_prompt_embeds: 6,996B, BPFP=0.5679 Using per-key quantization points (text_encoder_3-item5.t5_prompt_embeds: torch.Size([256])) for text_encoder_3-item5.t5_prompt_embeds text_encoder_3-item5.t5_prompt_embeds: 18,416B, BPFP=0.4671 Using per-key quantization points (vae.encoder_f0: torch.Size([256])) for vae.encoder_f0 vae.encoder_f0: 13,412B, BPFP=0.2047 Using per-key quantization points (vae.encoder_f1: torch.Size([256])) for vae.encoder_f1 vae.encoder_f1: 13,412B, BPFP=0.2047 Using per-key quantization points (vae.decoder: torch.Size([256])) for vae.decoder vae.decoder: 8,696B, BPFP=0.2654 ⌛️ [2/4] FRONTEND: Frontend time: 0.289s (Pack+Encode) [3/4] BACKEND: Decode + Unpack... Using per-key quantization points (text_encoder-item0.clip_pooled_prompt_embeds: torch.Size([256])) for text_encoder-item0.clip_pooled_prompt_embeds Using per-key quantization points (text_encoder-item0.clip_prompt_embeds: torch.Size([256])) for text_encoder-item0.clip_prompt_embeds Using per-key quantization points (text_encoder_2-item1.clip_pooled_prompt_embeds: torch.Size([256])) for text_encoder_2-item1.clip_pooled_prompt_embeds Using per-key quantization points (text_encoder_2-item1.clip_prompt_embeds: torch.Size([256])) for text_encoder_2-item1.clip_prompt_embeds Using per-key quantization points (text_encoder_3-item2.t5_prompt_embeds: torch.Size([256])) for text_encoder_3-item2.t5_prompt_embeds Using per-key quantization points (text_encoder-item3.clip_pooled_prompt_embeds: torch.Size([256])) for text_encoder-item3.clip_pooled_prompt_embeds Using per-key quantization points (text_encoder-item3.clip_prompt_embeds: torch.Size([256])) for text_encoder-item3.clip_prompt_embeds Using per-key quantization points (text_encoder_2-item4.clip_pooled: torch.Size([256])) for text_encoder_2-item4.clip_pooled_prompt_embeds Using per-key quantization points (text_encoder_2-item4.clip_prompt: torch.Size([256])) for text_encoder_2-item4.clip_prompt_embeds Using per-key quantization points (text_encoder_3-item5.t5_prompt_embeds: torch.Size([256])) for text_encoder_3-item5.t5_prompt_embeds Using per-key quantization points (vae.encoder_f0: torch.Size([256])) for vae.encoder_f0 Using per-key quantization points (vae.encoder_f1: torch.Size([256])) for vae.encoder_f1 Using per-key quantization points (vae.decoder: torch.Size([256])) for vae.decoder IndividualUnPacker: text_encoder-item0.clip_pooled_prompt_embeds: torch.Size([768]) text_encoder-item0.clip_prompt_embeds: torch.Size([77, 768]) text_encoder_2-item1.clip_pooled_prompt_embeds: torch.Size([1280]) text_encoder_2-item1.clip_prompt_embeds: torch.Size([77, 1280]) text_encoder_3-item2.t5_prompt_embeds: torch.Size([77, 4096]) text_encoder-item3.clip_pooled_prompt_embeds: torch.Size([768]) text_encoder-item3.clip_prompt_embeds: torch.Size([77, 768]) text_encoder_2-item4.clip_pooled_prompt_embeds: torch.Size([1280]) text_encoder_2-item4.clip_prompt_embeds: torch.Size([77, 1280]) text_encoder_3-item5.t5_prompt_embeds: torch.Size([77, 4096]) vae.decoder: torch.Size([16, 128, 128]) vae.encoder_f0: torch.Size([32, 128, 128]) vae.encoder_f1: torch.Size([32, 128, 128]) ⌛️ [3/4] BACKEND: Backend time: 0.444s [4/4] METRICS: Computing MSE Breakdown... Using per-key quantization points (text_encoder-item0.clip_pooled_prompt_embeds: torch.Size([256])) for text_encoder-item0.clip_pooled_prompt_embeds Using per-key quantization points (text_encoder-item0.clip_prompt_embeds: torch.Size([256])) for text_encoder-item0.clip_prompt_embeds Using per-key quantization points (text_encoder_2-item1.clip_pooled_prompt_embeds: torch.Size([256])) for text_encoder_2-item1.clip_pooled_prompt_embeds Using per-key quantization points (text_encoder_2-item1.clip_prompt_embeds: torch.Size([256])) for text_encoder_2-item1.clip_prompt_embeds Using per-key quantization points (text_encoder_3-item2.t5_prompt_embeds: torch.Size([256])) for text_encoder_3-item2.t5_prompt_embeds Using per-key quantization points (text_encoder-item3.clip_pooled_prompt_embeds: torch.Size([256])) for text_encoder-item3.clip_pooled_prompt_embeds Using per-key quantization points (text_encoder-item3.clip_prompt_embeds: torch.Size([256])) for text_encoder-item3.clip_prompt_embeds Using per-key quantization points (text_encoder_2-item4.clip_pooled: torch.Size([256])) for text_encoder_2-item4.clip_pooled_prompt_embeds Using per-key quantization points (text_encoder_2-item4.clip_prompt: torch.Size([256])) for text_encoder_2-item4.clip_prompt_embeds Using per-key quantization points (text_encoder_3-item5.t5_prompt_embeds: torch.Size([256])) for text_encoder_3-item5.t5_prompt_embeds Using per-key quantization points (vae.encoder_f0: torch.Size([256])) for vae.encoder_f0 Using per-key quantization points (vae.encoder_f1: torch.Size([256])) for vae.encoder_f1 Using per-key quantization points (vae.decoder: torch.Size([256])) for vae.decoder IndividualUnPacker: text_encoder-item0.clip_pooled_prompt_embeds: torch.Size([768]) text_encoder-item0.clip_prompt_embeds: torch.Size([77, 768]) text_encoder_2-item1.clip_pooled_prompt_embeds: torch.Size([1280]) text_encoder_2-item1.clip_prompt_embeds: torch.Size([77, 1280]) text_encoder_3-item2.t5_prompt_embeds: torch.Size([77, 4096]) text_encoder-item3.clip_pooled_prompt_embeds: torch.Size([768]) text_encoder-item3.clip_prompt_embeds: torch.Size([77, 768]) text_encoder_2-item4.clip_pooled_prompt_embeds: torch.Size([1280]) text_encoder_2-item4.clip_prompt_embeds: torch.Size([77, 1280]) text_encoder_3-item5.t5_prompt_embeds: torch.Size([77, 4096]) vae.decoder: torch.Size([16, 128, 128]) vae.encoder_f0: torch.Size([32, 128, 128]) vae.encoder_f1: torch.Size([32, 128, 128]) Per-key MSE Breakdown (Quant=Truncation+Quantization, Codec=Compression): Key Quant-MSE Total-MSE ------------------------------------------------------------------------------------- text_encoder-item0.clip_pooled_prompt_embeds 0.00021207 0.91777285 text_encoder-item0.clip_prompt_embeds 0.00022149 23.93123055 text_encoder_2-item1.clip_pooled_prompt_embeds 0.00018477 0.90661049 text_encoder_2-item1.clip_prompt_embeds 0.00103146 0.13397918 text_encoder_3-item2.t5_prompt_embeds 0.00000778 0.00239863 text_encoder-item3.clip_pooled_prompt_embeds 0.00841799 1.01420029 text_encoder-item3.clip_prompt_embeds 0.00023247 23.90570887 text_encoder_2-item4.clip_pooled_prompt_embeds 0.00016751 0.67051430 text_encoder_2-item4.clip_prompt_embeds 0.00009830 0.47049568 text_encoder_3-item5.t5_prompt_embeds 0.00001263 0.00227117 vae.encoder_f0 0.00564371 1.80442083 vae.encoder_f1 0.00565042 1.80473506 vae.decoder 0.00019980 0.07600767 ------------------------------------------------------------------------------------- TOTAL 0.00270919 2.12543860 (elements=2,260,992) ---------------------- -------------------------------------------------------- SAMPLE-WISE STATISTICS ---------------------- -------------------------------------------------------- Handler sd35 Strategy individual Architecture hyperprior-featurecoding ---------------------- -------------------------------------------------------- Total Elements 2260992 Total Bytes 110480 BPFP 0.3909 bits/point EBPFP 0.7818 equivalent bits/point MSE 2.125439 ---------------------- -------------------------------------------------------- Time: 0.742s Load: 0.009s, Pack+Encode: 0.289s, Decode+Unpack: 0.444s ---------------------- -------------------------------------------------------- Restored Feature Format: [Dict] with 8 keys key['text_encoder-item0']: [Dict] with 2 keys key['clip_pooled_prompt_embeds']: [Tensor] shape=torch.Size([768]), dtype=torch.float16, device=cpu key['clip_prompt_embeds']: [Tensor] shape=torch.Size([77, 768]), dtype=torch.float16, device=cpu key['text_encoder_2-item1']: [Dict] with 2 keys key['clip_pooled_prompt_embeds']: [Tensor] shape=torch.Size([1280]), dtype=torch.float16, device=cpu key['clip_prompt_embeds']: [Tensor] shape=torch.Size([77, 1280]), dtype=torch.float16, device=cpu key['text_encoder_3-item2']: [Dict] with 1 keys key['t5_prompt_embeds']: [Tensor] shape=torch.Size([77, 4096]), dtype=torch.float16, device=cpu key['text_encoder-item3']: [Dict] with 2 keys key['clip_pooled_prompt_embeds']: [Tensor] shape=torch.Size([768]), dtype=torch.float16, device=cpu key['clip_prompt_embeds']: [Tensor] shape=torch.Size([77, 768]), dtype=torch.float16, device=cpu key['text_encoder_2-item4']: [Dict] with 2 keys key['clip_pooled_prompt_embeds']: [Tensor] shape=torch.Size([1280]), dtype=torch.float16, device=cpu key['clip_prompt_embeds']: [Tensor] shape=torch.Size([77, 1280]), dtype=torch.float16, device=cpu key['text_encoder_3-item5']: [Dict] with 1 keys key['t5_prompt_embeds']: [Tensor] shape=torch.Size([77, 4096]), dtype=torch.float16, device=cpu key['vae.decoder']: [Tensor] shape=torch.Size([16, 128, 128]), dtype=torch.float16, device=cpu key['vae.encoder']: [Dict] with 2 keys key['vae.encoder_f0']: [Tensor] shape=torch.Size([32, 128, 128]), dtype=torch.float16, device=cpu key['vae.encoder_f1']: [Tensor] shape=torch.Size([32, 128, 128]), dtype=torch.float16, device=cpu 💾 Converting with 2.1254 MSE: from ../datasets/FeatureCoding-StableDiffusion3.5/100samples_features/000000361180.zst to output-fixed/sd35/lambda0.004/hyperprior-featurecoding-8bit-individual/sd35cond/000000361180.zst 💪 Processing: ../datasets/FeatureCoding-StableDiffusion3.5/100samples_features/000000361268.zst (61/100) [1/4] FRONTEND: Loading features from ../datasets/FeatureCoding-StableDiffusion3.5/100samples_features/000000361268.zst... Original data structure: root: [Dict] with 8 keys key['text_encoder-item0']: [Dict] with 2 keys key['clip_pooled_prompt_embeds']: [Tensor] shape=torch.Size([768]), dtype=torch.float16, device=cpu key['clip_prompt_embeds']: [Tensor] shape=torch.Size([77, 768]), dtype=torch.float16, device=cpu key['text_encoder_2-item1']: [Dict] with 2 keys key['clip_pooled_prompt_embeds']: [Tensor] shape=torch.Size([1280]), dtype=torch.float16, device=cpu key['clip_prompt_embeds']: [Tensor] shape=torch.Size([77, 1280]), dtype=torch.float16, device=cpu key['text_encoder_3-item2']: [Dict] with 1 keys key['t5_prompt_embeds']: [Tensor] shape=torch.Size([77, 4096]), dtype=torch.float16, device=cpu key['text_encoder-item3']: [Dict] with 2 keys key['clip_pooled_prompt_embeds']: [Tensor] shape=torch.Size([768]), dtype=torch.float16, device=cpu key['clip_prompt_embeds']: [Tensor] shape=torch.Size([77, 768]), dtype=torch.float16, device=cpu key['text_encoder_2-item4']: [Dict] with 2 keys key['clip_pooled_prompt_embeds']: [Tensor] shape=torch.Size([1280]), dtype=torch.float16, device=cpu key['clip_prompt_embeds']: [Tensor] shape=torch.Size([77, 1280]), dtype=torch.float16, device=cpu key['text_encoder_3-item5']: [Dict] with 1 keys key['t5_prompt_embeds']: [Tensor] shape=torch.Size([77, 4096]), dtype=torch.float16, device=cpu key['vae.encoder-item6']: [Dict] with 2 keys key['vae.encoder_f0']: [Tensor] shape=torch.Size([32, 128, 128]), dtype=torch.float16, device=cpu key['vae.encoder_f1']: [Tensor] shape=torch.Size([32, 128, 128]), dtype=torch.float16, device=cpu key['vae.decoder']: [Tensor] shape=torch.Size([16, 128, 128]), dtype=torch.float16, device=cpu ⌛️ [1/4] FRONTEND: Load time: 0.009s ------------------------------------------------------------ SD3.5 Features Summary ------------------------------------------------------------ Number of text encoders: 3 Encoder names: ['text_encoder', 'text_encoder_2', 'text_encoder_3'] Has VAE latents: True Has VAE encoder features: True Data type: torch.float16 text_encoder-item0: clip_pooled_prompt_embeds: torch.Size([768]), torch.float16 clip_prompt_embeds: torch.Size([77, 768]), torch.float16 text_encoder_2-item1: clip_pooled_prompt_embeds: torch.Size([1280]), torch.float16 clip_prompt_embeds: torch.Size([77, 1280]), torch.float16 text_encoder_3-item2: t5_prompt_embeds: torch.Size([77, 4096]), torch.float16 text_encoder-item3: clip_pooled_prompt_embeds: torch.Size([768]), torch.float16 clip_prompt_embeds: torch.Size([77, 768]), torch.float16 text_encoder_2-item4: clip_pooled_prompt_embeds: torch.Size([1280]), torch.float16 clip_prompt_embeds: torch.Size([77, 1280]), torch.float16 text_encoder_3-item5: t5_prompt_embeds: torch.Size([77, 4096]), torch.float16 vae.decoder latents: torch.Size([16, 128, 128]) vae.encoder features: vae.encoder_f0: torch.Size([32, 128, 128]), torch.float16 vae.encoder_f1: torch.Size([32, 128, 128]), torch.float16 ------------------------------------------------------------ [2/4] FRONTEND: Pack + Encode (strategy: individual)... IndividualPacker: text_encoder-item0.clip_pooled_prompt_embeds: torch.Size([768])->torch.Size([1, 1, 1, 768]) text_encoder-item0.clip_prompt_embeds: torch.Size([77, 768])->torch.Size([1, 1, 77, 768]) text_encoder_2-item1.clip_pooled_prompt_embeds: torch.Size([1280])->torch.Size([1, 1, 1, 1280]) text_encoder_2-item1.clip_prompt_embeds: torch.Size([77, 1280])->torch.Size([1, 1, 77, 1280]) text_encoder_3-item2.t5_prompt_embeds: torch.Size([77, 4096])->torch.Size([1, 1, 77, 4096]) text_encoder-item3.clip_pooled_prompt_embeds: torch.Size([768])->torch.Size([1, 1, 1, 768]) text_encoder-item3.clip_prompt_embeds: torch.Size([77, 768])->torch.Size([1, 1, 77, 768]) text_encoder_2-item4.clip_pooled_prompt_embeds: torch.Size([1280])->torch.Size([1, 1, 1, 1280]) text_encoder_2-item4.clip_prompt_embeds: torch.Size([77, 1280])->torch.Size([1, 1, 77, 1280]) text_encoder_3-item5.t5_prompt_embeds: torch.Size([77, 4096])->torch.Size([1, 1, 77, 4096]) vae.encoder_f0: torch.Size([32, 128, 128])->torch.Size([1, 1, 512, 1024]) vae.encoder_f1: torch.Size([32, 128, 128])->torch.Size([1, 1, 512, 1024]) vae.decoder: torch.Size([16, 128, 128])->torch.Size([1, 1, 512, 512]) Using per-key quantization points (text_encoder-item0.clip_pooled_prompt_embeds: torch.Size([256])) for text_encoder-item0.clip_pooled_prompt_embeds text_encoder-item0.clip_pooled_prompt_embeds: 732B, BPFP=7.6250 Using per-key quantization points (text_encoder-item0.clip_prompt_embeds: torch.Size([256])) for text_encoder-item0.clip_prompt_embeds text_encoder-item0.clip_prompt_embeds: 6,488B, BPFP=0.8777 Using per-key quantization points (text_encoder_2-item1.clip_pooled_prompt_embeds: torch.Size([256])) for text_encoder_2-item1.clip_pooled_prompt_embeds text_encoder_2-item1.clip_pooled_prompt_embeds: 1,188B, BPFP=7.4250 Using per-key quantization points (text_encoder_2-item1.clip_prompt_embeds: torch.Size([256])) for text_encoder_2-item1.clip_prompt_embeds text_encoder_2-item1.clip_prompt_embeds: 11,152B, BPFP=0.9052 Using per-key quantization points (text_encoder_3-item2.t5_prompt_embeds: torch.Size([256])) for text_encoder_3-item2.t5_prompt_embeds text_encoder_3-item2.t5_prompt_embeds: 25,736B, BPFP=0.6528 Using per-key quantization points (text_encoder-item3.clip_pooled_prompt_embeds: torch.Size([256])) for text_encoder-item3.clip_pooled_prompt_embeds text_encoder-item3.clip_pooled_prompt_embeds: 760B, BPFP=7.9167 Using per-key quantization points (text_encoder-item3.clip_prompt_embeds: torch.Size([256])) for text_encoder-item3.clip_prompt_embeds text_encoder-item3.clip_prompt_embeds: 4,024B, BPFP=0.5444 Using per-key quantization points (text_encoder_2-item4.clip_pooled: torch.Size([256])) for text_encoder_2-item4.clip_pooled_prompt_embeds text_encoder_2-item4.clip_pooled_prompt_embeds: 1,268B, BPFP=7.9250 Using per-key quantization points (text_encoder_2-item4.clip_prompt: torch.Size([256])) for text_encoder_2-item4.clip_prompt_embeds text_encoder_2-item4.clip_prompt_embeds: 6,996B, BPFP=0.5679 Using per-key quantization points (text_encoder_3-item5.t5_prompt_embeds: torch.Size([256])) for text_encoder_3-item5.t5_prompt_embeds text_encoder_3-item5.t5_prompt_embeds: 18,416B, BPFP=0.4671 Using per-key quantization points (vae.encoder_f0: torch.Size([256])) for vae.encoder_f0 vae.encoder_f0: 14,244B, BPFP=0.2173 Using per-key quantization points (vae.encoder_f1: torch.Size([256])) for vae.encoder_f1 vae.encoder_f1: 14,244B, BPFP=0.2173 Using per-key quantization points (vae.decoder: torch.Size([256])) for vae.decoder vae.decoder: 9,580B, BPFP=0.2924 ⌛️ [2/4] FRONTEND: Frontend time: 0.288s (Pack+Encode) [3/4] BACKEND: Decode + Unpack... Using per-key quantization points (text_encoder-item0.clip_pooled_prompt_embeds: torch.Size([256])) for text_encoder-item0.clip_pooled_prompt_embeds Using per-key quantization points (text_encoder-item0.clip_prompt_embeds: torch.Size([256])) for text_encoder-item0.clip_prompt_embeds Using per-key quantization points (text_encoder_2-item1.clip_pooled_prompt_embeds: torch.Size([256])) for text_encoder_2-item1.clip_pooled_prompt_embeds Using per-key quantization points (text_encoder_2-item1.clip_prompt_embeds: torch.Size([256])) for text_encoder_2-item1.clip_prompt_embeds Using per-key quantization points (text_encoder_3-item2.t5_prompt_embeds: torch.Size([256])) for text_encoder_3-item2.t5_prompt_embeds Using per-key quantization points (text_encoder-item3.clip_pooled_prompt_embeds: torch.Size([256])) for text_encoder-item3.clip_pooled_prompt_embeds Using per-key quantization points (text_encoder-item3.clip_prompt_embeds: torch.Size([256])) for text_encoder-item3.clip_prompt_embeds Using per-key quantization points (text_encoder_2-item4.clip_pooled: torch.Size([256])) for text_encoder_2-item4.clip_pooled_prompt_embeds Using per-key quantization points (text_encoder_2-item4.clip_prompt: torch.Size([256])) for text_encoder_2-item4.clip_prompt_embeds Using per-key quantization points (text_encoder_3-item5.t5_prompt_embeds: torch.Size([256])) for text_encoder_3-item5.t5_prompt_embeds Using per-key quantization points (vae.encoder_f0: torch.Size([256])) for vae.encoder_f0 Using per-key quantization points (vae.encoder_f1: torch.Size([256])) for vae.encoder_f1 Using per-key quantization points (vae.decoder: torch.Size([256])) for vae.decoder IndividualUnPacker: text_encoder-item0.clip_pooled_prompt_embeds: torch.Size([768]) text_encoder-item0.clip_prompt_embeds: torch.Size([77, 768]) text_encoder_2-item1.clip_pooled_prompt_embeds: torch.Size([1280]) text_encoder_2-item1.clip_prompt_embeds: torch.Size([77, 1280]) text_encoder_3-item2.t5_prompt_embeds: torch.Size([77, 4096]) text_encoder-item3.clip_pooled_prompt_embeds: torch.Size([768]) text_encoder-item3.clip_prompt_embeds: torch.Size([77, 768]) text_encoder_2-item4.clip_pooled_prompt_embeds: torch.Size([1280]) text_encoder_2-item4.clip_prompt_embeds: torch.Size([77, 1280]) text_encoder_3-item5.t5_prompt_embeds: torch.Size([77, 4096]) vae.decoder: torch.Size([16, 128, 128]) vae.encoder_f0: torch.Size([32, 128, 128]) vae.encoder_f1: torch.Size([32, 128, 128]) ⌛️ [3/4] BACKEND: Backend time: 0.443s [4/4] METRICS: Computing MSE Breakdown... Using per-key quantization points (text_encoder-item0.clip_pooled_prompt_embeds: torch.Size([256])) for text_encoder-item0.clip_pooled_prompt_embeds Using per-key quantization points (text_encoder-item0.clip_prompt_embeds: torch.Size([256])) for text_encoder-item0.clip_prompt_embeds Using per-key quantization points (text_encoder_2-item1.clip_pooled_prompt_embeds: torch.Size([256])) for text_encoder_2-item1.clip_pooled_prompt_embeds Using per-key quantization points (text_encoder_2-item1.clip_prompt_embeds: torch.Size([256])) for text_encoder_2-item1.clip_prompt_embeds Using per-key quantization points (text_encoder_3-item2.t5_prompt_embeds: torch.Size([256])) for text_encoder_3-item2.t5_prompt_embeds Using per-key quantization points (text_encoder-item3.clip_pooled_prompt_embeds: torch.Size([256])) for text_encoder-item3.clip_pooled_prompt_embeds Using per-key quantization points (text_encoder-item3.clip_prompt_embeds: torch.Size([256])) for text_encoder-item3.clip_prompt_embeds Using per-key quantization points (text_encoder_2-item4.clip_pooled: torch.Size([256])) for text_encoder_2-item4.clip_pooled_prompt_embeds Using per-key quantization points (text_encoder_2-item4.clip_prompt: torch.Size([256])) for text_encoder_2-item4.clip_prompt_embeds Using per-key quantization points (text_encoder_3-item5.t5_prompt_embeds: torch.Size([256])) for text_encoder_3-item5.t5_prompt_embeds Using per-key quantization points (vae.encoder_f0: torch.Size([256])) for vae.encoder_f0 Using per-key quantization points (vae.encoder_f1: torch.Size([256])) for vae.encoder_f1 Using per-key quantization points (vae.decoder: torch.Size([256])) for vae.decoder IndividualUnPacker: text_encoder-item0.clip_pooled_prompt_embeds: torch.Size([768]) text_encoder-item0.clip_prompt_embeds: torch.Size([77, 768]) text_encoder_2-item1.clip_pooled_prompt_embeds: torch.Size([1280]) text_encoder_2-item1.clip_prompt_embeds: torch.Size([77, 1280]) text_encoder_3-item2.t5_prompt_embeds: torch.Size([77, 4096]) text_encoder-item3.clip_pooled_prompt_embeds: torch.Size([768]) text_encoder-item3.clip_prompt_embeds: torch.Size([77, 768]) text_encoder_2-item4.clip_pooled_prompt_embeds: torch.Size([1280]) text_encoder_2-item4.clip_prompt_embeds: torch.Size([77, 1280]) text_encoder_3-item5.t5_prompt_embeds: torch.Size([77, 4096]) vae.decoder: torch.Size([16, 128, 128]) vae.encoder_f0: torch.Size([32, 128, 128]) vae.encoder_f1: torch.Size([32, 128, 128]) Per-key MSE Breakdown (Quant=Truncation+Quantization, Codec=Compression): Key Quant-MSE Total-MSE ------------------------------------------------------------------------------------- text_encoder-item0.clip_pooled_prompt_embeds 0.00017717 0.90015364 text_encoder-item0.clip_prompt_embeds 0.00022173 23.95726799 text_encoder_2-item1.clip_pooled_prompt_embeds 0.00022739 0.93097534 text_encoder_2-item1.clip_prompt_embeds 0.00103962 0.21871599 text_encoder_3-item2.t5_prompt_embeds 0.00000788 0.00264774 text_encoder-item3.clip_pooled_prompt_embeds 0.00841799 1.01420029 text_encoder-item3.clip_prompt_embeds 0.00023247 23.90570887 text_encoder_2-item4.clip_pooled_prompt_embeds 0.00016751 0.67051430 text_encoder_2-item4.clip_prompt_embeds 0.00009830 0.47049568 text_encoder_3-item5.t5_prompt_embeds 0.00001263 0.00227117 vae.encoder_f0 0.00576096 1.64696240 vae.encoder_f1 0.00576981 1.64633238 vae.decoder 0.00019592 0.07309705 ------------------------------------------------------------------------------------- TOTAL 0.00276400 2.05627535 (elements=2,260,992) ---------------------- -------------------------------------------------------- SAMPLE-WISE STATISTICS ---------------------- -------------------------------------------------------- Handler sd35 Strategy individual Architecture hyperprior-featurecoding ---------------------- -------------------------------------------------------- Total Elements 2260992 Total Bytes 114828 BPFP 0.4063 bits/point EBPFP 0.8126 equivalent bits/point MSE 2.056275 ---------------------- -------------------------------------------------------- Time: 0.740s Load: 0.009s, Pack+Encode: 0.288s, Decode+Unpack: 0.443s ---------------------- -------------------------------------------------------- Restored Feature Format: [Dict] with 8 keys key['text_encoder-item0']: [Dict] with 2 keys key['clip_pooled_prompt_embeds']: [Tensor] shape=torch.Size([768]), dtype=torch.float16, device=cpu key['clip_prompt_embeds']: [Tensor] shape=torch.Size([77, 768]), dtype=torch.float16, device=cpu key['text_encoder_2-item1']: [Dict] with 2 keys key['clip_pooled_prompt_embeds']: [Tensor] shape=torch.Size([1280]), dtype=torch.float16, device=cpu key['clip_prompt_embeds']: [Tensor] shape=torch.Size([77, 1280]), dtype=torch.float16, device=cpu key['text_encoder_3-item2']: [Dict] with 1 keys key['t5_prompt_embeds']: [Tensor] shape=torch.Size([77, 4096]), dtype=torch.float16, device=cpu key['text_encoder-item3']: [Dict] with 2 keys key['clip_pooled_prompt_embeds']: [Tensor] shape=torch.Size([768]), dtype=torch.float16, device=cpu key['clip_prompt_embeds']: [Tensor] shape=torch.Size([77, 768]), dtype=torch.float16, device=cpu key['text_encoder_2-item4']: [Dict] with 2 keys key['clip_pooled_prompt_embeds']: [Tensor] shape=torch.Size([1280]), dtype=torch.float16, device=cpu key['clip_prompt_embeds']: [Tensor] shape=torch.Size([77, 1280]), dtype=torch.float16, device=cpu key['text_encoder_3-item5']: [Dict] with 1 keys key['t5_prompt_embeds']: [Tensor] shape=torch.Size([77, 4096]), dtype=torch.float16, device=cpu key['vae.decoder']: [Tensor] shape=torch.Size([16, 128, 128]), dtype=torch.float16, device=cpu key['vae.encoder']: [Dict] with 2 keys key['vae.encoder_f0']: [Tensor] shape=torch.Size([32, 128, 128]), dtype=torch.float16, device=cpu key['vae.encoder_f1']: [Tensor] shape=torch.Size([32, 128, 128]), dtype=torch.float16, device=cpu 💾 Converting with 2.0563 MSE: from ../datasets/FeatureCoding-StableDiffusion3.5/100samples_features/000000361268.zst to output-fixed/sd35/lambda0.004/hyperprior-featurecoding-8bit-individual/sd35cond/000000361268.zst 💪 Processing: ../datasets/FeatureCoding-StableDiffusion3.5/100samples_features/000000367228.zst (62/100) [1/4] FRONTEND: Loading features from ../datasets/FeatureCoding-StableDiffusion3.5/100samples_features/000000367228.zst... Original data structure: root: [Dict] with 8 keys key['text_encoder-item0']: [Dict] with 2 keys key['clip_pooled_prompt_embeds']: [Tensor] shape=torch.Size([768]), dtype=torch.float16, device=cpu key['clip_prompt_embeds']: [Tensor] shape=torch.Size([77, 768]), dtype=torch.float16, device=cpu key['text_encoder_2-item1']: [Dict] with 2 keys key['clip_pooled_prompt_embeds']: [Tensor] shape=torch.Size([1280]), dtype=torch.float16, device=cpu key['clip_prompt_embeds']: [Tensor] shape=torch.Size([77, 1280]), dtype=torch.float16, device=cpu key['text_encoder_3-item2']: [Dict] with 1 keys key['t5_prompt_embeds']: [Tensor] shape=torch.Size([77, 4096]), dtype=torch.float16, device=cpu key['text_encoder-item3']: [Dict] with 2 keys key['clip_pooled_prompt_embeds']: [Tensor] shape=torch.Size([768]), dtype=torch.float16, device=cpu key['clip_prompt_embeds']: [Tensor] shape=torch.Size([77, 768]), dtype=torch.float16, device=cpu key['text_encoder_2-item4']: [Dict] with 2 keys key['clip_pooled_prompt_embeds']: [Tensor] shape=torch.Size([1280]), dtype=torch.float16, device=cpu key['clip_prompt_embeds']: [Tensor] shape=torch.Size([77, 1280]), dtype=torch.float16, device=cpu key['text_encoder_3-item5']: [Dict] with 1 keys key['t5_prompt_embeds']: [Tensor] shape=torch.Size([77, 4096]), dtype=torch.float16, device=cpu key['vae.encoder-item6']: [Dict] with 2 keys key['vae.encoder_f0']: [Tensor] shape=torch.Size([32, 128, 128]), dtype=torch.float16, device=cpu key['vae.encoder_f1']: [Tensor] shape=torch.Size([32, 128, 128]), dtype=torch.float16, device=cpu key['vae.decoder']: [Tensor] shape=torch.Size([16, 128, 128]), dtype=torch.float16, device=cpu ⌛️ [1/4] FRONTEND: Load time: 0.009s ------------------------------------------------------------ SD3.5 Features Summary ------------------------------------------------------------ Number of text encoders: 3 Encoder names: ['text_encoder', 'text_encoder_2', 'text_encoder_3'] Has VAE latents: True Has VAE encoder features: True Data type: torch.float16 text_encoder-item0: clip_pooled_prompt_embeds: torch.Size([768]), torch.float16 clip_prompt_embeds: torch.Size([77, 768]), torch.float16 text_encoder_2-item1: clip_pooled_prompt_embeds: torch.Size([1280]), torch.float16 clip_prompt_embeds: torch.Size([77, 1280]), torch.float16 text_encoder_3-item2: t5_prompt_embeds: torch.Size([77, 4096]), torch.float16 text_encoder-item3: clip_pooled_prompt_embeds: torch.Size([768]), torch.float16 clip_prompt_embeds: torch.Size([77, 768]), torch.float16 text_encoder_2-item4: clip_pooled_prompt_embeds: torch.Size([1280]), torch.float16 clip_prompt_embeds: torch.Size([77, 1280]), torch.float16 text_encoder_3-item5: t5_prompt_embeds: torch.Size([77, 4096]), torch.float16 vae.decoder latents: torch.Size([16, 128, 128]) vae.encoder features: vae.encoder_f0: torch.Size([32, 128, 128]), torch.float16 vae.encoder_f1: torch.Size([32, 128, 128]), torch.float16 ------------------------------------------------------------ [2/4] FRONTEND: Pack + Encode (strategy: individual)... IndividualPacker: text_encoder-item0.clip_pooled_prompt_embeds: torch.Size([768])->torch.Size([1, 1, 1, 768]) text_encoder-item0.clip_prompt_embeds: torch.Size([77, 768])->torch.Size([1, 1, 77, 768]) text_encoder_2-item1.clip_pooled_prompt_embeds: torch.Size([1280])->torch.Size([1, 1, 1, 1280]) text_encoder_2-item1.clip_prompt_embeds: torch.Size([77, 1280])->torch.Size([1, 1, 77, 1280]) text_encoder_3-item2.t5_prompt_embeds: torch.Size([77, 4096])->torch.Size([1, 1, 77, 4096]) text_encoder-item3.clip_pooled_prompt_embeds: torch.Size([768])->torch.Size([1, 1, 1, 768]) text_encoder-item3.clip_prompt_embeds: torch.Size([77, 768])->torch.Size([1, 1, 77, 768]) text_encoder_2-item4.clip_pooled_prompt_embeds: torch.Size([1280])->torch.Size([1, 1, 1, 1280]) text_encoder_2-item4.clip_prompt_embeds: torch.Size([77, 1280])->torch.Size([1, 1, 77, 1280]) text_encoder_3-item5.t5_prompt_embeds: torch.Size([77, 4096])->torch.Size([1, 1, 77, 4096]) vae.encoder_f0: torch.Size([32, 128, 128])->torch.Size([1, 1, 512, 1024]) vae.encoder_f1: torch.Size([32, 128, 128])->torch.Size([1, 1, 512, 1024]) vae.decoder: torch.Size([16, 128, 128])->torch.Size([1, 1, 512, 512]) Using per-key quantization points (text_encoder-item0.clip_pooled_prompt_embeds: torch.Size([256])) for text_encoder-item0.clip_pooled_prompt_embeds text_encoder-item0.clip_pooled_prompt_embeds: 744B, BPFP=7.7500 Using per-key quantization points (text_encoder-item0.clip_prompt_embeds: torch.Size([256])) for text_encoder-item0.clip_prompt_embeds text_encoder-item0.clip_prompt_embeds: 6,564B, BPFP=0.8880 Using per-key quantization points (text_encoder_2-item1.clip_pooled_prompt_embeds: torch.Size([256])) for text_encoder_2-item1.clip_pooled_prompt_embeds text_encoder_2-item1.clip_pooled_prompt_embeds: 1,192B, BPFP=7.4500 Using per-key quantization points (text_encoder_2-item1.clip_prompt_embeds: torch.Size([256])) for text_encoder_2-item1.clip_prompt_embeds text_encoder_2-item1.clip_prompt_embeds: 11,524B, BPFP=0.9354 Using per-key quantization points (text_encoder_3-item2.t5_prompt_embeds: torch.Size([256])) for text_encoder_3-item2.t5_prompt_embeds text_encoder_3-item2.t5_prompt_embeds: 29,840B, BPFP=0.7569 Using per-key quantization points (text_encoder-item3.clip_pooled_prompt_embeds: torch.Size([256])) for text_encoder-item3.clip_pooled_prompt_embeds text_encoder-item3.clip_pooled_prompt_embeds: 760B, BPFP=7.9167 Using per-key quantization points (text_encoder-item3.clip_prompt_embeds: torch.Size([256])) for text_encoder-item3.clip_prompt_embeds text_encoder-item3.clip_prompt_embeds: 4,024B, BPFP=0.5444 Using per-key quantization points (text_encoder_2-item4.clip_pooled: torch.Size([256])) for text_encoder_2-item4.clip_pooled_prompt_embeds text_encoder_2-item4.clip_pooled_prompt_embeds: 1,268B, BPFP=7.9250 Using per-key quantization points (text_encoder_2-item4.clip_prompt: torch.Size([256])) for text_encoder_2-item4.clip_prompt_embeds text_encoder_2-item4.clip_prompt_embeds: 6,996B, BPFP=0.5679 Using per-key quantization points (text_encoder_3-item5.t5_prompt_embeds: torch.Size([256])) for text_encoder_3-item5.t5_prompt_embeds text_encoder_3-item5.t5_prompt_embeds: 18,416B, BPFP=0.4671 Using per-key quantization points (vae.encoder_f0: torch.Size([256])) for vae.encoder_f0 vae.encoder_f0: 12,356B, BPFP=0.1885 Using per-key quantization points (vae.encoder_f1: torch.Size([256])) for vae.encoder_f1 vae.encoder_f1: 12,356B, BPFP=0.1885 Using per-key quantization points (vae.decoder: torch.Size([256])) for vae.decoder vae.decoder: 5,896B, BPFP=0.1799 ⌛️ [2/4] FRONTEND: Frontend time: 0.289s (Pack+Encode) [3/4] BACKEND: Decode + Unpack... Using per-key quantization points (text_encoder-item0.clip_pooled_prompt_embeds: torch.Size([256])) for text_encoder-item0.clip_pooled_prompt_embeds Using per-key quantization points (text_encoder-item0.clip_prompt_embeds: torch.Size([256])) for text_encoder-item0.clip_prompt_embeds Using per-key quantization points (text_encoder_2-item1.clip_pooled_prompt_embeds: torch.Size([256])) for text_encoder_2-item1.clip_pooled_prompt_embeds Using per-key quantization points (text_encoder_2-item1.clip_prompt_embeds: torch.Size([256])) for text_encoder_2-item1.clip_prompt_embeds Using per-key quantization points (text_encoder_3-item2.t5_prompt_embeds: torch.Size([256])) for text_encoder_3-item2.t5_prompt_embeds Using per-key quantization points (text_encoder-item3.clip_pooled_prompt_embeds: torch.Size([256])) for text_encoder-item3.clip_pooled_prompt_embeds Using per-key quantization points (text_encoder-item3.clip_prompt_embeds: torch.Size([256])) for text_encoder-item3.clip_prompt_embeds Using per-key quantization points (text_encoder_2-item4.clip_pooled: torch.Size([256])) for text_encoder_2-item4.clip_pooled_prompt_embeds Using per-key quantization points (text_encoder_2-item4.clip_prompt: torch.Size([256])) for text_encoder_2-item4.clip_prompt_embeds Using per-key quantization points (text_encoder_3-item5.t5_prompt_embeds: torch.Size([256])) for text_encoder_3-item5.t5_prompt_embeds Using per-key quantization points (vae.encoder_f0: torch.Size([256])) for vae.encoder_f0 Using per-key quantization points (vae.encoder_f1: torch.Size([256])) for vae.encoder_f1 Using per-key quantization points (vae.decoder: torch.Size([256])) for vae.decoder IndividualUnPacker: text_encoder-item0.clip_pooled_prompt_embeds: torch.Size([768]) text_encoder-item0.clip_prompt_embeds: torch.Size([77, 768]) text_encoder_2-item1.clip_pooled_prompt_embeds: torch.Size([1280]) text_encoder_2-item1.clip_prompt_embeds: torch.Size([77, 1280]) text_encoder_3-item2.t5_prompt_embeds: torch.Size([77, 4096]) text_encoder-item3.clip_pooled_prompt_embeds: torch.Size([768]) text_encoder-item3.clip_prompt_embeds: torch.Size([77, 768]) text_encoder_2-item4.clip_pooled_prompt_embeds: torch.Size([1280]) text_encoder_2-item4.clip_prompt_embeds: torch.Size([77, 1280]) text_encoder_3-item5.t5_prompt_embeds: torch.Size([77, 4096]) vae.decoder: torch.Size([16, 128, 128]) vae.encoder_f0: torch.Size([32, 128, 128]) vae.encoder_f1: torch.Size([32, 128, 128]) ⌛️ [3/4] BACKEND: Backend time: 0.442s [4/4] METRICS: Computing MSE Breakdown... Using per-key quantization points (text_encoder-item0.clip_pooled_prompt_embeds: torch.Size([256])) for text_encoder-item0.clip_pooled_prompt_embeds Using per-key quantization points (text_encoder-item0.clip_prompt_embeds: torch.Size([256])) for text_encoder-item0.clip_prompt_embeds Using per-key quantization points (text_encoder_2-item1.clip_pooled_prompt_embeds: torch.Size([256])) for text_encoder_2-item1.clip_pooled_prompt_embeds Using per-key quantization points (text_encoder_2-item1.clip_prompt_embeds: torch.Size([256])) for text_encoder_2-item1.clip_prompt_embeds Using per-key quantization points (text_encoder_3-item2.t5_prompt_embeds: torch.Size([256])) for text_encoder_3-item2.t5_prompt_embeds Using per-key quantization points (text_encoder-item3.clip_pooled_prompt_embeds: torch.Size([256])) for text_encoder-item3.clip_pooled_prompt_embeds Using per-key quantization points (text_encoder-item3.clip_prompt_embeds: torch.Size([256])) for text_encoder-item3.clip_prompt_embeds Using per-key quantization points (text_encoder_2-item4.clip_pooled: torch.Size([256])) for text_encoder_2-item4.clip_pooled_prompt_embeds Using per-key quantization points (text_encoder_2-item4.clip_prompt: torch.Size([256])) for text_encoder_2-item4.clip_prompt_embeds Using per-key quantization points (text_encoder_3-item5.t5_prompt_embeds: torch.Size([256])) for text_encoder_3-item5.t5_prompt_embeds Using per-key quantization points (vae.encoder_f0: torch.Size([256])) for vae.encoder_f0 Using per-key quantization points (vae.encoder_f1: torch.Size([256])) for vae.encoder_f1 Using per-key quantization points (vae.decoder: torch.Size([256])) for vae.decoder IndividualUnPacker: text_encoder-item0.clip_pooled_prompt_embeds: torch.Size([768]) text_encoder-item0.clip_prompt_embeds: torch.Size([77, 768]) text_encoder_2-item1.clip_pooled_prompt_embeds: torch.Size([1280]) text_encoder_2-item1.clip_prompt_embeds: torch.Size([77, 1280]) text_encoder_3-item2.t5_prompt_embeds: torch.Size([77, 4096]) text_encoder-item3.clip_pooled_prompt_embeds: torch.Size([768]) text_encoder-item3.clip_prompt_embeds: torch.Size([77, 768]) text_encoder_2-item4.clip_pooled_prompt_embeds: torch.Size([1280]) text_encoder_2-item4.clip_prompt_embeds: torch.Size([77, 1280]) text_encoder_3-item5.t5_prompt_embeds: torch.Size([77, 4096]) vae.decoder: torch.Size([16, 128, 128]) vae.encoder_f0: torch.Size([32, 128, 128]) vae.encoder_f1: torch.Size([32, 128, 128]) Per-key MSE Breakdown (Quant=Truncation+Quantization, Codec=Compression): Key Quant-MSE Total-MSE ------------------------------------------------------------------------------------- text_encoder-item0.clip_pooled_prompt_embeds 0.00024069 0.93638118 text_encoder-item0.clip_prompt_embeds 0.00025917 34.93605841 text_encoder_2-item1.clip_pooled_prompt_embeds 0.00023350 0.96450405 text_encoder_2-item1.clip_prompt_embeds 0.00019057 0.13600763 text_encoder_3-item2.t5_prompt_embeds 0.00000791 0.00361832 text_encoder-item3.clip_pooled_prompt_embeds 0.00841799 1.01420029 text_encoder-item3.clip_prompt_embeds 0.00023247 23.90570887 text_encoder_2-item4.clip_pooled_prompt_embeds 0.00016751 0.67051430 text_encoder_2-item4.clip_prompt_embeds 0.00009830 0.47049568 text_encoder_3-item5.t5_prompt_embeds 0.00001263 0.00227117 vae.encoder_f0 0.00594818 1.87497544 vae.encoder_f1 0.00595328 1.87472022 vae.decoder 0.00023462 0.05087871 ------------------------------------------------------------------------------------- TOTAL 0.00281845 2.44324176 (elements=2,260,992) ---------------------- -------------------------------------------------------- SAMPLE-WISE STATISTICS ---------------------- -------------------------------------------------------- Handler sd35 Strategy individual Architecture hyperprior-featurecoding ---------------------- -------------------------------------------------------- Total Elements 2260992 Total Bytes 111936 BPFP 0.3961 bits/point EBPFP 0.7921 equivalent bits/point MSE 2.443242 ---------------------- -------------------------------------------------------- Time: 0.739s Load: 0.009s, Pack+Encode: 0.289s, Decode+Unpack: 0.442s ---------------------- -------------------------------------------------------- Restored Feature Format: [Dict] with 8 keys key['text_encoder-item0']: [Dict] with 2 keys key['clip_pooled_prompt_embeds']: [Tensor] shape=torch.Size([768]), dtype=torch.float16, device=cpu key['clip_prompt_embeds']: [Tensor] shape=torch.Size([77, 768]), dtype=torch.float16, device=cpu key['text_encoder_2-item1']: [Dict] with 2 keys key['clip_pooled_prompt_embeds']: [Tensor] shape=torch.Size([1280]), dtype=torch.float16, device=cpu key['clip_prompt_embeds']: [Tensor] shape=torch.Size([77, 1280]), dtype=torch.float16, device=cpu key['text_encoder_3-item2']: [Dict] with 1 keys key['t5_prompt_embeds']: [Tensor] shape=torch.Size([77, 4096]), dtype=torch.float16, device=cpu key['text_encoder-item3']: [Dict] with 2 keys key['clip_pooled_prompt_embeds']: [Tensor] shape=torch.Size([768]), dtype=torch.float16, device=cpu key['clip_prompt_embeds']: [Tensor] shape=torch.Size([77, 768]), dtype=torch.float16, device=cpu key['text_encoder_2-item4']: [Dict] with 2 keys key['clip_pooled_prompt_embeds']: [Tensor] shape=torch.Size([1280]), dtype=torch.float16, device=cpu key['clip_prompt_embeds']: [Tensor] shape=torch.Size([77, 1280]), dtype=torch.float16, device=cpu key['text_encoder_3-item5']: [Dict] with 1 keys key['t5_prompt_embeds']: [Tensor] shape=torch.Size([77, 4096]), dtype=torch.float16, device=cpu key['vae.decoder']: [Tensor] shape=torch.Size([16, 128, 128]), dtype=torch.float16, device=cpu key['vae.encoder']: [Dict] with 2 keys key['vae.encoder_f0']: [Tensor] shape=torch.Size([32, 128, 128]), dtype=torch.float16, device=cpu key['vae.encoder_f1']: [Tensor] shape=torch.Size([32, 128, 128]), dtype=torch.float16, device=cpu 💾 Converting with 2.4432 MSE: from ../datasets/FeatureCoding-StableDiffusion3.5/100samples_features/000000367228.zst to output-fixed/sd35/lambda0.004/hyperprior-featurecoding-8bit-individual/sd35cond/000000367228.zst 💪 Processing: ../datasets/FeatureCoding-StableDiffusion3.5/100samples_features/000000369503.zst (63/100) [1/4] FRONTEND: Loading features from ../datasets/FeatureCoding-StableDiffusion3.5/100samples_features/000000369503.zst... Original data structure: root: [Dict] with 8 keys key['text_encoder-item0']: [Dict] with 2 keys key['clip_pooled_prompt_embeds']: [Tensor] shape=torch.Size([768]), dtype=torch.float16, device=cpu key['clip_prompt_embeds']: [Tensor] shape=torch.Size([77, 768]), dtype=torch.float16, device=cpu key['text_encoder_2-item1']: [Dict] with 2 keys key['clip_pooled_prompt_embeds']: [Tensor] shape=torch.Size([1280]), dtype=torch.float16, device=cpu key['clip_prompt_embeds']: [Tensor] shape=torch.Size([77, 1280]), dtype=torch.float16, device=cpu key['text_encoder_3-item2']: [Dict] with 1 keys key['t5_prompt_embeds']: [Tensor] shape=torch.Size([77, 4096]), dtype=torch.float16, device=cpu key['text_encoder-item3']: [Dict] with 2 keys key['clip_pooled_prompt_embeds']: [Tensor] shape=torch.Size([768]), dtype=torch.float16, device=cpu key['clip_prompt_embeds']: [Tensor] shape=torch.Size([77, 768]), dtype=torch.float16, device=cpu key['text_encoder_2-item4']: [Dict] with 2 keys key['clip_pooled_prompt_embeds']: [Tensor] shape=torch.Size([1280]), dtype=torch.float16, device=cpu key['clip_prompt_embeds']: [Tensor] shape=torch.Size([77, 1280]), dtype=torch.float16, device=cpu key['text_encoder_3-item5']: [Dict] with 1 keys key['t5_prompt_embeds']: [Tensor] shape=torch.Size([77, 4096]), dtype=torch.float16, device=cpu key['vae.encoder-item6']: [Dict] with 2 keys key['vae.encoder_f0']: [Tensor] shape=torch.Size([32, 128, 128]), dtype=torch.float16, device=cpu key['vae.encoder_f1']: [Tensor] shape=torch.Size([32, 128, 128]), dtype=torch.float16, device=cpu key['vae.decoder']: [Tensor] shape=torch.Size([16, 128, 128]), dtype=torch.float16, device=cpu ⌛️ [1/4] FRONTEND: Load time: 0.008s ------------------------------------------------------------ SD3.5 Features Summary ------------------------------------------------------------ Number of text encoders: 3 Encoder names: ['text_encoder', 'text_encoder_2', 'text_encoder_3'] Has VAE latents: True Has VAE encoder features: True Data type: torch.float16 text_encoder-item0: clip_pooled_prompt_embeds: torch.Size([768]), torch.float16 clip_prompt_embeds: torch.Size([77, 768]), torch.float16 text_encoder_2-item1: clip_pooled_prompt_embeds: torch.Size([1280]), torch.float16 clip_prompt_embeds: torch.Size([77, 1280]), torch.float16 text_encoder_3-item2: t5_prompt_embeds: torch.Size([77, 4096]), torch.float16 text_encoder-item3: clip_pooled_prompt_embeds: torch.Size([768]), torch.float16 clip_prompt_embeds: torch.Size([77, 768]), torch.float16 text_encoder_2-item4: clip_pooled_prompt_embeds: torch.Size([1280]), torch.float16 clip_prompt_embeds: torch.Size([77, 1280]), torch.float16 text_encoder_3-item5: t5_prompt_embeds: torch.Size([77, 4096]), torch.float16 vae.decoder latents: torch.Size([16, 128, 128]) vae.encoder features: vae.encoder_f0: torch.Size([32, 128, 128]), torch.float16 vae.encoder_f1: torch.Size([32, 128, 128]), torch.float16 ------------------------------------------------------------ [2/4] FRONTEND: Pack + Encode (strategy: individual)... IndividualPacker: text_encoder-item0.clip_pooled_prompt_embeds: torch.Size([768])->torch.Size([1, 1, 1, 768]) text_encoder-item0.clip_prompt_embeds: torch.Size([77, 768])->torch.Size([1, 1, 77, 768]) text_encoder_2-item1.clip_pooled_prompt_embeds: torch.Size([1280])->torch.Size([1, 1, 1, 1280]) text_encoder_2-item1.clip_prompt_embeds: torch.Size([77, 1280])->torch.Size([1, 1, 77, 1280]) text_encoder_3-item2.t5_prompt_embeds: torch.Size([77, 4096])->torch.Size([1, 1, 77, 4096]) text_encoder-item3.clip_pooled_prompt_embeds: torch.Size([768])->torch.Size([1, 1, 1, 768]) text_encoder-item3.clip_prompt_embeds: torch.Size([77, 768])->torch.Size([1, 1, 77, 768]) text_encoder_2-item4.clip_pooled_prompt_embeds: torch.Size([1280])->torch.Size([1, 1, 1, 1280]) text_encoder_2-item4.clip_prompt_embeds: torch.Size([77, 1280])->torch.Size([1, 1, 77, 1280]) text_encoder_3-item5.t5_prompt_embeds: torch.Size([77, 4096])->torch.Size([1, 1, 77, 4096]) vae.encoder_f0: torch.Size([32, 128, 128])->torch.Size([1, 1, 512, 1024]) vae.encoder_f1: torch.Size([32, 128, 128])->torch.Size([1, 1, 512, 1024]) vae.decoder: torch.Size([16, 128, 128])->torch.Size([1, 1, 512, 512]) Using per-key quantization points (text_encoder-item0.clip_pooled_prompt_embeds: torch.Size([256])) for text_encoder-item0.clip_pooled_prompt_embeds text_encoder-item0.clip_pooled_prompt_embeds: 744B, BPFP=7.7500 Using per-key quantization points (text_encoder-item0.clip_prompt_embeds: torch.Size([256])) for text_encoder-item0.clip_prompt_embeds text_encoder-item0.clip_prompt_embeds: 7,176B, BPFP=0.9708 Using per-key quantization points (text_encoder_2-item1.clip_pooled_prompt_embeds: torch.Size([256])) for text_encoder_2-item1.clip_pooled_prompt_embeds text_encoder_2-item1.clip_pooled_prompt_embeds: 1,200B, BPFP=7.5000 Using per-key quantization points (text_encoder_2-item1.clip_prompt_embeds: torch.Size([256])) for text_encoder_2-item1.clip_prompt_embeds text_encoder_2-item1.clip_prompt_embeds: 11,236B, BPFP=0.9120 Using per-key quantization points (text_encoder_3-item2.t5_prompt_embeds: torch.Size([256])) for text_encoder_3-item2.t5_prompt_embeds text_encoder_3-item2.t5_prompt_embeds: 29,960B, BPFP=0.7599 Using per-key quantization points (text_encoder-item3.clip_pooled_prompt_embeds: torch.Size([256])) for text_encoder-item3.clip_pooled_prompt_embeds text_encoder-item3.clip_pooled_prompt_embeds: 760B, BPFP=7.9167 Using per-key quantization points (text_encoder-item3.clip_prompt_embeds: torch.Size([256])) for text_encoder-item3.clip_prompt_embeds text_encoder-item3.clip_prompt_embeds: 4,024B, BPFP=0.5444 Using per-key quantization points (text_encoder_2-item4.clip_pooled: torch.Size([256])) for text_encoder_2-item4.clip_pooled_prompt_embeds text_encoder_2-item4.clip_pooled_prompt_embeds: 1,268B, BPFP=7.9250 Using per-key quantization points (text_encoder_2-item4.clip_prompt: torch.Size([256])) for text_encoder_2-item4.clip_prompt_embeds text_encoder_2-item4.clip_prompt_embeds: 6,996B, BPFP=0.5679 Using per-key quantization points (text_encoder_3-item5.t5_prompt_embeds: torch.Size([256])) for text_encoder_3-item5.t5_prompt_embeds text_encoder_3-item5.t5_prompt_embeds: 18,416B, BPFP=0.4671 Using per-key quantization points (vae.encoder_f0: torch.Size([256])) for vae.encoder_f0 vae.encoder_f0: 12,584B, BPFP=0.1920 Using per-key quantization points (vae.encoder_f1: torch.Size([256])) for vae.encoder_f1 vae.encoder_f1: 12,584B, BPFP=0.1920 Using per-key quantization points (vae.decoder: torch.Size([256])) for vae.decoder vae.decoder: 4,884B, BPFP=0.1490 ⌛️ [2/4] FRONTEND: Frontend time: 0.293s (Pack+Encode) [3/4] BACKEND: Decode + Unpack... Using per-key quantization points (text_encoder-item0.clip_pooled_prompt_embeds: torch.Size([256])) for text_encoder-item0.clip_pooled_prompt_embeds Using per-key quantization points (text_encoder-item0.clip_prompt_embeds: torch.Size([256])) for text_encoder-item0.clip_prompt_embeds Using per-key quantization points (text_encoder_2-item1.clip_pooled_prompt_embeds: torch.Size([256])) for text_encoder_2-item1.clip_pooled_prompt_embeds Using per-key quantization points (text_encoder_2-item1.clip_prompt_embeds: torch.Size([256])) for text_encoder_2-item1.clip_prompt_embeds Using per-key quantization points (text_encoder_3-item2.t5_prompt_embeds: torch.Size([256])) for text_encoder_3-item2.t5_prompt_embeds Using per-key quantization points (text_encoder-item3.clip_pooled_prompt_embeds: torch.Size([256])) for text_encoder-item3.clip_pooled_prompt_embeds Using per-key quantization points (text_encoder-item3.clip_prompt_embeds: torch.Size([256])) for text_encoder-item3.clip_prompt_embeds Using per-key quantization points (text_encoder_2-item4.clip_pooled: torch.Size([256])) for text_encoder_2-item4.clip_pooled_prompt_embeds Using per-key quantization points (text_encoder_2-item4.clip_prompt: torch.Size([256])) for text_encoder_2-item4.clip_prompt_embeds Using per-key quantization points (text_encoder_3-item5.t5_prompt_embeds: torch.Size([256])) for text_encoder_3-item5.t5_prompt_embeds Using per-key quantization points (vae.encoder_f0: torch.Size([256])) for vae.encoder_f0 Using per-key quantization points (vae.encoder_f1: torch.Size([256])) for vae.encoder_f1 Using per-key quantization points (vae.decoder: torch.Size([256])) for vae.decoder IndividualUnPacker: text_encoder-item0.clip_pooled_prompt_embeds: torch.Size([768]) text_encoder-item0.clip_prompt_embeds: torch.Size([77, 768]) text_encoder_2-item1.clip_pooled_prompt_embeds: torch.Size([1280]) text_encoder_2-item1.clip_prompt_embeds: torch.Size([77, 1280]) text_encoder_3-item2.t5_prompt_embeds: torch.Size([77, 4096]) text_encoder-item3.clip_pooled_prompt_embeds: torch.Size([768]) text_encoder-item3.clip_prompt_embeds: torch.Size([77, 768]) text_encoder_2-item4.clip_pooled_prompt_embeds: torch.Size([1280]) text_encoder_2-item4.clip_prompt_embeds: torch.Size([77, 1280]) text_encoder_3-item5.t5_prompt_embeds: torch.Size([77, 4096]) vae.decoder: torch.Size([16, 128, 128]) vae.encoder_f0: torch.Size([32, 128, 128]) vae.encoder_f1: torch.Size([32, 128, 128]) ⌛️ [3/4] BACKEND: Backend time: 0.457s [4/4] METRICS: Computing MSE Breakdown... Using per-key quantization points (text_encoder-item0.clip_pooled_prompt_embeds: torch.Size([256])) for text_encoder-item0.clip_pooled_prompt_embeds Using per-key quantization points (text_encoder-item0.clip_prompt_embeds: torch.Size([256])) for text_encoder-item0.clip_prompt_embeds Using per-key quantization points (text_encoder_2-item1.clip_pooled_prompt_embeds: torch.Size([256])) for text_encoder_2-item1.clip_pooled_prompt_embeds Using per-key quantization points (text_encoder_2-item1.clip_prompt_embeds: torch.Size([256])) for text_encoder_2-item1.clip_prompt_embeds Using per-key quantization points (text_encoder_3-item2.t5_prompt_embeds: torch.Size([256])) for text_encoder_3-item2.t5_prompt_embeds Using per-key quantization points (text_encoder-item3.clip_pooled_prompt_embeds: torch.Size([256])) for text_encoder-item3.clip_pooled_prompt_embeds Using per-key quantization points (text_encoder-item3.clip_prompt_embeds: torch.Size([256])) for text_encoder-item3.clip_prompt_embeds Using per-key quantization points (text_encoder_2-item4.clip_pooled: torch.Size([256])) for text_encoder_2-item4.clip_pooled_prompt_embeds Using per-key quantization points (text_encoder_2-item4.clip_prompt: torch.Size([256])) for text_encoder_2-item4.clip_prompt_embeds Using per-key quantization points (text_encoder_3-item5.t5_prompt_embeds: torch.Size([256])) for text_encoder_3-item5.t5_prompt_embeds Using per-key quantization points (vae.encoder_f0: torch.Size([256])) for vae.encoder_f0 Using per-key quantization points (vae.encoder_f1: torch.Size([256])) for vae.encoder_f1 Using per-key quantization points (vae.decoder: torch.Size([256])) for vae.decoder IndividualUnPacker: text_encoder-item0.clip_pooled_prompt_embeds: torch.Size([768]) text_encoder-item0.clip_prompt_embeds: torch.Size([77, 768]) text_encoder_2-item1.clip_pooled_prompt_embeds: torch.Size([1280]) text_encoder_2-item1.clip_prompt_embeds: torch.Size([77, 1280]) text_encoder_3-item2.t5_prompt_embeds: torch.Size([77, 4096]) text_encoder-item3.clip_pooled_prompt_embeds: torch.Size([768]) text_encoder-item3.clip_prompt_embeds: torch.Size([77, 768]) text_encoder_2-item4.clip_pooled_prompt_embeds: torch.Size([1280]) text_encoder_2-item4.clip_prompt_embeds: torch.Size([77, 1280]) text_encoder_3-item5.t5_prompt_embeds: torch.Size([77, 4096]) vae.decoder: torch.Size([16, 128, 128]) vae.encoder_f0: torch.Size([32, 128, 128]) vae.encoder_f1: torch.Size([32, 128, 128]) Per-key MSE Breakdown (Quant=Truncation+Quantization, Codec=Compression): Key Quant-MSE Total-MSE ------------------------------------------------------------------------------------- text_encoder-item0.clip_pooled_prompt_embeds 0.00022245 0.90805984 text_encoder-item0.clip_prompt_embeds 0.00022579 35.97949219 text_encoder_2-item1.clip_pooled_prompt_embeds 0.00020263 0.91401119 text_encoder_2-item1.clip_prompt_embeds 0.00017578 0.16031427 text_encoder_3-item2.t5_prompt_embeds 0.00000800 0.00384470 text_encoder-item3.clip_pooled_prompt_embeds 0.00841799 1.01420029 text_encoder-item3.clip_prompt_embeds 0.00023247 23.90570887 text_encoder_2-item4.clip_pooled_prompt_embeds 0.00016751 0.67051430 text_encoder_2-item4.clip_prompt_embeds 0.00009830 0.47049568 text_encoder_3-item5.t5_prompt_embeds 0.00001263 0.00227117 vae.encoder_f0 0.85445058 7.23124504 vae.encoder_f1 0.85445166 7.23127222 vae.decoder 0.00025257 0.02433285 ------------------------------------------------------------------------------------- TOTAL 0.39632643 4.95264036 (elements=2,260,992) ---------------------- -------------------------------------------------------- SAMPLE-WISE STATISTICS ---------------------- -------------------------------------------------------- Handler sd35 Strategy individual Architecture hyperprior-featurecoding ---------------------- -------------------------------------------------------- Total Elements 2260992 Total Bytes 111832 BPFP 0.3957 bits/point EBPFP 0.7914 equivalent bits/point MSE 4.952640 ---------------------- -------------------------------------------------------- Time: 0.759s Load: 0.008s, Pack+Encode: 0.293s, Decode+Unpack: 0.457s ---------------------- -------------------------------------------------------- Restored Feature Format: [Dict] with 8 keys key['text_encoder-item0']: [Dict] with 2 keys key['clip_pooled_prompt_embeds']: [Tensor] shape=torch.Size([768]), dtype=torch.float16, device=cpu key['clip_prompt_embeds']: [Tensor] shape=torch.Size([77, 768]), dtype=torch.float16, device=cpu key['text_encoder_2-item1']: [Dict] with 2 keys key['clip_pooled_prompt_embeds']: [Tensor] shape=torch.Size([1280]), dtype=torch.float16, device=cpu key['clip_prompt_embeds']: [Tensor] shape=torch.Size([77, 1280]), dtype=torch.float16, device=cpu key['text_encoder_3-item2']: [Dict] with 1 keys key['t5_prompt_embeds']: [Tensor] shape=torch.Size([77, 4096]), dtype=torch.float16, device=cpu key['text_encoder-item3']: [Dict] with 2 keys key['clip_pooled_prompt_embeds']: [Tensor] shape=torch.Size([768]), dtype=torch.float16, device=cpu key['clip_prompt_embeds']: [Tensor] shape=torch.Size([77, 768]), dtype=torch.float16, device=cpu key['text_encoder_2-item4']: [Dict] with 2 keys key['clip_pooled_prompt_embeds']: [Tensor] shape=torch.Size([1280]), dtype=torch.float16, device=cpu key['clip_prompt_embeds']: [Tensor] shape=torch.Size([77, 1280]), dtype=torch.float16, device=cpu key['text_encoder_3-item5']: [Dict] with 1 keys key['t5_prompt_embeds']: [Tensor] shape=torch.Size([77, 4096]), dtype=torch.float16, device=cpu key['vae.decoder']: [Tensor] shape=torch.Size([16, 128, 128]), dtype=torch.float16, device=cpu key['vae.encoder']: [Dict] with 2 keys key['vae.encoder_f0']: [Tensor] shape=torch.Size([32, 128, 128]), dtype=torch.float16, device=cpu key['vae.encoder_f1']: [Tensor] shape=torch.Size([32, 128, 128]), dtype=torch.float16, device=cpu 💾 Converting with 4.9526 MSE: from ../datasets/FeatureCoding-StableDiffusion3.5/100samples_features/000000369503.zst to output-fixed/sd35/lambda0.004/hyperprior-featurecoding-8bit-individual/sd35cond/000000369503.zst 💪 Processing: ../datasets/FeatureCoding-StableDiffusion3.5/100samples_features/000000370486.zst (64/100) [1/4] FRONTEND: Loading features from ../datasets/FeatureCoding-StableDiffusion3.5/100samples_features/000000370486.zst... Original data structure: root: [Dict] with 8 keys key['text_encoder-item0']: [Dict] with 2 keys key['clip_pooled_prompt_embeds']: [Tensor] shape=torch.Size([768]), dtype=torch.float16, device=cpu key['clip_prompt_embeds']: [Tensor] shape=torch.Size([77, 768]), dtype=torch.float16, device=cpu key['text_encoder_2-item1']: [Dict] with 2 keys key['clip_pooled_prompt_embeds']: [Tensor] shape=torch.Size([1280]), dtype=torch.float16, device=cpu key['clip_prompt_embeds']: [Tensor] shape=torch.Size([77, 1280]), dtype=torch.float16, device=cpu key['text_encoder_3-item2']: [Dict] with 1 keys key['t5_prompt_embeds']: [Tensor] shape=torch.Size([77, 4096]), dtype=torch.float16, device=cpu key['text_encoder-item3']: [Dict] with 2 keys key['clip_pooled_prompt_embeds']: [Tensor] shape=torch.Size([768]), dtype=torch.float16, device=cpu key['clip_prompt_embeds']: [Tensor] shape=torch.Size([77, 768]), dtype=torch.float16, device=cpu key['text_encoder_2-item4']: [Dict] with 2 keys key['clip_pooled_prompt_embeds']: [Tensor] shape=torch.Size([1280]), dtype=torch.float16, device=cpu key['clip_prompt_embeds']: [Tensor] shape=torch.Size([77, 1280]), dtype=torch.float16, device=cpu key['text_encoder_3-item5']: [Dict] with 1 keys key['t5_prompt_embeds']: [Tensor] shape=torch.Size([77, 4096]), dtype=torch.float16, device=cpu key['vae.encoder-item6']: [Dict] with 2 keys key['vae.encoder_f0']: [Tensor] shape=torch.Size([32, 128, 128]), dtype=torch.float16, device=cpu key['vae.encoder_f1']: [Tensor] shape=torch.Size([32, 128, 128]), dtype=torch.float16, device=cpu key['vae.decoder']: [Tensor] shape=torch.Size([16, 128, 128]), dtype=torch.float16, device=cpu ⌛️ [1/4] FRONTEND: Load time: 0.009s ------------------------------------------------------------ SD3.5 Features Summary ------------------------------------------------------------ Number of text encoders: 3 Encoder names: ['text_encoder', 'text_encoder_2', 'text_encoder_3'] Has VAE latents: True Has VAE encoder features: True Data type: torch.float16 text_encoder-item0: clip_pooled_prompt_embeds: torch.Size([768]), torch.float16 clip_prompt_embeds: torch.Size([77, 768]), torch.float16 text_encoder_2-item1: clip_pooled_prompt_embeds: torch.Size([1280]), torch.float16 clip_prompt_embeds: torch.Size([77, 1280]), torch.float16 text_encoder_3-item2: t5_prompt_embeds: torch.Size([77, 4096]), torch.float16 text_encoder-item3: clip_pooled_prompt_embeds: torch.Size([768]), torch.float16 clip_prompt_embeds: torch.Size([77, 768]), torch.float16 text_encoder_2-item4: clip_pooled_prompt_embeds: torch.Size([1280]), torch.float16 clip_prompt_embeds: torch.Size([77, 1280]), torch.float16 text_encoder_3-item5: t5_prompt_embeds: torch.Size([77, 4096]), torch.float16 vae.decoder latents: torch.Size([16, 128, 128]) vae.encoder features: vae.encoder_f0: torch.Size([32, 128, 128]), torch.float16 vae.encoder_f1: torch.Size([32, 128, 128]), torch.float16 ------------------------------------------------------------ [2/4] FRONTEND: Pack + Encode (strategy: individual)... IndividualPacker: text_encoder-item0.clip_pooled_prompt_embeds: torch.Size([768])->torch.Size([1, 1, 1, 768]) text_encoder-item0.clip_prompt_embeds: torch.Size([77, 768])->torch.Size([1, 1, 77, 768]) text_encoder_2-item1.clip_pooled_prompt_embeds: torch.Size([1280])->torch.Size([1, 1, 1, 1280]) text_encoder_2-item1.clip_prompt_embeds: torch.Size([77, 1280])->torch.Size([1, 1, 77, 1280]) text_encoder_3-item2.t5_prompt_embeds: torch.Size([77, 4096])->torch.Size([1, 1, 77, 4096]) text_encoder-item3.clip_pooled_prompt_embeds: torch.Size([768])->torch.Size([1, 1, 1, 768]) text_encoder-item3.clip_prompt_embeds: torch.Size([77, 768])->torch.Size([1, 1, 77, 768]) text_encoder_2-item4.clip_pooled_prompt_embeds: torch.Size([1280])->torch.Size([1, 1, 1, 1280]) text_encoder_2-item4.clip_prompt_embeds: torch.Size([77, 1280])->torch.Size([1, 1, 77, 1280]) text_encoder_3-item5.t5_prompt_embeds: torch.Size([77, 4096])->torch.Size([1, 1, 77, 4096]) vae.encoder_f0: torch.Size([32, 128, 128])->torch.Size([1, 1, 512, 1024]) vae.encoder_f1: torch.Size([32, 128, 128])->torch.Size([1, 1, 512, 1024]) vae.decoder: torch.Size([16, 128, 128])->torch.Size([1, 1, 512, 512]) Using per-key quantization points (text_encoder-item0.clip_pooled_prompt_embeds: torch.Size([256])) for text_encoder-item0.clip_pooled_prompt_embeds text_encoder-item0.clip_pooled_prompt_embeds: 728B, BPFP=7.5833 Using per-key quantization points (text_encoder-item0.clip_prompt_embeds: torch.Size([256])) for text_encoder-item0.clip_prompt_embeds text_encoder-item0.clip_prompt_embeds: 6,232B, BPFP=0.8431 Using per-key quantization points (text_encoder_2-item1.clip_pooled_prompt_embeds: torch.Size([256])) for text_encoder_2-item1.clip_pooled_prompt_embeds text_encoder_2-item1.clip_pooled_prompt_embeds: 1,204B, BPFP=7.5250 Using per-key quantization points (text_encoder_2-item1.clip_prompt_embeds: torch.Size([256])) for text_encoder_2-item1.clip_prompt_embeds text_encoder_2-item1.clip_prompt_embeds: 11,000B, BPFP=0.8929 Using per-key quantization points (text_encoder_3-item2.t5_prompt_embeds: torch.Size([256])) for text_encoder_3-item2.t5_prompt_embeds text_encoder_3-item2.t5_prompt_embeds: 25,508B, BPFP=0.6470 Using per-key quantization points (text_encoder-item3.clip_pooled_prompt_embeds: torch.Size([256])) for text_encoder-item3.clip_pooled_prompt_embeds text_encoder-item3.clip_pooled_prompt_embeds: 760B, BPFP=7.9167 Using per-key quantization points (text_encoder-item3.clip_prompt_embeds: torch.Size([256])) for text_encoder-item3.clip_prompt_embeds text_encoder-item3.clip_prompt_embeds: 4,024B, BPFP=0.5444 Using per-key quantization points (text_encoder_2-item4.clip_pooled: torch.Size([256])) for text_encoder_2-item4.clip_pooled_prompt_embeds text_encoder_2-item4.clip_pooled_prompt_embeds: 1,268B, BPFP=7.9250 Using per-key quantization points (text_encoder_2-item4.clip_prompt: torch.Size([256])) for text_encoder_2-item4.clip_prompt_embeds text_encoder_2-item4.clip_prompt_embeds: 6,996B, BPFP=0.5679 Using per-key quantization points (text_encoder_3-item5.t5_prompt_embeds: torch.Size([256])) for text_encoder_3-item5.t5_prompt_embeds text_encoder_3-item5.t5_prompt_embeds: 18,416B, BPFP=0.4671 Using per-key quantization points (vae.encoder_f0: torch.Size([256])) for vae.encoder_f0 vae.encoder_f0: 22,796B, BPFP=0.3478 Using per-key quantization points (vae.encoder_f1: torch.Size([256])) for vae.encoder_f1 vae.encoder_f1: 22,796B, BPFP=0.3478 Using per-key quantization points (vae.decoder: torch.Size([256])) for vae.decoder vae.decoder: 9,280B, BPFP=0.2832 ⌛️ [2/4] FRONTEND: Frontend time: 0.298s (Pack+Encode) [3/4] BACKEND: Decode + Unpack... Using per-key quantization points (text_encoder-item0.clip_pooled_prompt_embeds: torch.Size([256])) for text_encoder-item0.clip_pooled_prompt_embeds Using per-key quantization points (text_encoder-item0.clip_prompt_embeds: torch.Size([256])) for text_encoder-item0.clip_prompt_embeds Using per-key quantization points (text_encoder_2-item1.clip_pooled_prompt_embeds: torch.Size([256])) for text_encoder_2-item1.clip_pooled_prompt_embeds Using per-key quantization points (text_encoder_2-item1.clip_prompt_embeds: torch.Size([256])) for text_encoder_2-item1.clip_prompt_embeds Using per-key quantization points (text_encoder_3-item2.t5_prompt_embeds: torch.Size([256])) for text_encoder_3-item2.t5_prompt_embeds Using per-key quantization points (text_encoder-item3.clip_pooled_prompt_embeds: torch.Size([256])) for text_encoder-item3.clip_pooled_prompt_embeds Using per-key quantization points (text_encoder-item3.clip_prompt_embeds: torch.Size([256])) for text_encoder-item3.clip_prompt_embeds Using per-key quantization points (text_encoder_2-item4.clip_pooled: torch.Size([256])) for text_encoder_2-item4.clip_pooled_prompt_embeds Using per-key quantization points (text_encoder_2-item4.clip_prompt: torch.Size([256])) for text_encoder_2-item4.clip_prompt_embeds Using per-key quantization points (text_encoder_3-item5.t5_prompt_embeds: torch.Size([256])) for text_encoder_3-item5.t5_prompt_embeds Using per-key quantization points (vae.encoder_f0: torch.Size([256])) for vae.encoder_f0 Using per-key quantization points (vae.encoder_f1: torch.Size([256])) for vae.encoder_f1 Using per-key quantization points (vae.decoder: torch.Size([256])) for vae.decoder IndividualUnPacker: text_encoder-item0.clip_pooled_prompt_embeds: torch.Size([768]) text_encoder-item0.clip_prompt_embeds: torch.Size([77, 768]) text_encoder_2-item1.clip_pooled_prompt_embeds: torch.Size([1280]) text_encoder_2-item1.clip_prompt_embeds: torch.Size([77, 1280]) text_encoder_3-item2.t5_prompt_embeds: torch.Size([77, 4096]) text_encoder-item3.clip_pooled_prompt_embeds: torch.Size([768]) text_encoder-item3.clip_prompt_embeds: torch.Size([77, 768]) text_encoder_2-item4.clip_pooled_prompt_embeds: torch.Size([1280]) text_encoder_2-item4.clip_prompt_embeds: torch.Size([77, 1280]) text_encoder_3-item5.t5_prompt_embeds: torch.Size([77, 4096]) vae.decoder: torch.Size([16, 128, 128]) vae.encoder_f0: torch.Size([32, 128, 128]) vae.encoder_f1: torch.Size([32, 128, 128]) ⌛️ [3/4] BACKEND: Backend time: 0.438s [4/4] METRICS: Computing MSE Breakdown... Using per-key quantization points (text_encoder-item0.clip_pooled_prompt_embeds: torch.Size([256])) for text_encoder-item0.clip_pooled_prompt_embeds Using per-key quantization points (text_encoder-item0.clip_prompt_embeds: torch.Size([256])) for text_encoder-item0.clip_prompt_embeds Using per-key quantization points (text_encoder_2-item1.clip_pooled_prompt_embeds: torch.Size([256])) for text_encoder_2-item1.clip_pooled_prompt_embeds Using per-key quantization points (text_encoder_2-item1.clip_prompt_embeds: torch.Size([256])) for text_encoder_2-item1.clip_prompt_embeds Using per-key quantization points (text_encoder_3-item2.t5_prompt_embeds: torch.Size([256])) for text_encoder_3-item2.t5_prompt_embeds Using per-key quantization points (text_encoder-item3.clip_pooled_prompt_embeds: torch.Size([256])) for text_encoder-item3.clip_pooled_prompt_embeds Using per-key quantization points (text_encoder-item3.clip_prompt_embeds: torch.Size([256])) for text_encoder-item3.clip_prompt_embeds Using per-key quantization points (text_encoder_2-item4.clip_pooled: torch.Size([256])) for text_encoder_2-item4.clip_pooled_prompt_embeds Using per-key quantization points (text_encoder_2-item4.clip_prompt: torch.Size([256])) for text_encoder_2-item4.clip_prompt_embeds Using per-key quantization points (text_encoder_3-item5.t5_prompt_embeds: torch.Size([256])) for text_encoder_3-item5.t5_prompt_embeds Using per-key quantization points (vae.encoder_f0: torch.Size([256])) for vae.encoder_f0 Using per-key quantization points (vae.encoder_f1: torch.Size([256])) for vae.encoder_f1 Using per-key quantization points (vae.decoder: torch.Size([256])) for vae.decoder IndividualUnPacker: text_encoder-item0.clip_pooled_prompt_embeds: torch.Size([768]) text_encoder-item0.clip_prompt_embeds: torch.Size([77, 768]) text_encoder_2-item1.clip_pooled_prompt_embeds: torch.Size([1280]) text_encoder_2-item1.clip_prompt_embeds: torch.Size([77, 1280]) text_encoder_3-item2.t5_prompt_embeds: torch.Size([77, 4096]) text_encoder-item3.clip_pooled_prompt_embeds: torch.Size([768]) text_encoder-item3.clip_prompt_embeds: torch.Size([77, 768]) text_encoder_2-item4.clip_pooled_prompt_embeds: torch.Size([1280]) text_encoder_2-item4.clip_prompt_embeds: torch.Size([77, 1280]) text_encoder_3-item5.t5_prompt_embeds: torch.Size([77, 4096]) vae.decoder: torch.Size([16, 128, 128]) vae.encoder_f0: torch.Size([32, 128, 128]) vae.encoder_f1: torch.Size([32, 128, 128]) Per-key MSE Breakdown (Quant=Truncation+Quantization, Codec=Compression): Key Quant-MSE Total-MSE ------------------------------------------------------------------------------------- text_encoder-item0.clip_pooled_prompt_embeds 0.00057152 0.93954388 text_encoder-item0.clip_prompt_embeds 0.00025458 23.96035199 text_encoder_2-item1.clip_pooled_prompt_embeds 0.00158787 0.92514687 text_encoder_2-item1.clip_prompt_embeds 0.00016969 0.13684623 text_encoder_3-item2.t5_prompt_embeds 0.00000826 0.00268806 text_encoder-item3.clip_pooled_prompt_embeds 0.00841799 1.01420029 text_encoder-item3.clip_prompt_embeds 0.00023247 23.90570887 text_encoder_2-item4.clip_pooled_prompt_embeds 0.00016751 0.67051430 text_encoder_2-item4.clip_prompt_embeds 0.00009830 0.47049568 text_encoder_3-item5.t5_prompt_embeds 0.00001263 0.00227117 vae.encoder_f0 0.00628510 2.58958292 vae.encoder_f1 0.00629234 2.58969927 vae.decoder 0.00023521 0.07789175 ------------------------------------------------------------------------------------- TOTAL 0.00297516 2.49068921 (elements=2,260,992) ---------------------- -------------------------------------------------------- SAMPLE-WISE STATISTICS ---------------------- -------------------------------------------------------- Handler sd35 Strategy individual Architecture hyperprior-featurecoding ---------------------- -------------------------------------------------------- Total Elements 2260992 Total Bytes 131008 BPFP 0.4635 bits/point EBPFP 0.9271 equivalent bits/point MSE 2.490689 ---------------------- -------------------------------------------------------- Time: 0.745s Load: 0.009s, Pack+Encode: 0.298s, Decode+Unpack: 0.438s ---------------------- -------------------------------------------------------- Restored Feature Format: [Dict] with 8 keys key['text_encoder-item0']: [Dict] with 2 keys key['clip_pooled_prompt_embeds']: [Tensor] shape=torch.Size([768]), dtype=torch.float16, device=cpu key['clip_prompt_embeds']: [Tensor] shape=torch.Size([77, 768]), dtype=torch.float16, device=cpu key['text_encoder_2-item1']: [Dict] with 2 keys key['clip_pooled_prompt_embeds']: [Tensor] shape=torch.Size([1280]), dtype=torch.float16, device=cpu key['clip_prompt_embeds']: [Tensor] shape=torch.Size([77, 1280]), dtype=torch.float16, device=cpu key['text_encoder_3-item2']: [Dict] with 1 keys key['t5_prompt_embeds']: [Tensor] shape=torch.Size([77, 4096]), dtype=torch.float16, device=cpu key['text_encoder-item3']: [Dict] with 2 keys key['clip_pooled_prompt_embeds']: [Tensor] shape=torch.Size([768]), dtype=torch.float16, device=cpu key['clip_prompt_embeds']: [Tensor] shape=torch.Size([77, 768]), dtype=torch.float16, device=cpu key['text_encoder_2-item4']: [Dict] with 2 keys key['clip_pooled_prompt_embeds']: [Tensor] shape=torch.Size([1280]), dtype=torch.float16, device=cpu key['clip_prompt_embeds']: [Tensor] shape=torch.Size([77, 1280]), dtype=torch.float16, device=cpu key['text_encoder_3-item5']: [Dict] with 1 keys key['t5_prompt_embeds']: [Tensor] shape=torch.Size([77, 4096]), dtype=torch.float16, device=cpu key['vae.decoder']: [Tensor] shape=torch.Size([16, 128, 128]), dtype=torch.float16, device=cpu key['vae.encoder']: [Dict] with 2 keys key['vae.encoder_f0']: [Tensor] shape=torch.Size([32, 128, 128]), dtype=torch.float16, device=cpu key['vae.encoder_f1']: [Tensor] shape=torch.Size([32, 128, 128]), dtype=torch.float16, device=cpu 💾 Converting with 2.4907 MSE: from ../datasets/FeatureCoding-StableDiffusion3.5/100samples_features/000000370486.zst to output-fixed/sd35/lambda0.004/hyperprior-featurecoding-8bit-individual/sd35cond/000000370486.zst 💪 Processing: ../datasets/FeatureCoding-StableDiffusion3.5/100samples_features/000000377635.zst (65/100) [1/4] FRONTEND: Loading features from ../datasets/FeatureCoding-StableDiffusion3.5/100samples_features/000000377635.zst... Original data structure: root: [Dict] with 8 keys key['text_encoder-item0']: [Dict] with 2 keys key['clip_pooled_prompt_embeds']: [Tensor] shape=torch.Size([768]), dtype=torch.float16, device=cpu key['clip_prompt_embeds']: [Tensor] shape=torch.Size([77, 768]), dtype=torch.float16, device=cpu key['text_encoder_2-item1']: [Dict] with 2 keys key['clip_pooled_prompt_embeds']: [Tensor] shape=torch.Size([1280]), dtype=torch.float16, device=cpu key['clip_prompt_embeds']: [Tensor] shape=torch.Size([77, 1280]), dtype=torch.float16, device=cpu key['text_encoder_3-item2']: [Dict] with 1 keys key['t5_prompt_embeds']: [Tensor] shape=torch.Size([77, 4096]), dtype=torch.float16, device=cpu key['text_encoder-item3']: [Dict] with 2 keys key['clip_pooled_prompt_embeds']: [Tensor] shape=torch.Size([768]), dtype=torch.float16, device=cpu key['clip_prompt_embeds']: [Tensor] shape=torch.Size([77, 768]), dtype=torch.float16, device=cpu key['text_encoder_2-item4']: [Dict] with 2 keys key['clip_pooled_prompt_embeds']: [Tensor] shape=torch.Size([1280]), dtype=torch.float16, device=cpu key['clip_prompt_embeds']: [Tensor] shape=torch.Size([77, 1280]), dtype=torch.float16, device=cpu key['text_encoder_3-item5']: [Dict] with 1 keys key['t5_prompt_embeds']: [Tensor] shape=torch.Size([77, 4096]), dtype=torch.float16, device=cpu key['vae.encoder-item6']: [Dict] with 2 keys key['vae.encoder_f0']: [Tensor] shape=torch.Size([32, 128, 128]), dtype=torch.float16, device=cpu key['vae.encoder_f1']: [Tensor] shape=torch.Size([32, 128, 128]), dtype=torch.float16, device=cpu key['vae.decoder']: [Tensor] shape=torch.Size([16, 128, 128]), dtype=torch.float16, device=cpu ⌛️ [1/4] FRONTEND: Load time: 0.009s ------------------------------------------------------------ SD3.5 Features Summary ------------------------------------------------------------ Number of text encoders: 3 Encoder names: ['text_encoder', 'text_encoder_2', 'text_encoder_3'] Has VAE latents: True Has VAE encoder features: True Data type: torch.float16 text_encoder-item0: clip_pooled_prompt_embeds: torch.Size([768]), torch.float16 clip_prompt_embeds: torch.Size([77, 768]), torch.float16 text_encoder_2-item1: clip_pooled_prompt_embeds: torch.Size([1280]), torch.float16 clip_prompt_embeds: torch.Size([77, 1280]), torch.float16 text_encoder_3-item2: t5_prompt_embeds: torch.Size([77, 4096]), torch.float16 text_encoder-item3: clip_pooled_prompt_embeds: torch.Size([768]), torch.float16 clip_prompt_embeds: torch.Size([77, 768]), torch.float16 text_encoder_2-item4: clip_pooled_prompt_embeds: torch.Size([1280]), torch.float16 clip_prompt_embeds: torch.Size([77, 1280]), torch.float16 text_encoder_3-item5: t5_prompt_embeds: torch.Size([77, 4096]), torch.float16 vae.decoder latents: torch.Size([16, 128, 128]) vae.encoder features: vae.encoder_f0: torch.Size([32, 128, 128]), torch.float16 vae.encoder_f1: torch.Size([32, 128, 128]), torch.float16 ------------------------------------------------------------ [2/4] FRONTEND: Pack + Encode (strategy: individual)... IndividualPacker: text_encoder-item0.clip_pooled_prompt_embeds: torch.Size([768])->torch.Size([1, 1, 1, 768]) text_encoder-item0.clip_prompt_embeds: torch.Size([77, 768])->torch.Size([1, 1, 77, 768]) text_encoder_2-item1.clip_pooled_prompt_embeds: torch.Size([1280])->torch.Size([1, 1, 1, 1280]) text_encoder_2-item1.clip_prompt_embeds: torch.Size([77, 1280])->torch.Size([1, 1, 77, 1280]) text_encoder_3-item2.t5_prompt_embeds: torch.Size([77, 4096])->torch.Size([1, 1, 77, 4096]) text_encoder-item3.clip_pooled_prompt_embeds: torch.Size([768])->torch.Size([1, 1, 1, 768]) text_encoder-item3.clip_prompt_embeds: torch.Size([77, 768])->torch.Size([1, 1, 77, 768]) text_encoder_2-item4.clip_pooled_prompt_embeds: torch.Size([1280])->torch.Size([1, 1, 1, 1280]) text_encoder_2-item4.clip_prompt_embeds: torch.Size([77, 1280])->torch.Size([1, 1, 77, 1280]) text_encoder_3-item5.t5_prompt_embeds: torch.Size([77, 4096])->torch.Size([1, 1, 77, 4096]) vae.encoder_f0: torch.Size([32, 128, 128])->torch.Size([1, 1, 512, 1024]) vae.encoder_f1: torch.Size([32, 128, 128])->torch.Size([1, 1, 512, 1024]) vae.decoder: torch.Size([16, 128, 128])->torch.Size([1, 1, 512, 512]) Using per-key quantization points (text_encoder-item0.clip_pooled_prompt_embeds: torch.Size([256])) for text_encoder-item0.clip_pooled_prompt_embeds text_encoder-item0.clip_pooled_prompt_embeds: 728B, BPFP=7.5833 Using per-key quantization points (text_encoder-item0.clip_prompt_embeds: torch.Size([256])) for text_encoder-item0.clip_prompt_embeds text_encoder-item0.clip_prompt_embeds: 5,840B, BPFP=0.7900 Using per-key quantization points (text_encoder_2-item1.clip_pooled_prompt_embeds: torch.Size([256])) for text_encoder_2-item1.clip_pooled_prompt_embeds text_encoder_2-item1.clip_pooled_prompt_embeds: 1,212B, BPFP=7.5750 Using per-key quantization points (text_encoder_2-item1.clip_prompt_embeds: torch.Size([256])) for text_encoder_2-item1.clip_prompt_embeds text_encoder_2-item1.clip_prompt_embeds: 10,680B, BPFP=0.8669 Using per-key quantization points (text_encoder_3-item2.t5_prompt_embeds: torch.Size([256])) for text_encoder_3-item2.t5_prompt_embeds text_encoder_3-item2.t5_prompt_embeds: 27,084B, BPFP=0.6870 Using per-key quantization points (text_encoder-item3.clip_pooled_prompt_embeds: torch.Size([256])) for text_encoder-item3.clip_pooled_prompt_embeds text_encoder-item3.clip_pooled_prompt_embeds: 760B, BPFP=7.9167 Using per-key quantization points (text_encoder-item3.clip_prompt_embeds: torch.Size([256])) for text_encoder-item3.clip_prompt_embeds text_encoder-item3.clip_prompt_embeds: 4,024B, BPFP=0.5444 Using per-key quantization points (text_encoder_2-item4.clip_pooled: torch.Size([256])) for text_encoder_2-item4.clip_pooled_prompt_embeds text_encoder_2-item4.clip_pooled_prompt_embeds: 1,268B, BPFP=7.9250 Using per-key quantization points (text_encoder_2-item4.clip_prompt: torch.Size([256])) for text_encoder_2-item4.clip_prompt_embeds text_encoder_2-item4.clip_prompt_embeds: 6,996B, BPFP=0.5679 Using per-key quantization points (text_encoder_3-item5.t5_prompt_embeds: torch.Size([256])) for text_encoder_3-item5.t5_prompt_embeds text_encoder_3-item5.t5_prompt_embeds: 18,416B, BPFP=0.4671 Using per-key quantization points (vae.encoder_f0: torch.Size([256])) for vae.encoder_f0 vae.encoder_f0: 14,800B, BPFP=0.2258 Using per-key quantization points (vae.encoder_f1: torch.Size([256])) for vae.encoder_f1 vae.encoder_f1: 14,804B, BPFP=0.2259 Using per-key quantization points (vae.decoder: torch.Size([256])) for vae.decoder vae.decoder: 7,520B, BPFP=0.2295 ⌛️ [2/4] FRONTEND: Frontend time: 0.290s (Pack+Encode) [3/4] BACKEND: Decode + Unpack... Using per-key quantization points (text_encoder-item0.clip_pooled_prompt_embeds: torch.Size([256])) for text_encoder-item0.clip_pooled_prompt_embeds Using per-key quantization points (text_encoder-item0.clip_prompt_embeds: torch.Size([256])) for text_encoder-item0.clip_prompt_embeds Using per-key quantization points (text_encoder_2-item1.clip_pooled_prompt_embeds: torch.Size([256])) for text_encoder_2-item1.clip_pooled_prompt_embeds Using per-key quantization points (text_encoder_2-item1.clip_prompt_embeds: torch.Size([256])) for text_encoder_2-item1.clip_prompt_embeds Using per-key quantization points (text_encoder_3-item2.t5_prompt_embeds: torch.Size([256])) for text_encoder_3-item2.t5_prompt_embeds Using per-key quantization points (text_encoder-item3.clip_pooled_prompt_embeds: torch.Size([256])) for text_encoder-item3.clip_pooled_prompt_embeds Using per-key quantization points (text_encoder-item3.clip_prompt_embeds: torch.Size([256])) for text_encoder-item3.clip_prompt_embeds Using per-key quantization points (text_encoder_2-item4.clip_pooled: torch.Size([256])) for text_encoder_2-item4.clip_pooled_prompt_embeds Using per-key quantization points (text_encoder_2-item4.clip_prompt: torch.Size([256])) for text_encoder_2-item4.clip_prompt_embeds Using per-key quantization points (text_encoder_3-item5.t5_prompt_embeds: torch.Size([256])) for text_encoder_3-item5.t5_prompt_embeds Using per-key quantization points (vae.encoder_f0: torch.Size([256])) for vae.encoder_f0 Using per-key quantization points (vae.encoder_f1: torch.Size([256])) for vae.encoder_f1 Using per-key quantization points (vae.decoder: torch.Size([256])) for vae.decoder IndividualUnPacker: text_encoder-item0.clip_pooled_prompt_embeds: torch.Size([768]) text_encoder-item0.clip_prompt_embeds: torch.Size([77, 768]) text_encoder_2-item1.clip_pooled_prompt_embeds: torch.Size([1280]) text_encoder_2-item1.clip_prompt_embeds: torch.Size([77, 1280]) text_encoder_3-item2.t5_prompt_embeds: torch.Size([77, 4096]) text_encoder-item3.clip_pooled_prompt_embeds: torch.Size([768]) text_encoder-item3.clip_prompt_embeds: torch.Size([77, 768]) text_encoder_2-item4.clip_pooled_prompt_embeds: torch.Size([1280]) text_encoder_2-item4.clip_prompt_embeds: torch.Size([77, 1280]) text_encoder_3-item5.t5_prompt_embeds: torch.Size([77, 4096]) vae.decoder: torch.Size([16, 128, 128]) vae.encoder_f0: torch.Size([32, 128, 128]) vae.encoder_f1: torch.Size([32, 128, 128]) ⌛️ [3/4] BACKEND: Backend time: 0.437s [4/4] METRICS: Computing MSE Breakdown... Using per-key quantization points (text_encoder-item0.clip_pooled_prompt_embeds: torch.Size([256])) for text_encoder-item0.clip_pooled_prompt_embeds Using per-key quantization points (text_encoder-item0.clip_prompt_embeds: torch.Size([256])) for text_encoder-item0.clip_prompt_embeds Using per-key quantization points (text_encoder_2-item1.clip_pooled_prompt_embeds: torch.Size([256])) for text_encoder_2-item1.clip_pooled_prompt_embeds Using per-key quantization points (text_encoder_2-item1.clip_prompt_embeds: torch.Size([256])) for text_encoder_2-item1.clip_prompt_embeds Using per-key quantization points (text_encoder_3-item2.t5_prompt_embeds: torch.Size([256])) for text_encoder_3-item2.t5_prompt_embeds Using per-key quantization points (text_encoder-item3.clip_pooled_prompt_embeds: torch.Size([256])) for text_encoder-item3.clip_pooled_prompt_embeds Using per-key quantization points (text_encoder-item3.clip_prompt_embeds: torch.Size([256])) for text_encoder-item3.clip_prompt_embeds Using per-key quantization points (text_encoder_2-item4.clip_pooled: torch.Size([256])) for text_encoder_2-item4.clip_pooled_prompt_embeds Using per-key quantization points (text_encoder_2-item4.clip_prompt: torch.Size([256])) for text_encoder_2-item4.clip_prompt_embeds Using per-key quantization points (text_encoder_3-item5.t5_prompt_embeds: torch.Size([256])) for text_encoder_3-item5.t5_prompt_embeds Using per-key quantization points (vae.encoder_f0: torch.Size([256])) for vae.encoder_f0 Using per-key quantization points (vae.encoder_f1: torch.Size([256])) for vae.encoder_f1 Using per-key quantization points (vae.decoder: torch.Size([256])) for vae.decoder IndividualUnPacker: text_encoder-item0.clip_pooled_prompt_embeds: torch.Size([768]) text_encoder-item0.clip_prompt_embeds: torch.Size([77, 768]) text_encoder_2-item1.clip_pooled_prompt_embeds: torch.Size([1280]) text_encoder_2-item1.clip_prompt_embeds: torch.Size([77, 1280]) text_encoder_3-item2.t5_prompt_embeds: torch.Size([77, 4096]) text_encoder-item3.clip_pooled_prompt_embeds: torch.Size([768]) text_encoder-item3.clip_prompt_embeds: torch.Size([77, 768]) text_encoder_2-item4.clip_pooled_prompt_embeds: torch.Size([1280]) text_encoder_2-item4.clip_prompt_embeds: torch.Size([77, 1280]) text_encoder_3-item5.t5_prompt_embeds: torch.Size([77, 4096]) vae.decoder: torch.Size([16, 128, 128]) vae.encoder_f0: torch.Size([32, 128, 128]) vae.encoder_f1: torch.Size([32, 128, 128]) Per-key MSE Breakdown (Quant=Truncation+Quantization, Codec=Compression): Key Quant-MSE Total-MSE ------------------------------------------------------------------------------------- text_encoder-item0.clip_pooled_prompt_embeds 0.00037564 0.97767448 text_encoder-item0.clip_prompt_embeds 0.00022807 23.93931573 text_encoder_2-item1.clip_pooled_prompt_embeds 0.00029471 1.00986891 text_encoder_2-item1.clip_prompt_embeds 0.00018746 0.12058989 text_encoder_3-item2.t5_prompt_embeds 0.00000782 0.00393192 text_encoder-item3.clip_pooled_prompt_embeds 0.00841799 1.01420029 text_encoder-item3.clip_prompt_embeds 0.00023247 23.90570887 text_encoder_2-item4.clip_pooled_prompt_embeds 0.00016751 0.67051430 text_encoder_2-item4.clip_prompt_embeds 0.00009830 0.47049568 text_encoder_3-item5.t5_prompt_embeds 0.00001263 0.00227117 vae.encoder_f0 0.00573429 1.80714881 vae.encoder_f1 0.00574192 1.80755830 vae.decoder 0.00017875 0.06089570 ------------------------------------------------------------------------------------- TOTAL 0.00271248 2.12489422 (elements=2,260,992) ---------------------- -------------------------------------------------------- SAMPLE-WISE STATISTICS ---------------------- -------------------------------------------------------- Handler sd35 Strategy individual Architecture hyperprior-featurecoding ---------------------- -------------------------------------------------------- Total Elements 2260992 Total Bytes 114132 BPFP 0.4038 bits/point EBPFP 0.8077 equivalent bits/point MSE 2.124894 ---------------------- -------------------------------------------------------- Time: 0.736s Load: 0.009s, Pack+Encode: 0.290s, Decode+Unpack: 0.437s ---------------------- -------------------------------------------------------- Restored Feature Format: [Dict] with 8 keys key['text_encoder-item0']: [Dict] with 2 keys key['clip_pooled_prompt_embeds']: [Tensor] shape=torch.Size([768]), dtype=torch.float16, device=cpu key['clip_prompt_embeds']: [Tensor] shape=torch.Size([77, 768]), dtype=torch.float16, device=cpu key['text_encoder_2-item1']: [Dict] with 2 keys key['clip_pooled_prompt_embeds']: [Tensor] shape=torch.Size([1280]), dtype=torch.float16, device=cpu key['clip_prompt_embeds']: [Tensor] shape=torch.Size([77, 1280]), dtype=torch.float16, device=cpu key['text_encoder_3-item2']: [Dict] with 1 keys key['t5_prompt_embeds']: [Tensor] shape=torch.Size([77, 4096]), dtype=torch.float16, device=cpu key['text_encoder-item3']: [Dict] with 2 keys key['clip_pooled_prompt_embeds']: [Tensor] shape=torch.Size([768]), dtype=torch.float16, device=cpu key['clip_prompt_embeds']: [Tensor] shape=torch.Size([77, 768]), dtype=torch.float16, device=cpu key['text_encoder_2-item4']: [Dict] with 2 keys key['clip_pooled_prompt_embeds']: [Tensor] shape=torch.Size([1280]), dtype=torch.float16, device=cpu key['clip_prompt_embeds']: [Tensor] shape=torch.Size([77, 1280]), dtype=torch.float16, device=cpu key['text_encoder_3-item5']: [Dict] with 1 keys key['t5_prompt_embeds']: [Tensor] shape=torch.Size([77, 4096]), dtype=torch.float16, device=cpu key['vae.decoder']: [Tensor] shape=torch.Size([16, 128, 128]), dtype=torch.float16, device=cpu key['vae.encoder']: [Dict] with 2 keys key['vae.encoder_f0']: [Tensor] shape=torch.Size([32, 128, 128]), dtype=torch.float16, device=cpu key['vae.encoder_f1']: [Tensor] shape=torch.Size([32, 128, 128]), dtype=torch.float16, device=cpu 💾 Converting with 2.1249 MSE: from ../datasets/FeatureCoding-StableDiffusion3.5/100samples_features/000000377635.zst to output-fixed/sd35/lambda0.004/hyperprior-featurecoding-8bit-individual/sd35cond/000000377635.zst 💪 Processing: ../datasets/FeatureCoding-StableDiffusion3.5/100samples_features/000000377814.zst (66/100) [1/4] FRONTEND: Loading features from ../datasets/FeatureCoding-StableDiffusion3.5/100samples_features/000000377814.zst... Original data structure: root: [Dict] with 8 keys key['text_encoder-item0']: [Dict] with 2 keys key['clip_pooled_prompt_embeds']: [Tensor] shape=torch.Size([768]), dtype=torch.float16, device=cpu key['clip_prompt_embeds']: [Tensor] shape=torch.Size([77, 768]), dtype=torch.float16, device=cpu key['text_encoder_2-item1']: [Dict] with 2 keys key['clip_pooled_prompt_embeds']: [Tensor] shape=torch.Size([1280]), dtype=torch.float16, device=cpu key['clip_prompt_embeds']: [Tensor] shape=torch.Size([77, 1280]), dtype=torch.float16, device=cpu key['text_encoder_3-item2']: [Dict] with 1 keys key['t5_prompt_embeds']: [Tensor] shape=torch.Size([77, 4096]), dtype=torch.float16, device=cpu key['text_encoder-item3']: [Dict] with 2 keys key['clip_pooled_prompt_embeds']: [Tensor] shape=torch.Size([768]), dtype=torch.float16, device=cpu key['clip_prompt_embeds']: [Tensor] shape=torch.Size([77, 768]), dtype=torch.float16, device=cpu key['text_encoder_2-item4']: [Dict] with 2 keys key['clip_pooled_prompt_embeds']: [Tensor] shape=torch.Size([1280]), dtype=torch.float16, device=cpu key['clip_prompt_embeds']: [Tensor] shape=torch.Size([77, 1280]), dtype=torch.float16, device=cpu key['text_encoder_3-item5']: [Dict] with 1 keys key['t5_prompt_embeds']: [Tensor] shape=torch.Size([77, 4096]), dtype=torch.float16, device=cpu key['vae.encoder-item6']: [Dict] with 2 keys key['vae.encoder_f0']: [Tensor] shape=torch.Size([32, 128, 128]), dtype=torch.float16, device=cpu key['vae.encoder_f1']: [Tensor] shape=torch.Size([32, 128, 128]), dtype=torch.float16, device=cpu key['vae.decoder']: [Tensor] shape=torch.Size([16, 128, 128]), dtype=torch.float16, device=cpu ⌛️ [1/4] FRONTEND: Load time: 0.009s ------------------------------------------------------------ SD3.5 Features Summary ------------------------------------------------------------ Number of text encoders: 3 Encoder names: ['text_encoder', 'text_encoder_2', 'text_encoder_3'] Has VAE latents: True Has VAE encoder features: True Data type: torch.float16 text_encoder-item0: clip_pooled_prompt_embeds: torch.Size([768]), torch.float16 clip_prompt_embeds: torch.Size([77, 768]), torch.float16 text_encoder_2-item1: clip_pooled_prompt_embeds: torch.Size([1280]), torch.float16 clip_prompt_embeds: torch.Size([77, 1280]), torch.float16 text_encoder_3-item2: t5_prompt_embeds: torch.Size([77, 4096]), torch.float16 text_encoder-item3: clip_pooled_prompt_embeds: torch.Size([768]), torch.float16 clip_prompt_embeds: torch.Size([77, 768]), torch.float16 text_encoder_2-item4: clip_pooled_prompt_embeds: torch.Size([1280]), torch.float16 clip_prompt_embeds: torch.Size([77, 1280]), torch.float16 text_encoder_3-item5: t5_prompt_embeds: torch.Size([77, 4096]), torch.float16 vae.decoder latents: torch.Size([16, 128, 128]) vae.encoder features: vae.encoder_f0: torch.Size([32, 128, 128]), torch.float16 vae.encoder_f1: torch.Size([32, 128, 128]), torch.float16 ------------------------------------------------------------ [2/4] FRONTEND: Pack + Encode (strategy: individual)... IndividualPacker: text_encoder-item0.clip_pooled_prompt_embeds: torch.Size([768])->torch.Size([1, 1, 1, 768]) text_encoder-item0.clip_prompt_embeds: torch.Size([77, 768])->torch.Size([1, 1, 77, 768]) text_encoder_2-item1.clip_pooled_prompt_embeds: torch.Size([1280])->torch.Size([1, 1, 1, 1280]) text_encoder_2-item1.clip_prompt_embeds: torch.Size([77, 1280])->torch.Size([1, 1, 77, 1280]) text_encoder_3-item2.t5_prompt_embeds: torch.Size([77, 4096])->torch.Size([1, 1, 77, 4096]) text_encoder-item3.clip_pooled_prompt_embeds: torch.Size([768])->torch.Size([1, 1, 1, 768]) text_encoder-item3.clip_prompt_embeds: torch.Size([77, 768])->torch.Size([1, 1, 77, 768]) text_encoder_2-item4.clip_pooled_prompt_embeds: torch.Size([1280])->torch.Size([1, 1, 1, 1280]) text_encoder_2-item4.clip_prompt_embeds: torch.Size([77, 1280])->torch.Size([1, 1, 77, 1280]) text_encoder_3-item5.t5_prompt_embeds: torch.Size([77, 4096])->torch.Size([1, 1, 77, 4096]) vae.encoder_f0: torch.Size([32, 128, 128])->torch.Size([1, 1, 512, 1024]) vae.encoder_f1: torch.Size([32, 128, 128])->torch.Size([1, 1, 512, 1024]) vae.decoder: torch.Size([16, 128, 128])->torch.Size([1, 1, 512, 512]) Using per-key quantization points (text_encoder-item0.clip_pooled_prompt_embeds: torch.Size([256])) for text_encoder-item0.clip_pooled_prompt_embeds text_encoder-item0.clip_pooled_prompt_embeds: 740B, BPFP=7.7083 Using per-key quantization points (text_encoder-item0.clip_prompt_embeds: torch.Size([256])) for text_encoder-item0.clip_prompt_embeds text_encoder-item0.clip_prompt_embeds: 7,460B, BPFP=1.0092 Using per-key quantization points (text_encoder_2-item1.clip_pooled_prompt_embeds: torch.Size([256])) for text_encoder_2-item1.clip_pooled_prompt_embeds text_encoder_2-item1.clip_pooled_prompt_embeds: 1,200B, BPFP=7.5000 Using per-key quantization points (text_encoder_2-item1.clip_prompt_embeds: torch.Size([256])) for text_encoder_2-item1.clip_prompt_embeds text_encoder_2-item1.clip_prompt_embeds: 11,668B, BPFP=0.9471 Using per-key quantization points (text_encoder_3-item2.t5_prompt_embeds: torch.Size([256])) for text_encoder_3-item2.t5_prompt_embeds text_encoder_3-item2.t5_prompt_embeds: 37,244B, BPFP=0.9447 Using per-key quantization points (text_encoder-item3.clip_pooled_prompt_embeds: torch.Size([256])) for text_encoder-item3.clip_pooled_prompt_embeds text_encoder-item3.clip_pooled_prompt_embeds: 760B, BPFP=7.9167 Using per-key quantization points (text_encoder-item3.clip_prompt_embeds: torch.Size([256])) for text_encoder-item3.clip_prompt_embeds text_encoder-item3.clip_prompt_embeds: 4,024B, BPFP=0.5444 Using per-key quantization points (text_encoder_2-item4.clip_pooled: torch.Size([256])) for text_encoder_2-item4.clip_pooled_prompt_embeds text_encoder_2-item4.clip_pooled_prompt_embeds: 1,268B, BPFP=7.9250 Using per-key quantization points (text_encoder_2-item4.clip_prompt: torch.Size([256])) for text_encoder_2-item4.clip_prompt_embeds text_encoder_2-item4.clip_prompt_embeds: 6,996B, BPFP=0.5679 Using per-key quantization points (text_encoder_3-item5.t5_prompt_embeds: torch.Size([256])) for text_encoder_3-item5.t5_prompt_embeds text_encoder_3-item5.t5_prompt_embeds: 18,416B, BPFP=0.4671 Using per-key quantization points (vae.encoder_f0: torch.Size([256])) for vae.encoder_f0 vae.encoder_f0: 20,204B, BPFP=0.3083 Using per-key quantization points (vae.encoder_f1: torch.Size([256])) for vae.encoder_f1 vae.encoder_f1: 20,204B, BPFP=0.3083 Using per-key quantization points (vae.decoder: torch.Size([256])) for vae.decoder vae.decoder: 7,780B, BPFP=0.2374 ⌛️ [2/4] FRONTEND: Frontend time: 0.290s (Pack+Encode) [3/4] BACKEND: Decode + Unpack... Using per-key quantization points (text_encoder-item0.clip_pooled_prompt_embeds: torch.Size([256])) for text_encoder-item0.clip_pooled_prompt_embeds Using per-key quantization points (text_encoder-item0.clip_prompt_embeds: torch.Size([256])) for text_encoder-item0.clip_prompt_embeds Using per-key quantization points (text_encoder_2-item1.clip_pooled_prompt_embeds: torch.Size([256])) for text_encoder_2-item1.clip_pooled_prompt_embeds Using per-key quantization points (text_encoder_2-item1.clip_prompt_embeds: torch.Size([256])) for text_encoder_2-item1.clip_prompt_embeds Using per-key quantization points (text_encoder_3-item2.t5_prompt_embeds: torch.Size([256])) for text_encoder_3-item2.t5_prompt_embeds Using per-key quantization points (text_encoder-item3.clip_pooled_prompt_embeds: torch.Size([256])) for text_encoder-item3.clip_pooled_prompt_embeds Using per-key quantization points (text_encoder-item3.clip_prompt_embeds: torch.Size([256])) for text_encoder-item3.clip_prompt_embeds Using per-key quantization points (text_encoder_2-item4.clip_pooled: torch.Size([256])) for text_encoder_2-item4.clip_pooled_prompt_embeds Using per-key quantization points (text_encoder_2-item4.clip_prompt: torch.Size([256])) for text_encoder_2-item4.clip_prompt_embeds Using per-key quantization points (text_encoder_3-item5.t5_prompt_embeds: torch.Size([256])) for text_encoder_3-item5.t5_prompt_embeds Using per-key quantization points (vae.encoder_f0: torch.Size([256])) for vae.encoder_f0 Using per-key quantization points (vae.encoder_f1: torch.Size([256])) for vae.encoder_f1 Using per-key quantization points (vae.decoder: torch.Size([256])) for vae.decoder IndividualUnPacker: text_encoder-item0.clip_pooled_prompt_embeds: torch.Size([768]) text_encoder-item0.clip_prompt_embeds: torch.Size([77, 768]) text_encoder_2-item1.clip_pooled_prompt_embeds: torch.Size([1280]) text_encoder_2-item1.clip_prompt_embeds: torch.Size([77, 1280]) text_encoder_3-item2.t5_prompt_embeds: torch.Size([77, 4096]) text_encoder-item3.clip_pooled_prompt_embeds: torch.Size([768]) text_encoder-item3.clip_prompt_embeds: torch.Size([77, 768]) text_encoder_2-item4.clip_pooled_prompt_embeds: torch.Size([1280]) text_encoder_2-item4.clip_prompt_embeds: torch.Size([77, 1280]) text_encoder_3-item5.t5_prompt_embeds: torch.Size([77, 4096]) vae.decoder: torch.Size([16, 128, 128]) vae.encoder_f0: torch.Size([32, 128, 128]) vae.encoder_f1: torch.Size([32, 128, 128]) ⌛️ [3/4] BACKEND: Backend time: 0.438s [4/4] METRICS: Computing MSE Breakdown... Using per-key quantization points (text_encoder-item0.clip_pooled_prompt_embeds: torch.Size([256])) for text_encoder-item0.clip_pooled_prompt_embeds Using per-key quantization points (text_encoder-item0.clip_prompt_embeds: torch.Size([256])) for text_encoder-item0.clip_prompt_embeds Using per-key quantization points (text_encoder_2-item1.clip_pooled_prompt_embeds: torch.Size([256])) for text_encoder_2-item1.clip_pooled_prompt_embeds Using per-key quantization points (text_encoder_2-item1.clip_prompt_embeds: torch.Size([256])) for text_encoder_2-item1.clip_prompt_embeds Using per-key quantization points (text_encoder_3-item2.t5_prompt_embeds: torch.Size([256])) for text_encoder_3-item2.t5_prompt_embeds Using per-key quantization points (text_encoder-item3.clip_pooled_prompt_embeds: torch.Size([256])) for text_encoder-item3.clip_pooled_prompt_embeds Using per-key quantization points (text_encoder-item3.clip_prompt_embeds: torch.Size([256])) for text_encoder-item3.clip_prompt_embeds Using per-key quantization points (text_encoder_2-item4.clip_pooled: torch.Size([256])) for text_encoder_2-item4.clip_pooled_prompt_embeds Using per-key quantization points (text_encoder_2-item4.clip_prompt: torch.Size([256])) for text_encoder_2-item4.clip_prompt_embeds Using per-key quantization points (text_encoder_3-item5.t5_prompt_embeds: torch.Size([256])) for text_encoder_3-item5.t5_prompt_embeds Using per-key quantization points (vae.encoder_f0: torch.Size([256])) for vae.encoder_f0 Using per-key quantization points (vae.encoder_f1: torch.Size([256])) for vae.encoder_f1 Using per-key quantization points (vae.decoder: torch.Size([256])) for vae.decoder IndividualUnPacker: text_encoder-item0.clip_pooled_prompt_embeds: torch.Size([768]) text_encoder-item0.clip_prompt_embeds: torch.Size([77, 768]) text_encoder_2-item1.clip_pooled_prompt_embeds: torch.Size([1280]) text_encoder_2-item1.clip_prompt_embeds: torch.Size([77, 1280]) text_encoder_3-item2.t5_prompt_embeds: torch.Size([77, 4096]) text_encoder-item3.clip_pooled_prompt_embeds: torch.Size([768]) text_encoder-item3.clip_prompt_embeds: torch.Size([77, 768]) text_encoder_2-item4.clip_pooled_prompt_embeds: torch.Size([1280]) text_encoder_2-item4.clip_prompt_embeds: torch.Size([77, 1280]) text_encoder_3-item5.t5_prompt_embeds: torch.Size([77, 4096]) vae.decoder: torch.Size([16, 128, 128]) vae.encoder_f0: torch.Size([32, 128, 128]) vae.encoder_f1: torch.Size([32, 128, 128]) Per-key MSE Breakdown (Quant=Truncation+Quantization, Codec=Compression): Key Quant-MSE Total-MSE ------------------------------------------------------------------------------------- text_encoder-item0.clip_pooled_prompt_embeds 0.00017150 0.91770617 text_encoder-item0.clip_prompt_embeds 0.00027120 34.96352898 text_encoder_2-item1.clip_pooled_prompt_embeds 0.00023509 0.89880629 text_encoder_2-item1.clip_prompt_embeds 0.00019567 0.16719725 text_encoder_3-item2.t5_prompt_embeds 0.00000829 0.00442290 text_encoder-item3.clip_pooled_prompt_embeds 0.00841799 1.01420029 text_encoder-item3.clip_prompt_embeds 0.00023247 23.90570887 text_encoder_2-item4.clip_pooled_prompt_embeds 0.00016751 0.67051430 text_encoder_2-item4.clip_prompt_embeds 0.00009830 0.47049568 text_encoder_3-item5.t5_prompt_embeds 0.00001263 0.00227117 vae.encoder_f0 0.00781570 3.31092930 vae.encoder_f1 0.00781878 3.31031370 vae.decoder 0.00029724 0.07037102 ------------------------------------------------------------------------------------- TOTAL 0.00369190 3.11351458 (elements=2,260,992) ---------------------- -------------------------------------------------------- SAMPLE-WISE STATISTICS ---------------------- -------------------------------------------------------- Handler sd35 Strategy individual Architecture hyperprior-featurecoding ---------------------- -------------------------------------------------------- Total Elements 2260992 Total Bytes 137964 BPFP 0.4882 bits/point EBPFP 0.9763 equivalent bits/point MSE 3.113515 ---------------------- -------------------------------------------------------- Time: 0.737s Load: 0.009s, Pack+Encode: 0.290s, Decode+Unpack: 0.438s ---------------------- -------------------------------------------------------- Restored Feature Format: [Dict] with 8 keys key['text_encoder-item0']: [Dict] with 2 keys key['clip_pooled_prompt_embeds']: [Tensor] shape=torch.Size([768]), dtype=torch.float16, device=cpu key['clip_prompt_embeds']: [Tensor] shape=torch.Size([77, 768]), dtype=torch.float16, device=cpu key['text_encoder_2-item1']: [Dict] with 2 keys key['clip_pooled_prompt_embeds']: [Tensor] shape=torch.Size([1280]), dtype=torch.float16, device=cpu key['clip_prompt_embeds']: [Tensor] shape=torch.Size([77, 1280]), dtype=torch.float16, device=cpu key['text_encoder_3-item2']: [Dict] with 1 keys key['t5_prompt_embeds']: [Tensor] shape=torch.Size([77, 4096]), dtype=torch.float16, device=cpu key['text_encoder-item3']: [Dict] with 2 keys key['clip_pooled_prompt_embeds']: [Tensor] shape=torch.Size([768]), dtype=torch.float16, device=cpu key['clip_prompt_embeds']: [Tensor] shape=torch.Size([77, 768]), dtype=torch.float16, device=cpu key['text_encoder_2-item4']: [Dict] with 2 keys key['clip_pooled_prompt_embeds']: [Tensor] shape=torch.Size([1280]), dtype=torch.float16, device=cpu key['clip_prompt_embeds']: [Tensor] shape=torch.Size([77, 1280]), dtype=torch.float16, device=cpu key['text_encoder_3-item5']: [Dict] with 1 keys key['t5_prompt_embeds']: [Tensor] shape=torch.Size([77, 4096]), dtype=torch.float16, device=cpu key['vae.decoder']: [Tensor] shape=torch.Size([16, 128, 128]), dtype=torch.float16, device=cpu key['vae.encoder']: [Dict] with 2 keys key['vae.encoder_f0']: [Tensor] shape=torch.Size([32, 128, 128]), dtype=torch.float16, device=cpu key['vae.encoder_f1']: [Tensor] shape=torch.Size([32, 128, 128]), dtype=torch.float16, device=cpu 💾 Converting with 3.1135 MSE: from ../datasets/FeatureCoding-StableDiffusion3.5/100samples_features/000000377814.zst to output-fixed/sd35/lambda0.004/hyperprior-featurecoding-8bit-individual/sd35cond/000000377814.zst 💪 Processing: ../datasets/FeatureCoding-StableDiffusion3.5/100samples_features/000000379800.zst (67/100) [1/4] FRONTEND: Loading features from ../datasets/FeatureCoding-StableDiffusion3.5/100samples_features/000000379800.zst... Original data structure: root: [Dict] with 8 keys key['text_encoder-item0']: [Dict] with 2 keys key['clip_pooled_prompt_embeds']: [Tensor] shape=torch.Size([768]), dtype=torch.float16, device=cpu key['clip_prompt_embeds']: [Tensor] shape=torch.Size([77, 768]), dtype=torch.float16, device=cpu key['text_encoder_2-item1']: [Dict] with 2 keys key['clip_pooled_prompt_embeds']: [Tensor] shape=torch.Size([1280]), dtype=torch.float16, device=cpu key['clip_prompt_embeds']: [Tensor] shape=torch.Size([77, 1280]), dtype=torch.float16, device=cpu key['text_encoder_3-item2']: [Dict] with 1 keys key['t5_prompt_embeds']: [Tensor] shape=torch.Size([77, 4096]), dtype=torch.float16, device=cpu key['text_encoder-item3']: [Dict] with 2 keys key['clip_pooled_prompt_embeds']: [Tensor] shape=torch.Size([768]), dtype=torch.float16, device=cpu key['clip_prompt_embeds']: [Tensor] shape=torch.Size([77, 768]), dtype=torch.float16, device=cpu key['text_encoder_2-item4']: [Dict] with 2 keys key['clip_pooled_prompt_embeds']: [Tensor] shape=torch.Size([1280]), dtype=torch.float16, device=cpu key['clip_prompt_embeds']: [Tensor] shape=torch.Size([77, 1280]), dtype=torch.float16, device=cpu key['text_encoder_3-item5']: [Dict] with 1 keys key['t5_prompt_embeds']: [Tensor] shape=torch.Size([77, 4096]), dtype=torch.float16, device=cpu key['vae.encoder-item6']: [Dict] with 2 keys key['vae.encoder_f0']: [Tensor] shape=torch.Size([32, 128, 128]), dtype=torch.float16, device=cpu key['vae.encoder_f1']: [Tensor] shape=torch.Size([32, 128, 128]), dtype=torch.float16, device=cpu key['vae.decoder']: [Tensor] shape=torch.Size([16, 128, 128]), dtype=torch.float16, device=cpu ⌛️ [1/4] FRONTEND: Load time: 0.008s ------------------------------------------------------------ SD3.5 Features Summary ------------------------------------------------------------ Number of text encoders: 3 Encoder names: ['text_encoder', 'text_encoder_2', 'text_encoder_3'] Has VAE latents: True Has VAE encoder features: True Data type: torch.float16 text_encoder-item0: clip_pooled_prompt_embeds: torch.Size([768]), torch.float16 clip_prompt_embeds: torch.Size([77, 768]), torch.float16 text_encoder_2-item1: clip_pooled_prompt_embeds: torch.Size([1280]), torch.float16 clip_prompt_embeds: torch.Size([77, 1280]), torch.float16 text_encoder_3-item2: t5_prompt_embeds: torch.Size([77, 4096]), torch.float16 text_encoder-item3: clip_pooled_prompt_embeds: torch.Size([768]), torch.float16 clip_prompt_embeds: torch.Size([77, 768]), torch.float16 text_encoder_2-item4: clip_pooled_prompt_embeds: torch.Size([1280]), torch.float16 clip_prompt_embeds: torch.Size([77, 1280]), torch.float16 text_encoder_3-item5: t5_prompt_embeds: torch.Size([77, 4096]), torch.float16 vae.decoder latents: torch.Size([16, 128, 128]) vae.encoder features: vae.encoder_f0: torch.Size([32, 128, 128]), torch.float16 vae.encoder_f1: torch.Size([32, 128, 128]), torch.float16 ------------------------------------------------------------ [2/4] FRONTEND: Pack + Encode (strategy: individual)... IndividualPacker: text_encoder-item0.clip_pooled_prompt_embeds: torch.Size([768])->torch.Size([1, 1, 1, 768]) text_encoder-item0.clip_prompt_embeds: torch.Size([77, 768])->torch.Size([1, 1, 77, 768]) text_encoder_2-item1.clip_pooled_prompt_embeds: torch.Size([1280])->torch.Size([1, 1, 1, 1280]) text_encoder_2-item1.clip_prompt_embeds: torch.Size([77, 1280])->torch.Size([1, 1, 77, 1280]) text_encoder_3-item2.t5_prompt_embeds: torch.Size([77, 4096])->torch.Size([1, 1, 77, 4096]) text_encoder-item3.clip_pooled_prompt_embeds: torch.Size([768])->torch.Size([1, 1, 1, 768]) text_encoder-item3.clip_prompt_embeds: torch.Size([77, 768])->torch.Size([1, 1, 77, 768]) text_encoder_2-item4.clip_pooled_prompt_embeds: torch.Size([1280])->torch.Size([1, 1, 1, 1280]) text_encoder_2-item4.clip_prompt_embeds: torch.Size([77, 1280])->torch.Size([1, 1, 77, 1280]) text_encoder_3-item5.t5_prompt_embeds: torch.Size([77, 4096])->torch.Size([1, 1, 77, 4096]) vae.encoder_f0: torch.Size([32, 128, 128])->torch.Size([1, 1, 512, 1024]) vae.encoder_f1: torch.Size([32, 128, 128])->torch.Size([1, 1, 512, 1024]) vae.decoder: torch.Size([16, 128, 128])->torch.Size([1, 1, 512, 512]) Using per-key quantization points (text_encoder-item0.clip_pooled_prompt_embeds: torch.Size([256])) for text_encoder-item0.clip_pooled_prompt_embeds text_encoder-item0.clip_pooled_prompt_embeds: 744B, BPFP=7.7500 Using per-key quantization points (text_encoder-item0.clip_prompt_embeds: torch.Size([256])) for text_encoder-item0.clip_prompt_embeds text_encoder-item0.clip_prompt_embeds: 6,464B, BPFP=0.8745 Using per-key quantization points (text_encoder_2-item1.clip_pooled_prompt_embeds: torch.Size([256])) for text_encoder_2-item1.clip_pooled_prompt_embeds text_encoder_2-item1.clip_pooled_prompt_embeds: 1,200B, BPFP=7.5000 Using per-key quantization points (text_encoder_2-item1.clip_prompt_embeds: torch.Size([256])) for text_encoder_2-item1.clip_prompt_embeds text_encoder_2-item1.clip_prompt_embeds: 11,820B, BPFP=0.9594 Using per-key quantization points (text_encoder_3-item2.t5_prompt_embeds: torch.Size([256])) for text_encoder_3-item2.t5_prompt_embeds text_encoder_3-item2.t5_prompt_embeds: 29,484B, BPFP=0.7479 Using per-key quantization points (text_encoder-item3.clip_pooled_prompt_embeds: torch.Size([256])) for text_encoder-item3.clip_pooled_prompt_embeds text_encoder-item3.clip_pooled_prompt_embeds: 760B, BPFP=7.9167 Using per-key quantization points (text_encoder-item3.clip_prompt_embeds: torch.Size([256])) for text_encoder-item3.clip_prompt_embeds text_encoder-item3.clip_prompt_embeds: 4,024B, BPFP=0.5444 Using per-key quantization points (text_encoder_2-item4.clip_pooled: torch.Size([256])) for text_encoder_2-item4.clip_pooled_prompt_embeds text_encoder_2-item4.clip_pooled_prompt_embeds: 1,268B, BPFP=7.9250 Using per-key quantization points (text_encoder_2-item4.clip_prompt: torch.Size([256])) for text_encoder_2-item4.clip_prompt_embeds text_encoder_2-item4.clip_prompt_embeds: 6,996B, BPFP=0.5679 Using per-key quantization points (text_encoder_3-item5.t5_prompt_embeds: torch.Size([256])) for text_encoder_3-item5.t5_prompt_embeds text_encoder_3-item5.t5_prompt_embeds: 18,416B, BPFP=0.4671 Using per-key quantization points (vae.encoder_f0: torch.Size([256])) for vae.encoder_f0 vae.encoder_f0: 17,604B, BPFP=0.2686 Using per-key quantization points (vae.encoder_f1: torch.Size([256])) for vae.encoder_f1 vae.encoder_f1: 17,604B, BPFP=0.2686 Using per-key quantization points (vae.decoder: torch.Size([256])) for vae.decoder vae.decoder: 10,988B, BPFP=0.3353 ⌛️ [2/4] FRONTEND: Frontend time: 0.291s (Pack+Encode) [3/4] BACKEND: Decode + Unpack... Using per-key quantization points (text_encoder-item0.clip_pooled_prompt_embeds: torch.Size([256])) for text_encoder-item0.clip_pooled_prompt_embeds Using per-key quantization points (text_encoder-item0.clip_prompt_embeds: torch.Size([256])) for text_encoder-item0.clip_prompt_embeds Using per-key quantization points (text_encoder_2-item1.clip_pooled_prompt_embeds: torch.Size([256])) for text_encoder_2-item1.clip_pooled_prompt_embeds Using per-key quantization points (text_encoder_2-item1.clip_prompt_embeds: torch.Size([256])) for text_encoder_2-item1.clip_prompt_embeds Using per-key quantization points (text_encoder_3-item2.t5_prompt_embeds: torch.Size([256])) for text_encoder_3-item2.t5_prompt_embeds Using per-key quantization points (text_encoder-item3.clip_pooled_prompt_embeds: torch.Size([256])) for text_encoder-item3.clip_pooled_prompt_embeds Using per-key quantization points (text_encoder-item3.clip_prompt_embeds: torch.Size([256])) for text_encoder-item3.clip_prompt_embeds Using per-key quantization points (text_encoder_2-item4.clip_pooled: torch.Size([256])) for text_encoder_2-item4.clip_pooled_prompt_embeds Using per-key quantization points (text_encoder_2-item4.clip_prompt: torch.Size([256])) for text_encoder_2-item4.clip_prompt_embeds Using per-key quantization points (text_encoder_3-item5.t5_prompt_embeds: torch.Size([256])) for text_encoder_3-item5.t5_prompt_embeds Using per-key quantization points (vae.encoder_f0: torch.Size([256])) for vae.encoder_f0 Using per-key quantization points (vae.encoder_f1: torch.Size([256])) for vae.encoder_f1 Using per-key quantization points (vae.decoder: torch.Size([256])) for vae.decoder IndividualUnPacker: text_encoder-item0.clip_pooled_prompt_embeds: torch.Size([768]) text_encoder-item0.clip_prompt_embeds: torch.Size([77, 768]) text_encoder_2-item1.clip_pooled_prompt_embeds: torch.Size([1280]) text_encoder_2-item1.clip_prompt_embeds: torch.Size([77, 1280]) text_encoder_3-item2.t5_prompt_embeds: torch.Size([77, 4096]) text_encoder-item3.clip_pooled_prompt_embeds: torch.Size([768]) text_encoder-item3.clip_prompt_embeds: torch.Size([77, 768]) text_encoder_2-item4.clip_pooled_prompt_embeds: torch.Size([1280]) text_encoder_2-item4.clip_prompt_embeds: torch.Size([77, 1280]) text_encoder_3-item5.t5_prompt_embeds: torch.Size([77, 4096]) vae.decoder: torch.Size([16, 128, 128]) vae.encoder_f0: torch.Size([32, 128, 128]) vae.encoder_f1: torch.Size([32, 128, 128]) ⌛️ [3/4] BACKEND: Backend time: 0.453s [4/4] METRICS: Computing MSE Breakdown... Using per-key quantization points (text_encoder-item0.clip_pooled_prompt_embeds: torch.Size([256])) for text_encoder-item0.clip_pooled_prompt_embeds Using per-key quantization points (text_encoder-item0.clip_prompt_embeds: torch.Size([256])) for text_encoder-item0.clip_prompt_embeds Using per-key quantization points (text_encoder_2-item1.clip_pooled_prompt_embeds: torch.Size([256])) for text_encoder_2-item1.clip_pooled_prompt_embeds Using per-key quantization points (text_encoder_2-item1.clip_prompt_embeds: torch.Size([256])) for text_encoder_2-item1.clip_prompt_embeds Using per-key quantization points (text_encoder_3-item2.t5_prompt_embeds: torch.Size([256])) for text_encoder_3-item2.t5_prompt_embeds Using per-key quantization points (text_encoder-item3.clip_pooled_prompt_embeds: torch.Size([256])) for text_encoder-item3.clip_pooled_prompt_embeds Using per-key quantization points (text_encoder-item3.clip_prompt_embeds: torch.Size([256])) for text_encoder-item3.clip_prompt_embeds Using per-key quantization points (text_encoder_2-item4.clip_pooled: torch.Size([256])) for text_encoder_2-item4.clip_pooled_prompt_embeds Using per-key quantization points (text_encoder_2-item4.clip_prompt: torch.Size([256])) for text_encoder_2-item4.clip_prompt_embeds Using per-key quantization points (text_encoder_3-item5.t5_prompt_embeds: torch.Size([256])) for text_encoder_3-item5.t5_prompt_embeds Using per-key quantization points (vae.encoder_f0: torch.Size([256])) for vae.encoder_f0 Using per-key quantization points (vae.encoder_f1: torch.Size([256])) for vae.encoder_f1 Using per-key quantization points (vae.decoder: torch.Size([256])) for vae.decoder IndividualUnPacker: text_encoder-item0.clip_pooled_prompt_embeds: torch.Size([768]) text_encoder-item0.clip_prompt_embeds: torch.Size([77, 768]) text_encoder_2-item1.clip_pooled_prompt_embeds: torch.Size([1280]) text_encoder_2-item1.clip_prompt_embeds: torch.Size([77, 1280]) text_encoder_3-item2.t5_prompt_embeds: torch.Size([77, 4096]) text_encoder-item3.clip_pooled_prompt_embeds: torch.Size([768]) text_encoder-item3.clip_prompt_embeds: torch.Size([77, 768]) text_encoder_2-item4.clip_pooled_prompt_embeds: torch.Size([1280]) text_encoder_2-item4.clip_prompt_embeds: torch.Size([77, 1280]) text_encoder_3-item5.t5_prompt_embeds: torch.Size([77, 4096]) vae.decoder: torch.Size([16, 128, 128]) vae.encoder_f0: torch.Size([32, 128, 128]) vae.encoder_f1: torch.Size([32, 128, 128]) Per-key MSE Breakdown (Quant=Truncation+Quantization, Codec=Compression): Key Quant-MSE Total-MSE ------------------------------------------------------------------------------------- text_encoder-item0.clip_pooled_prompt_embeds 0.00018216 0.95708966 text_encoder-item0.clip_prompt_embeds 0.00022930 34.89980511 text_encoder_2-item1.clip_pooled_prompt_embeds 0.00047978 0.81547565 text_encoder_2-item1.clip_prompt_embeds 0.00018160 0.13374667 text_encoder_3-item2.t5_prompt_embeds 0.00000828 0.00325211 text_encoder-item3.clip_pooled_prompt_embeds 0.00841799 1.01420029 text_encoder-item3.clip_prompt_embeds 0.00023247 23.90570887 text_encoder_2-item4.clip_pooled_prompt_embeds 0.00016751 0.67051430 text_encoder_2-item4.clip_prompt_embeds 0.00009830 0.47049568 text_encoder_3-item5.t5_prompt_embeds 0.00001263 0.00227117 vae.encoder_f0 0.00577752 1.88577557 vae.encoder_f1 0.00578475 1.88526547 vae.decoder 0.00024190 0.07867203 ------------------------------------------------------------------------------------- TOTAL 0.00273964 2.45023865 (elements=2,260,992) ---------------------- -------------------------------------------------------- SAMPLE-WISE STATISTICS ---------------------- -------------------------------------------------------- Handler sd35 Strategy individual Architecture hyperprior-featurecoding ---------------------- -------------------------------------------------------- Total Elements 2260992 Total Bytes 127372 BPFP 0.4507 bits/point EBPFP 0.9014 equivalent bits/point MSE 2.450239 ---------------------- -------------------------------------------------------- Time: 0.752s Load: 0.008s, Pack+Encode: 0.291s, Decode+Unpack: 0.453s ---------------------- -------------------------------------------------------- Restored Feature Format: [Dict] with 8 keys key['text_encoder-item0']: [Dict] with 2 keys key['clip_pooled_prompt_embeds']: [Tensor] shape=torch.Size([768]), dtype=torch.float16, device=cpu key['clip_prompt_embeds']: [Tensor] shape=torch.Size([77, 768]), dtype=torch.float16, device=cpu key['text_encoder_2-item1']: [Dict] with 2 keys key['clip_pooled_prompt_embeds']: [Tensor] shape=torch.Size([1280]), dtype=torch.float16, device=cpu key['clip_prompt_embeds']: [Tensor] shape=torch.Size([77, 1280]), dtype=torch.float16, device=cpu key['text_encoder_3-item2']: [Dict] with 1 keys key['t5_prompt_embeds']: [Tensor] shape=torch.Size([77, 4096]), dtype=torch.float16, device=cpu key['text_encoder-item3']: [Dict] with 2 keys key['clip_pooled_prompt_embeds']: [Tensor] shape=torch.Size([768]), dtype=torch.float16, device=cpu key['clip_prompt_embeds']: [Tensor] shape=torch.Size([77, 768]), dtype=torch.float16, device=cpu key['text_encoder_2-item4']: [Dict] with 2 keys key['clip_pooled_prompt_embeds']: [Tensor] shape=torch.Size([1280]), dtype=torch.float16, device=cpu key['clip_prompt_embeds']: [Tensor] shape=torch.Size([77, 1280]), dtype=torch.float16, device=cpu key['text_encoder_3-item5']: [Dict] with 1 keys key['t5_prompt_embeds']: [Tensor] shape=torch.Size([77, 4096]), dtype=torch.float16, device=cpu key['vae.decoder']: [Tensor] shape=torch.Size([16, 128, 128]), dtype=torch.float16, device=cpu key['vae.encoder']: [Dict] with 2 keys key['vae.encoder_f0']: [Tensor] shape=torch.Size([32, 128, 128]), dtype=torch.float16, device=cpu key['vae.encoder_f1']: [Tensor] shape=torch.Size([32, 128, 128]), dtype=torch.float16, device=cpu 💾 Converting with 2.4502 MSE: from ../datasets/FeatureCoding-StableDiffusion3.5/100samples_features/000000379800.zst to output-fixed/sd35/lambda0.004/hyperprior-featurecoding-8bit-individual/sd35cond/000000379800.zst 💪 Processing: ../datasets/FeatureCoding-StableDiffusion3.5/100samples_features/000000384808.zst (68/100) [1/4] FRONTEND: Loading features from ../datasets/FeatureCoding-StableDiffusion3.5/100samples_features/000000384808.zst... Original data structure: root: [Dict] with 8 keys key['text_encoder-item0']: [Dict] with 2 keys key['clip_pooled_prompt_embeds']: [Tensor] shape=torch.Size([768]), dtype=torch.float16, device=cpu key['clip_prompt_embeds']: [Tensor] shape=torch.Size([77, 768]), dtype=torch.float16, device=cpu key['text_encoder_2-item1']: [Dict] with 2 keys key['clip_pooled_prompt_embeds']: [Tensor] shape=torch.Size([1280]), dtype=torch.float16, device=cpu key['clip_prompt_embeds']: [Tensor] shape=torch.Size([77, 1280]), dtype=torch.float16, device=cpu key['text_encoder_3-item2']: [Dict] with 1 keys key['t5_prompt_embeds']: [Tensor] shape=torch.Size([77, 4096]), dtype=torch.float16, device=cpu key['text_encoder-item3']: [Dict] with 2 keys key['clip_pooled_prompt_embeds']: [Tensor] shape=torch.Size([768]), dtype=torch.float16, device=cpu key['clip_prompt_embeds']: [Tensor] shape=torch.Size([77, 768]), dtype=torch.float16, device=cpu key['text_encoder_2-item4']: [Dict] with 2 keys key['clip_pooled_prompt_embeds']: [Tensor] shape=torch.Size([1280]), dtype=torch.float16, device=cpu key['clip_prompt_embeds']: [Tensor] shape=torch.Size([77, 1280]), dtype=torch.float16, device=cpu key['text_encoder_3-item5']: [Dict] with 1 keys key['t5_prompt_embeds']: [Tensor] shape=torch.Size([77, 4096]), dtype=torch.float16, device=cpu key['vae.encoder-item6']: [Dict] with 2 keys key['vae.encoder_f0']: [Tensor] shape=torch.Size([32, 128, 128]), dtype=torch.float16, device=cpu key['vae.encoder_f1']: [Tensor] shape=torch.Size([32, 128, 128]), dtype=torch.float16, device=cpu key['vae.decoder']: [Tensor] shape=torch.Size([16, 128, 128]), dtype=torch.float16, device=cpu ⌛️ [1/4] FRONTEND: Load time: 0.009s ------------------------------------------------------------ SD3.5 Features Summary ------------------------------------------------------------ Number of text encoders: 3 Encoder names: ['text_encoder', 'text_encoder_2', 'text_encoder_3'] Has VAE latents: True Has VAE encoder features: True Data type: torch.float16 text_encoder-item0: clip_pooled_prompt_embeds: torch.Size([768]), torch.float16 clip_prompt_embeds: torch.Size([77, 768]), torch.float16 text_encoder_2-item1: clip_pooled_prompt_embeds: torch.Size([1280]), torch.float16 clip_prompt_embeds: torch.Size([77, 1280]), torch.float16 text_encoder_3-item2: t5_prompt_embeds: torch.Size([77, 4096]), torch.float16 text_encoder-item3: clip_pooled_prompt_embeds: torch.Size([768]), torch.float16 clip_prompt_embeds: torch.Size([77, 768]), torch.float16 text_encoder_2-item4: clip_pooled_prompt_embeds: torch.Size([1280]), torch.float16 clip_prompt_embeds: torch.Size([77, 1280]), torch.float16 text_encoder_3-item5: t5_prompt_embeds: torch.Size([77, 4096]), torch.float16 vae.decoder latents: torch.Size([16, 128, 128]) vae.encoder features: vae.encoder_f0: torch.Size([32, 128, 128]), torch.float16 vae.encoder_f1: torch.Size([32, 128, 128]), torch.float16 ------------------------------------------------------------ [2/4] FRONTEND: Pack + Encode (strategy: individual)... IndividualPacker: text_encoder-item0.clip_pooled_prompt_embeds: torch.Size([768])->torch.Size([1, 1, 1, 768]) text_encoder-item0.clip_prompt_embeds: torch.Size([77, 768])->torch.Size([1, 1, 77, 768]) text_encoder_2-item1.clip_pooled_prompt_embeds: torch.Size([1280])->torch.Size([1, 1, 1, 1280]) text_encoder_2-item1.clip_prompt_embeds: torch.Size([77, 1280])->torch.Size([1, 1, 77, 1280]) text_encoder_3-item2.t5_prompt_embeds: torch.Size([77, 4096])->torch.Size([1, 1, 77, 4096]) text_encoder-item3.clip_pooled_prompt_embeds: torch.Size([768])->torch.Size([1, 1, 1, 768]) text_encoder-item3.clip_prompt_embeds: torch.Size([77, 768])->torch.Size([1, 1, 77, 768]) text_encoder_2-item4.clip_pooled_prompt_embeds: torch.Size([1280])->torch.Size([1, 1, 1, 1280]) text_encoder_2-item4.clip_prompt_embeds: torch.Size([77, 1280])->torch.Size([1, 1, 77, 1280]) text_encoder_3-item5.t5_prompt_embeds: torch.Size([77, 4096])->torch.Size([1, 1, 77, 4096]) vae.encoder_f0: torch.Size([32, 128, 128])->torch.Size([1, 1, 512, 1024]) vae.encoder_f1: torch.Size([32, 128, 128])->torch.Size([1, 1, 512, 1024]) vae.decoder: torch.Size([16, 128, 128])->torch.Size([1, 1, 512, 512]) Using per-key quantization points (text_encoder-item0.clip_pooled_prompt_embeds: torch.Size([256])) for text_encoder-item0.clip_pooled_prompt_embeds text_encoder-item0.clip_pooled_prompt_embeds: 728B, BPFP=7.5833 Using per-key quantization points (text_encoder-item0.clip_prompt_embeds: torch.Size([256])) for text_encoder-item0.clip_prompt_embeds text_encoder-item0.clip_prompt_embeds: 6,644B, BPFP=0.8988 Using per-key quantization points (text_encoder_2-item1.clip_pooled_prompt_embeds: torch.Size([256])) for text_encoder_2-item1.clip_pooled_prompt_embeds text_encoder_2-item1.clip_pooled_prompt_embeds: 1,196B, BPFP=7.4750 Using per-key quantization points (text_encoder_2-item1.clip_prompt_embeds: torch.Size([256])) for text_encoder_2-item1.clip_prompt_embeds text_encoder_2-item1.clip_prompt_embeds: 11,124B, BPFP=0.9029 Using per-key quantization points (text_encoder_3-item2.t5_prompt_embeds: torch.Size([256])) for text_encoder_3-item2.t5_prompt_embeds text_encoder_3-item2.t5_prompt_embeds: 30,236B, BPFP=0.7669 Using per-key quantization points (text_encoder-item3.clip_pooled_prompt_embeds: torch.Size([256])) for text_encoder-item3.clip_pooled_prompt_embeds text_encoder-item3.clip_pooled_prompt_embeds: 760B, BPFP=7.9167 Using per-key quantization points (text_encoder-item3.clip_prompt_embeds: torch.Size([256])) for text_encoder-item3.clip_prompt_embeds text_encoder-item3.clip_prompt_embeds: 4,024B, BPFP=0.5444 Using per-key quantization points (text_encoder_2-item4.clip_pooled: torch.Size([256])) for text_encoder_2-item4.clip_pooled_prompt_embeds text_encoder_2-item4.clip_pooled_prompt_embeds: 1,268B, BPFP=7.9250 Using per-key quantization points (text_encoder_2-item4.clip_prompt: torch.Size([256])) for text_encoder_2-item4.clip_prompt_embeds text_encoder_2-item4.clip_prompt_embeds: 6,996B, BPFP=0.5679 Using per-key quantization points (text_encoder_3-item5.t5_prompt_embeds: torch.Size([256])) for text_encoder_3-item5.t5_prompt_embeds text_encoder_3-item5.t5_prompt_embeds: 18,416B, BPFP=0.4671 Using per-key quantization points (vae.encoder_f0: torch.Size([256])) for vae.encoder_f0 vae.encoder_f0: 21,472B, BPFP=0.3276 Using per-key quantization points (vae.encoder_f1: torch.Size([256])) for vae.encoder_f1 vae.encoder_f1: 21,472B, BPFP=0.3276 Using per-key quantization points (vae.decoder: torch.Size([256])) for vae.decoder vae.decoder: 4,732B, BPFP=0.1444 ⌛️ [2/4] FRONTEND: Frontend time: 0.300s (Pack+Encode) [3/4] BACKEND: Decode + Unpack... Using per-key quantization points (text_encoder-item0.clip_pooled_prompt_embeds: torch.Size([256])) for text_encoder-item0.clip_pooled_prompt_embeds Using per-key quantization points (text_encoder-item0.clip_prompt_embeds: torch.Size([256])) for text_encoder-item0.clip_prompt_embeds Using per-key quantization points (text_encoder_2-item1.clip_pooled_prompt_embeds: torch.Size([256])) for text_encoder_2-item1.clip_pooled_prompt_embeds Using per-key quantization points (text_encoder_2-item1.clip_prompt_embeds: torch.Size([256])) for text_encoder_2-item1.clip_prompt_embeds Using per-key quantization points (text_encoder_3-item2.t5_prompt_embeds: torch.Size([256])) for text_encoder_3-item2.t5_prompt_embeds Using per-key quantization points (text_encoder-item3.clip_pooled_prompt_embeds: torch.Size([256])) for text_encoder-item3.clip_pooled_prompt_embeds Using per-key quantization points (text_encoder-item3.clip_prompt_embeds: torch.Size([256])) for text_encoder-item3.clip_prompt_embeds Using per-key quantization points (text_encoder_2-item4.clip_pooled: torch.Size([256])) for text_encoder_2-item4.clip_pooled_prompt_embeds Using per-key quantization points (text_encoder_2-item4.clip_prompt: torch.Size([256])) for text_encoder_2-item4.clip_prompt_embeds Using per-key quantization points (text_encoder_3-item5.t5_prompt_embeds: torch.Size([256])) for text_encoder_3-item5.t5_prompt_embeds Using per-key quantization points (vae.encoder_f0: torch.Size([256])) for vae.encoder_f0 Using per-key quantization points (vae.encoder_f1: torch.Size([256])) for vae.encoder_f1 Using per-key quantization points (vae.decoder: torch.Size([256])) for vae.decoder IndividualUnPacker: text_encoder-item0.clip_pooled_prompt_embeds: torch.Size([768]) text_encoder-item0.clip_prompt_embeds: torch.Size([77, 768]) text_encoder_2-item1.clip_pooled_prompt_embeds: torch.Size([1280]) text_encoder_2-item1.clip_prompt_embeds: torch.Size([77, 1280]) text_encoder_3-item2.t5_prompt_embeds: torch.Size([77, 4096]) text_encoder-item3.clip_pooled_prompt_embeds: torch.Size([768]) text_encoder-item3.clip_prompt_embeds: torch.Size([77, 768]) text_encoder_2-item4.clip_pooled_prompt_embeds: torch.Size([1280]) text_encoder_2-item4.clip_prompt_embeds: torch.Size([77, 1280]) text_encoder_3-item5.t5_prompt_embeds: torch.Size([77, 4096]) vae.decoder: torch.Size([16, 128, 128]) vae.encoder_f0: torch.Size([32, 128, 128]) vae.encoder_f1: torch.Size([32, 128, 128]) ⌛️ [3/4] BACKEND: Backend time: 0.458s [4/4] METRICS: Computing MSE Breakdown... Using per-key quantization points (text_encoder-item0.clip_pooled_prompt_embeds: torch.Size([256])) for text_encoder-item0.clip_pooled_prompt_embeds Using per-key quantization points (text_encoder-item0.clip_prompt_embeds: torch.Size([256])) for text_encoder-item0.clip_prompt_embeds Using per-key quantization points (text_encoder_2-item1.clip_pooled_prompt_embeds: torch.Size([256])) for text_encoder_2-item1.clip_pooled_prompt_embeds Using per-key quantization points (text_encoder_2-item1.clip_prompt_embeds: torch.Size([256])) for text_encoder_2-item1.clip_prompt_embeds Using per-key quantization points (text_encoder_3-item2.t5_prompt_embeds: torch.Size([256])) for text_encoder_3-item2.t5_prompt_embeds Using per-key quantization points (text_encoder-item3.clip_pooled_prompt_embeds: torch.Size([256])) for text_encoder-item3.clip_pooled_prompt_embeds Using per-key quantization points (text_encoder-item3.clip_prompt_embeds: torch.Size([256])) for text_encoder-item3.clip_prompt_embeds Using per-key quantization points (text_encoder_2-item4.clip_pooled: torch.Size([256])) for text_encoder_2-item4.clip_pooled_prompt_embeds Using per-key quantization points (text_encoder_2-item4.clip_prompt: torch.Size([256])) for text_encoder_2-item4.clip_prompt_embeds Using per-key quantization points (text_encoder_3-item5.t5_prompt_embeds: torch.Size([256])) for text_encoder_3-item5.t5_prompt_embeds Using per-key quantization points (vae.encoder_f0: torch.Size([256])) for vae.encoder_f0 Using per-key quantization points (vae.encoder_f1: torch.Size([256])) for vae.encoder_f1 Using per-key quantization points (vae.decoder: torch.Size([256])) for vae.decoder IndividualUnPacker: text_encoder-item0.clip_pooled_prompt_embeds: torch.Size([768]) text_encoder-item0.clip_prompt_embeds: torch.Size([77, 768]) text_encoder_2-item1.clip_pooled_prompt_embeds: torch.Size([1280]) text_encoder_2-item1.clip_prompt_embeds: torch.Size([77, 1280]) text_encoder_3-item2.t5_prompt_embeds: torch.Size([77, 4096]) text_encoder-item3.clip_pooled_prompt_embeds: torch.Size([768]) text_encoder-item3.clip_prompt_embeds: torch.Size([77, 768]) text_encoder_2-item4.clip_pooled_prompt_embeds: torch.Size([1280]) text_encoder_2-item4.clip_prompt_embeds: torch.Size([77, 1280]) text_encoder_3-item5.t5_prompt_embeds: torch.Size([77, 4096]) vae.decoder: torch.Size([16, 128, 128]) vae.encoder_f0: torch.Size([32, 128, 128]) vae.encoder_f1: torch.Size([32, 128, 128]) Per-key MSE Breakdown (Quant=Truncation+Quantization, Codec=Compression): Key Quant-MSE Total-MSE ------------------------------------------------------------------------------------- text_encoder-item0.clip_pooled_prompt_embeds 0.00047293 0.99003704 text_encoder-item0.clip_prompt_embeds 0.00028764 23.98071606 text_encoder_2-item1.clip_pooled_prompt_embeds 0.00021081 1.00480852 text_encoder_2-item1.clip_prompt_embeds 0.00018283 0.12935021 text_encoder_3-item2.t5_prompt_embeds 0.00000777 0.00363175 text_encoder-item3.clip_pooled_prompt_embeds 0.00841799 1.01420029 text_encoder-item3.clip_prompt_embeds 0.00023247 23.90570887 text_encoder_2-item4.clip_pooled_prompt_embeds 0.00016751 0.67051430 text_encoder_2-item4.clip_prompt_embeds 0.00009830 0.47049568 text_encoder_3-item5.t5_prompt_embeds 0.00001263 0.00227117 vae.encoder_f0 0.03343784 4.14136887 vae.encoder_f1 0.03344063 4.14278078 vae.decoder 0.00016139 0.02989344 ------------------------------------------------------------------------------------- TOTAL 0.01555870 3.20549320 (elements=2,260,992) ---------------------- -------------------------------------------------------- SAMPLE-WISE STATISTICS ---------------------- -------------------------------------------------------- Handler sd35 Strategy individual Architecture hyperprior-featurecoding ---------------------- -------------------------------------------------------- Total Elements 2260992 Total Bytes 129068 BPFP 0.4567 bits/point EBPFP 0.9134 equivalent bits/point MSE 3.205493 ---------------------- -------------------------------------------------------- Time: 0.767s Load: 0.009s, Pack+Encode: 0.300s, Decode+Unpack: 0.458s ---------------------- -------------------------------------------------------- Restored Feature Format: [Dict] with 8 keys key['text_encoder-item0']: [Dict] with 2 keys key['clip_pooled_prompt_embeds']: [Tensor] shape=torch.Size([768]), dtype=torch.float16, device=cpu key['clip_prompt_embeds']: [Tensor] shape=torch.Size([77, 768]), dtype=torch.float16, device=cpu key['text_encoder_2-item1']: [Dict] with 2 keys key['clip_pooled_prompt_embeds']: [Tensor] shape=torch.Size([1280]), dtype=torch.float16, device=cpu key['clip_prompt_embeds']: [Tensor] shape=torch.Size([77, 1280]), dtype=torch.float16, device=cpu key['text_encoder_3-item2']: [Dict] with 1 keys key['t5_prompt_embeds']: [Tensor] shape=torch.Size([77, 4096]), dtype=torch.float16, device=cpu key['text_encoder-item3']: [Dict] with 2 keys key['clip_pooled_prompt_embeds']: [Tensor] shape=torch.Size([768]), dtype=torch.float16, device=cpu key['clip_prompt_embeds']: [Tensor] shape=torch.Size([77, 768]), dtype=torch.float16, device=cpu key['text_encoder_2-item4']: [Dict] with 2 keys key['clip_pooled_prompt_embeds']: [Tensor] shape=torch.Size([1280]), dtype=torch.float16, device=cpu key['clip_prompt_embeds']: [Tensor] shape=torch.Size([77, 1280]), dtype=torch.float16, device=cpu key['text_encoder_3-item5']: [Dict] with 1 keys key['t5_prompt_embeds']: [Tensor] shape=torch.Size([77, 4096]), dtype=torch.float16, device=cpu key['vae.decoder']: [Tensor] shape=torch.Size([16, 128, 128]), dtype=torch.float16, device=cpu key['vae.encoder']: [Dict] with 2 keys key['vae.encoder_f0']: [Tensor] shape=torch.Size([32, 128, 128]), dtype=torch.float16, device=cpu key['vae.encoder_f1']: [Tensor] shape=torch.Size([32, 128, 128]), dtype=torch.float16, device=cpu 💾 Converting with 3.2055 MSE: from ../datasets/FeatureCoding-StableDiffusion3.5/100samples_features/000000384808.zst to output-fixed/sd35/lambda0.004/hyperprior-featurecoding-8bit-individual/sd35cond/000000384808.zst 💪 Processing: ../datasets/FeatureCoding-StableDiffusion3.5/100samples_features/000000396338.zst (69/100) [1/4] FRONTEND: Loading features from ../datasets/FeatureCoding-StableDiffusion3.5/100samples_features/000000396338.zst... Original data structure: root: [Dict] with 8 keys key['text_encoder-item0']: [Dict] with 2 keys key['clip_pooled_prompt_embeds']: [Tensor] shape=torch.Size([768]), dtype=torch.float16, device=cpu key['clip_prompt_embeds']: [Tensor] shape=torch.Size([77, 768]), dtype=torch.float16, device=cpu key['text_encoder_2-item1']: [Dict] with 2 keys key['clip_pooled_prompt_embeds']: [Tensor] shape=torch.Size([1280]), dtype=torch.float16, device=cpu key['clip_prompt_embeds']: [Tensor] shape=torch.Size([77, 1280]), dtype=torch.float16, device=cpu key['text_encoder_3-item2']: [Dict] with 1 keys key['t5_prompt_embeds']: [Tensor] shape=torch.Size([77, 4096]), dtype=torch.float16, device=cpu key['text_encoder-item3']: [Dict] with 2 keys key['clip_pooled_prompt_embeds']: [Tensor] shape=torch.Size([768]), dtype=torch.float16, device=cpu key['clip_prompt_embeds']: [Tensor] shape=torch.Size([77, 768]), dtype=torch.float16, device=cpu key['text_encoder_2-item4']: [Dict] with 2 keys key['clip_pooled_prompt_embeds']: [Tensor] shape=torch.Size([1280]), dtype=torch.float16, device=cpu key['clip_prompt_embeds']: [Tensor] shape=torch.Size([77, 1280]), dtype=torch.float16, device=cpu key['text_encoder_3-item5']: [Dict] with 1 keys key['t5_prompt_embeds']: [Tensor] shape=torch.Size([77, 4096]), dtype=torch.float16, device=cpu key['vae.encoder-item6']: [Dict] with 2 keys key['vae.encoder_f0']: [Tensor] shape=torch.Size([32, 128, 128]), dtype=torch.float16, device=cpu key['vae.encoder_f1']: [Tensor] shape=torch.Size([32, 128, 128]), dtype=torch.float16, device=cpu key['vae.decoder']: [Tensor] shape=torch.Size([16, 128, 128]), dtype=torch.float16, device=cpu ⌛️ [1/4] FRONTEND: Load time: 0.009s ------------------------------------------------------------ SD3.5 Features Summary ------------------------------------------------------------ Number of text encoders: 3 Encoder names: ['text_encoder', 'text_encoder_2', 'text_encoder_3'] Has VAE latents: True Has VAE encoder features: True Data type: torch.float16 text_encoder-item0: clip_pooled_prompt_embeds: torch.Size([768]), torch.float16 clip_prompt_embeds: torch.Size([77, 768]), torch.float16 text_encoder_2-item1: clip_pooled_prompt_embeds: torch.Size([1280]), torch.float16 clip_prompt_embeds: torch.Size([77, 1280]), torch.float16 text_encoder_3-item2: t5_prompt_embeds: torch.Size([77, 4096]), torch.float16 text_encoder-item3: clip_pooled_prompt_embeds: torch.Size([768]), torch.float16 clip_prompt_embeds: torch.Size([77, 768]), torch.float16 text_encoder_2-item4: clip_pooled_prompt_embeds: torch.Size([1280]), torch.float16 clip_prompt_embeds: torch.Size([77, 1280]), torch.float16 text_encoder_3-item5: t5_prompt_embeds: torch.Size([77, 4096]), torch.float16 vae.decoder latents: torch.Size([16, 128, 128]) vae.encoder features: vae.encoder_f0: torch.Size([32, 128, 128]), torch.float16 vae.encoder_f1: torch.Size([32, 128, 128]), torch.float16 ------------------------------------------------------------ [2/4] FRONTEND: Pack + Encode (strategy: individual)... IndividualPacker: text_encoder-item0.clip_pooled_prompt_embeds: torch.Size([768])->torch.Size([1, 1, 1, 768]) text_encoder-item0.clip_prompt_embeds: torch.Size([77, 768])->torch.Size([1, 1, 77, 768]) text_encoder_2-item1.clip_pooled_prompt_embeds: torch.Size([1280])->torch.Size([1, 1, 1, 1280]) text_encoder_2-item1.clip_prompt_embeds: torch.Size([77, 1280])->torch.Size([1, 1, 77, 1280]) text_encoder_3-item2.t5_prompt_embeds: torch.Size([77, 4096])->torch.Size([1, 1, 77, 4096]) text_encoder-item3.clip_pooled_prompt_embeds: torch.Size([768])->torch.Size([1, 1, 1, 768]) text_encoder-item3.clip_prompt_embeds: torch.Size([77, 768])->torch.Size([1, 1, 77, 768]) text_encoder_2-item4.clip_pooled_prompt_embeds: torch.Size([1280])->torch.Size([1, 1, 1, 1280]) text_encoder_2-item4.clip_prompt_embeds: torch.Size([77, 1280])->torch.Size([1, 1, 77, 1280]) text_encoder_3-item5.t5_prompt_embeds: torch.Size([77, 4096])->torch.Size([1, 1, 77, 4096]) vae.encoder_f0: torch.Size([32, 128, 128])->torch.Size([1, 1, 512, 1024]) vae.encoder_f1: torch.Size([32, 128, 128])->torch.Size([1, 1, 512, 1024]) vae.decoder: torch.Size([16, 128, 128])->torch.Size([1, 1, 512, 512]) Using per-key quantization points (text_encoder-item0.clip_pooled_prompt_embeds: torch.Size([256])) for text_encoder-item0.clip_pooled_prompt_embeds text_encoder-item0.clip_pooled_prompt_embeds: 732B, BPFP=7.6250 Using per-key quantization points (text_encoder-item0.clip_prompt_embeds: torch.Size([256])) for text_encoder-item0.clip_prompt_embeds text_encoder-item0.clip_prompt_embeds: 5,800B, BPFP=0.7846 Using per-key quantization points (text_encoder_2-item1.clip_pooled_prompt_embeds: torch.Size([256])) for text_encoder_2-item1.clip_pooled_prompt_embeds text_encoder_2-item1.clip_pooled_prompt_embeds: 1,204B, BPFP=7.5250 Using per-key quantization points (text_encoder_2-item1.clip_prompt_embeds: torch.Size([256])) for text_encoder_2-item1.clip_prompt_embeds text_encoder_2-item1.clip_prompt_embeds: 10,612B, BPFP=0.8614 Using per-key quantization points (text_encoder_3-item2.t5_prompt_embeds: torch.Size([256])) for text_encoder_3-item2.t5_prompt_embeds text_encoder_3-item2.t5_prompt_embeds: 26,100B, BPFP=0.6620 Using per-key quantization points (text_encoder-item3.clip_pooled_prompt_embeds: torch.Size([256])) for text_encoder-item3.clip_pooled_prompt_embeds text_encoder-item3.clip_pooled_prompt_embeds: 760B, BPFP=7.9167 Using per-key quantization points (text_encoder-item3.clip_prompt_embeds: torch.Size([256])) for text_encoder-item3.clip_prompt_embeds text_encoder-item3.clip_prompt_embeds: 4,024B, BPFP=0.5444 Using per-key quantization points (text_encoder_2-item4.clip_pooled: torch.Size([256])) for text_encoder_2-item4.clip_pooled_prompt_embeds text_encoder_2-item4.clip_pooled_prompt_embeds: 1,268B, BPFP=7.9250 Using per-key quantization points (text_encoder_2-item4.clip_prompt: torch.Size([256])) for text_encoder_2-item4.clip_prompt_embeds text_encoder_2-item4.clip_prompt_embeds: 6,996B, BPFP=0.5679 Using per-key quantization points (text_encoder_3-item5.t5_prompt_embeds: torch.Size([256])) for text_encoder_3-item5.t5_prompt_embeds text_encoder_3-item5.t5_prompt_embeds: 18,416B, BPFP=0.4671 Using per-key quantization points (vae.encoder_f0: torch.Size([256])) for vae.encoder_f0 vae.encoder_f0: 21,476B, BPFP=0.3277 Using per-key quantization points (vae.encoder_f1: torch.Size([256])) for vae.encoder_f1 vae.encoder_f1: 21,476B, BPFP=0.3277 Using per-key quantization points (vae.decoder: torch.Size([256])) for vae.decoder vae.decoder: 8,344B, BPFP=0.2546 ⌛️ [2/4] FRONTEND: Frontend time: 0.295s (Pack+Encode) [3/4] BACKEND: Decode + Unpack... Using per-key quantization points (text_encoder-item0.clip_pooled_prompt_embeds: torch.Size([256])) for text_encoder-item0.clip_pooled_prompt_embeds Using per-key quantization points (text_encoder-item0.clip_prompt_embeds: torch.Size([256])) for text_encoder-item0.clip_prompt_embeds Using per-key quantization points (text_encoder_2-item1.clip_pooled_prompt_embeds: torch.Size([256])) for text_encoder_2-item1.clip_pooled_prompt_embeds Using per-key quantization points (text_encoder_2-item1.clip_prompt_embeds: torch.Size([256])) for text_encoder_2-item1.clip_prompt_embeds Using per-key quantization points (text_encoder_3-item2.t5_prompt_embeds: torch.Size([256])) for text_encoder_3-item2.t5_prompt_embeds Using per-key quantization points (text_encoder-item3.clip_pooled_prompt_embeds: torch.Size([256])) for text_encoder-item3.clip_pooled_prompt_embeds Using per-key quantization points (text_encoder-item3.clip_prompt_embeds: torch.Size([256])) for text_encoder-item3.clip_prompt_embeds Using per-key quantization points (text_encoder_2-item4.clip_pooled: torch.Size([256])) for text_encoder_2-item4.clip_pooled_prompt_embeds Using per-key quantization points (text_encoder_2-item4.clip_prompt: torch.Size([256])) for text_encoder_2-item4.clip_prompt_embeds Using per-key quantization points (text_encoder_3-item5.t5_prompt_embeds: torch.Size([256])) for text_encoder_3-item5.t5_prompt_embeds Using per-key quantization points (vae.encoder_f0: torch.Size([256])) for vae.encoder_f0 Using per-key quantization points (vae.encoder_f1: torch.Size([256])) for vae.encoder_f1 Using per-key quantization points (vae.decoder: torch.Size([256])) for vae.decoder IndividualUnPacker: text_encoder-item0.clip_pooled_prompt_embeds: torch.Size([768]) text_encoder-item0.clip_prompt_embeds: torch.Size([77, 768]) text_encoder_2-item1.clip_pooled_prompt_embeds: torch.Size([1280]) text_encoder_2-item1.clip_prompt_embeds: torch.Size([77, 1280]) text_encoder_3-item2.t5_prompt_embeds: torch.Size([77, 4096]) text_encoder-item3.clip_pooled_prompt_embeds: torch.Size([768]) text_encoder-item3.clip_prompt_embeds: torch.Size([77, 768]) text_encoder_2-item4.clip_pooled_prompt_embeds: torch.Size([1280]) text_encoder_2-item4.clip_prompt_embeds: torch.Size([77, 1280]) text_encoder_3-item5.t5_prompt_embeds: torch.Size([77, 4096]) vae.decoder: torch.Size([16, 128, 128]) vae.encoder_f0: torch.Size([32, 128, 128]) vae.encoder_f1: torch.Size([32, 128, 128]) ⌛️ [3/4] BACKEND: Backend time: 0.449s [4/4] METRICS: Computing MSE Breakdown... Using per-key quantization points (text_encoder-item0.clip_pooled_prompt_embeds: torch.Size([256])) for text_encoder-item0.clip_pooled_prompt_embeds Using per-key quantization points (text_encoder-item0.clip_prompt_embeds: torch.Size([256])) for text_encoder-item0.clip_prompt_embeds Using per-key quantization points (text_encoder_2-item1.clip_pooled_prompt_embeds: torch.Size([256])) for text_encoder_2-item1.clip_pooled_prompt_embeds Using per-key quantization points (text_encoder_2-item1.clip_prompt_embeds: torch.Size([256])) for text_encoder_2-item1.clip_prompt_embeds Using per-key quantization points (text_encoder_3-item2.t5_prompt_embeds: torch.Size([256])) for text_encoder_3-item2.t5_prompt_embeds Using per-key quantization points (text_encoder-item3.clip_pooled_prompt_embeds: torch.Size([256])) for text_encoder-item3.clip_pooled_prompt_embeds Using per-key quantization points (text_encoder-item3.clip_prompt_embeds: torch.Size([256])) for text_encoder-item3.clip_prompt_embeds Using per-key quantization points (text_encoder_2-item4.clip_pooled: torch.Size([256])) for text_encoder_2-item4.clip_pooled_prompt_embeds Using per-key quantization points (text_encoder_2-item4.clip_prompt: torch.Size([256])) for text_encoder_2-item4.clip_prompt_embeds Using per-key quantization points (text_encoder_3-item5.t5_prompt_embeds: torch.Size([256])) for text_encoder_3-item5.t5_prompt_embeds Using per-key quantization points (vae.encoder_f0: torch.Size([256])) for vae.encoder_f0 Using per-key quantization points (vae.encoder_f1: torch.Size([256])) for vae.encoder_f1 Using per-key quantization points (vae.decoder: torch.Size([256])) for vae.decoder IndividualUnPacker: text_encoder-item0.clip_pooled_prompt_embeds: torch.Size([768]) text_encoder-item0.clip_prompt_embeds: torch.Size([77, 768]) text_encoder_2-item1.clip_pooled_prompt_embeds: torch.Size([1280]) text_encoder_2-item1.clip_prompt_embeds: torch.Size([77, 1280]) text_encoder_3-item2.t5_prompt_embeds: torch.Size([77, 4096]) text_encoder-item3.clip_pooled_prompt_embeds: torch.Size([768]) text_encoder-item3.clip_prompt_embeds: torch.Size([77, 768]) text_encoder_2-item4.clip_pooled_prompt_embeds: torch.Size([1280]) text_encoder_2-item4.clip_prompt_embeds: torch.Size([77, 1280]) text_encoder_3-item5.t5_prompt_embeds: torch.Size([77, 4096]) vae.decoder: torch.Size([16, 128, 128]) vae.encoder_f0: torch.Size([32, 128, 128]) vae.encoder_f1: torch.Size([32, 128, 128]) Per-key MSE Breakdown (Quant=Truncation+Quantization, Codec=Compression): Key Quant-MSE Total-MSE ------------------------------------------------------------------------------------- text_encoder-item0.clip_pooled_prompt_embeds 0.00559742 0.94486284 text_encoder-item0.clip_prompt_embeds 0.00023094 23.94424927 text_encoder_2-item1.clip_pooled_prompt_embeds 0.00027942 0.88454590 text_encoder_2-item1.clip_prompt_embeds 0.00018965 0.12744388 text_encoder_3-item2.t5_prompt_embeds 0.00000768 0.00315106 text_encoder-item3.clip_pooled_prompt_embeds 0.00841799 1.01420029 text_encoder-item3.clip_prompt_embeds 0.00023247 23.90570887 text_encoder_2-item4.clip_pooled_prompt_embeds 0.00016751 0.67051430 text_encoder_2-item4.clip_prompt_embeds 0.00009830 0.47049568 text_encoder_3-item5.t5_prompt_embeds 0.00001263 0.00227117 vae.encoder_f0 0.00637455 2.92587972 vae.encoder_f1 0.00637988 2.92553234 vae.decoder 0.00020059 0.06826786 ------------------------------------------------------------------------------------- TOTAL 0.00301333 2.64464198 (elements=2,260,992) ---------------------- -------------------------------------------------------- SAMPLE-WISE STATISTICS ---------------------- -------------------------------------------------------- Handler sd35 Strategy individual Architecture hyperprior-featurecoding ---------------------- -------------------------------------------------------- Total Elements 2260992 Total Bytes 127208 BPFP 0.4501 bits/point EBPFP 0.9002 equivalent bits/point MSE 2.644642 ---------------------- -------------------------------------------------------- Time: 0.753s Load: 0.009s, Pack+Encode: 0.295s, Decode+Unpack: 0.449s ---------------------- -------------------------------------------------------- Restored Feature Format: [Dict] with 8 keys key['text_encoder-item0']: [Dict] with 2 keys key['clip_pooled_prompt_embeds']: [Tensor] shape=torch.Size([768]), dtype=torch.float16, device=cpu key['clip_prompt_embeds']: [Tensor] shape=torch.Size([77, 768]), dtype=torch.float16, device=cpu key['text_encoder_2-item1']: [Dict] with 2 keys key['clip_pooled_prompt_embeds']: [Tensor] shape=torch.Size([1280]), dtype=torch.float16, device=cpu key['clip_prompt_embeds']: [Tensor] shape=torch.Size([77, 1280]), dtype=torch.float16, device=cpu key['text_encoder_3-item2']: [Dict] with 1 keys key['t5_prompt_embeds']: [Tensor] shape=torch.Size([77, 4096]), dtype=torch.float16, device=cpu key['text_encoder-item3']: [Dict] with 2 keys key['clip_pooled_prompt_embeds']: [Tensor] shape=torch.Size([768]), dtype=torch.float16, device=cpu key['clip_prompt_embeds']: [Tensor] shape=torch.Size([77, 768]), dtype=torch.float16, device=cpu key['text_encoder_2-item4']: [Dict] with 2 keys key['clip_pooled_prompt_embeds']: [Tensor] shape=torch.Size([1280]), dtype=torch.float16, device=cpu key['clip_prompt_embeds']: [Tensor] shape=torch.Size([77, 1280]), dtype=torch.float16, device=cpu key['text_encoder_3-item5']: [Dict] with 1 keys key['t5_prompt_embeds']: [Tensor] shape=torch.Size([77, 4096]), dtype=torch.float16, device=cpu key['vae.decoder']: [Tensor] shape=torch.Size([16, 128, 128]), dtype=torch.float16, device=cpu key['vae.encoder']: [Dict] with 2 keys key['vae.encoder_f0']: [Tensor] shape=torch.Size([32, 128, 128]), dtype=torch.float16, device=cpu key['vae.encoder_f1']: [Tensor] shape=torch.Size([32, 128, 128]), dtype=torch.float16, device=cpu 💾 Converting with 2.6446 MSE: from ../datasets/FeatureCoding-StableDiffusion3.5/100samples_features/000000396338.zst to output-fixed/sd35/lambda0.004/hyperprior-featurecoding-8bit-individual/sd35cond/000000396338.zst 💪 Processing: ../datasets/FeatureCoding-StableDiffusion3.5/100samples_features/000000397303.zst (70/100) [1/4] FRONTEND: Loading features from ../datasets/FeatureCoding-StableDiffusion3.5/100samples_features/000000397303.zst... Original data structure: root: [Dict] with 8 keys key['text_encoder-item0']: [Dict] with 2 keys key['clip_pooled_prompt_embeds']: [Tensor] shape=torch.Size([768]), dtype=torch.float16, device=cpu key['clip_prompt_embeds']: [Tensor] shape=torch.Size([77, 768]), dtype=torch.float16, device=cpu key['text_encoder_2-item1']: [Dict] with 2 keys key['clip_pooled_prompt_embeds']: [Tensor] shape=torch.Size([1280]), dtype=torch.float16, device=cpu key['clip_prompt_embeds']: [Tensor] shape=torch.Size([77, 1280]), dtype=torch.float16, device=cpu key['text_encoder_3-item2']: [Dict] with 1 keys key['t5_prompt_embeds']: [Tensor] shape=torch.Size([77, 4096]), dtype=torch.float16, device=cpu key['text_encoder-item3']: [Dict] with 2 keys key['clip_pooled_prompt_embeds']: [Tensor] shape=torch.Size([768]), dtype=torch.float16, device=cpu key['clip_prompt_embeds']: [Tensor] shape=torch.Size([77, 768]), dtype=torch.float16, device=cpu key['text_encoder_2-item4']: [Dict] with 2 keys key['clip_pooled_prompt_embeds']: [Tensor] shape=torch.Size([1280]), dtype=torch.float16, device=cpu key['clip_prompt_embeds']: [Tensor] shape=torch.Size([77, 1280]), dtype=torch.float16, device=cpu key['text_encoder_3-item5']: [Dict] with 1 keys key['t5_prompt_embeds']: [Tensor] shape=torch.Size([77, 4096]), dtype=torch.float16, device=cpu key['vae.encoder-item6']: [Dict] with 2 keys key['vae.encoder_f0']: [Tensor] shape=torch.Size([32, 128, 128]), dtype=torch.float16, device=cpu key['vae.encoder_f1']: [Tensor] shape=torch.Size([32, 128, 128]), dtype=torch.float16, device=cpu key['vae.decoder']: [Tensor] shape=torch.Size([16, 128, 128]), dtype=torch.float16, device=cpu ⌛️ [1/4] FRONTEND: Load time: 0.009s ------------------------------------------------------------ SD3.5 Features Summary ------------------------------------------------------------ Number of text encoders: 3 Encoder names: ['text_encoder', 'text_encoder_2', 'text_encoder_3'] Has VAE latents: True Has VAE encoder features: True Data type: torch.float16 text_encoder-item0: clip_pooled_prompt_embeds: torch.Size([768]), torch.float16 clip_prompt_embeds: torch.Size([77, 768]), torch.float16 text_encoder_2-item1: clip_pooled_prompt_embeds: torch.Size([1280]), torch.float16 clip_prompt_embeds: torch.Size([77, 1280]), torch.float16 text_encoder_3-item2: t5_prompt_embeds: torch.Size([77, 4096]), torch.float16 text_encoder-item3: clip_pooled_prompt_embeds: torch.Size([768]), torch.float16 clip_prompt_embeds: torch.Size([77, 768]), torch.float16 text_encoder_2-item4: clip_pooled_prompt_embeds: torch.Size([1280]), torch.float16 clip_prompt_embeds: torch.Size([77, 1280]), torch.float16 text_encoder_3-item5: t5_prompt_embeds: torch.Size([77, 4096]), torch.float16 vae.decoder latents: torch.Size([16, 128, 128]) vae.encoder features: vae.encoder_f0: torch.Size([32, 128, 128]), torch.float16 vae.encoder_f1: torch.Size([32, 128, 128]), torch.float16 ------------------------------------------------------------ [2/4] FRONTEND: Pack + Encode (strategy: individual)... IndividualPacker: text_encoder-item0.clip_pooled_prompt_embeds: torch.Size([768])->torch.Size([1, 1, 1, 768]) text_encoder-item0.clip_prompt_embeds: torch.Size([77, 768])->torch.Size([1, 1, 77, 768]) text_encoder_2-item1.clip_pooled_prompt_embeds: torch.Size([1280])->torch.Size([1, 1, 1, 1280]) text_encoder_2-item1.clip_prompt_embeds: torch.Size([77, 1280])->torch.Size([1, 1, 77, 1280]) text_encoder_3-item2.t5_prompt_embeds: torch.Size([77, 4096])->torch.Size([1, 1, 77, 4096]) text_encoder-item3.clip_pooled_prompt_embeds: torch.Size([768])->torch.Size([1, 1, 1, 768]) text_encoder-item3.clip_prompt_embeds: torch.Size([77, 768])->torch.Size([1, 1, 77, 768]) text_encoder_2-item4.clip_pooled_prompt_embeds: torch.Size([1280])->torch.Size([1, 1, 1, 1280]) text_encoder_2-item4.clip_prompt_embeds: torch.Size([77, 1280])->torch.Size([1, 1, 77, 1280]) text_encoder_3-item5.t5_prompt_embeds: torch.Size([77, 4096])->torch.Size([1, 1, 77, 4096]) vae.encoder_f0: torch.Size([32, 128, 128])->torch.Size([1, 1, 512, 1024]) vae.encoder_f1: torch.Size([32, 128, 128])->torch.Size([1, 1, 512, 1024]) vae.decoder: torch.Size([16, 128, 128])->torch.Size([1, 1, 512, 512]) Using per-key quantization points (text_encoder-item0.clip_pooled_prompt_embeds: torch.Size([256])) for text_encoder-item0.clip_pooled_prompt_embeds text_encoder-item0.clip_pooled_prompt_embeds: 728B, BPFP=7.5833 Using per-key quantization points (text_encoder-item0.clip_prompt_embeds: torch.Size([256])) for text_encoder-item0.clip_prompt_embeds text_encoder-item0.clip_prompt_embeds: 6,176B, BPFP=0.8355 Using per-key quantization points (text_encoder_2-item1.clip_pooled_prompt_embeds: torch.Size([256])) for text_encoder_2-item1.clip_pooled_prompt_embeds text_encoder_2-item1.clip_pooled_prompt_embeds: 1,212B, BPFP=7.5750 Using per-key quantization points (text_encoder_2-item1.clip_prompt_embeds: torch.Size([256])) for text_encoder_2-item1.clip_prompt_embeds text_encoder_2-item1.clip_prompt_embeds: 11,020B, BPFP=0.8945 Using per-key quantization points (text_encoder_3-item2.t5_prompt_embeds: torch.Size([256])) for text_encoder_3-item2.t5_prompt_embeds text_encoder_3-item2.t5_prompt_embeds: 28,080B, BPFP=0.7123 Using per-key quantization points (text_encoder-item3.clip_pooled_prompt_embeds: torch.Size([256])) for text_encoder-item3.clip_pooled_prompt_embeds text_encoder-item3.clip_pooled_prompt_embeds: 760B, BPFP=7.9167 Using per-key quantization points (text_encoder-item3.clip_prompt_embeds: torch.Size([256])) for text_encoder-item3.clip_prompt_embeds text_encoder-item3.clip_prompt_embeds: 4,024B, BPFP=0.5444 Using per-key quantization points (text_encoder_2-item4.clip_pooled: torch.Size([256])) for text_encoder_2-item4.clip_pooled_prompt_embeds text_encoder_2-item4.clip_pooled_prompt_embeds: 1,268B, BPFP=7.9250 Using per-key quantization points (text_encoder_2-item4.clip_prompt: torch.Size([256])) for text_encoder_2-item4.clip_prompt_embeds text_encoder_2-item4.clip_prompt_embeds: 6,996B, BPFP=0.5679 Using per-key quantization points (text_encoder_3-item5.t5_prompt_embeds: torch.Size([256])) for text_encoder_3-item5.t5_prompt_embeds text_encoder_3-item5.t5_prompt_embeds: 18,416B, BPFP=0.4671 Using per-key quantization points (vae.encoder_f0: torch.Size([256])) for vae.encoder_f0 vae.encoder_f0: 16,876B, BPFP=0.2575 Using per-key quantization points (vae.encoder_f1: torch.Size([256])) for vae.encoder_f1 vae.encoder_f1: 16,876B, BPFP=0.2575 Using per-key quantization points (vae.decoder: torch.Size([256])) for vae.decoder vae.decoder: 8,384B, BPFP=0.2559 ⌛️ [2/4] FRONTEND: Frontend time: 0.297s (Pack+Encode) [3/4] BACKEND: Decode + Unpack... Using per-key quantization points (text_encoder-item0.clip_pooled_prompt_embeds: torch.Size([256])) for text_encoder-item0.clip_pooled_prompt_embeds Using per-key quantization points (text_encoder-item0.clip_prompt_embeds: torch.Size([256])) for text_encoder-item0.clip_prompt_embeds Using per-key quantization points (text_encoder_2-item1.clip_pooled_prompt_embeds: torch.Size([256])) for text_encoder_2-item1.clip_pooled_prompt_embeds Using per-key quantization points (text_encoder_2-item1.clip_prompt_embeds: torch.Size([256])) for text_encoder_2-item1.clip_prompt_embeds Using per-key quantization points (text_encoder_3-item2.t5_prompt_embeds: torch.Size([256])) for text_encoder_3-item2.t5_prompt_embeds Using per-key quantization points (text_encoder-item3.clip_pooled_prompt_embeds: torch.Size([256])) for text_encoder-item3.clip_pooled_prompt_embeds Using per-key quantization points (text_encoder-item3.clip_prompt_embeds: torch.Size([256])) for text_encoder-item3.clip_prompt_embeds Using per-key quantization points (text_encoder_2-item4.clip_pooled: torch.Size([256])) for text_encoder_2-item4.clip_pooled_prompt_embeds Using per-key quantization points (text_encoder_2-item4.clip_prompt: torch.Size([256])) for text_encoder_2-item4.clip_prompt_embeds Using per-key quantization points (text_encoder_3-item5.t5_prompt_embeds: torch.Size([256])) for text_encoder_3-item5.t5_prompt_embeds Using per-key quantization points (vae.encoder_f0: torch.Size([256])) for vae.encoder_f0 Using per-key quantization points (vae.encoder_f1: torch.Size([256])) for vae.encoder_f1 Using per-key quantization points (vae.decoder: torch.Size([256])) for vae.decoder IndividualUnPacker: text_encoder-item0.clip_pooled_prompt_embeds: torch.Size([768]) text_encoder-item0.clip_prompt_embeds: torch.Size([77, 768]) text_encoder_2-item1.clip_pooled_prompt_embeds: torch.Size([1280]) text_encoder_2-item1.clip_prompt_embeds: torch.Size([77, 1280]) text_encoder_3-item2.t5_prompt_embeds: torch.Size([77, 4096]) text_encoder-item3.clip_pooled_prompt_embeds: torch.Size([768]) text_encoder-item3.clip_prompt_embeds: torch.Size([77, 768]) text_encoder_2-item4.clip_pooled_prompt_embeds: torch.Size([1280]) text_encoder_2-item4.clip_prompt_embeds: torch.Size([77, 1280]) text_encoder_3-item5.t5_prompt_embeds: torch.Size([77, 4096]) vae.decoder: torch.Size([16, 128, 128]) vae.encoder_f0: torch.Size([32, 128, 128]) vae.encoder_f1: torch.Size([32, 128, 128]) ⌛️ [3/4] BACKEND: Backend time: 0.447s [4/4] METRICS: Computing MSE Breakdown... Using per-key quantization points (text_encoder-item0.clip_pooled_prompt_embeds: torch.Size([256])) for text_encoder-item0.clip_pooled_prompt_embeds Using per-key quantization points (text_encoder-item0.clip_prompt_embeds: torch.Size([256])) for text_encoder-item0.clip_prompt_embeds Using per-key quantization points (text_encoder_2-item1.clip_pooled_prompt_embeds: torch.Size([256])) for text_encoder_2-item1.clip_pooled_prompt_embeds Using per-key quantization points (text_encoder_2-item1.clip_prompt_embeds: torch.Size([256])) for text_encoder_2-item1.clip_prompt_embeds Using per-key quantization points (text_encoder_3-item2.t5_prompt_embeds: torch.Size([256])) for text_encoder_3-item2.t5_prompt_embeds Using per-key quantization points (text_encoder-item3.clip_pooled_prompt_embeds: torch.Size([256])) for text_encoder-item3.clip_pooled_prompt_embeds Using per-key quantization points (text_encoder-item3.clip_prompt_embeds: torch.Size([256])) for text_encoder-item3.clip_prompt_embeds Using per-key quantization points (text_encoder_2-item4.clip_pooled: torch.Size([256])) for text_encoder_2-item4.clip_pooled_prompt_embeds Using per-key quantization points (text_encoder_2-item4.clip_prompt: torch.Size([256])) for text_encoder_2-item4.clip_prompt_embeds Using per-key quantization points (text_encoder_3-item5.t5_prompt_embeds: torch.Size([256])) for text_encoder_3-item5.t5_prompt_embeds Using per-key quantization points (vae.encoder_f0: torch.Size([256])) for vae.encoder_f0 Using per-key quantization points (vae.encoder_f1: torch.Size([256])) for vae.encoder_f1 Using per-key quantization points (vae.decoder: torch.Size([256])) for vae.decoder IndividualUnPacker: text_encoder-item0.clip_pooled_prompt_embeds: torch.Size([768]) text_encoder-item0.clip_prompt_embeds: torch.Size([77, 768]) text_encoder_2-item1.clip_pooled_prompt_embeds: torch.Size([1280]) text_encoder_2-item1.clip_prompt_embeds: torch.Size([77, 1280]) text_encoder_3-item2.t5_prompt_embeds: torch.Size([77, 4096]) text_encoder-item3.clip_pooled_prompt_embeds: torch.Size([768]) text_encoder-item3.clip_prompt_embeds: torch.Size([77, 768]) text_encoder_2-item4.clip_pooled_prompt_embeds: torch.Size([1280]) text_encoder_2-item4.clip_prompt_embeds: torch.Size([77, 1280]) text_encoder_3-item5.t5_prompt_embeds: torch.Size([77, 4096]) vae.decoder: torch.Size([16, 128, 128]) vae.encoder_f0: torch.Size([32, 128, 128]) vae.encoder_f1: torch.Size([32, 128, 128]) Per-key MSE Breakdown (Quant=Truncation+Quantization, Codec=Compression): Key Quant-MSE Total-MSE ------------------------------------------------------------------------------------- text_encoder-item0.clip_pooled_prompt_embeds 0.00036729 0.96959186 text_encoder-item0.clip_prompt_embeds 0.00025217 23.94428732 text_encoder_2-item1.clip_pooled_prompt_embeds 0.00026091 1.03209095 text_encoder_2-item1.clip_prompt_embeds 0.00018200 0.13086605 text_encoder_3-item2.t5_prompt_embeds 0.00000809 0.00352439 text_encoder-item3.clip_pooled_prompt_embeds 0.00841799 1.01420029 text_encoder-item3.clip_prompt_embeds 0.00023247 23.90570887 text_encoder_2-item4.clip_pooled_prompt_embeds 0.00016751 0.67051430 text_encoder_2-item4.clip_prompt_embeds 0.00009830 0.47049568 text_encoder_3-item5.t5_prompt_embeds 0.00001263 0.00227117 vae.encoder_f0 0.00581597 1.85434699 vae.encoder_f1 0.00582356 1.85452151 vae.decoder 0.00019494 0.06740627 ------------------------------------------------------------------------------------- TOTAL 0.00275264 2.14801457 (elements=2,260,992) ---------------------- -------------------------------------------------------- SAMPLE-WISE STATISTICS ---------------------- -------------------------------------------------------- Handler sd35 Strategy individual Architecture hyperprior-featurecoding ---------------------- -------------------------------------------------------- Total Elements 2260992 Total Bytes 120816 BPFP 0.4275 bits/point EBPFP 0.8550 equivalent bits/point MSE 2.148015 ---------------------- -------------------------------------------------------- Time: 0.753s Load: 0.009s, Pack+Encode: 0.297s, Decode+Unpack: 0.447s ---------------------- -------------------------------------------------------- Restored Feature Format: [Dict] with 8 keys key['text_encoder-item0']: [Dict] with 2 keys key['clip_pooled_prompt_embeds']: [Tensor] shape=torch.Size([768]), dtype=torch.float16, device=cpu key['clip_prompt_embeds']: [Tensor] shape=torch.Size([77, 768]), dtype=torch.float16, device=cpu key['text_encoder_2-item1']: [Dict] with 2 keys key['clip_pooled_prompt_embeds']: [Tensor] shape=torch.Size([1280]), dtype=torch.float16, device=cpu key['clip_prompt_embeds']: [Tensor] shape=torch.Size([77, 1280]), dtype=torch.float16, device=cpu key['text_encoder_3-item2']: [Dict] with 1 keys key['t5_prompt_embeds']: [Tensor] shape=torch.Size([77, 4096]), dtype=torch.float16, device=cpu key['text_encoder-item3']: [Dict] with 2 keys key['clip_pooled_prompt_embeds']: [Tensor] shape=torch.Size([768]), dtype=torch.float16, device=cpu key['clip_prompt_embeds']: [Tensor] shape=torch.Size([77, 768]), dtype=torch.float16, device=cpu key['text_encoder_2-item4']: [Dict] with 2 keys key['clip_pooled_prompt_embeds']: [Tensor] shape=torch.Size([1280]), dtype=torch.float16, device=cpu key['clip_prompt_embeds']: [Tensor] shape=torch.Size([77, 1280]), dtype=torch.float16, device=cpu key['text_encoder_3-item5']: [Dict] with 1 keys key['t5_prompt_embeds']: [Tensor] shape=torch.Size([77, 4096]), dtype=torch.float16, device=cpu key['vae.decoder']: [Tensor] shape=torch.Size([16, 128, 128]), dtype=torch.float16, device=cpu key['vae.encoder']: [Dict] with 2 keys key['vae.encoder_f0']: [Tensor] shape=torch.Size([32, 128, 128]), dtype=torch.float16, device=cpu key['vae.encoder_f1']: [Tensor] shape=torch.Size([32, 128, 128]), dtype=torch.float16, device=cpu 💾 Converting with 2.1480 MSE: from ../datasets/FeatureCoding-StableDiffusion3.5/100samples_features/000000397303.zst to output-fixed/sd35/lambda0.004/hyperprior-featurecoding-8bit-individual/sd35cond/000000397303.zst 💪 Processing: ../datasets/FeatureCoding-StableDiffusion3.5/100samples_features/000000402473.zst (71/100) [1/4] FRONTEND: Loading features from ../datasets/FeatureCoding-StableDiffusion3.5/100samples_features/000000402473.zst... Original data structure: root: [Dict] with 8 keys key['text_encoder-item0']: [Dict] with 2 keys key['clip_pooled_prompt_embeds']: [Tensor] shape=torch.Size([768]), dtype=torch.float16, device=cpu key['clip_prompt_embeds']: [Tensor] shape=torch.Size([77, 768]), dtype=torch.float16, device=cpu key['text_encoder_2-item1']: [Dict] with 2 keys key['clip_pooled_prompt_embeds']: [Tensor] shape=torch.Size([1280]), dtype=torch.float16, device=cpu key['clip_prompt_embeds']: [Tensor] shape=torch.Size([77, 1280]), dtype=torch.float16, device=cpu key['text_encoder_3-item2']: [Dict] with 1 keys key['t5_prompt_embeds']: [Tensor] shape=torch.Size([77, 4096]), dtype=torch.float16, device=cpu key['text_encoder-item3']: [Dict] with 2 keys key['clip_pooled_prompt_embeds']: [Tensor] shape=torch.Size([768]), dtype=torch.float16, device=cpu key['clip_prompt_embeds']: [Tensor] shape=torch.Size([77, 768]), dtype=torch.float16, device=cpu key['text_encoder_2-item4']: [Dict] with 2 keys key['clip_pooled_prompt_embeds']: [Tensor] shape=torch.Size([1280]), dtype=torch.float16, device=cpu key['clip_prompt_embeds']: [Tensor] shape=torch.Size([77, 1280]), dtype=torch.float16, device=cpu key['text_encoder_3-item5']: [Dict] with 1 keys key['t5_prompt_embeds']: [Tensor] shape=torch.Size([77, 4096]), dtype=torch.float16, device=cpu key['vae.encoder-item6']: [Dict] with 2 keys key['vae.encoder_f0']: [Tensor] shape=torch.Size([32, 128, 128]), dtype=torch.float16, device=cpu key['vae.encoder_f1']: [Tensor] shape=torch.Size([32, 128, 128]), dtype=torch.float16, device=cpu key['vae.decoder']: [Tensor] shape=torch.Size([16, 128, 128]), dtype=torch.float16, device=cpu ⌛️ [1/4] FRONTEND: Load time: 0.008s ------------------------------------------------------------ SD3.5 Features Summary ------------------------------------------------------------ Number of text encoders: 3 Encoder names: ['text_encoder', 'text_encoder_2', 'text_encoder_3'] Has VAE latents: True Has VAE encoder features: True Data type: torch.float16 text_encoder-item0: clip_pooled_prompt_embeds: torch.Size([768]), torch.float16 clip_prompt_embeds: torch.Size([77, 768]), torch.float16 text_encoder_2-item1: clip_pooled_prompt_embeds: torch.Size([1280]), torch.float16 clip_prompt_embeds: torch.Size([77, 1280]), torch.float16 text_encoder_3-item2: t5_prompt_embeds: torch.Size([77, 4096]), torch.float16 text_encoder-item3: clip_pooled_prompt_embeds: torch.Size([768]), torch.float16 clip_prompt_embeds: torch.Size([77, 768]), torch.float16 text_encoder_2-item4: clip_pooled_prompt_embeds: torch.Size([1280]), torch.float16 clip_prompt_embeds: torch.Size([77, 1280]), torch.float16 text_encoder_3-item5: t5_prompt_embeds: torch.Size([77, 4096]), torch.float16 vae.decoder latents: torch.Size([16, 128, 128]) vae.encoder features: vae.encoder_f0: torch.Size([32, 128, 128]), torch.float16 vae.encoder_f1: torch.Size([32, 128, 128]), torch.float16 ------------------------------------------------------------ [2/4] FRONTEND: Pack + Encode (strategy: individual)... IndividualPacker: text_encoder-item0.clip_pooled_prompt_embeds: torch.Size([768])->torch.Size([1, 1, 1, 768]) text_encoder-item0.clip_prompt_embeds: torch.Size([77, 768])->torch.Size([1, 1, 77, 768]) text_encoder_2-item1.clip_pooled_prompt_embeds: torch.Size([1280])->torch.Size([1, 1, 1, 1280]) text_encoder_2-item1.clip_prompt_embeds: torch.Size([77, 1280])->torch.Size([1, 1, 77, 1280]) text_encoder_3-item2.t5_prompt_embeds: torch.Size([77, 4096])->torch.Size([1, 1, 77, 4096]) text_encoder-item3.clip_pooled_prompt_embeds: torch.Size([768])->torch.Size([1, 1, 1, 768]) text_encoder-item3.clip_prompt_embeds: torch.Size([77, 768])->torch.Size([1, 1, 77, 768]) text_encoder_2-item4.clip_pooled_prompt_embeds: torch.Size([1280])->torch.Size([1, 1, 1, 1280]) text_encoder_2-item4.clip_prompt_embeds: torch.Size([77, 1280])->torch.Size([1, 1, 77, 1280]) text_encoder_3-item5.t5_prompt_embeds: torch.Size([77, 4096])->torch.Size([1, 1, 77, 4096]) vae.encoder_f0: torch.Size([32, 128, 128])->torch.Size([1, 1, 512, 1024]) vae.encoder_f1: torch.Size([32, 128, 128])->torch.Size([1, 1, 512, 1024]) vae.decoder: torch.Size([16, 128, 128])->torch.Size([1, 1, 512, 512]) Using per-key quantization points (text_encoder-item0.clip_pooled_prompt_embeds: torch.Size([256])) for text_encoder-item0.clip_pooled_prompt_embeds text_encoder-item0.clip_pooled_prompt_embeds: 716B, BPFP=7.4583 Using per-key quantization points (text_encoder-item0.clip_prompt_embeds: torch.Size([256])) for text_encoder-item0.clip_prompt_embeds text_encoder-item0.clip_prompt_embeds: 5,412B, BPFP=0.7321 Using per-key quantization points (text_encoder_2-item1.clip_pooled_prompt_embeds: torch.Size([256])) for text_encoder_2-item1.clip_pooled_prompt_embeds text_encoder_2-item1.clip_pooled_prompt_embeds: 1,204B, BPFP=7.5250 Using per-key quantization points (text_encoder_2-item1.clip_prompt_embeds: torch.Size([256])) for text_encoder_2-item1.clip_prompt_embeds text_encoder_2-item1.clip_prompt_embeds: 9,532B, BPFP=0.7737 Using per-key quantization points (text_encoder_3-item2.t5_prompt_embeds: torch.Size([256])) for text_encoder_3-item2.t5_prompt_embeds text_encoder_3-item2.t5_prompt_embeds: 25,568B, BPFP=0.6485 Using per-key quantization points (text_encoder-item3.clip_pooled_prompt_embeds: torch.Size([256])) for text_encoder-item3.clip_pooled_prompt_embeds text_encoder-item3.clip_pooled_prompt_embeds: 760B, BPFP=7.9167 Using per-key quantization points (text_encoder-item3.clip_prompt_embeds: torch.Size([256])) for text_encoder-item3.clip_prompt_embeds text_encoder-item3.clip_prompt_embeds: 4,024B, BPFP=0.5444 Using per-key quantization points (text_encoder_2-item4.clip_pooled: torch.Size([256])) for text_encoder_2-item4.clip_pooled_prompt_embeds text_encoder_2-item4.clip_pooled_prompt_embeds: 1,268B, BPFP=7.9250 Using per-key quantization points (text_encoder_2-item4.clip_prompt: torch.Size([256])) for text_encoder_2-item4.clip_prompt_embeds text_encoder_2-item4.clip_prompt_embeds: 6,996B, BPFP=0.5679 Using per-key quantization points (text_encoder_3-item5.t5_prompt_embeds: torch.Size([256])) for text_encoder_3-item5.t5_prompt_embeds text_encoder_3-item5.t5_prompt_embeds: 18,416B, BPFP=0.4671 Using per-key quantization points (vae.encoder_f0: torch.Size([256])) for vae.encoder_f0 vae.encoder_f0: 10,984B, BPFP=0.1676 Using per-key quantization points (vae.encoder_f1: torch.Size([256])) for vae.encoder_f1 vae.encoder_f1: 10,984B, BPFP=0.1676 Using per-key quantization points (vae.decoder: torch.Size([256])) for vae.decoder vae.decoder: 5,652B, BPFP=0.1725 ⌛️ [2/4] FRONTEND: Frontend time: 0.296s (Pack+Encode) [3/4] BACKEND: Decode + Unpack... Using per-key quantization points (text_encoder-item0.clip_pooled_prompt_embeds: torch.Size([256])) for text_encoder-item0.clip_pooled_prompt_embeds Using per-key quantization points (text_encoder-item0.clip_prompt_embeds: torch.Size([256])) for text_encoder-item0.clip_prompt_embeds Using per-key quantization points (text_encoder_2-item1.clip_pooled_prompt_embeds: torch.Size([256])) for text_encoder_2-item1.clip_pooled_prompt_embeds Using per-key quantization points (text_encoder_2-item1.clip_prompt_embeds: torch.Size([256])) for text_encoder_2-item1.clip_prompt_embeds Using per-key quantization points (text_encoder_3-item2.t5_prompt_embeds: torch.Size([256])) for text_encoder_3-item2.t5_prompt_embeds Using per-key quantization points (text_encoder-item3.clip_pooled_prompt_embeds: torch.Size([256])) for text_encoder-item3.clip_pooled_prompt_embeds Using per-key quantization points (text_encoder-item3.clip_prompt_embeds: torch.Size([256])) for text_encoder-item3.clip_prompt_embeds Using per-key quantization points (text_encoder_2-item4.clip_pooled: torch.Size([256])) for text_encoder_2-item4.clip_pooled_prompt_embeds Using per-key quantization points (text_encoder_2-item4.clip_prompt: torch.Size([256])) for text_encoder_2-item4.clip_prompt_embeds Using per-key quantization points (text_encoder_3-item5.t5_prompt_embeds: torch.Size([256])) for text_encoder_3-item5.t5_prompt_embeds Using per-key quantization points (vae.encoder_f0: torch.Size([256])) for vae.encoder_f0 Using per-key quantization points (vae.encoder_f1: torch.Size([256])) for vae.encoder_f1 Using per-key quantization points (vae.decoder: torch.Size([256])) for vae.decoder IndividualUnPacker: text_encoder-item0.clip_pooled_prompt_embeds: torch.Size([768]) text_encoder-item0.clip_prompt_embeds: torch.Size([77, 768]) text_encoder_2-item1.clip_pooled_prompt_embeds: torch.Size([1280]) text_encoder_2-item1.clip_prompt_embeds: torch.Size([77, 1280]) text_encoder_3-item2.t5_prompt_embeds: torch.Size([77, 4096]) text_encoder-item3.clip_pooled_prompt_embeds: torch.Size([768]) text_encoder-item3.clip_prompt_embeds: torch.Size([77, 768]) text_encoder_2-item4.clip_pooled_prompt_embeds: torch.Size([1280]) text_encoder_2-item4.clip_prompt_embeds: torch.Size([77, 1280]) text_encoder_3-item5.t5_prompt_embeds: torch.Size([77, 4096]) vae.decoder: torch.Size([16, 128, 128]) vae.encoder_f0: torch.Size([32, 128, 128]) vae.encoder_f1: torch.Size([32, 128, 128]) ⌛️ [3/4] BACKEND: Backend time: 0.451s [4/4] METRICS: Computing MSE Breakdown... Using per-key quantization points (text_encoder-item0.clip_pooled_prompt_embeds: torch.Size([256])) for text_encoder-item0.clip_pooled_prompt_embeds Using per-key quantization points (text_encoder-item0.clip_prompt_embeds: torch.Size([256])) for text_encoder-item0.clip_prompt_embeds Using per-key quantization points (text_encoder_2-item1.clip_pooled_prompt_embeds: torch.Size([256])) for text_encoder_2-item1.clip_pooled_prompt_embeds Using per-key quantization points (text_encoder_2-item1.clip_prompt_embeds: torch.Size([256])) for text_encoder_2-item1.clip_prompt_embeds Using per-key quantization points (text_encoder_3-item2.t5_prompt_embeds: torch.Size([256])) for text_encoder_3-item2.t5_prompt_embeds Using per-key quantization points (text_encoder-item3.clip_pooled_prompt_embeds: torch.Size([256])) for text_encoder-item3.clip_pooled_prompt_embeds Using per-key quantization points (text_encoder-item3.clip_prompt_embeds: torch.Size([256])) for text_encoder-item3.clip_prompt_embeds Using per-key quantization points (text_encoder_2-item4.clip_pooled: torch.Size([256])) for text_encoder_2-item4.clip_pooled_prompt_embeds Using per-key quantization points (text_encoder_2-item4.clip_prompt: torch.Size([256])) for text_encoder_2-item4.clip_prompt_embeds Using per-key quantization points (text_encoder_3-item5.t5_prompt_embeds: torch.Size([256])) for text_encoder_3-item5.t5_prompt_embeds Using per-key quantization points (vae.encoder_f0: torch.Size([256])) for vae.encoder_f0 Using per-key quantization points (vae.encoder_f1: torch.Size([256])) for vae.encoder_f1 Using per-key quantization points (vae.decoder: torch.Size([256])) for vae.decoder IndividualUnPacker: text_encoder-item0.clip_pooled_prompt_embeds: torch.Size([768]) text_encoder-item0.clip_prompt_embeds: torch.Size([77, 768]) text_encoder_2-item1.clip_pooled_prompt_embeds: torch.Size([1280]) text_encoder_2-item1.clip_prompt_embeds: torch.Size([77, 1280]) text_encoder_3-item2.t5_prompt_embeds: torch.Size([77, 4096]) text_encoder-item3.clip_pooled_prompt_embeds: torch.Size([768]) text_encoder-item3.clip_prompt_embeds: torch.Size([77, 768]) text_encoder_2-item4.clip_pooled_prompt_embeds: torch.Size([1280]) text_encoder_2-item4.clip_prompt_embeds: torch.Size([77, 1280]) text_encoder_3-item5.t5_prompt_embeds: torch.Size([77, 4096]) vae.decoder: torch.Size([16, 128, 128]) vae.encoder_f0: torch.Size([32, 128, 128]) vae.encoder_f1: torch.Size([32, 128, 128]) Per-key MSE Breakdown (Quant=Truncation+Quantization, Codec=Compression): Key Quant-MSE Total-MSE ------------------------------------------------------------------------------------- text_encoder-item0.clip_pooled_prompt_embeds 0.00799810 0.99352590 text_encoder-item0.clip_prompt_embeds 0.00026975 23.94204038 text_encoder_2-item1.clip_pooled_prompt_embeds 0.00022593 0.94448957 text_encoder_2-item1.clip_prompt_embeds 0.00015480 0.11280751 text_encoder_3-item2.t5_prompt_embeds 0.00000862 0.00307184 text_encoder-item3.clip_pooled_prompt_embeds 0.00841799 1.01420029 text_encoder-item3.clip_prompt_embeds 0.00023247 23.90570887 text_encoder_2-item4.clip_pooled_prompt_embeds 0.00016751 0.67051430 text_encoder_2-item4.clip_prompt_embeds 0.00009830 0.47049568 text_encoder_3-item5.t5_prompt_embeds 0.00001263 0.00227117 vae.encoder_f0 1.11695218 6.05053043 vae.encoder_f1 1.11695278 6.04967594 vae.decoder 0.00019720 0.05043061 ------------------------------------------------------------------------------------- TOTAL 0.51806274 4.09091329 (elements=2,260,992) ---------------------- -------------------------------------------------------- SAMPLE-WISE STATISTICS ---------------------- -------------------------------------------------------- Handler sd35 Strategy individual Architecture hyperprior-featurecoding ---------------------- -------------------------------------------------------- Total Elements 2260992 Total Bytes 101516 BPFP 0.3592 bits/point EBPFP 0.7184 equivalent bits/point MSE 4.090913 ---------------------- -------------------------------------------------------- Time: 0.755s Load: 0.008s, Pack+Encode: 0.296s, Decode+Unpack: 0.451s ---------------------- -------------------------------------------------------- Restored Feature Format: [Dict] with 8 keys key['text_encoder-item0']: [Dict] with 2 keys key['clip_pooled_prompt_embeds']: [Tensor] shape=torch.Size([768]), dtype=torch.float16, device=cpu key['clip_prompt_embeds']: [Tensor] shape=torch.Size([77, 768]), dtype=torch.float16, device=cpu key['text_encoder_2-item1']: [Dict] with 2 keys key['clip_pooled_prompt_embeds']: [Tensor] shape=torch.Size([1280]), dtype=torch.float16, device=cpu key['clip_prompt_embeds']: [Tensor] shape=torch.Size([77, 1280]), dtype=torch.float16, device=cpu key['text_encoder_3-item2']: [Dict] with 1 keys key['t5_prompt_embeds']: [Tensor] shape=torch.Size([77, 4096]), dtype=torch.float16, device=cpu key['text_encoder-item3']: [Dict] with 2 keys key['clip_pooled_prompt_embeds']: [Tensor] shape=torch.Size([768]), dtype=torch.float16, device=cpu key['clip_prompt_embeds']: [Tensor] shape=torch.Size([77, 768]), dtype=torch.float16, device=cpu key['text_encoder_2-item4']: [Dict] with 2 keys key['clip_pooled_prompt_embeds']: [Tensor] shape=torch.Size([1280]), dtype=torch.float16, device=cpu key['clip_prompt_embeds']: [Tensor] shape=torch.Size([77, 1280]), dtype=torch.float16, device=cpu key['text_encoder_3-item5']: [Dict] with 1 keys key['t5_prompt_embeds']: [Tensor] shape=torch.Size([77, 4096]), dtype=torch.float16, device=cpu key['vae.decoder']: [Tensor] shape=torch.Size([16, 128, 128]), dtype=torch.float16, device=cpu key['vae.encoder']: [Dict] with 2 keys key['vae.encoder_f0']: [Tensor] shape=torch.Size([32, 128, 128]), dtype=torch.float16, device=cpu key['vae.encoder_f1']: [Tensor] shape=torch.Size([32, 128, 128]), dtype=torch.float16, device=cpu 💾 Converting with 4.0909 MSE: from ../datasets/FeatureCoding-StableDiffusion3.5/100samples_features/000000402473.zst to output-fixed/sd35/lambda0.004/hyperprior-featurecoding-8bit-individual/sd35cond/000000402473.zst 💪 Processing: ../datasets/FeatureCoding-StableDiffusion3.5/100samples_features/000000409211.zst (72/100) [1/4] FRONTEND: Loading features from ../datasets/FeatureCoding-StableDiffusion3.5/100samples_features/000000409211.zst... Original data structure: root: [Dict] with 8 keys key['text_encoder-item0']: [Dict] with 2 keys key['clip_pooled_prompt_embeds']: [Tensor] shape=torch.Size([768]), dtype=torch.float16, device=cpu key['clip_prompt_embeds']: [Tensor] shape=torch.Size([77, 768]), dtype=torch.float16, device=cpu key['text_encoder_2-item1']: [Dict] with 2 keys key['clip_pooled_prompt_embeds']: [Tensor] shape=torch.Size([1280]), dtype=torch.float16, device=cpu key['clip_prompt_embeds']: [Tensor] shape=torch.Size([77, 1280]), dtype=torch.float16, device=cpu key['text_encoder_3-item2']: [Dict] with 1 keys key['t5_prompt_embeds']: [Tensor] shape=torch.Size([77, 4096]), dtype=torch.float16, device=cpu key['text_encoder-item3']: [Dict] with 2 keys key['clip_pooled_prompt_embeds']: [Tensor] shape=torch.Size([768]), dtype=torch.float16, device=cpu key['clip_prompt_embeds']: [Tensor] shape=torch.Size([77, 768]), dtype=torch.float16, device=cpu key['text_encoder_2-item4']: [Dict] with 2 keys key['clip_pooled_prompt_embeds']: [Tensor] shape=torch.Size([1280]), dtype=torch.float16, device=cpu key['clip_prompt_embeds']: [Tensor] shape=torch.Size([77, 1280]), dtype=torch.float16, device=cpu key['text_encoder_3-item5']: [Dict] with 1 keys key['t5_prompt_embeds']: [Tensor] shape=torch.Size([77, 4096]), dtype=torch.float16, device=cpu key['vae.encoder-item6']: [Dict] with 2 keys key['vae.encoder_f0']: [Tensor] shape=torch.Size([32, 128, 128]), dtype=torch.float16, device=cpu key['vae.encoder_f1']: [Tensor] shape=torch.Size([32, 128, 128]), dtype=torch.float16, device=cpu key['vae.decoder']: [Tensor] shape=torch.Size([16, 128, 128]), dtype=torch.float16, device=cpu ⌛️ [1/4] FRONTEND: Load time: 0.009s ------------------------------------------------------------ SD3.5 Features Summary ------------------------------------------------------------ Number of text encoders: 3 Encoder names: ['text_encoder', 'text_encoder_2', 'text_encoder_3'] Has VAE latents: True Has VAE encoder features: True Data type: torch.float16 text_encoder-item0: clip_pooled_prompt_embeds: torch.Size([768]), torch.float16 clip_prompt_embeds: torch.Size([77, 768]), torch.float16 text_encoder_2-item1: clip_pooled_prompt_embeds: torch.Size([1280]), torch.float16 clip_prompt_embeds: torch.Size([77, 1280]), torch.float16 text_encoder_3-item2: t5_prompt_embeds: torch.Size([77, 4096]), torch.float16 text_encoder-item3: clip_pooled_prompt_embeds: torch.Size([768]), torch.float16 clip_prompt_embeds: torch.Size([77, 768]), torch.float16 text_encoder_2-item4: clip_pooled_prompt_embeds: torch.Size([1280]), torch.float16 clip_prompt_embeds: torch.Size([77, 1280]), torch.float16 text_encoder_3-item5: t5_prompt_embeds: torch.Size([77, 4096]), torch.float16 vae.decoder latents: torch.Size([16, 128, 128]) vae.encoder features: vae.encoder_f0: torch.Size([32, 128, 128]), torch.float16 vae.encoder_f1: torch.Size([32, 128, 128]), torch.float16 ------------------------------------------------------------ [2/4] FRONTEND: Pack + Encode (strategy: individual)... IndividualPacker: text_encoder-item0.clip_pooled_prompt_embeds: torch.Size([768])->torch.Size([1, 1, 1, 768]) text_encoder-item0.clip_prompt_embeds: torch.Size([77, 768])->torch.Size([1, 1, 77, 768]) text_encoder_2-item1.clip_pooled_prompt_embeds: torch.Size([1280])->torch.Size([1, 1, 1, 1280]) text_encoder_2-item1.clip_prompt_embeds: torch.Size([77, 1280])->torch.Size([1, 1, 77, 1280]) text_encoder_3-item2.t5_prompt_embeds: torch.Size([77, 4096])->torch.Size([1, 1, 77, 4096]) text_encoder-item3.clip_pooled_prompt_embeds: torch.Size([768])->torch.Size([1, 1, 1, 768]) text_encoder-item3.clip_prompt_embeds: torch.Size([77, 768])->torch.Size([1, 1, 77, 768]) text_encoder_2-item4.clip_pooled_prompt_embeds: torch.Size([1280])->torch.Size([1, 1, 1, 1280]) text_encoder_2-item4.clip_prompt_embeds: torch.Size([77, 1280])->torch.Size([1, 1, 77, 1280]) text_encoder_3-item5.t5_prompt_embeds: torch.Size([77, 4096])->torch.Size([1, 1, 77, 4096]) vae.encoder_f0: torch.Size([32, 128, 128])->torch.Size([1, 1, 512, 1024]) vae.encoder_f1: torch.Size([32, 128, 128])->torch.Size([1, 1, 512, 1024]) vae.decoder: torch.Size([16, 128, 128])->torch.Size([1, 1, 512, 512]) Using per-key quantization points (text_encoder-item0.clip_pooled_prompt_embeds: torch.Size([256])) for text_encoder-item0.clip_pooled_prompt_embeds text_encoder-item0.clip_pooled_prompt_embeds: 728B, BPFP=7.5833 Using per-key quantization points (text_encoder-item0.clip_prompt_embeds: torch.Size([256])) for text_encoder-item0.clip_prompt_embeds text_encoder-item0.clip_prompt_embeds: 6,320B, BPFP=0.8550 Using per-key quantization points (text_encoder_2-item1.clip_pooled_prompt_embeds: torch.Size([256])) for text_encoder_2-item1.clip_pooled_prompt_embeds text_encoder_2-item1.clip_pooled_prompt_embeds: 1,196B, BPFP=7.4750 Using per-key quantization points (text_encoder_2-item1.clip_prompt_embeds: torch.Size([256])) for text_encoder_2-item1.clip_prompt_embeds text_encoder_2-item1.clip_prompt_embeds: 11,284B, BPFP=0.9159 Using per-key quantization points (text_encoder_3-item2.t5_prompt_embeds: torch.Size([256])) for text_encoder_3-item2.t5_prompt_embeds text_encoder_3-item2.t5_prompt_embeds: 29,392B, BPFP=0.7455 Using per-key quantization points (text_encoder-item3.clip_pooled_prompt_embeds: torch.Size([256])) for text_encoder-item3.clip_pooled_prompt_embeds text_encoder-item3.clip_pooled_prompt_embeds: 760B, BPFP=7.9167 Using per-key quantization points (text_encoder-item3.clip_prompt_embeds: torch.Size([256])) for text_encoder-item3.clip_prompt_embeds text_encoder-item3.clip_prompt_embeds: 4,024B, BPFP=0.5444 Using per-key quantization points (text_encoder_2-item4.clip_pooled: torch.Size([256])) for text_encoder_2-item4.clip_pooled_prompt_embeds text_encoder_2-item4.clip_pooled_prompt_embeds: 1,268B, BPFP=7.9250 Using per-key quantization points (text_encoder_2-item4.clip_prompt: torch.Size([256])) for text_encoder_2-item4.clip_prompt_embeds text_encoder_2-item4.clip_prompt_embeds: 6,996B, BPFP=0.5679 Using per-key quantization points (text_encoder_3-item5.t5_prompt_embeds: torch.Size([256])) for text_encoder_3-item5.t5_prompt_embeds text_encoder_3-item5.t5_prompt_embeds: 18,416B, BPFP=0.4671 Using per-key quantization points (vae.encoder_f0: torch.Size([256])) for vae.encoder_f0 vae.encoder_f0: 18,448B, BPFP=0.2815 Using per-key quantization points (vae.encoder_f1: torch.Size([256])) for vae.encoder_f1 vae.encoder_f1: 18,444B, BPFP=0.2814 Using per-key quantization points (vae.decoder: torch.Size([256])) for vae.decoder vae.decoder: 7,904B, BPFP=0.2412 ⌛️ [2/4] FRONTEND: Frontend time: 0.298s (Pack+Encode) [3/4] BACKEND: Decode + Unpack... Using per-key quantization points (text_encoder-item0.clip_pooled_prompt_embeds: torch.Size([256])) for text_encoder-item0.clip_pooled_prompt_embeds Using per-key quantization points (text_encoder-item0.clip_prompt_embeds: torch.Size([256])) for text_encoder-item0.clip_prompt_embeds Using per-key quantization points (text_encoder_2-item1.clip_pooled_prompt_embeds: torch.Size([256])) for text_encoder_2-item1.clip_pooled_prompt_embeds Using per-key quantization points (text_encoder_2-item1.clip_prompt_embeds: torch.Size([256])) for text_encoder_2-item1.clip_prompt_embeds Using per-key quantization points (text_encoder_3-item2.t5_prompt_embeds: torch.Size([256])) for text_encoder_3-item2.t5_prompt_embeds Using per-key quantization points (text_encoder-item3.clip_pooled_prompt_embeds: torch.Size([256])) for text_encoder-item3.clip_pooled_prompt_embeds Using per-key quantization points (text_encoder-item3.clip_prompt_embeds: torch.Size([256])) for text_encoder-item3.clip_prompt_embeds Using per-key quantization points (text_encoder_2-item4.clip_pooled: torch.Size([256])) for text_encoder_2-item4.clip_pooled_prompt_embeds Using per-key quantization points (text_encoder_2-item4.clip_prompt: torch.Size([256])) for text_encoder_2-item4.clip_prompt_embeds Using per-key quantization points (text_encoder_3-item5.t5_prompt_embeds: torch.Size([256])) for text_encoder_3-item5.t5_prompt_embeds Using per-key quantization points (vae.encoder_f0: torch.Size([256])) for vae.encoder_f0 Using per-key quantization points (vae.encoder_f1: torch.Size([256])) for vae.encoder_f1 Using per-key quantization points (vae.decoder: torch.Size([256])) for vae.decoder IndividualUnPacker: text_encoder-item0.clip_pooled_prompt_embeds: torch.Size([768]) text_encoder-item0.clip_prompt_embeds: torch.Size([77, 768]) text_encoder_2-item1.clip_pooled_prompt_embeds: torch.Size([1280]) text_encoder_2-item1.clip_prompt_embeds: torch.Size([77, 1280]) text_encoder_3-item2.t5_prompt_embeds: torch.Size([77, 4096]) text_encoder-item3.clip_pooled_prompt_embeds: torch.Size([768]) text_encoder-item3.clip_prompt_embeds: torch.Size([77, 768]) text_encoder_2-item4.clip_pooled_prompt_embeds: torch.Size([1280]) text_encoder_2-item4.clip_prompt_embeds: torch.Size([77, 1280]) text_encoder_3-item5.t5_prompt_embeds: torch.Size([77, 4096]) vae.decoder: torch.Size([16, 128, 128]) vae.encoder_f0: torch.Size([32, 128, 128]) vae.encoder_f1: torch.Size([32, 128, 128]) ⌛️ [3/4] BACKEND: Backend time: 0.447s [4/4] METRICS: Computing MSE Breakdown... Using per-key quantization points (text_encoder-item0.clip_pooled_prompt_embeds: torch.Size([256])) for text_encoder-item0.clip_pooled_prompt_embeds Using per-key quantization points (text_encoder-item0.clip_prompt_embeds: torch.Size([256])) for text_encoder-item0.clip_prompt_embeds Using per-key quantization points (text_encoder_2-item1.clip_pooled_prompt_embeds: torch.Size([256])) for text_encoder_2-item1.clip_pooled_prompt_embeds Using per-key quantization points (text_encoder_2-item1.clip_prompt_embeds: torch.Size([256])) for text_encoder_2-item1.clip_prompt_embeds Using per-key quantization points (text_encoder_3-item2.t5_prompt_embeds: torch.Size([256])) for text_encoder_3-item2.t5_prompt_embeds Using per-key quantization points (text_encoder-item3.clip_pooled_prompt_embeds: torch.Size([256])) for text_encoder-item3.clip_pooled_prompt_embeds Using per-key quantization points (text_encoder-item3.clip_prompt_embeds: torch.Size([256])) for text_encoder-item3.clip_prompt_embeds Using per-key quantization points (text_encoder_2-item4.clip_pooled: torch.Size([256])) for text_encoder_2-item4.clip_pooled_prompt_embeds Using per-key quantization points (text_encoder_2-item4.clip_prompt: torch.Size([256])) for text_encoder_2-item4.clip_prompt_embeds Using per-key quantization points (text_encoder_3-item5.t5_prompt_embeds: torch.Size([256])) for text_encoder_3-item5.t5_prompt_embeds Using per-key quantization points (vae.encoder_f0: torch.Size([256])) for vae.encoder_f0 Using per-key quantization points (vae.encoder_f1: torch.Size([256])) for vae.encoder_f1 Using per-key quantization points (vae.decoder: torch.Size([256])) for vae.decoder IndividualUnPacker: text_encoder-item0.clip_pooled_prompt_embeds: torch.Size([768]) text_encoder-item0.clip_prompt_embeds: torch.Size([77, 768]) text_encoder_2-item1.clip_pooled_prompt_embeds: torch.Size([1280]) text_encoder_2-item1.clip_prompt_embeds: torch.Size([77, 1280]) text_encoder_3-item2.t5_prompt_embeds: torch.Size([77, 4096]) text_encoder-item3.clip_pooled_prompt_embeds: torch.Size([768]) text_encoder-item3.clip_prompt_embeds: torch.Size([77, 768]) text_encoder_2-item4.clip_pooled_prompt_embeds: torch.Size([1280]) text_encoder_2-item4.clip_prompt_embeds: torch.Size([77, 1280]) text_encoder_3-item5.t5_prompt_embeds: torch.Size([77, 4096]) vae.decoder: torch.Size([16, 128, 128]) vae.encoder_f0: torch.Size([32, 128, 128]) vae.encoder_f1: torch.Size([32, 128, 128]) Per-key MSE Breakdown (Quant=Truncation+Quantization, Codec=Compression): Key Quant-MSE Total-MSE ------------------------------------------------------------------------------------- text_encoder-item0.clip_pooled_prompt_embeds 0.00023525 0.94369245 text_encoder-item0.clip_prompt_embeds 0.00025545 23.94648987 text_encoder_2-item1.clip_pooled_prompt_embeds 0.00018422 0.82373486 text_encoder_2-item1.clip_prompt_embeds 0.00016916 0.11683742 text_encoder_3-item2.t5_prompt_embeds 0.00000823 0.00322552 text_encoder-item3.clip_pooled_prompt_embeds 0.00841799 1.01420029 text_encoder-item3.clip_prompt_embeds 0.00023247 23.90570887 text_encoder_2-item4.clip_pooled_prompt_embeds 0.00016751 0.67051430 text_encoder_2-item4.clip_prompt_embeds 0.00009830 0.47049568 text_encoder_3-item5.t5_prompt_embeds 0.00001263 0.00227117 vae.encoder_f0 0.01535016 3.52688074 vae.encoder_f1 0.01535382 3.52612185 vae.decoder 0.00021460 0.06178664 ------------------------------------------------------------------------------------- TOTAL 0.00717511 2.92209203 (elements=2,260,992) ---------------------- -------------------------------------------------------- SAMPLE-WISE STATISTICS ---------------------- -------------------------------------------------------- Handler sd35 Strategy individual Architecture hyperprior-featurecoding ---------------------- -------------------------------------------------------- Total Elements 2260992 Total Bytes 125180 BPFP 0.4429 bits/point EBPFP 0.8858 equivalent bits/point MSE 2.922092 ---------------------- -------------------------------------------------------- Time: 0.755s Load: 0.009s, Pack+Encode: 0.298s, Decode+Unpack: 0.447s ---------------------- -------------------------------------------------------- Restored Feature Format: [Dict] with 8 keys key['text_encoder-item0']: [Dict] with 2 keys key['clip_pooled_prompt_embeds']: [Tensor] shape=torch.Size([768]), dtype=torch.float16, device=cpu key['clip_prompt_embeds']: [Tensor] shape=torch.Size([77, 768]), dtype=torch.float16, device=cpu key['text_encoder_2-item1']: [Dict] with 2 keys key['clip_pooled_prompt_embeds']: [Tensor] shape=torch.Size([1280]), dtype=torch.float16, device=cpu key['clip_prompt_embeds']: [Tensor] shape=torch.Size([77, 1280]), dtype=torch.float16, device=cpu key['text_encoder_3-item2']: [Dict] with 1 keys key['t5_prompt_embeds']: [Tensor] shape=torch.Size([77, 4096]), dtype=torch.float16, device=cpu key['text_encoder-item3']: [Dict] with 2 keys key['clip_pooled_prompt_embeds']: [Tensor] shape=torch.Size([768]), dtype=torch.float16, device=cpu key['clip_prompt_embeds']: [Tensor] shape=torch.Size([77, 768]), dtype=torch.float16, device=cpu key['text_encoder_2-item4']: [Dict] with 2 keys key['clip_pooled_prompt_embeds']: [Tensor] shape=torch.Size([1280]), dtype=torch.float16, device=cpu key['clip_prompt_embeds']: [Tensor] shape=torch.Size([77, 1280]), dtype=torch.float16, device=cpu key['text_encoder_3-item5']: [Dict] with 1 keys key['t5_prompt_embeds']: [Tensor] shape=torch.Size([77, 4096]), dtype=torch.float16, device=cpu key['vae.decoder']: [Tensor] shape=torch.Size([16, 128, 128]), dtype=torch.float16, device=cpu key['vae.encoder']: [Dict] with 2 keys key['vae.encoder_f0']: [Tensor] shape=torch.Size([32, 128, 128]), dtype=torch.float16, device=cpu key['vae.encoder_f1']: [Tensor] shape=torch.Size([32, 128, 128]), dtype=torch.float16, device=cpu 💾 Converting with 2.9221 MSE: from ../datasets/FeatureCoding-StableDiffusion3.5/100samples_features/000000409211.zst to output-fixed/sd35/lambda0.004/hyperprior-featurecoding-8bit-individual/sd35cond/000000409211.zst 💪 Processing: ../datasets/FeatureCoding-StableDiffusion3.5/100samples_features/000000427500.zst (73/100) [1/4] FRONTEND: Loading features from ../datasets/FeatureCoding-StableDiffusion3.5/100samples_features/000000427500.zst... Original data structure: root: [Dict] with 8 keys key['text_encoder-item0']: [Dict] with 2 keys key['clip_pooled_prompt_embeds']: [Tensor] shape=torch.Size([768]), dtype=torch.float16, device=cpu key['clip_prompt_embeds']: [Tensor] shape=torch.Size([77, 768]), dtype=torch.float16, device=cpu key['text_encoder_2-item1']: [Dict] with 2 keys key['clip_pooled_prompt_embeds']: [Tensor] shape=torch.Size([1280]), dtype=torch.float16, device=cpu key['clip_prompt_embeds']: [Tensor] shape=torch.Size([77, 1280]), dtype=torch.float16, device=cpu key['text_encoder_3-item2']: [Dict] with 1 keys key['t5_prompt_embeds']: [Tensor] shape=torch.Size([77, 4096]), dtype=torch.float16, device=cpu key['text_encoder-item3']: [Dict] with 2 keys key['clip_pooled_prompt_embeds']: [Tensor] shape=torch.Size([768]), dtype=torch.float16, device=cpu key['clip_prompt_embeds']: [Tensor] shape=torch.Size([77, 768]), dtype=torch.float16, device=cpu key['text_encoder_2-item4']: [Dict] with 2 keys key['clip_pooled_prompt_embeds']: [Tensor] shape=torch.Size([1280]), dtype=torch.float16, device=cpu key['clip_prompt_embeds']: [Tensor] shape=torch.Size([77, 1280]), dtype=torch.float16, device=cpu key['text_encoder_3-item5']: [Dict] with 1 keys key['t5_prompt_embeds']: [Tensor] shape=torch.Size([77, 4096]), dtype=torch.float16, device=cpu key['vae.encoder-item6']: [Dict] with 2 keys key['vae.encoder_f0']: [Tensor] shape=torch.Size([32, 128, 128]), dtype=torch.float16, device=cpu key['vae.encoder_f1']: [Tensor] shape=torch.Size([32, 128, 128]), dtype=torch.float16, device=cpu key['vae.decoder']: [Tensor] shape=torch.Size([16, 128, 128]), dtype=torch.float16, device=cpu ⌛️ [1/4] FRONTEND: Load time: 0.008s ------------------------------------------------------------ SD3.5 Features Summary ------------------------------------------------------------ Number of text encoders: 3 Encoder names: ['text_encoder', 'text_encoder_2', 'text_encoder_3'] Has VAE latents: True Has VAE encoder features: True Data type: torch.float16 text_encoder-item0: clip_pooled_prompt_embeds: torch.Size([768]), torch.float16 clip_prompt_embeds: torch.Size([77, 768]), torch.float16 text_encoder_2-item1: clip_pooled_prompt_embeds: torch.Size([1280]), torch.float16 clip_prompt_embeds: torch.Size([77, 1280]), torch.float16 text_encoder_3-item2: t5_prompt_embeds: torch.Size([77, 4096]), torch.float16 text_encoder-item3: clip_pooled_prompt_embeds: torch.Size([768]), torch.float16 clip_prompt_embeds: torch.Size([77, 768]), torch.float16 text_encoder_2-item4: clip_pooled_prompt_embeds: torch.Size([1280]), torch.float16 clip_prompt_embeds: torch.Size([77, 1280]), torch.float16 text_encoder_3-item5: t5_prompt_embeds: torch.Size([77, 4096]), torch.float16 vae.decoder latents: torch.Size([16, 128, 128]) vae.encoder features: vae.encoder_f0: torch.Size([32, 128, 128]), torch.float16 vae.encoder_f1: torch.Size([32, 128, 128]), torch.float16 ------------------------------------------------------------ [2/4] FRONTEND: Pack + Encode (strategy: individual)... IndividualPacker: text_encoder-item0.clip_pooled_prompt_embeds: torch.Size([768])->torch.Size([1, 1, 1, 768]) text_encoder-item0.clip_prompt_embeds: torch.Size([77, 768])->torch.Size([1, 1, 77, 768]) text_encoder_2-item1.clip_pooled_prompt_embeds: torch.Size([1280])->torch.Size([1, 1, 1, 1280]) text_encoder_2-item1.clip_prompt_embeds: torch.Size([77, 1280])->torch.Size([1, 1, 77, 1280]) text_encoder_3-item2.t5_prompt_embeds: torch.Size([77, 4096])->torch.Size([1, 1, 77, 4096]) text_encoder-item3.clip_pooled_prompt_embeds: torch.Size([768])->torch.Size([1, 1, 1, 768]) text_encoder-item3.clip_prompt_embeds: torch.Size([77, 768])->torch.Size([1, 1, 77, 768]) text_encoder_2-item4.clip_pooled_prompt_embeds: torch.Size([1280])->torch.Size([1, 1, 1, 1280]) text_encoder_2-item4.clip_prompt_embeds: torch.Size([77, 1280])->torch.Size([1, 1, 77, 1280]) text_encoder_3-item5.t5_prompt_embeds: torch.Size([77, 4096])->torch.Size([1, 1, 77, 4096]) vae.encoder_f0: torch.Size([32, 128, 128])->torch.Size([1, 1, 512, 1024]) vae.encoder_f1: torch.Size([32, 128, 128])->torch.Size([1, 1, 512, 1024]) vae.decoder: torch.Size([16, 128, 128])->torch.Size([1, 1, 512, 512]) Using per-key quantization points (text_encoder-item0.clip_pooled_prompt_embeds: torch.Size([256])) for text_encoder-item0.clip_pooled_prompt_embeds text_encoder-item0.clip_pooled_prompt_embeds: 736B, BPFP=7.6667 Using per-key quantization points (text_encoder-item0.clip_prompt_embeds: torch.Size([256])) for text_encoder-item0.clip_prompt_embeds text_encoder-item0.clip_prompt_embeds: 6,804B, BPFP=0.9205 Using per-key quantization points (text_encoder_2-item1.clip_pooled_prompt_embeds: torch.Size([256])) for text_encoder_2-item1.clip_pooled_prompt_embeds text_encoder_2-item1.clip_pooled_prompt_embeds: 1,204B, BPFP=7.5250 Using per-key quantization points (text_encoder_2-item1.clip_prompt_embeds: torch.Size([256])) for text_encoder_2-item1.clip_prompt_embeds text_encoder_2-item1.clip_prompt_embeds: 11,060B, BPFP=0.8977 Using per-key quantization points (text_encoder_3-item2.t5_prompt_embeds: torch.Size([256])) for text_encoder_3-item2.t5_prompt_embeds text_encoder_3-item2.t5_prompt_embeds: 28,000B, BPFP=0.7102 Using per-key quantization points (text_encoder-item3.clip_pooled_prompt_embeds: torch.Size([256])) for text_encoder-item3.clip_pooled_prompt_embeds text_encoder-item3.clip_pooled_prompt_embeds: 760B, BPFP=7.9167 Using per-key quantization points (text_encoder-item3.clip_prompt_embeds: torch.Size([256])) for text_encoder-item3.clip_prompt_embeds text_encoder-item3.clip_prompt_embeds: 4,024B, BPFP=0.5444 Using per-key quantization points (text_encoder_2-item4.clip_pooled: torch.Size([256])) for text_encoder_2-item4.clip_pooled_prompt_embeds text_encoder_2-item4.clip_pooled_prompt_embeds: 1,268B, BPFP=7.9250 Using per-key quantization points (text_encoder_2-item4.clip_prompt: torch.Size([256])) for text_encoder_2-item4.clip_prompt_embeds text_encoder_2-item4.clip_prompt_embeds: 6,996B, BPFP=0.5679 Using per-key quantization points (text_encoder_3-item5.t5_prompt_embeds: torch.Size([256])) for text_encoder_3-item5.t5_prompt_embeds text_encoder_3-item5.t5_prompt_embeds: 18,416B, BPFP=0.4671 Using per-key quantization points (vae.encoder_f0: torch.Size([256])) for vae.encoder_f0 vae.encoder_f0: 13,836B, BPFP=0.2111 Using per-key quantization points (vae.encoder_f1: torch.Size([256])) for vae.encoder_f1 vae.encoder_f1: 13,836B, BPFP=0.2111 Using per-key quantization points (vae.decoder: torch.Size([256])) for vae.decoder vae.decoder: 9,608B, BPFP=0.2932 ⌛️ [2/4] FRONTEND: Frontend time: 0.292s (Pack+Encode) [3/4] BACKEND: Decode + Unpack... Using per-key quantization points (text_encoder-item0.clip_pooled_prompt_embeds: torch.Size([256])) for text_encoder-item0.clip_pooled_prompt_embeds Using per-key quantization points (text_encoder-item0.clip_prompt_embeds: torch.Size([256])) for text_encoder-item0.clip_prompt_embeds Using per-key quantization points (text_encoder_2-item1.clip_pooled_prompt_embeds: torch.Size([256])) for text_encoder_2-item1.clip_pooled_prompt_embeds Using per-key quantization points (text_encoder_2-item1.clip_prompt_embeds: torch.Size([256])) for text_encoder_2-item1.clip_prompt_embeds Using per-key quantization points (text_encoder_3-item2.t5_prompt_embeds: torch.Size([256])) for text_encoder_3-item2.t5_prompt_embeds Using per-key quantization points (text_encoder-item3.clip_pooled_prompt_embeds: torch.Size([256])) for text_encoder-item3.clip_pooled_prompt_embeds Using per-key quantization points (text_encoder-item3.clip_prompt_embeds: torch.Size([256])) for text_encoder-item3.clip_prompt_embeds Using per-key quantization points (text_encoder_2-item4.clip_pooled: torch.Size([256])) for text_encoder_2-item4.clip_pooled_prompt_embeds Using per-key quantization points (text_encoder_2-item4.clip_prompt: torch.Size([256])) for text_encoder_2-item4.clip_prompt_embeds Using per-key quantization points (text_encoder_3-item5.t5_prompt_embeds: torch.Size([256])) for text_encoder_3-item5.t5_prompt_embeds Using per-key quantization points (vae.encoder_f0: torch.Size([256])) for vae.encoder_f0 Using per-key quantization points (vae.encoder_f1: torch.Size([256])) for vae.encoder_f1 Using per-key quantization points (vae.decoder: torch.Size([256])) for vae.decoder IndividualUnPacker: text_encoder-item0.clip_pooled_prompt_embeds: torch.Size([768]) text_encoder-item0.clip_prompt_embeds: torch.Size([77, 768]) text_encoder_2-item1.clip_pooled_prompt_embeds: torch.Size([1280]) text_encoder_2-item1.clip_prompt_embeds: torch.Size([77, 1280]) text_encoder_3-item2.t5_prompt_embeds: torch.Size([77, 4096]) text_encoder-item3.clip_pooled_prompt_embeds: torch.Size([768]) text_encoder-item3.clip_prompt_embeds: torch.Size([77, 768]) text_encoder_2-item4.clip_pooled_prompt_embeds: torch.Size([1280]) text_encoder_2-item4.clip_prompt_embeds: torch.Size([77, 1280]) text_encoder_3-item5.t5_prompt_embeds: torch.Size([77, 4096]) vae.decoder: torch.Size([16, 128, 128]) vae.encoder_f0: torch.Size([32, 128, 128]) vae.encoder_f1: torch.Size([32, 128, 128]) ⌛️ [3/4] BACKEND: Backend time: 0.446s [4/4] METRICS: Computing MSE Breakdown... Using per-key quantization points (text_encoder-item0.clip_pooled_prompt_embeds: torch.Size([256])) for text_encoder-item0.clip_pooled_prompt_embeds Using per-key quantization points (text_encoder-item0.clip_prompt_embeds: torch.Size([256])) for text_encoder-item0.clip_prompt_embeds Using per-key quantization points (text_encoder_2-item1.clip_pooled_prompt_embeds: torch.Size([256])) for text_encoder_2-item1.clip_pooled_prompt_embeds Using per-key quantization points (text_encoder_2-item1.clip_prompt_embeds: torch.Size([256])) for text_encoder_2-item1.clip_prompt_embeds Using per-key quantization points (text_encoder_3-item2.t5_prompt_embeds: torch.Size([256])) for text_encoder_3-item2.t5_prompt_embeds Using per-key quantization points (text_encoder-item3.clip_pooled_prompt_embeds: torch.Size([256])) for text_encoder-item3.clip_pooled_prompt_embeds Using per-key quantization points (text_encoder-item3.clip_prompt_embeds: torch.Size([256])) for text_encoder-item3.clip_prompt_embeds Using per-key quantization points (text_encoder_2-item4.clip_pooled: torch.Size([256])) for text_encoder_2-item4.clip_pooled_prompt_embeds Using per-key quantization points (text_encoder_2-item4.clip_prompt: torch.Size([256])) for text_encoder_2-item4.clip_prompt_embeds Using per-key quantization points (text_encoder_3-item5.t5_prompt_embeds: torch.Size([256])) for text_encoder_3-item5.t5_prompt_embeds Using per-key quantization points (vae.encoder_f0: torch.Size([256])) for vae.encoder_f0 Using per-key quantization points (vae.encoder_f1: torch.Size([256])) for vae.encoder_f1 Using per-key quantization points (vae.decoder: torch.Size([256])) for vae.decoder IndividualUnPacker: text_encoder-item0.clip_pooled_prompt_embeds: torch.Size([768]) text_encoder-item0.clip_prompt_embeds: torch.Size([77, 768]) text_encoder_2-item1.clip_pooled_prompt_embeds: torch.Size([1280]) text_encoder_2-item1.clip_prompt_embeds: torch.Size([77, 1280]) text_encoder_3-item2.t5_prompt_embeds: torch.Size([77, 4096]) text_encoder-item3.clip_pooled_prompt_embeds: torch.Size([768]) text_encoder-item3.clip_prompt_embeds: torch.Size([77, 768]) text_encoder_2-item4.clip_pooled_prompt_embeds: torch.Size([1280]) text_encoder_2-item4.clip_prompt_embeds: torch.Size([77, 1280]) text_encoder_3-item5.t5_prompt_embeds: torch.Size([77, 4096]) vae.decoder: torch.Size([16, 128, 128]) vae.encoder_f0: torch.Size([32, 128, 128]) vae.encoder_f1: torch.Size([32, 128, 128]) Per-key MSE Breakdown (Quant=Truncation+Quantization, Codec=Compression): Key Quant-MSE Total-MSE ------------------------------------------------------------------------------------- text_encoder-item0.clip_pooled_prompt_embeds 0.00020648 0.95495764 text_encoder-item0.clip_prompt_embeds 0.00022628 23.95352028 text_encoder_2-item1.clip_pooled_prompt_embeds 0.00027089 0.94877110 text_encoder_2-item1.clip_prompt_embeds 0.00017658 0.13721140 text_encoder_3-item2.t5_prompt_embeds 0.00000761 0.00368198 text_encoder-item3.clip_pooled_prompt_embeds 0.00841799 1.01420029 text_encoder-item3.clip_prompt_embeds 0.00023247 23.90570887 text_encoder_2-item4.clip_pooled_prompt_embeds 0.00016751 0.67051430 text_encoder_2-item4.clip_prompt_embeds 0.00009830 0.47049568 text_encoder_3-item5.t5_prompt_embeds 0.00001263 0.00227117 vae.encoder_f0 0.00589589 1.63149714 vae.encoder_f1 0.00590398 1.63158572 vae.decoder 0.00017838 0.08846619 ------------------------------------------------------------------------------------- TOTAL 0.00278687 2.04757364 (elements=2,260,992) ---------------------- -------------------------------------------------------- SAMPLE-WISE STATISTICS ---------------------- -------------------------------------------------------- Handler sd35 Strategy individual Architecture hyperprior-featurecoding ---------------------- -------------------------------------------------------- Total Elements 2260992 Total Bytes 116548 BPFP 0.4124 bits/point EBPFP 0.8248 equivalent bits/point MSE 2.047574 ---------------------- -------------------------------------------------------- Time: 0.747s Load: 0.008s, Pack+Encode: 0.292s, Decode+Unpack: 0.446s ---------------------- -------------------------------------------------------- Restored Feature Format: [Dict] with 8 keys key['text_encoder-item0']: [Dict] with 2 keys key['clip_pooled_prompt_embeds']: [Tensor] shape=torch.Size([768]), dtype=torch.float16, device=cpu key['clip_prompt_embeds']: [Tensor] shape=torch.Size([77, 768]), dtype=torch.float16, device=cpu key['text_encoder_2-item1']: [Dict] with 2 keys key['clip_pooled_prompt_embeds']: [Tensor] shape=torch.Size([1280]), dtype=torch.float16, device=cpu key['clip_prompt_embeds']: [Tensor] shape=torch.Size([77, 1280]), dtype=torch.float16, device=cpu key['text_encoder_3-item2']: [Dict] with 1 keys key['t5_prompt_embeds']: [Tensor] shape=torch.Size([77, 4096]), dtype=torch.float16, device=cpu key['text_encoder-item3']: [Dict] with 2 keys key['clip_pooled_prompt_embeds']: [Tensor] shape=torch.Size([768]), dtype=torch.float16, device=cpu key['clip_prompt_embeds']: [Tensor] shape=torch.Size([77, 768]), dtype=torch.float16, device=cpu key['text_encoder_2-item4']: [Dict] with 2 keys key['clip_pooled_prompt_embeds']: [Tensor] shape=torch.Size([1280]), dtype=torch.float16, device=cpu key['clip_prompt_embeds']: [Tensor] shape=torch.Size([77, 1280]), dtype=torch.float16, device=cpu key['text_encoder_3-item5']: [Dict] with 1 keys key['t5_prompt_embeds']: [Tensor] shape=torch.Size([77, 4096]), dtype=torch.float16, device=cpu key['vae.decoder']: [Tensor] shape=torch.Size([16, 128, 128]), dtype=torch.float16, device=cpu key['vae.encoder']: [Dict] with 2 keys key['vae.encoder_f0']: [Tensor] shape=torch.Size([32, 128, 128]), dtype=torch.float16, device=cpu key['vae.encoder_f1']: [Tensor] shape=torch.Size([32, 128, 128]), dtype=torch.float16, device=cpu 💾 Converting with 2.0476 MSE: from ../datasets/FeatureCoding-StableDiffusion3.5/100samples_features/000000427500.zst to output-fixed/sd35/lambda0.004/hyperprior-featurecoding-8bit-individual/sd35cond/000000427500.zst 💪 Processing: ../datasets/FeatureCoding-StableDiffusion3.5/100samples_features/000000435208.zst (74/100) [1/4] FRONTEND: Loading features from ../datasets/FeatureCoding-StableDiffusion3.5/100samples_features/000000435208.zst... Original data structure: root: [Dict] with 8 keys key['text_encoder-item0']: [Dict] with 2 keys key['clip_pooled_prompt_embeds']: [Tensor] shape=torch.Size([768]), dtype=torch.float16, device=cpu key['clip_prompt_embeds']: [Tensor] shape=torch.Size([77, 768]), dtype=torch.float16, device=cpu key['text_encoder_2-item1']: [Dict] with 2 keys key['clip_pooled_prompt_embeds']: [Tensor] shape=torch.Size([1280]), dtype=torch.float16, device=cpu key['clip_prompt_embeds']: [Tensor] shape=torch.Size([77, 1280]), dtype=torch.float16, device=cpu key['text_encoder_3-item2']: [Dict] with 1 keys key['t5_prompt_embeds']: [Tensor] shape=torch.Size([77, 4096]), dtype=torch.float16, device=cpu key['text_encoder-item3']: [Dict] with 2 keys key['clip_pooled_prompt_embeds']: [Tensor] shape=torch.Size([768]), dtype=torch.float16, device=cpu key['clip_prompt_embeds']: [Tensor] shape=torch.Size([77, 768]), dtype=torch.float16, device=cpu key['text_encoder_2-item4']: [Dict] with 2 keys key['clip_pooled_prompt_embeds']: [Tensor] shape=torch.Size([1280]), dtype=torch.float16, device=cpu key['clip_prompt_embeds']: [Tensor] shape=torch.Size([77, 1280]), dtype=torch.float16, device=cpu key['text_encoder_3-item5']: [Dict] with 1 keys key['t5_prompt_embeds']: [Tensor] shape=torch.Size([77, 4096]), dtype=torch.float16, device=cpu key['vae.encoder-item6']: [Dict] with 2 keys key['vae.encoder_f0']: [Tensor] shape=torch.Size([32, 128, 128]), dtype=torch.float16, device=cpu key['vae.encoder_f1']: [Tensor] shape=torch.Size([32, 128, 128]), dtype=torch.float16, device=cpu key['vae.decoder']: [Tensor] shape=torch.Size([16, 128, 128]), dtype=torch.float16, device=cpu ⌛️ [1/4] FRONTEND: Load time: 0.009s ------------------------------------------------------------ SD3.5 Features Summary ------------------------------------------------------------ Number of text encoders: 3 Encoder names: ['text_encoder', 'text_encoder_2', 'text_encoder_3'] Has VAE latents: True Has VAE encoder features: True Data type: torch.float16 text_encoder-item0: clip_pooled_prompt_embeds: torch.Size([768]), torch.float16 clip_prompt_embeds: torch.Size([77, 768]), torch.float16 text_encoder_2-item1: clip_pooled_prompt_embeds: torch.Size([1280]), torch.float16 clip_prompt_embeds: torch.Size([77, 1280]), torch.float16 text_encoder_3-item2: t5_prompt_embeds: torch.Size([77, 4096]), torch.float16 text_encoder-item3: clip_pooled_prompt_embeds: torch.Size([768]), torch.float16 clip_prompt_embeds: torch.Size([77, 768]), torch.float16 text_encoder_2-item4: clip_pooled_prompt_embeds: torch.Size([1280]), torch.float16 clip_prompt_embeds: torch.Size([77, 1280]), torch.float16 text_encoder_3-item5: t5_prompt_embeds: torch.Size([77, 4096]), torch.float16 vae.decoder latents: torch.Size([16, 128, 128]) vae.encoder features: vae.encoder_f0: torch.Size([32, 128, 128]), torch.float16 vae.encoder_f1: torch.Size([32, 128, 128]), torch.float16 ------------------------------------------------------------ [2/4] FRONTEND: Pack + Encode (strategy: individual)... IndividualPacker: text_encoder-item0.clip_pooled_prompt_embeds: torch.Size([768])->torch.Size([1, 1, 1, 768]) text_encoder-item0.clip_prompt_embeds: torch.Size([77, 768])->torch.Size([1, 1, 77, 768]) text_encoder_2-item1.clip_pooled_prompt_embeds: torch.Size([1280])->torch.Size([1, 1, 1, 1280]) text_encoder_2-item1.clip_prompt_embeds: torch.Size([77, 1280])->torch.Size([1, 1, 77, 1280]) text_encoder_3-item2.t5_prompt_embeds: torch.Size([77, 4096])->torch.Size([1, 1, 77, 4096]) text_encoder-item3.clip_pooled_prompt_embeds: torch.Size([768])->torch.Size([1, 1, 1, 768]) text_encoder-item3.clip_prompt_embeds: torch.Size([77, 768])->torch.Size([1, 1, 77, 768]) text_encoder_2-item4.clip_pooled_prompt_embeds: torch.Size([1280])->torch.Size([1, 1, 1, 1280]) text_encoder_2-item4.clip_prompt_embeds: torch.Size([77, 1280])->torch.Size([1, 1, 77, 1280]) text_encoder_3-item5.t5_prompt_embeds: torch.Size([77, 4096])->torch.Size([1, 1, 77, 4096]) vae.encoder_f0: torch.Size([32, 128, 128])->torch.Size([1, 1, 512, 1024]) vae.encoder_f1: torch.Size([32, 128, 128])->torch.Size([1, 1, 512, 1024]) vae.decoder: torch.Size([16, 128, 128])->torch.Size([1, 1, 512, 512]) Using per-key quantization points (text_encoder-item0.clip_pooled_prompt_embeds: torch.Size([256])) for text_encoder-item0.clip_pooled_prompt_embeds text_encoder-item0.clip_pooled_prompt_embeds: 732B, BPFP=7.6250 Using per-key quantization points (text_encoder-item0.clip_prompt_embeds: torch.Size([256])) for text_encoder-item0.clip_prompt_embeds text_encoder-item0.clip_prompt_embeds: 6,092B, BPFP=0.8241 Using per-key quantization points (text_encoder_2-item1.clip_pooled_prompt_embeds: torch.Size([256])) for text_encoder_2-item1.clip_pooled_prompt_embeds text_encoder_2-item1.clip_pooled_prompt_embeds: 1,188B, BPFP=7.4250 Using per-key quantization points (text_encoder_2-item1.clip_prompt_embeds: torch.Size([256])) for text_encoder_2-item1.clip_prompt_embeds text_encoder_2-item1.clip_prompt_embeds: 11,080B, BPFP=0.8994 Using per-key quantization points (text_encoder_3-item2.t5_prompt_embeds: torch.Size([256])) for text_encoder_3-item2.t5_prompt_embeds text_encoder_3-item2.t5_prompt_embeds: 25,320B, BPFP=0.6422 Using per-key quantization points (text_encoder-item3.clip_pooled_prompt_embeds: torch.Size([256])) for text_encoder-item3.clip_pooled_prompt_embeds text_encoder-item3.clip_pooled_prompt_embeds: 760B, BPFP=7.9167 Using per-key quantization points (text_encoder-item3.clip_prompt_embeds: torch.Size([256])) for text_encoder-item3.clip_prompt_embeds text_encoder-item3.clip_prompt_embeds: 4,024B, BPFP=0.5444 Using per-key quantization points (text_encoder_2-item4.clip_pooled: torch.Size([256])) for text_encoder_2-item4.clip_pooled_prompt_embeds text_encoder_2-item4.clip_pooled_prompt_embeds: 1,268B, BPFP=7.9250 Using per-key quantization points (text_encoder_2-item4.clip_prompt: torch.Size([256])) for text_encoder_2-item4.clip_prompt_embeds text_encoder_2-item4.clip_prompt_embeds: 6,996B, BPFP=0.5679 Using per-key quantization points (text_encoder_3-item5.t5_prompt_embeds: torch.Size([256])) for text_encoder_3-item5.t5_prompt_embeds text_encoder_3-item5.t5_prompt_embeds: 18,416B, BPFP=0.4671 Using per-key quantization points (vae.encoder_f0: torch.Size([256])) for vae.encoder_f0 vae.encoder_f0: 22,392B, BPFP=0.3417 Using per-key quantization points (vae.encoder_f1: torch.Size([256])) for vae.encoder_f1 vae.encoder_f1: 22,392B, BPFP=0.3417 Using per-key quantization points (vae.decoder: torch.Size([256])) for vae.decoder vae.decoder: 5,360B, BPFP=0.1636 ⌛️ [2/4] FRONTEND: Frontend time: 0.294s (Pack+Encode) [3/4] BACKEND: Decode + Unpack... Using per-key quantization points (text_encoder-item0.clip_pooled_prompt_embeds: torch.Size([256])) for text_encoder-item0.clip_pooled_prompt_embeds Using per-key quantization points (text_encoder-item0.clip_prompt_embeds: torch.Size([256])) for text_encoder-item0.clip_prompt_embeds Using per-key quantization points (text_encoder_2-item1.clip_pooled_prompt_embeds: torch.Size([256])) for text_encoder_2-item1.clip_pooled_prompt_embeds Using per-key quantization points (text_encoder_2-item1.clip_prompt_embeds: torch.Size([256])) for text_encoder_2-item1.clip_prompt_embeds Using per-key quantization points (text_encoder_3-item2.t5_prompt_embeds: torch.Size([256])) for text_encoder_3-item2.t5_prompt_embeds Using per-key quantization points (text_encoder-item3.clip_pooled_prompt_embeds: torch.Size([256])) for text_encoder-item3.clip_pooled_prompt_embeds Using per-key quantization points (text_encoder-item3.clip_prompt_embeds: torch.Size([256])) for text_encoder-item3.clip_prompt_embeds Using per-key quantization points (text_encoder_2-item4.clip_pooled: torch.Size([256])) for text_encoder_2-item4.clip_pooled_prompt_embeds Using per-key quantization points (text_encoder_2-item4.clip_prompt: torch.Size([256])) for text_encoder_2-item4.clip_prompt_embeds Using per-key quantization points (text_encoder_3-item5.t5_prompt_embeds: torch.Size([256])) for text_encoder_3-item5.t5_prompt_embeds Using per-key quantization points (vae.encoder_f0: torch.Size([256])) for vae.encoder_f0 Using per-key quantization points (vae.encoder_f1: torch.Size([256])) for vae.encoder_f1 Using per-key quantization points (vae.decoder: torch.Size([256])) for vae.decoder IndividualUnPacker: text_encoder-item0.clip_pooled_prompt_embeds: torch.Size([768]) text_encoder-item0.clip_prompt_embeds: torch.Size([77, 768]) text_encoder_2-item1.clip_pooled_prompt_embeds: torch.Size([1280]) text_encoder_2-item1.clip_prompt_embeds: torch.Size([77, 1280]) text_encoder_3-item2.t5_prompt_embeds: torch.Size([77, 4096]) text_encoder-item3.clip_pooled_prompt_embeds: torch.Size([768]) text_encoder-item3.clip_prompt_embeds: torch.Size([77, 768]) text_encoder_2-item4.clip_pooled_prompt_embeds: torch.Size([1280]) text_encoder_2-item4.clip_prompt_embeds: torch.Size([77, 1280]) text_encoder_3-item5.t5_prompt_embeds: torch.Size([77, 4096]) vae.decoder: torch.Size([16, 128, 128]) vae.encoder_f0: torch.Size([32, 128, 128]) vae.encoder_f1: torch.Size([32, 128, 128]) ⌛️ [3/4] BACKEND: Backend time: 0.450s [4/4] METRICS: Computing MSE Breakdown... Using per-key quantization points (text_encoder-item0.clip_pooled_prompt_embeds: torch.Size([256])) for text_encoder-item0.clip_pooled_prompt_embeds Using per-key quantization points (text_encoder-item0.clip_prompt_embeds: torch.Size([256])) for text_encoder-item0.clip_prompt_embeds Using per-key quantization points (text_encoder_2-item1.clip_pooled_prompt_embeds: torch.Size([256])) for text_encoder_2-item1.clip_pooled_prompt_embeds Using per-key quantization points (text_encoder_2-item1.clip_prompt_embeds: torch.Size([256])) for text_encoder_2-item1.clip_prompt_embeds Using per-key quantization points (text_encoder_3-item2.t5_prompt_embeds: torch.Size([256])) for text_encoder_3-item2.t5_prompt_embeds Using per-key quantization points (text_encoder-item3.clip_pooled_prompt_embeds: torch.Size([256])) for text_encoder-item3.clip_pooled_prompt_embeds Using per-key quantization points (text_encoder-item3.clip_prompt_embeds: torch.Size([256])) for text_encoder-item3.clip_prompt_embeds Using per-key quantization points (text_encoder_2-item4.clip_pooled: torch.Size([256])) for text_encoder_2-item4.clip_pooled_prompt_embeds Using per-key quantization points (text_encoder_2-item4.clip_prompt: torch.Size([256])) for text_encoder_2-item4.clip_prompt_embeds Using per-key quantization points (text_encoder_3-item5.t5_prompt_embeds: torch.Size([256])) for text_encoder_3-item5.t5_prompt_embeds Using per-key quantization points (vae.encoder_f0: torch.Size([256])) for vae.encoder_f0 Using per-key quantization points (vae.encoder_f1: torch.Size([256])) for vae.encoder_f1 Using per-key quantization points (vae.decoder: torch.Size([256])) for vae.decoder IndividualUnPacker: text_encoder-item0.clip_pooled_prompt_embeds: torch.Size([768]) text_encoder-item0.clip_prompt_embeds: torch.Size([77, 768]) text_encoder_2-item1.clip_pooled_prompt_embeds: torch.Size([1280]) text_encoder_2-item1.clip_prompt_embeds: torch.Size([77, 1280]) text_encoder_3-item2.t5_prompt_embeds: torch.Size([77, 4096]) text_encoder-item3.clip_pooled_prompt_embeds: torch.Size([768]) text_encoder-item3.clip_prompt_embeds: torch.Size([77, 768]) text_encoder_2-item4.clip_pooled_prompt_embeds: torch.Size([1280]) text_encoder_2-item4.clip_prompt_embeds: torch.Size([77, 1280]) text_encoder_3-item5.t5_prompt_embeds: torch.Size([77, 4096]) vae.decoder: torch.Size([16, 128, 128]) vae.encoder_f0: torch.Size([32, 128, 128]) vae.encoder_f1: torch.Size([32, 128, 128]) Per-key MSE Breakdown (Quant=Truncation+Quantization, Codec=Compression): Key Quant-MSE Total-MSE ------------------------------------------------------------------------------------- text_encoder-item0.clip_pooled_prompt_embeds 0.00045802 0.91433032 text_encoder-item0.clip_prompt_embeds 0.00031548 23.94811536 text_encoder_2-item1.clip_pooled_prompt_embeds 0.00020720 0.93400612 text_encoder_2-item1.clip_prompt_embeds 0.00018318 0.13739471 text_encoder_3-item2.t5_prompt_embeds 0.00000772 0.00282103 text_encoder-item3.clip_pooled_prompt_embeds 0.00841799 1.01420029 text_encoder-item3.clip_prompt_embeds 0.00023247 23.90570887 text_encoder_2-item4.clip_pooled_prompt_embeds 0.00016751 0.67051430 text_encoder_2-item4.clip_prompt_embeds 0.00009830 0.47049568 text_encoder_3-item5.t5_prompt_embeds 0.00001263 0.00227117 vae.encoder_f0 0.00725484 3.50162029 vae.encoder_f1 0.00725992 3.50131130 vae.decoder 0.00019960 0.04445163 ------------------------------------------------------------------------------------- TOTAL 0.00342155 2.90940618 (elements=2,260,992) ---------------------- -------------------------------------------------------- SAMPLE-WISE STATISTICS ---------------------- -------------------------------------------------------- Handler sd35 Strategy individual Architecture hyperprior-featurecoding ---------------------- -------------------------------------------------------- Total Elements 2260992 Total Bytes 126020 BPFP 0.4459 bits/point EBPFP 0.8918 equivalent bits/point MSE 2.909406 ---------------------- -------------------------------------------------------- Time: 0.752s Load: 0.009s, Pack+Encode: 0.294s, Decode+Unpack: 0.450s ---------------------- -------------------------------------------------------- Restored Feature Format: [Dict] with 8 keys key['text_encoder-item0']: [Dict] with 2 keys key['clip_pooled_prompt_embeds']: [Tensor] shape=torch.Size([768]), dtype=torch.float16, device=cpu key['clip_prompt_embeds']: [Tensor] shape=torch.Size([77, 768]), dtype=torch.float16, device=cpu key['text_encoder_2-item1']: [Dict] with 2 keys key['clip_pooled_prompt_embeds']: [Tensor] shape=torch.Size([1280]), dtype=torch.float16, device=cpu key['clip_prompt_embeds']: [Tensor] shape=torch.Size([77, 1280]), dtype=torch.float16, device=cpu key['text_encoder_3-item2']: [Dict] with 1 keys key['t5_prompt_embeds']: [Tensor] shape=torch.Size([77, 4096]), dtype=torch.float16, device=cpu key['text_encoder-item3']: [Dict] with 2 keys key['clip_pooled_prompt_embeds']: [Tensor] shape=torch.Size([768]), dtype=torch.float16, device=cpu key['clip_prompt_embeds']: [Tensor] shape=torch.Size([77, 768]), dtype=torch.float16, device=cpu key['text_encoder_2-item4']: [Dict] with 2 keys key['clip_pooled_prompt_embeds']: [Tensor] shape=torch.Size([1280]), dtype=torch.float16, device=cpu key['clip_prompt_embeds']: [Tensor] shape=torch.Size([77, 1280]), dtype=torch.float16, device=cpu key['text_encoder_3-item5']: [Dict] with 1 keys key['t5_prompt_embeds']: [Tensor] shape=torch.Size([77, 4096]), dtype=torch.float16, device=cpu key['vae.decoder']: [Tensor] shape=torch.Size([16, 128, 128]), dtype=torch.float16, device=cpu key['vae.encoder']: [Dict] with 2 keys key['vae.encoder_f0']: [Tensor] shape=torch.Size([32, 128, 128]), dtype=torch.float16, device=cpu key['vae.encoder_f1']: [Tensor] shape=torch.Size([32, 128, 128]), dtype=torch.float16, device=cpu 💾 Converting with 2.9094 MSE: from ../datasets/FeatureCoding-StableDiffusion3.5/100samples_features/000000435208.zst to output-fixed/sd35/lambda0.004/hyperprior-featurecoding-8bit-individual/sd35cond/000000435208.zst 💪 Processing: ../datasets/FeatureCoding-StableDiffusion3.5/100samples_features/000000435880.zst (75/100) [1/4] FRONTEND: Loading features from ../datasets/FeatureCoding-StableDiffusion3.5/100samples_features/000000435880.zst... Original data structure: root: [Dict] with 8 keys key['text_encoder-item0']: [Dict] with 2 keys key['clip_pooled_prompt_embeds']: [Tensor] shape=torch.Size([768]), dtype=torch.float16, device=cpu key['clip_prompt_embeds']: [Tensor] shape=torch.Size([77, 768]), dtype=torch.float16, device=cpu key['text_encoder_2-item1']: [Dict] with 2 keys key['clip_pooled_prompt_embeds']: [Tensor] shape=torch.Size([1280]), dtype=torch.float16, device=cpu key['clip_prompt_embeds']: [Tensor] shape=torch.Size([77, 1280]), dtype=torch.float16, device=cpu key['text_encoder_3-item2']: [Dict] with 1 keys key['t5_prompt_embeds']: [Tensor] shape=torch.Size([77, 4096]), dtype=torch.float16, device=cpu key['text_encoder-item3']: [Dict] with 2 keys key['clip_pooled_prompt_embeds']: [Tensor] shape=torch.Size([768]), dtype=torch.float16, device=cpu key['clip_prompt_embeds']: [Tensor] shape=torch.Size([77, 768]), dtype=torch.float16, device=cpu key['text_encoder_2-item4']: [Dict] with 2 keys key['clip_pooled_prompt_embeds']: [Tensor] shape=torch.Size([1280]), dtype=torch.float16, device=cpu key['clip_prompt_embeds']: [Tensor] shape=torch.Size([77, 1280]), dtype=torch.float16, device=cpu key['text_encoder_3-item5']: [Dict] with 1 keys key['t5_prompt_embeds']: [Tensor] shape=torch.Size([77, 4096]), dtype=torch.float16, device=cpu key['vae.encoder-item6']: [Dict] with 2 keys key['vae.encoder_f0']: [Tensor] shape=torch.Size([32, 128, 128]), dtype=torch.float16, device=cpu key['vae.encoder_f1']: [Tensor] shape=torch.Size([32, 128, 128]), dtype=torch.float16, device=cpu key['vae.decoder']: [Tensor] shape=torch.Size([16, 128, 128]), dtype=torch.float16, device=cpu ⌛️ [1/4] FRONTEND: Load time: 0.009s ------------------------------------------------------------ SD3.5 Features Summary ------------------------------------------------------------ Number of text encoders: 3 Encoder names: ['text_encoder', 'text_encoder_2', 'text_encoder_3'] Has VAE latents: True Has VAE encoder features: True Data type: torch.float16 text_encoder-item0: clip_pooled_prompt_embeds: torch.Size([768]), torch.float16 clip_prompt_embeds: torch.Size([77, 768]), torch.float16 text_encoder_2-item1: clip_pooled_prompt_embeds: torch.Size([1280]), torch.float16 clip_prompt_embeds: torch.Size([77, 1280]), torch.float16 text_encoder_3-item2: t5_prompt_embeds: torch.Size([77, 4096]), torch.float16 text_encoder-item3: clip_pooled_prompt_embeds: torch.Size([768]), torch.float16 clip_prompt_embeds: torch.Size([77, 768]), torch.float16 text_encoder_2-item4: clip_pooled_prompt_embeds: torch.Size([1280]), torch.float16 clip_prompt_embeds: torch.Size([77, 1280]), torch.float16 text_encoder_3-item5: t5_prompt_embeds: torch.Size([77, 4096]), torch.float16 vae.decoder latents: torch.Size([16, 128, 128]) vae.encoder features: vae.encoder_f0: torch.Size([32, 128, 128]), torch.float16 vae.encoder_f1: torch.Size([32, 128, 128]), torch.float16 ------------------------------------------------------------ [2/4] FRONTEND: Pack + Encode (strategy: individual)... IndividualPacker: text_encoder-item0.clip_pooled_prompt_embeds: torch.Size([768])->torch.Size([1, 1, 1, 768]) text_encoder-item0.clip_prompt_embeds: torch.Size([77, 768])->torch.Size([1, 1, 77, 768]) text_encoder_2-item1.clip_pooled_prompt_embeds: torch.Size([1280])->torch.Size([1, 1, 1, 1280]) text_encoder_2-item1.clip_prompt_embeds: torch.Size([77, 1280])->torch.Size([1, 1, 77, 1280]) text_encoder_3-item2.t5_prompt_embeds: torch.Size([77, 4096])->torch.Size([1, 1, 77, 4096]) text_encoder-item3.clip_pooled_prompt_embeds: torch.Size([768])->torch.Size([1, 1, 1, 768]) text_encoder-item3.clip_prompt_embeds: torch.Size([77, 768])->torch.Size([1, 1, 77, 768]) text_encoder_2-item4.clip_pooled_prompt_embeds: torch.Size([1280])->torch.Size([1, 1, 1, 1280]) text_encoder_2-item4.clip_prompt_embeds: torch.Size([77, 1280])->torch.Size([1, 1, 77, 1280]) text_encoder_3-item5.t5_prompt_embeds: torch.Size([77, 4096])->torch.Size([1, 1, 77, 4096]) vae.encoder_f0: torch.Size([32, 128, 128])->torch.Size([1, 1, 512, 1024]) vae.encoder_f1: torch.Size([32, 128, 128])->torch.Size([1, 1, 512, 1024]) vae.decoder: torch.Size([16, 128, 128])->torch.Size([1, 1, 512, 512]) Using per-key quantization points (text_encoder-item0.clip_pooled_prompt_embeds: torch.Size([256])) for text_encoder-item0.clip_pooled_prompt_embeds text_encoder-item0.clip_pooled_prompt_embeds: 728B, BPFP=7.5833 Using per-key quantization points (text_encoder-item0.clip_prompt_embeds: torch.Size([256])) for text_encoder-item0.clip_prompt_embeds text_encoder-item0.clip_prompt_embeds: 5,200B, BPFP=0.7035 Using per-key quantization points (text_encoder_2-item1.clip_pooled_prompt_embeds: torch.Size([256])) for text_encoder_2-item1.clip_pooled_prompt_embeds text_encoder_2-item1.clip_pooled_prompt_embeds: 1,212B, BPFP=7.5750 Using per-key quantization points (text_encoder_2-item1.clip_prompt_embeds: torch.Size([256])) for text_encoder_2-item1.clip_prompt_embeds text_encoder_2-item1.clip_prompt_embeds: 9,504B, BPFP=0.7714 Using per-key quantization points (text_encoder_3-item2.t5_prompt_embeds: torch.Size([256])) for text_encoder_3-item2.t5_prompt_embeds text_encoder_3-item2.t5_prompt_embeds: 23,468B, BPFP=0.5953 Using per-key quantization points (text_encoder-item3.clip_pooled_prompt_embeds: torch.Size([256])) for text_encoder-item3.clip_pooled_prompt_embeds text_encoder-item3.clip_pooled_prompt_embeds: 760B, BPFP=7.9167 Using per-key quantization points (text_encoder-item3.clip_prompt_embeds: torch.Size([256])) for text_encoder-item3.clip_prompt_embeds text_encoder-item3.clip_prompt_embeds: 4,024B, BPFP=0.5444 Using per-key quantization points (text_encoder_2-item4.clip_pooled: torch.Size([256])) for text_encoder_2-item4.clip_pooled_prompt_embeds text_encoder_2-item4.clip_pooled_prompt_embeds: 1,268B, BPFP=7.9250 Using per-key quantization points (text_encoder_2-item4.clip_prompt: torch.Size([256])) for text_encoder_2-item4.clip_prompt_embeds text_encoder_2-item4.clip_prompt_embeds: 6,996B, BPFP=0.5679 Using per-key quantization points (text_encoder_3-item5.t5_prompt_embeds: torch.Size([256])) for text_encoder_3-item5.t5_prompt_embeds text_encoder_3-item5.t5_prompt_embeds: 18,416B, BPFP=0.4671 Using per-key quantization points (vae.encoder_f0: torch.Size([256])) for vae.encoder_f0 vae.encoder_f0: 19,772B, BPFP=0.3017 Using per-key quantization points (vae.encoder_f1: torch.Size([256])) for vae.encoder_f1 vae.encoder_f1: 19,772B, BPFP=0.3017 Using per-key quantization points (vae.decoder: torch.Size([256])) for vae.decoder vae.decoder: 6,416B, BPFP=0.1958 ⌛️ [2/4] FRONTEND: Frontend time: 0.293s (Pack+Encode) [3/4] BACKEND: Decode + Unpack... Using per-key quantization points (text_encoder-item0.clip_pooled_prompt_embeds: torch.Size([256])) for text_encoder-item0.clip_pooled_prompt_embeds Using per-key quantization points (text_encoder-item0.clip_prompt_embeds: torch.Size([256])) for text_encoder-item0.clip_prompt_embeds Using per-key quantization points (text_encoder_2-item1.clip_pooled_prompt_embeds: torch.Size([256])) for text_encoder_2-item1.clip_pooled_prompt_embeds Using per-key quantization points (text_encoder_2-item1.clip_prompt_embeds: torch.Size([256])) for text_encoder_2-item1.clip_prompt_embeds Using per-key quantization points (text_encoder_3-item2.t5_prompt_embeds: torch.Size([256])) for text_encoder_3-item2.t5_prompt_embeds Using per-key quantization points (text_encoder-item3.clip_pooled_prompt_embeds: torch.Size([256])) for text_encoder-item3.clip_pooled_prompt_embeds Using per-key quantization points (text_encoder-item3.clip_prompt_embeds: torch.Size([256])) for text_encoder-item3.clip_prompt_embeds Using per-key quantization points (text_encoder_2-item4.clip_pooled: torch.Size([256])) for text_encoder_2-item4.clip_pooled_prompt_embeds Using per-key quantization points (text_encoder_2-item4.clip_prompt: torch.Size([256])) for text_encoder_2-item4.clip_prompt_embeds Using per-key quantization points (text_encoder_3-item5.t5_prompt_embeds: torch.Size([256])) for text_encoder_3-item5.t5_prompt_embeds Using per-key quantization points (vae.encoder_f0: torch.Size([256])) for vae.encoder_f0 Using per-key quantization points (vae.encoder_f1: torch.Size([256])) for vae.encoder_f1 Using per-key quantization points (vae.decoder: torch.Size([256])) for vae.decoder IndividualUnPacker: text_encoder-item0.clip_pooled_prompt_embeds: torch.Size([768]) text_encoder-item0.clip_prompt_embeds: torch.Size([77, 768]) text_encoder_2-item1.clip_pooled_prompt_embeds: torch.Size([1280]) text_encoder_2-item1.clip_prompt_embeds: torch.Size([77, 1280]) text_encoder_3-item2.t5_prompt_embeds: torch.Size([77, 4096]) text_encoder-item3.clip_pooled_prompt_embeds: torch.Size([768]) text_encoder-item3.clip_prompt_embeds: torch.Size([77, 768]) text_encoder_2-item4.clip_pooled_prompt_embeds: torch.Size([1280]) text_encoder_2-item4.clip_prompt_embeds: torch.Size([77, 1280]) text_encoder_3-item5.t5_prompt_embeds: torch.Size([77, 4096]) vae.decoder: torch.Size([16, 128, 128]) vae.encoder_f0: torch.Size([32, 128, 128]) vae.encoder_f1: torch.Size([32, 128, 128]) ⌛️ [3/4] BACKEND: Backend time: 0.447s [4/4] METRICS: Computing MSE Breakdown... Using per-key quantization points (text_encoder-item0.clip_pooled_prompt_embeds: torch.Size([256])) for text_encoder-item0.clip_pooled_prompt_embeds Using per-key quantization points (text_encoder-item0.clip_prompt_embeds: torch.Size([256])) for text_encoder-item0.clip_prompt_embeds Using per-key quantization points (text_encoder_2-item1.clip_pooled_prompt_embeds: torch.Size([256])) for text_encoder_2-item1.clip_pooled_prompt_embeds Using per-key quantization points (text_encoder_2-item1.clip_prompt_embeds: torch.Size([256])) for text_encoder_2-item1.clip_prompt_embeds Using per-key quantization points (text_encoder_3-item2.t5_prompt_embeds: torch.Size([256])) for text_encoder_3-item2.t5_prompt_embeds Using per-key quantization points (text_encoder-item3.clip_pooled_prompt_embeds: torch.Size([256])) for text_encoder-item3.clip_pooled_prompt_embeds Using per-key quantization points (text_encoder-item3.clip_prompt_embeds: torch.Size([256])) for text_encoder-item3.clip_prompt_embeds Using per-key quantization points (text_encoder_2-item4.clip_pooled: torch.Size([256])) for text_encoder_2-item4.clip_pooled_prompt_embeds Using per-key quantization points (text_encoder_2-item4.clip_prompt: torch.Size([256])) for text_encoder_2-item4.clip_prompt_embeds Using per-key quantization points (text_encoder_3-item5.t5_prompt_embeds: torch.Size([256])) for text_encoder_3-item5.t5_prompt_embeds Using per-key quantization points (vae.encoder_f0: torch.Size([256])) for vae.encoder_f0 Using per-key quantization points (vae.encoder_f1: torch.Size([256])) for vae.encoder_f1 Using per-key quantization points (vae.decoder: torch.Size([256])) for vae.decoder IndividualUnPacker: text_encoder-item0.clip_pooled_prompt_embeds: torch.Size([768]) text_encoder-item0.clip_prompt_embeds: torch.Size([77, 768]) text_encoder_2-item1.clip_pooled_prompt_embeds: torch.Size([1280]) text_encoder_2-item1.clip_prompt_embeds: torch.Size([77, 1280]) text_encoder_3-item2.t5_prompt_embeds: torch.Size([77, 4096]) text_encoder-item3.clip_pooled_prompt_embeds: torch.Size([768]) text_encoder-item3.clip_prompt_embeds: torch.Size([77, 768]) text_encoder_2-item4.clip_pooled_prompt_embeds: torch.Size([1280]) text_encoder_2-item4.clip_prompt_embeds: torch.Size([77, 1280]) text_encoder_3-item5.t5_prompt_embeds: torch.Size([77, 4096]) vae.decoder: torch.Size([16, 128, 128]) vae.encoder_f0: torch.Size([32, 128, 128]) vae.encoder_f1: torch.Size([32, 128, 128]) Per-key MSE Breakdown (Quant=Truncation+Quantization, Codec=Compression): Key Quant-MSE Total-MSE ------------------------------------------------------------------------------------- text_encoder-item0.clip_pooled_prompt_embeds 0.00061068 0.93807300 text_encoder-item0.clip_prompt_embeds 0.00021831 144.57315341 text_encoder_2-item1.clip_pooled_prompt_embeds 0.00025602 1.10824051 text_encoder_2-item1.clip_prompt_embeds 0.00016110 0.11763276 text_encoder_3-item2.t5_prompt_embeds 0.00000740 0.00279928 text_encoder-item3.clip_pooled_prompt_embeds 0.00841799 1.01420029 text_encoder-item3.clip_prompt_embeds 0.00023247 23.90570887 text_encoder_2-item4.clip_pooled_prompt_embeds 0.00016751 0.67051430 text_encoder_2-item4.clip_prompt_embeds 0.00009830 0.47049568 text_encoder_3-item5.t5_prompt_embeds 0.00001263 0.00227117 vae.encoder_f0 0.00923516 3.12357950 vae.encoder_f1 0.00923823 3.12349415 vae.decoder 0.00019521 0.04057125 ------------------------------------------------------------------------------------- TOTAL 0.00433552 5.88786185 (elements=2,260,992) ---------------------- -------------------------------------------------------- SAMPLE-WISE STATISTICS ---------------------- -------------------------------------------------------- Handler sd35 Strategy individual Architecture hyperprior-featurecoding ---------------------- -------------------------------------------------------- Total Elements 2260992 Total Bytes 117536 BPFP 0.4159 bits/point EBPFP 0.8317 equivalent bits/point MSE 5.887862 ---------------------- -------------------------------------------------------- Time: 0.749s Load: 0.009s, Pack+Encode: 0.293s, Decode+Unpack: 0.447s ---------------------- -------------------------------------------------------- Restored Feature Format: [Dict] with 8 keys key['text_encoder-item0']: [Dict] with 2 keys key['clip_pooled_prompt_embeds']: [Tensor] shape=torch.Size([768]), dtype=torch.float16, device=cpu key['clip_prompt_embeds']: [Tensor] shape=torch.Size([77, 768]), dtype=torch.float16, device=cpu key['text_encoder_2-item1']: [Dict] with 2 keys key['clip_pooled_prompt_embeds']: [Tensor] shape=torch.Size([1280]), dtype=torch.float16, device=cpu key['clip_prompt_embeds']: [Tensor] shape=torch.Size([77, 1280]), dtype=torch.float16, device=cpu key['text_encoder_3-item2']: [Dict] with 1 keys key['t5_prompt_embeds']: [Tensor] shape=torch.Size([77, 4096]), dtype=torch.float16, device=cpu key['text_encoder-item3']: [Dict] with 2 keys key['clip_pooled_prompt_embeds']: [Tensor] shape=torch.Size([768]), dtype=torch.float16, device=cpu key['clip_prompt_embeds']: [Tensor] shape=torch.Size([77, 768]), dtype=torch.float16, device=cpu key['text_encoder_2-item4']: [Dict] with 2 keys key['clip_pooled_prompt_embeds']: [Tensor] shape=torch.Size([1280]), dtype=torch.float16, device=cpu key['clip_prompt_embeds']: [Tensor] shape=torch.Size([77, 1280]), dtype=torch.float16, device=cpu key['text_encoder_3-item5']: [Dict] with 1 keys key['t5_prompt_embeds']: [Tensor] shape=torch.Size([77, 4096]), dtype=torch.float16, device=cpu key['vae.decoder']: [Tensor] shape=torch.Size([16, 128, 128]), dtype=torch.float16, device=cpu key['vae.encoder']: [Dict] with 2 keys key['vae.encoder_f0']: [Tensor] shape=torch.Size([32, 128, 128]), dtype=torch.float16, device=cpu key['vae.encoder_f1']: [Tensor] shape=torch.Size([32, 128, 128]), dtype=torch.float16, device=cpu 💾 Converting with 5.8879 MSE: from ../datasets/FeatureCoding-StableDiffusion3.5/100samples_features/000000435880.zst to output-fixed/sd35/lambda0.004/hyperprior-featurecoding-8bit-individual/sd35cond/000000435880.zst 💪 Processing: ../datasets/FeatureCoding-StableDiffusion3.5/100samples_features/000000439593.zst (76/100) [1/4] FRONTEND: Loading features from ../datasets/FeatureCoding-StableDiffusion3.5/100samples_features/000000439593.zst... Original data structure: root: [Dict] with 8 keys key['text_encoder-item0']: [Dict] with 2 keys key['clip_pooled_prompt_embeds']: [Tensor] shape=torch.Size([768]), dtype=torch.float16, device=cpu key['clip_prompt_embeds']: [Tensor] shape=torch.Size([77, 768]), dtype=torch.float16, device=cpu key['text_encoder_2-item1']: [Dict] with 2 keys key['clip_pooled_prompt_embeds']: [Tensor] shape=torch.Size([1280]), dtype=torch.float16, device=cpu key['clip_prompt_embeds']: [Tensor] shape=torch.Size([77, 1280]), dtype=torch.float16, device=cpu key['text_encoder_3-item2']: [Dict] with 1 keys key['t5_prompt_embeds']: [Tensor] shape=torch.Size([77, 4096]), dtype=torch.float16, device=cpu key['text_encoder-item3']: [Dict] with 2 keys key['clip_pooled_prompt_embeds']: [Tensor] shape=torch.Size([768]), dtype=torch.float16, device=cpu key['clip_prompt_embeds']: [Tensor] shape=torch.Size([77, 768]), dtype=torch.float16, device=cpu key['text_encoder_2-item4']: [Dict] with 2 keys key['clip_pooled_prompt_embeds']: [Tensor] shape=torch.Size([1280]), dtype=torch.float16, device=cpu key['clip_prompt_embeds']: [Tensor] shape=torch.Size([77, 1280]), dtype=torch.float16, device=cpu key['text_encoder_3-item5']: [Dict] with 1 keys key['t5_prompt_embeds']: [Tensor] shape=torch.Size([77, 4096]), dtype=torch.float16, device=cpu key['vae.encoder-item6']: [Dict] with 2 keys key['vae.encoder_f0']: [Tensor] shape=torch.Size([32, 128, 128]), dtype=torch.float16, device=cpu key['vae.encoder_f1']: [Tensor] shape=torch.Size([32, 128, 128]), dtype=torch.float16, device=cpu key['vae.decoder']: [Tensor] shape=torch.Size([16, 128, 128]), dtype=torch.float16, device=cpu ⌛️ [1/4] FRONTEND: Load time: 0.009s ------------------------------------------------------------ SD3.5 Features Summary ------------------------------------------------------------ Number of text encoders: 3 Encoder names: ['text_encoder', 'text_encoder_2', 'text_encoder_3'] Has VAE latents: True Has VAE encoder features: True Data type: torch.float16 text_encoder-item0: clip_pooled_prompt_embeds: torch.Size([768]), torch.float16 clip_prompt_embeds: torch.Size([77, 768]), torch.float16 text_encoder_2-item1: clip_pooled_prompt_embeds: torch.Size([1280]), torch.float16 clip_prompt_embeds: torch.Size([77, 1280]), torch.float16 text_encoder_3-item2: t5_prompt_embeds: torch.Size([77, 4096]), torch.float16 text_encoder-item3: clip_pooled_prompt_embeds: torch.Size([768]), torch.float16 clip_prompt_embeds: torch.Size([77, 768]), torch.float16 text_encoder_2-item4: clip_pooled_prompt_embeds: torch.Size([1280]), torch.float16 clip_prompt_embeds: torch.Size([77, 1280]), torch.float16 text_encoder_3-item5: t5_prompt_embeds: torch.Size([77, 4096]), torch.float16 vae.decoder latents: torch.Size([16, 128, 128]) vae.encoder features: vae.encoder_f0: torch.Size([32, 128, 128]), torch.float16 vae.encoder_f1: torch.Size([32, 128, 128]), torch.float16 ------------------------------------------------------------ [2/4] FRONTEND: Pack + Encode (strategy: individual)... IndividualPacker: text_encoder-item0.clip_pooled_prompt_embeds: torch.Size([768])->torch.Size([1, 1, 1, 768]) text_encoder-item0.clip_prompt_embeds: torch.Size([77, 768])->torch.Size([1, 1, 77, 768]) text_encoder_2-item1.clip_pooled_prompt_embeds: torch.Size([1280])->torch.Size([1, 1, 1, 1280]) text_encoder_2-item1.clip_prompt_embeds: torch.Size([77, 1280])->torch.Size([1, 1, 77, 1280]) text_encoder_3-item2.t5_prompt_embeds: torch.Size([77, 4096])->torch.Size([1, 1, 77, 4096]) text_encoder-item3.clip_pooled_prompt_embeds: torch.Size([768])->torch.Size([1, 1, 1, 768]) text_encoder-item3.clip_prompt_embeds: torch.Size([77, 768])->torch.Size([1, 1, 77, 768]) text_encoder_2-item4.clip_pooled_prompt_embeds: torch.Size([1280])->torch.Size([1, 1, 1, 1280]) text_encoder_2-item4.clip_prompt_embeds: torch.Size([77, 1280])->torch.Size([1, 1, 77, 1280]) text_encoder_3-item5.t5_prompt_embeds: torch.Size([77, 4096])->torch.Size([1, 1, 77, 4096]) vae.encoder_f0: torch.Size([32, 128, 128])->torch.Size([1, 1, 512, 1024]) vae.encoder_f1: torch.Size([32, 128, 128])->torch.Size([1, 1, 512, 1024]) vae.decoder: torch.Size([16, 128, 128])->torch.Size([1, 1, 512, 512]) Using per-key quantization points (text_encoder-item0.clip_pooled_prompt_embeds: torch.Size([256])) for text_encoder-item0.clip_pooled_prompt_embeds text_encoder-item0.clip_pooled_prompt_embeds: 724B, BPFP=7.5417 Using per-key quantization points (text_encoder-item0.clip_prompt_embeds: torch.Size([256])) for text_encoder-item0.clip_prompt_embeds text_encoder-item0.clip_prompt_embeds: 5,720B, BPFP=0.7738 Using per-key quantization points (text_encoder_2-item1.clip_pooled_prompt_embeds: torch.Size([256])) for text_encoder_2-item1.clip_pooled_prompt_embeds text_encoder_2-item1.clip_pooled_prompt_embeds: 1,200B, BPFP=7.5000 Using per-key quantization points (text_encoder_2-item1.clip_prompt_embeds: torch.Size([256])) for text_encoder_2-item1.clip_prompt_embeds text_encoder_2-item1.clip_prompt_embeds: 10,996B, BPFP=0.8925 Using per-key quantization points (text_encoder_3-item2.t5_prompt_embeds: torch.Size([256])) for text_encoder_3-item2.t5_prompt_embeds text_encoder_3-item2.t5_prompt_embeds: 29,268B, BPFP=0.7424 Using per-key quantization points (text_encoder-item3.clip_pooled_prompt_embeds: torch.Size([256])) for text_encoder-item3.clip_pooled_prompt_embeds text_encoder-item3.clip_pooled_prompt_embeds: 760B, BPFP=7.9167 Using per-key quantization points (text_encoder-item3.clip_prompt_embeds: torch.Size([256])) for text_encoder-item3.clip_prompt_embeds text_encoder-item3.clip_prompt_embeds: 4,024B, BPFP=0.5444 Using per-key quantization points (text_encoder_2-item4.clip_pooled: torch.Size([256])) for text_encoder_2-item4.clip_pooled_prompt_embeds text_encoder_2-item4.clip_pooled_prompt_embeds: 1,268B, BPFP=7.9250 Using per-key quantization points (text_encoder_2-item4.clip_prompt: torch.Size([256])) for text_encoder_2-item4.clip_prompt_embeds text_encoder_2-item4.clip_prompt_embeds: 6,996B, BPFP=0.5679 Using per-key quantization points (text_encoder_3-item5.t5_prompt_embeds: torch.Size([256])) for text_encoder_3-item5.t5_prompt_embeds text_encoder_3-item5.t5_prompt_embeds: 18,416B, BPFP=0.4671 Using per-key quantization points (vae.encoder_f0: torch.Size([256])) for vae.encoder_f0 vae.encoder_f0: 20,712B, BPFP=0.3160 Using per-key quantization points (vae.encoder_f1: torch.Size([256])) for vae.encoder_f1 vae.encoder_f1: 20,712B, BPFP=0.3160 Using per-key quantization points (vae.decoder: torch.Size([256])) for vae.decoder vae.decoder: 7,156B, BPFP=0.2184 ⌛️ [2/4] FRONTEND: Frontend time: 0.293s (Pack+Encode) [3/4] BACKEND: Decode + Unpack... Using per-key quantization points (text_encoder-item0.clip_pooled_prompt_embeds: torch.Size([256])) for text_encoder-item0.clip_pooled_prompt_embeds Using per-key quantization points (text_encoder-item0.clip_prompt_embeds: torch.Size([256])) for text_encoder-item0.clip_prompt_embeds Using per-key quantization points (text_encoder_2-item1.clip_pooled_prompt_embeds: torch.Size([256])) for text_encoder_2-item1.clip_pooled_prompt_embeds Using per-key quantization points (text_encoder_2-item1.clip_prompt_embeds: torch.Size([256])) for text_encoder_2-item1.clip_prompt_embeds Using per-key quantization points (text_encoder_3-item2.t5_prompt_embeds: torch.Size([256])) for text_encoder_3-item2.t5_prompt_embeds Using per-key quantization points (text_encoder-item3.clip_pooled_prompt_embeds: torch.Size([256])) for text_encoder-item3.clip_pooled_prompt_embeds Using per-key quantization points (text_encoder-item3.clip_prompt_embeds: torch.Size([256])) for text_encoder-item3.clip_prompt_embeds Using per-key quantization points (text_encoder_2-item4.clip_pooled: torch.Size([256])) for text_encoder_2-item4.clip_pooled_prompt_embeds Using per-key quantization points (text_encoder_2-item4.clip_prompt: torch.Size([256])) for text_encoder_2-item4.clip_prompt_embeds Using per-key quantization points (text_encoder_3-item5.t5_prompt_embeds: torch.Size([256])) for text_encoder_3-item5.t5_prompt_embeds Using per-key quantization points (vae.encoder_f0: torch.Size([256])) for vae.encoder_f0 Using per-key quantization points (vae.encoder_f1: torch.Size([256])) for vae.encoder_f1 Using per-key quantization points (vae.decoder: torch.Size([256])) for vae.decoder IndividualUnPacker: text_encoder-item0.clip_pooled_prompt_embeds: torch.Size([768]) text_encoder-item0.clip_prompt_embeds: torch.Size([77, 768]) text_encoder_2-item1.clip_pooled_prompt_embeds: torch.Size([1280]) text_encoder_2-item1.clip_prompt_embeds: torch.Size([77, 1280]) text_encoder_3-item2.t5_prompt_embeds: torch.Size([77, 4096]) text_encoder-item3.clip_pooled_prompt_embeds: torch.Size([768]) text_encoder-item3.clip_prompt_embeds: torch.Size([77, 768]) text_encoder_2-item4.clip_pooled_prompt_embeds: torch.Size([1280]) text_encoder_2-item4.clip_prompt_embeds: torch.Size([77, 1280]) text_encoder_3-item5.t5_prompt_embeds: torch.Size([77, 4096]) vae.decoder: torch.Size([16, 128, 128]) vae.encoder_f0: torch.Size([32, 128, 128]) vae.encoder_f1: torch.Size([32, 128, 128]) ⌛️ [3/4] BACKEND: Backend time: 0.451s [4/4] METRICS: Computing MSE Breakdown... Using per-key quantization points (text_encoder-item0.clip_pooled_prompt_embeds: torch.Size([256])) for text_encoder-item0.clip_pooled_prompt_embeds Using per-key quantization points (text_encoder-item0.clip_prompt_embeds: torch.Size([256])) for text_encoder-item0.clip_prompt_embeds Using per-key quantization points (text_encoder_2-item1.clip_pooled_prompt_embeds: torch.Size([256])) for text_encoder_2-item1.clip_pooled_prompt_embeds Using per-key quantization points (text_encoder_2-item1.clip_prompt_embeds: torch.Size([256])) for text_encoder_2-item1.clip_prompt_embeds Using per-key quantization points (text_encoder_3-item2.t5_prompt_embeds: torch.Size([256])) for text_encoder_3-item2.t5_prompt_embeds Using per-key quantization points (text_encoder-item3.clip_pooled_prompt_embeds: torch.Size([256])) for text_encoder-item3.clip_pooled_prompt_embeds Using per-key quantization points (text_encoder-item3.clip_prompt_embeds: torch.Size([256])) for text_encoder-item3.clip_prompt_embeds Using per-key quantization points (text_encoder_2-item4.clip_pooled: torch.Size([256])) for text_encoder_2-item4.clip_pooled_prompt_embeds Using per-key quantization points (text_encoder_2-item4.clip_prompt: torch.Size([256])) for text_encoder_2-item4.clip_prompt_embeds Using per-key quantization points (text_encoder_3-item5.t5_prompt_embeds: torch.Size([256])) for text_encoder_3-item5.t5_prompt_embeds Using per-key quantization points (vae.encoder_f0: torch.Size([256])) for vae.encoder_f0 Using per-key quantization points (vae.encoder_f1: torch.Size([256])) for vae.encoder_f1 Using per-key quantization points (vae.decoder: torch.Size([256])) for vae.decoder IndividualUnPacker: text_encoder-item0.clip_pooled_prompt_embeds: torch.Size([768]) text_encoder-item0.clip_prompt_embeds: torch.Size([77, 768]) text_encoder_2-item1.clip_pooled_prompt_embeds: torch.Size([1280]) text_encoder_2-item1.clip_prompt_embeds: torch.Size([77, 1280]) text_encoder_3-item2.t5_prompt_embeds: torch.Size([77, 4096]) text_encoder-item3.clip_pooled_prompt_embeds: torch.Size([768]) text_encoder-item3.clip_prompt_embeds: torch.Size([77, 768]) text_encoder_2-item4.clip_pooled_prompt_embeds: torch.Size([1280]) text_encoder_2-item4.clip_prompt_embeds: torch.Size([77, 1280]) text_encoder_3-item5.t5_prompt_embeds: torch.Size([77, 4096]) vae.decoder: torch.Size([16, 128, 128]) vae.encoder_f0: torch.Size([32, 128, 128]) vae.encoder_f1: torch.Size([32, 128, 128]) Per-key MSE Breakdown (Quant=Truncation+Quantization, Codec=Compression): Key Quant-MSE Total-MSE ------------------------------------------------------------------------------------- text_encoder-item0.clip_pooled_prompt_embeds 0.00028585 0.93011427 text_encoder-item0.clip_prompt_embeds 0.00062166 96.06972910 text_encoder_2-item1.clip_pooled_prompt_embeds 0.00050487 0.89883842 text_encoder_2-item1.clip_prompt_embeds 0.00018638 0.12416455 text_encoder_3-item2.t5_prompt_embeds 0.00000762 0.00321192 text_encoder-item3.clip_pooled_prompt_embeds 0.00841799 1.01420029 text_encoder-item3.clip_prompt_embeds 0.00023247 23.90570887 text_encoder_2-item4.clip_pooled_prompt_embeds 0.00016751 0.67051430 text_encoder_2-item4.clip_prompt_embeds 0.00009830 0.47049568 text_encoder_3-item5.t5_prompt_embeds 0.00001263 0.00227117 vae.encoder_f0 0.00831779 2.97977066 vae.encoder_f1 0.00832197 2.97941566 vae.decoder 0.00023271 0.05285780 ------------------------------------------------------------------------------------- TOTAL 0.00392639 4.55414914 (elements=2,260,992) ---------------------- -------------------------------------------------------- SAMPLE-WISE STATISTICS ---------------------- -------------------------------------------------------- Handler sd35 Strategy individual Architecture hyperprior-featurecoding ---------------------- -------------------------------------------------------- Total Elements 2260992 Total Bytes 127952 BPFP 0.4527 bits/point EBPFP 0.9055 equivalent bits/point MSE 4.554149 ---------------------- -------------------------------------------------------- Time: 0.753s Load: 0.009s, Pack+Encode: 0.293s, Decode+Unpack: 0.451s ---------------------- -------------------------------------------------------- Restored Feature Format: [Dict] with 8 keys key['text_encoder-item0']: [Dict] with 2 keys key['clip_pooled_prompt_embeds']: [Tensor] shape=torch.Size([768]), dtype=torch.float16, device=cpu key['clip_prompt_embeds']: [Tensor] shape=torch.Size([77, 768]), dtype=torch.float16, device=cpu key['text_encoder_2-item1']: [Dict] with 2 keys key['clip_pooled_prompt_embeds']: [Tensor] shape=torch.Size([1280]), dtype=torch.float16, device=cpu key['clip_prompt_embeds']: [Tensor] shape=torch.Size([77, 1280]), dtype=torch.float16, device=cpu key['text_encoder_3-item2']: [Dict] with 1 keys key['t5_prompt_embeds']: [Tensor] shape=torch.Size([77, 4096]), dtype=torch.float16, device=cpu key['text_encoder-item3']: [Dict] with 2 keys key['clip_pooled_prompt_embeds']: [Tensor] shape=torch.Size([768]), dtype=torch.float16, device=cpu key['clip_prompt_embeds']: [Tensor] shape=torch.Size([77, 768]), dtype=torch.float16, device=cpu key['text_encoder_2-item4']: [Dict] with 2 keys key['clip_pooled_prompt_embeds']: [Tensor] shape=torch.Size([1280]), dtype=torch.float16, device=cpu key['clip_prompt_embeds']: [Tensor] shape=torch.Size([77, 1280]), dtype=torch.float16, device=cpu key['text_encoder_3-item5']: [Dict] with 1 keys key['t5_prompt_embeds']: [Tensor] shape=torch.Size([77, 4096]), dtype=torch.float16, device=cpu key['vae.decoder']: [Tensor] shape=torch.Size([16, 128, 128]), dtype=torch.float16, device=cpu key['vae.encoder']: [Dict] with 2 keys key['vae.encoder_f0']: [Tensor] shape=torch.Size([32, 128, 128]), dtype=torch.float16, device=cpu key['vae.encoder_f1']: [Tensor] shape=torch.Size([32, 128, 128]), dtype=torch.float16, device=cpu 💾 Converting with 4.5541 MSE: from ../datasets/FeatureCoding-StableDiffusion3.5/100samples_features/000000439593.zst to output-fixed/sd35/lambda0.004/hyperprior-featurecoding-8bit-individual/sd35cond/000000439593.zst 💪 Processing: ../datasets/FeatureCoding-StableDiffusion3.5/100samples_features/000000441286.zst (77/100) [1/4] FRONTEND: Loading features from ../datasets/FeatureCoding-StableDiffusion3.5/100samples_features/000000441286.zst... Original data structure: root: [Dict] with 8 keys key['text_encoder-item0']: [Dict] with 2 keys key['clip_pooled_prompt_embeds']: [Tensor] shape=torch.Size([768]), dtype=torch.float16, device=cpu key['clip_prompt_embeds']: [Tensor] shape=torch.Size([77, 768]), dtype=torch.float16, device=cpu key['text_encoder_2-item1']: [Dict] with 2 keys key['clip_pooled_prompt_embeds']: [Tensor] shape=torch.Size([1280]), dtype=torch.float16, device=cpu key['clip_prompt_embeds']: [Tensor] shape=torch.Size([77, 1280]), dtype=torch.float16, device=cpu key['text_encoder_3-item2']: [Dict] with 1 keys key['t5_prompt_embeds']: [Tensor] shape=torch.Size([77, 4096]), dtype=torch.float16, device=cpu key['text_encoder-item3']: [Dict] with 2 keys key['clip_pooled_prompt_embeds']: [Tensor] shape=torch.Size([768]), dtype=torch.float16, device=cpu key['clip_prompt_embeds']: [Tensor] shape=torch.Size([77, 768]), dtype=torch.float16, device=cpu key['text_encoder_2-item4']: [Dict] with 2 keys key['clip_pooled_prompt_embeds']: [Tensor] shape=torch.Size([1280]), dtype=torch.float16, device=cpu key['clip_prompt_embeds']: [Tensor] shape=torch.Size([77, 1280]), dtype=torch.float16, device=cpu key['text_encoder_3-item5']: [Dict] with 1 keys key['t5_prompt_embeds']: [Tensor] shape=torch.Size([77, 4096]), dtype=torch.float16, device=cpu key['vae.encoder-item6']: [Dict] with 2 keys key['vae.encoder_f0']: [Tensor] shape=torch.Size([32, 128, 128]), dtype=torch.float16, device=cpu key['vae.encoder_f1']: [Tensor] shape=torch.Size([32, 128, 128]), dtype=torch.float16, device=cpu key['vae.decoder']: [Tensor] shape=torch.Size([16, 128, 128]), dtype=torch.float16, device=cpu ⌛️ [1/4] FRONTEND: Load time: 0.009s ------------------------------------------------------------ SD3.5 Features Summary ------------------------------------------------------------ Number of text encoders: 3 Encoder names: ['text_encoder', 'text_encoder_2', 'text_encoder_3'] Has VAE latents: True Has VAE encoder features: True Data type: torch.float16 text_encoder-item0: clip_pooled_prompt_embeds: torch.Size([768]), torch.float16 clip_prompt_embeds: torch.Size([77, 768]), torch.float16 text_encoder_2-item1: clip_pooled_prompt_embeds: torch.Size([1280]), torch.float16 clip_prompt_embeds: torch.Size([77, 1280]), torch.float16 text_encoder_3-item2: t5_prompt_embeds: torch.Size([77, 4096]), torch.float16 text_encoder-item3: clip_pooled_prompt_embeds: torch.Size([768]), torch.float16 clip_prompt_embeds: torch.Size([77, 768]), torch.float16 text_encoder_2-item4: clip_pooled_prompt_embeds: torch.Size([1280]), torch.float16 clip_prompt_embeds: torch.Size([77, 1280]), torch.float16 text_encoder_3-item5: t5_prompt_embeds: torch.Size([77, 4096]), torch.float16 vae.decoder latents: torch.Size([16, 128, 128]) vae.encoder features: vae.encoder_f0: torch.Size([32, 128, 128]), torch.float16 vae.encoder_f1: torch.Size([32, 128, 128]), torch.float16 ------------------------------------------------------------ [2/4] FRONTEND: Pack + Encode (strategy: individual)... IndividualPacker: text_encoder-item0.clip_pooled_prompt_embeds: torch.Size([768])->torch.Size([1, 1, 1, 768]) text_encoder-item0.clip_prompt_embeds: torch.Size([77, 768])->torch.Size([1, 1, 77, 768]) text_encoder_2-item1.clip_pooled_prompt_embeds: torch.Size([1280])->torch.Size([1, 1, 1, 1280]) text_encoder_2-item1.clip_prompt_embeds: torch.Size([77, 1280])->torch.Size([1, 1, 77, 1280]) text_encoder_3-item2.t5_prompt_embeds: torch.Size([77, 4096])->torch.Size([1, 1, 77, 4096]) text_encoder-item3.clip_pooled_prompt_embeds: torch.Size([768])->torch.Size([1, 1, 1, 768]) text_encoder-item3.clip_prompt_embeds: torch.Size([77, 768])->torch.Size([1, 1, 77, 768]) text_encoder_2-item4.clip_pooled_prompt_embeds: torch.Size([1280])->torch.Size([1, 1, 1, 1280]) text_encoder_2-item4.clip_prompt_embeds: torch.Size([77, 1280])->torch.Size([1, 1, 77, 1280]) text_encoder_3-item5.t5_prompt_embeds: torch.Size([77, 4096])->torch.Size([1, 1, 77, 4096]) vae.encoder_f0: torch.Size([32, 128, 128])->torch.Size([1, 1, 512, 1024]) vae.encoder_f1: torch.Size([32, 128, 128])->torch.Size([1, 1, 512, 1024]) vae.decoder: torch.Size([16, 128, 128])->torch.Size([1, 1, 512, 512]) Using per-key quantization points (text_encoder-item0.clip_pooled_prompt_embeds: torch.Size([256])) for text_encoder-item0.clip_pooled_prompt_embeds text_encoder-item0.clip_pooled_prompt_embeds: 740B, BPFP=7.7083 Using per-key quantization points (text_encoder-item0.clip_prompt_embeds: torch.Size([256])) for text_encoder-item0.clip_prompt_embeds text_encoder-item0.clip_prompt_embeds: 6,096B, BPFP=0.8247 Using per-key quantization points (text_encoder_2-item1.clip_pooled_prompt_embeds: torch.Size([256])) for text_encoder_2-item1.clip_pooled_prompt_embeds text_encoder_2-item1.clip_pooled_prompt_embeds: 1,196B, BPFP=7.4750 Using per-key quantization points (text_encoder_2-item1.clip_prompt_embeds: torch.Size([256])) for text_encoder_2-item1.clip_prompt_embeds text_encoder_2-item1.clip_prompt_embeds: 10,712B, BPFP=0.8695 Using per-key quantization points (text_encoder_3-item2.t5_prompt_embeds: torch.Size([256])) for text_encoder_3-item2.t5_prompt_embeds text_encoder_3-item2.t5_prompt_embeds: 27,868B, BPFP=0.7069 Using per-key quantization points (text_encoder-item3.clip_pooled_prompt_embeds: torch.Size([256])) for text_encoder-item3.clip_pooled_prompt_embeds text_encoder-item3.clip_pooled_prompt_embeds: 760B, BPFP=7.9167 Using per-key quantization points (text_encoder-item3.clip_prompt_embeds: torch.Size([256])) for text_encoder-item3.clip_prompt_embeds text_encoder-item3.clip_prompt_embeds: 4,024B, BPFP=0.5444 Using per-key quantization points (text_encoder_2-item4.clip_pooled: torch.Size([256])) for text_encoder_2-item4.clip_pooled_prompt_embeds text_encoder_2-item4.clip_pooled_prompt_embeds: 1,268B, BPFP=7.9250 Using per-key quantization points (text_encoder_2-item4.clip_prompt: torch.Size([256])) for text_encoder_2-item4.clip_prompt_embeds text_encoder_2-item4.clip_prompt_embeds: 6,996B, BPFP=0.5679 Using per-key quantization points (text_encoder_3-item5.t5_prompt_embeds: torch.Size([256])) for text_encoder_3-item5.t5_prompt_embeds text_encoder_3-item5.t5_prompt_embeds: 18,416B, BPFP=0.4671 Using per-key quantization points (vae.encoder_f0: torch.Size([256])) for vae.encoder_f0 vae.encoder_f0: 15,568B, BPFP=0.2375 Using per-key quantization points (vae.encoder_f1: torch.Size([256])) for vae.encoder_f1 vae.encoder_f1: 15,568B, BPFP=0.2375 Using per-key quantization points (vae.decoder: torch.Size([256])) for vae.decoder vae.decoder: 5,216B, BPFP=0.1592 ⌛️ [2/4] FRONTEND: Frontend time: 0.294s (Pack+Encode) [3/4] BACKEND: Decode + Unpack... Using per-key quantization points (text_encoder-item0.clip_pooled_prompt_embeds: torch.Size([256])) for text_encoder-item0.clip_pooled_prompt_embeds Using per-key quantization points (text_encoder-item0.clip_prompt_embeds: torch.Size([256])) for text_encoder-item0.clip_prompt_embeds Using per-key quantization points (text_encoder_2-item1.clip_pooled_prompt_embeds: torch.Size([256])) for text_encoder_2-item1.clip_pooled_prompt_embeds Using per-key quantization points (text_encoder_2-item1.clip_prompt_embeds: torch.Size([256])) for text_encoder_2-item1.clip_prompt_embeds Using per-key quantization points (text_encoder_3-item2.t5_prompt_embeds: torch.Size([256])) for text_encoder_3-item2.t5_prompt_embeds Using per-key quantization points (text_encoder-item3.clip_pooled_prompt_embeds: torch.Size([256])) for text_encoder-item3.clip_pooled_prompt_embeds Using per-key quantization points (text_encoder-item3.clip_prompt_embeds: torch.Size([256])) for text_encoder-item3.clip_prompt_embeds Using per-key quantization points (text_encoder_2-item4.clip_pooled: torch.Size([256])) for text_encoder_2-item4.clip_pooled_prompt_embeds Using per-key quantization points (text_encoder_2-item4.clip_prompt: torch.Size([256])) for text_encoder_2-item4.clip_prompt_embeds Using per-key quantization points (text_encoder_3-item5.t5_prompt_embeds: torch.Size([256])) for text_encoder_3-item5.t5_prompt_embeds Using per-key quantization points (vae.encoder_f0: torch.Size([256])) for vae.encoder_f0 Using per-key quantization points (vae.encoder_f1: torch.Size([256])) for vae.encoder_f1 Using per-key quantization points (vae.decoder: torch.Size([256])) for vae.decoder IndividualUnPacker: text_encoder-item0.clip_pooled_prompt_embeds: torch.Size([768]) text_encoder-item0.clip_prompt_embeds: torch.Size([77, 768]) text_encoder_2-item1.clip_pooled_prompt_embeds: torch.Size([1280]) text_encoder_2-item1.clip_prompt_embeds: torch.Size([77, 1280]) text_encoder_3-item2.t5_prompt_embeds: torch.Size([77, 4096]) text_encoder-item3.clip_pooled_prompt_embeds: torch.Size([768]) text_encoder-item3.clip_prompt_embeds: torch.Size([77, 768]) text_encoder_2-item4.clip_pooled_prompt_embeds: torch.Size([1280]) text_encoder_2-item4.clip_prompt_embeds: torch.Size([77, 1280]) text_encoder_3-item5.t5_prompt_embeds: torch.Size([77, 4096]) vae.decoder: torch.Size([16, 128, 128]) vae.encoder_f0: torch.Size([32, 128, 128]) vae.encoder_f1: torch.Size([32, 128, 128]) ⌛️ [3/4] BACKEND: Backend time: 0.446s [4/4] METRICS: Computing MSE Breakdown... Using per-key quantization points (text_encoder-item0.clip_pooled_prompt_embeds: torch.Size([256])) for text_encoder-item0.clip_pooled_prompt_embeds Using per-key quantization points (text_encoder-item0.clip_prompt_embeds: torch.Size([256])) for text_encoder-item0.clip_prompt_embeds Using per-key quantization points (text_encoder_2-item1.clip_pooled_prompt_embeds: torch.Size([256])) for text_encoder_2-item1.clip_pooled_prompt_embeds Using per-key quantization points (text_encoder_2-item1.clip_prompt_embeds: torch.Size([256])) for text_encoder_2-item1.clip_prompt_embeds Using per-key quantization points (text_encoder_3-item2.t5_prompt_embeds: torch.Size([256])) for text_encoder_3-item2.t5_prompt_embeds Using per-key quantization points (text_encoder-item3.clip_pooled_prompt_embeds: torch.Size([256])) for text_encoder-item3.clip_pooled_prompt_embeds Using per-key quantization points (text_encoder-item3.clip_prompt_embeds: torch.Size([256])) for text_encoder-item3.clip_prompt_embeds Using per-key quantization points (text_encoder_2-item4.clip_pooled: torch.Size([256])) for text_encoder_2-item4.clip_pooled_prompt_embeds Using per-key quantization points (text_encoder_2-item4.clip_prompt: torch.Size([256])) for text_encoder_2-item4.clip_prompt_embeds Using per-key quantization points (text_encoder_3-item5.t5_prompt_embeds: torch.Size([256])) for text_encoder_3-item5.t5_prompt_embeds Using per-key quantization points (vae.encoder_f0: torch.Size([256])) for vae.encoder_f0 Using per-key quantization points (vae.encoder_f1: torch.Size([256])) for vae.encoder_f1 Using per-key quantization points (vae.decoder: torch.Size([256])) for vae.decoder IndividualUnPacker: text_encoder-item0.clip_pooled_prompt_embeds: torch.Size([768]) text_encoder-item0.clip_prompt_embeds: torch.Size([77, 768]) text_encoder_2-item1.clip_pooled_prompt_embeds: torch.Size([1280]) text_encoder_2-item1.clip_prompt_embeds: torch.Size([77, 1280]) text_encoder_3-item2.t5_prompt_embeds: torch.Size([77, 4096]) text_encoder-item3.clip_pooled_prompt_embeds: torch.Size([768]) text_encoder-item3.clip_prompt_embeds: torch.Size([77, 768]) text_encoder_2-item4.clip_pooled_prompt_embeds: torch.Size([1280]) text_encoder_2-item4.clip_prompt_embeds: torch.Size([77, 1280]) text_encoder_3-item5.t5_prompt_embeds: torch.Size([77, 4096]) vae.decoder: torch.Size([16, 128, 128]) vae.encoder_f0: torch.Size([32, 128, 128]) vae.encoder_f1: torch.Size([32, 128, 128]) Per-key MSE Breakdown (Quant=Truncation+Quantization, Codec=Compression): Key Quant-MSE Total-MSE ------------------------------------------------------------------------------------- text_encoder-item0.clip_pooled_prompt_embeds 0.00019770 0.94746765 text_encoder-item0.clip_prompt_embeds 0.00022938 23.95260713 text_encoder_2-item1.clip_pooled_prompt_embeds 0.00028331 0.89992113 text_encoder_2-item1.clip_prompt_embeds 0.00016501 0.12026917 text_encoder_3-item2.t5_prompt_embeds 0.00000786 0.00338437 text_encoder-item3.clip_pooled_prompt_embeds 0.00841799 1.01420029 text_encoder-item3.clip_prompt_embeds 0.00023247 23.90570887 text_encoder_2-item4.clip_pooled_prompt_embeds 0.00016751 0.67051430 text_encoder_2-item4.clip_prompt_embeds 0.00009830 0.47049568 text_encoder_3-item5.t5_prompt_embeds 0.00001263 0.00227117 vae.encoder_f0 0.00626977 2.34134245 vae.encoder_f1 0.00627489 2.34046507 vae.decoder 0.00017842 0.05376314 ------------------------------------------------------------------------------------- TOTAL 0.00295919 2.37169560 (elements=2,260,992) ---------------------- -------------------------------------------------------- SAMPLE-WISE STATISTICS ---------------------- -------------------------------------------------------- Handler sd35 Strategy individual Architecture hyperprior-featurecoding ---------------------- -------------------------------------------------------- Total Elements 2260992 Total Bytes 114428 BPFP 0.4049 bits/point EBPFP 0.8098 equivalent bits/point MSE 2.371696 ---------------------- -------------------------------------------------------- Time: 0.749s Load: 0.009s, Pack+Encode: 0.294s, Decode+Unpack: 0.446s ---------------------- -------------------------------------------------------- Restored Feature Format: [Dict] with 8 keys key['text_encoder-item0']: [Dict] with 2 keys key['clip_pooled_prompt_embeds']: [Tensor] shape=torch.Size([768]), dtype=torch.float16, device=cpu key['clip_prompt_embeds']: [Tensor] shape=torch.Size([77, 768]), dtype=torch.float16, device=cpu key['text_encoder_2-item1']: [Dict] with 2 keys key['clip_pooled_prompt_embeds']: [Tensor] shape=torch.Size([1280]), dtype=torch.float16, device=cpu key['clip_prompt_embeds']: [Tensor] shape=torch.Size([77, 1280]), dtype=torch.float16, device=cpu key['text_encoder_3-item2']: [Dict] with 1 keys key['t5_prompt_embeds']: [Tensor] shape=torch.Size([77, 4096]), dtype=torch.float16, device=cpu key['text_encoder-item3']: [Dict] with 2 keys key['clip_pooled_prompt_embeds']: [Tensor] shape=torch.Size([768]), dtype=torch.float16, device=cpu key['clip_prompt_embeds']: [Tensor] shape=torch.Size([77, 768]), dtype=torch.float16, device=cpu key['text_encoder_2-item4']: [Dict] with 2 keys key['clip_pooled_prompt_embeds']: [Tensor] shape=torch.Size([1280]), dtype=torch.float16, device=cpu key['clip_prompt_embeds']: [Tensor] shape=torch.Size([77, 1280]), dtype=torch.float16, device=cpu key['text_encoder_3-item5']: [Dict] with 1 keys key['t5_prompt_embeds']: [Tensor] shape=torch.Size([77, 4096]), dtype=torch.float16, device=cpu key['vae.decoder']: [Tensor] shape=torch.Size([16, 128, 128]), dtype=torch.float16, device=cpu key['vae.encoder']: [Dict] with 2 keys key['vae.encoder_f0']: [Tensor] shape=torch.Size([32, 128, 128]), dtype=torch.float16, device=cpu key['vae.encoder_f1']: [Tensor] shape=torch.Size([32, 128, 128]), dtype=torch.float16, device=cpu 💾 Converting with 2.3717 MSE: from ../datasets/FeatureCoding-StableDiffusion3.5/100samples_features/000000441286.zst to output-fixed/sd35/lambda0.004/hyperprior-featurecoding-8bit-individual/sd35cond/000000441286.zst 💪 Processing: ../datasets/FeatureCoding-StableDiffusion3.5/100samples_features/000000445365.zst (78/100) [1/4] FRONTEND: Loading features from ../datasets/FeatureCoding-StableDiffusion3.5/100samples_features/000000445365.zst... Original data structure: root: [Dict] with 8 keys key['text_encoder-item0']: [Dict] with 2 keys key['clip_pooled_prompt_embeds']: [Tensor] shape=torch.Size([768]), dtype=torch.float16, device=cpu key['clip_prompt_embeds']: [Tensor] shape=torch.Size([77, 768]), dtype=torch.float16, device=cpu key['text_encoder_2-item1']: [Dict] with 2 keys key['clip_pooled_prompt_embeds']: [Tensor] shape=torch.Size([1280]), dtype=torch.float16, device=cpu key['clip_prompt_embeds']: [Tensor] shape=torch.Size([77, 1280]), dtype=torch.float16, device=cpu key['text_encoder_3-item2']: [Dict] with 1 keys key['t5_prompt_embeds']: [Tensor] shape=torch.Size([77, 4096]), dtype=torch.float16, device=cpu key['text_encoder-item3']: [Dict] with 2 keys key['clip_pooled_prompt_embeds']: [Tensor] shape=torch.Size([768]), dtype=torch.float16, device=cpu key['clip_prompt_embeds']: [Tensor] shape=torch.Size([77, 768]), dtype=torch.float16, device=cpu key['text_encoder_2-item4']: [Dict] with 2 keys key['clip_pooled_prompt_embeds']: [Tensor] shape=torch.Size([1280]), dtype=torch.float16, device=cpu key['clip_prompt_embeds']: [Tensor] shape=torch.Size([77, 1280]), dtype=torch.float16, device=cpu key['text_encoder_3-item5']: [Dict] with 1 keys key['t5_prompt_embeds']: [Tensor] shape=torch.Size([77, 4096]), dtype=torch.float16, device=cpu key['vae.encoder-item6']: [Dict] with 2 keys key['vae.encoder_f0']: [Tensor] shape=torch.Size([32, 128, 128]), dtype=torch.float16, device=cpu key['vae.encoder_f1']: [Tensor] shape=torch.Size([32, 128, 128]), dtype=torch.float16, device=cpu key['vae.decoder']: [Tensor] shape=torch.Size([16, 128, 128]), dtype=torch.float16, device=cpu ⌛️ [1/4] FRONTEND: Load time: 0.008s ------------------------------------------------------------ SD3.5 Features Summary ------------------------------------------------------------ Number of text encoders: 3 Encoder names: ['text_encoder', 'text_encoder_2', 'text_encoder_3'] Has VAE latents: True Has VAE encoder features: True Data type: torch.float16 text_encoder-item0: clip_pooled_prompt_embeds: torch.Size([768]), torch.float16 clip_prompt_embeds: torch.Size([77, 768]), torch.float16 text_encoder_2-item1: clip_pooled_prompt_embeds: torch.Size([1280]), torch.float16 clip_prompt_embeds: torch.Size([77, 1280]), torch.float16 text_encoder_3-item2: t5_prompt_embeds: torch.Size([77, 4096]), torch.float16 text_encoder-item3: clip_pooled_prompt_embeds: torch.Size([768]), torch.float16 clip_prompt_embeds: torch.Size([77, 768]), torch.float16 text_encoder_2-item4: clip_pooled_prompt_embeds: torch.Size([1280]), torch.float16 clip_prompt_embeds: torch.Size([77, 1280]), torch.float16 text_encoder_3-item5: t5_prompt_embeds: torch.Size([77, 4096]), torch.float16 vae.decoder latents: torch.Size([16, 128, 128]) vae.encoder features: vae.encoder_f0: torch.Size([32, 128, 128]), torch.float16 vae.encoder_f1: torch.Size([32, 128, 128]), torch.float16 ------------------------------------------------------------ [2/4] FRONTEND: Pack + Encode (strategy: individual)... IndividualPacker: text_encoder-item0.clip_pooled_prompt_embeds: torch.Size([768])->torch.Size([1, 1, 1, 768]) text_encoder-item0.clip_prompt_embeds: torch.Size([77, 768])->torch.Size([1, 1, 77, 768]) text_encoder_2-item1.clip_pooled_prompt_embeds: torch.Size([1280])->torch.Size([1, 1, 1, 1280]) text_encoder_2-item1.clip_prompt_embeds: torch.Size([77, 1280])->torch.Size([1, 1, 77, 1280]) text_encoder_3-item2.t5_prompt_embeds: torch.Size([77, 4096])->torch.Size([1, 1, 77, 4096]) text_encoder-item3.clip_pooled_prompt_embeds: torch.Size([768])->torch.Size([1, 1, 1, 768]) text_encoder-item3.clip_prompt_embeds: torch.Size([77, 768])->torch.Size([1, 1, 77, 768]) text_encoder_2-item4.clip_pooled_prompt_embeds: torch.Size([1280])->torch.Size([1, 1, 1, 1280]) text_encoder_2-item4.clip_prompt_embeds: torch.Size([77, 1280])->torch.Size([1, 1, 77, 1280]) text_encoder_3-item5.t5_prompt_embeds: torch.Size([77, 4096])->torch.Size([1, 1, 77, 4096]) vae.encoder_f0: torch.Size([32, 128, 128])->torch.Size([1, 1, 512, 1024]) vae.encoder_f1: torch.Size([32, 128, 128])->torch.Size([1, 1, 512, 1024]) vae.decoder: torch.Size([16, 128, 128])->torch.Size([1, 1, 512, 512]) Using per-key quantization points (text_encoder-item0.clip_pooled_prompt_embeds: torch.Size([256])) for text_encoder-item0.clip_pooled_prompt_embeds text_encoder-item0.clip_pooled_prompt_embeds: 744B, BPFP=7.7500 Using per-key quantization points (text_encoder-item0.clip_prompt_embeds: torch.Size([256])) for text_encoder-item0.clip_prompt_embeds text_encoder-item0.clip_prompt_embeds: 6,672B, BPFP=0.9026 Using per-key quantization points (text_encoder_2-item1.clip_pooled_prompt_embeds: torch.Size([256])) for text_encoder_2-item1.clip_pooled_prompt_embeds text_encoder_2-item1.clip_pooled_prompt_embeds: 1,196B, BPFP=7.4750 Using per-key quantization points (text_encoder_2-item1.clip_prompt_embeds: torch.Size([256])) for text_encoder_2-item1.clip_prompt_embeds text_encoder_2-item1.clip_prompt_embeds: 11,084B, BPFP=0.8997 Using per-key quantization points (text_encoder_3-item2.t5_prompt_embeds: torch.Size([256])) for text_encoder_3-item2.t5_prompt_embeds text_encoder_3-item2.t5_prompt_embeds: 28,716B, BPFP=0.7284 Using per-key quantization points (text_encoder-item3.clip_pooled_prompt_embeds: torch.Size([256])) for text_encoder-item3.clip_pooled_prompt_embeds text_encoder-item3.clip_pooled_prompt_embeds: 760B, BPFP=7.9167 Using per-key quantization points (text_encoder-item3.clip_prompt_embeds: torch.Size([256])) for text_encoder-item3.clip_prompt_embeds text_encoder-item3.clip_prompt_embeds: 4,024B, BPFP=0.5444 Using per-key quantization points (text_encoder_2-item4.clip_pooled: torch.Size([256])) for text_encoder_2-item4.clip_pooled_prompt_embeds text_encoder_2-item4.clip_pooled_prompt_embeds: 1,268B, BPFP=7.9250 Using per-key quantization points (text_encoder_2-item4.clip_prompt: torch.Size([256])) for text_encoder_2-item4.clip_prompt_embeds text_encoder_2-item4.clip_prompt_embeds: 6,996B, BPFP=0.5679 Using per-key quantization points (text_encoder_3-item5.t5_prompt_embeds: torch.Size([256])) for text_encoder_3-item5.t5_prompt_embeds text_encoder_3-item5.t5_prompt_embeds: 18,416B, BPFP=0.4671 Using per-key quantization points (vae.encoder_f0: torch.Size([256])) for vae.encoder_f0 vae.encoder_f0: 15,848B, BPFP=0.2418 Using per-key quantization points (vae.encoder_f1: torch.Size([256])) for vae.encoder_f1 vae.encoder_f1: 15,852B, BPFP=0.2419 Using per-key quantization points (vae.decoder: torch.Size([256])) for vae.decoder vae.decoder: 8,720B, BPFP=0.2661 ⌛️ [2/4] FRONTEND: Frontend time: 0.292s (Pack+Encode) [3/4] BACKEND: Decode + Unpack... Using per-key quantization points (text_encoder-item0.clip_pooled_prompt_embeds: torch.Size([256])) for text_encoder-item0.clip_pooled_prompt_embeds Using per-key quantization points (text_encoder-item0.clip_prompt_embeds: torch.Size([256])) for text_encoder-item0.clip_prompt_embeds Using per-key quantization points (text_encoder_2-item1.clip_pooled_prompt_embeds: torch.Size([256])) for text_encoder_2-item1.clip_pooled_prompt_embeds Using per-key quantization points (text_encoder_2-item1.clip_prompt_embeds: torch.Size([256])) for text_encoder_2-item1.clip_prompt_embeds Using per-key quantization points (text_encoder_3-item2.t5_prompt_embeds: torch.Size([256])) for text_encoder_3-item2.t5_prompt_embeds Using per-key quantization points (text_encoder-item3.clip_pooled_prompt_embeds: torch.Size([256])) for text_encoder-item3.clip_pooled_prompt_embeds Using per-key quantization points (text_encoder-item3.clip_prompt_embeds: torch.Size([256])) for text_encoder-item3.clip_prompt_embeds Using per-key quantization points (text_encoder_2-item4.clip_pooled: torch.Size([256])) for text_encoder_2-item4.clip_pooled_prompt_embeds Using per-key quantization points (text_encoder_2-item4.clip_prompt: torch.Size([256])) for text_encoder_2-item4.clip_prompt_embeds Using per-key quantization points (text_encoder_3-item5.t5_prompt_embeds: torch.Size([256])) for text_encoder_3-item5.t5_prompt_embeds Using per-key quantization points (vae.encoder_f0: torch.Size([256])) for vae.encoder_f0 Using per-key quantization points (vae.encoder_f1: torch.Size([256])) for vae.encoder_f1 Using per-key quantization points (vae.decoder: torch.Size([256])) for vae.decoder IndividualUnPacker: text_encoder-item0.clip_pooled_prompt_embeds: torch.Size([768]) text_encoder-item0.clip_prompt_embeds: torch.Size([77, 768]) text_encoder_2-item1.clip_pooled_prompt_embeds: torch.Size([1280]) text_encoder_2-item1.clip_prompt_embeds: torch.Size([77, 1280]) text_encoder_3-item2.t5_prompt_embeds: torch.Size([77, 4096]) text_encoder-item3.clip_pooled_prompt_embeds: torch.Size([768]) text_encoder-item3.clip_prompt_embeds: torch.Size([77, 768]) text_encoder_2-item4.clip_pooled_prompt_embeds: torch.Size([1280]) text_encoder_2-item4.clip_prompt_embeds: torch.Size([77, 1280]) text_encoder_3-item5.t5_prompt_embeds: torch.Size([77, 4096]) vae.decoder: torch.Size([16, 128, 128]) vae.encoder_f0: torch.Size([32, 128, 128]) vae.encoder_f1: torch.Size([32, 128, 128]) ⌛️ [3/4] BACKEND: Backend time: 0.447s [4/4] METRICS: Computing MSE Breakdown... Using per-key quantization points (text_encoder-item0.clip_pooled_prompt_embeds: torch.Size([256])) for text_encoder-item0.clip_pooled_prompt_embeds Using per-key quantization points (text_encoder-item0.clip_prompt_embeds: torch.Size([256])) for text_encoder-item0.clip_prompt_embeds Using per-key quantization points (text_encoder_2-item1.clip_pooled_prompt_embeds: torch.Size([256])) for text_encoder_2-item1.clip_pooled_prompt_embeds Using per-key quantization points (text_encoder_2-item1.clip_prompt_embeds: torch.Size([256])) for text_encoder_2-item1.clip_prompt_embeds Using per-key quantization points (text_encoder_3-item2.t5_prompt_embeds: torch.Size([256])) for text_encoder_3-item2.t5_prompt_embeds Using per-key quantization points (text_encoder-item3.clip_pooled_prompt_embeds: torch.Size([256])) for text_encoder-item3.clip_pooled_prompt_embeds Using per-key quantization points (text_encoder-item3.clip_prompt_embeds: torch.Size([256])) for text_encoder-item3.clip_prompt_embeds Using per-key quantization points (text_encoder_2-item4.clip_pooled: torch.Size([256])) for text_encoder_2-item4.clip_pooled_prompt_embeds Using per-key quantization points (text_encoder_2-item4.clip_prompt: torch.Size([256])) for text_encoder_2-item4.clip_prompt_embeds Using per-key quantization points (text_encoder_3-item5.t5_prompt_embeds: torch.Size([256])) for text_encoder_3-item5.t5_prompt_embeds Using per-key quantization points (vae.encoder_f0: torch.Size([256])) for vae.encoder_f0 Using per-key quantization points (vae.encoder_f1: torch.Size([256])) for vae.encoder_f1 Using per-key quantization points (vae.decoder: torch.Size([256])) for vae.decoder IndividualUnPacker: text_encoder-item0.clip_pooled_prompt_embeds: torch.Size([768]) text_encoder-item0.clip_prompt_embeds: torch.Size([77, 768]) text_encoder_2-item1.clip_pooled_prompt_embeds: torch.Size([1280]) text_encoder_2-item1.clip_prompt_embeds: torch.Size([77, 1280]) text_encoder_3-item2.t5_prompt_embeds: torch.Size([77, 4096]) text_encoder-item3.clip_pooled_prompt_embeds: torch.Size([768]) text_encoder-item3.clip_prompt_embeds: torch.Size([77, 768]) text_encoder_2-item4.clip_pooled_prompt_embeds: torch.Size([1280]) text_encoder_2-item4.clip_prompt_embeds: torch.Size([77, 1280]) text_encoder_3-item5.t5_prompt_embeds: torch.Size([77, 4096]) vae.decoder: torch.Size([16, 128, 128]) vae.encoder_f0: torch.Size([32, 128, 128]) vae.encoder_f1: torch.Size([32, 128, 128]) Per-key MSE Breakdown (Quant=Truncation+Quantization, Codec=Compression): Key Quant-MSE Total-MSE ------------------------------------------------------------------------------------- text_encoder-item0.clip_pooled_prompt_embeds 0.00022406 0.87804802 text_encoder-item0.clip_prompt_embeds 0.00022180 35.97835498 text_encoder_2-item1.clip_pooled_prompt_embeds 0.00120074 0.89294500 text_encoder_2-item1.clip_prompt_embeds 0.00017918 0.13705883 text_encoder_3-item2.t5_prompt_embeds 0.00000774 0.00363033 text_encoder-item3.clip_pooled_prompt_embeds 0.00841799 1.01420029 text_encoder-item3.clip_prompt_embeds 0.00023247 23.90570887 text_encoder_2-item4.clip_pooled_prompt_embeds 0.00016751 0.67051430 text_encoder_2-item4.clip_prompt_embeds 0.00009830 0.47049568 text_encoder_3-item5.t5_prompt_embeds 0.00001263 0.00227117 vae.encoder_f0 0.00585720 1.93750107 vae.encoder_f1 0.00586586 1.93775511 vae.decoder 0.00016520 0.07783050 ------------------------------------------------------------------------------------- TOTAL 0.00276807 2.50273042 (elements=2,260,992) ---------------------- -------------------------------------------------------- SAMPLE-WISE STATISTICS ---------------------- -------------------------------------------------------- Handler sd35 Strategy individual Architecture hyperprior-featurecoding ---------------------- -------------------------------------------------------- Total Elements 2260992 Total Bytes 120296 BPFP 0.4256 bits/point EBPFP 0.8513 equivalent bits/point MSE 2.502730 ---------------------- -------------------------------------------------------- Time: 0.748s Load: 0.008s, Pack+Encode: 0.292s, Decode+Unpack: 0.447s ---------------------- -------------------------------------------------------- Restored Feature Format: [Dict] with 8 keys key['text_encoder-item0']: [Dict] with 2 keys key['clip_pooled_prompt_embeds']: [Tensor] shape=torch.Size([768]), dtype=torch.float16, device=cpu key['clip_prompt_embeds']: [Tensor] shape=torch.Size([77, 768]), dtype=torch.float16, device=cpu key['text_encoder_2-item1']: [Dict] with 2 keys key['clip_pooled_prompt_embeds']: [Tensor] shape=torch.Size([1280]), dtype=torch.float16, device=cpu key['clip_prompt_embeds']: [Tensor] shape=torch.Size([77, 1280]), dtype=torch.float16, device=cpu key['text_encoder_3-item2']: [Dict] with 1 keys key['t5_prompt_embeds']: [Tensor] shape=torch.Size([77, 4096]), dtype=torch.float16, device=cpu key['text_encoder-item3']: [Dict] with 2 keys key['clip_pooled_prompt_embeds']: [Tensor] shape=torch.Size([768]), dtype=torch.float16, device=cpu key['clip_prompt_embeds']: [Tensor] shape=torch.Size([77, 768]), dtype=torch.float16, device=cpu key['text_encoder_2-item4']: [Dict] with 2 keys key['clip_pooled_prompt_embeds']: [Tensor] shape=torch.Size([1280]), dtype=torch.float16, device=cpu key['clip_prompt_embeds']: [Tensor] shape=torch.Size([77, 1280]), dtype=torch.float16, device=cpu key['text_encoder_3-item5']: [Dict] with 1 keys key['t5_prompt_embeds']: [Tensor] shape=torch.Size([77, 4096]), dtype=torch.float16, device=cpu key['vae.decoder']: [Tensor] shape=torch.Size([16, 128, 128]), dtype=torch.float16, device=cpu key['vae.encoder']: [Dict] with 2 keys key['vae.encoder_f0']: [Tensor] shape=torch.Size([32, 128, 128]), dtype=torch.float16, device=cpu key['vae.encoder_f1']: [Tensor] shape=torch.Size([32, 128, 128]), dtype=torch.float16, device=cpu 💾 Converting with 2.5027 MSE: from ../datasets/FeatureCoding-StableDiffusion3.5/100samples_features/000000445365.zst to output-fixed/sd35/lambda0.004/hyperprior-featurecoding-8bit-individual/sd35cond/000000445365.zst 💪 Processing: ../datasets/FeatureCoding-StableDiffusion3.5/100samples_features/000000449996.zst (79/100) [1/4] FRONTEND: Loading features from ../datasets/FeatureCoding-StableDiffusion3.5/100samples_features/000000449996.zst... Original data structure: root: [Dict] with 8 keys key['text_encoder-item0']: [Dict] with 2 keys key['clip_pooled_prompt_embeds']: [Tensor] shape=torch.Size([768]), dtype=torch.float16, device=cpu key['clip_prompt_embeds']: [Tensor] shape=torch.Size([77, 768]), dtype=torch.float16, device=cpu key['text_encoder_2-item1']: [Dict] with 2 keys key['clip_pooled_prompt_embeds']: [Tensor] shape=torch.Size([1280]), dtype=torch.float16, device=cpu key['clip_prompt_embeds']: [Tensor] shape=torch.Size([77, 1280]), dtype=torch.float16, device=cpu key['text_encoder_3-item2']: [Dict] with 1 keys key['t5_prompt_embeds']: [Tensor] shape=torch.Size([77, 4096]), dtype=torch.float16, device=cpu key['text_encoder-item3']: [Dict] with 2 keys key['clip_pooled_prompt_embeds']: [Tensor] shape=torch.Size([768]), dtype=torch.float16, device=cpu key['clip_prompt_embeds']: [Tensor] shape=torch.Size([77, 768]), dtype=torch.float16, device=cpu key['text_encoder_2-item4']: [Dict] with 2 keys key['clip_pooled_prompt_embeds']: [Tensor] shape=torch.Size([1280]), dtype=torch.float16, device=cpu key['clip_prompt_embeds']: [Tensor] shape=torch.Size([77, 1280]), dtype=torch.float16, device=cpu key['text_encoder_3-item5']: [Dict] with 1 keys key['t5_prompt_embeds']: [Tensor] shape=torch.Size([77, 4096]), dtype=torch.float16, device=cpu key['vae.encoder-item6']: [Dict] with 2 keys key['vae.encoder_f0']: [Tensor] shape=torch.Size([32, 128, 128]), dtype=torch.float16, device=cpu key['vae.encoder_f1']: [Tensor] shape=torch.Size([32, 128, 128]), dtype=torch.float16, device=cpu key['vae.decoder']: [Tensor] shape=torch.Size([16, 128, 128]), dtype=torch.float16, device=cpu ⌛️ [1/4] FRONTEND: Load time: 0.008s ------------------------------------------------------------ SD3.5 Features Summary ------------------------------------------------------------ Number of text encoders: 3 Encoder names: ['text_encoder', 'text_encoder_2', 'text_encoder_3'] Has VAE latents: True Has VAE encoder features: True Data type: torch.float16 text_encoder-item0: clip_pooled_prompt_embeds: torch.Size([768]), torch.float16 clip_prompt_embeds: torch.Size([77, 768]), torch.float16 text_encoder_2-item1: clip_pooled_prompt_embeds: torch.Size([1280]), torch.float16 clip_prompt_embeds: torch.Size([77, 1280]), torch.float16 text_encoder_3-item2: t5_prompt_embeds: torch.Size([77, 4096]), torch.float16 text_encoder-item3: clip_pooled_prompt_embeds: torch.Size([768]), torch.float16 clip_prompt_embeds: torch.Size([77, 768]), torch.float16 text_encoder_2-item4: clip_pooled_prompt_embeds: torch.Size([1280]), torch.float16 clip_prompt_embeds: torch.Size([77, 1280]), torch.float16 text_encoder_3-item5: t5_prompt_embeds: torch.Size([77, 4096]), torch.float16 vae.decoder latents: torch.Size([16, 128, 128]) vae.encoder features: vae.encoder_f0: torch.Size([32, 128, 128]), torch.float16 vae.encoder_f1: torch.Size([32, 128, 128]), torch.float16 ------------------------------------------------------------ [2/4] FRONTEND: Pack + Encode (strategy: individual)... IndividualPacker: text_encoder-item0.clip_pooled_prompt_embeds: torch.Size([768])->torch.Size([1, 1, 1, 768]) text_encoder-item0.clip_prompt_embeds: torch.Size([77, 768])->torch.Size([1, 1, 77, 768]) text_encoder_2-item1.clip_pooled_prompt_embeds: torch.Size([1280])->torch.Size([1, 1, 1, 1280]) text_encoder_2-item1.clip_prompt_embeds: torch.Size([77, 1280])->torch.Size([1, 1, 77, 1280]) text_encoder_3-item2.t5_prompt_embeds: torch.Size([77, 4096])->torch.Size([1, 1, 77, 4096]) text_encoder-item3.clip_pooled_prompt_embeds: torch.Size([768])->torch.Size([1, 1, 1, 768]) text_encoder-item3.clip_prompt_embeds: torch.Size([77, 768])->torch.Size([1, 1, 77, 768]) text_encoder_2-item4.clip_pooled_prompt_embeds: torch.Size([1280])->torch.Size([1, 1, 1, 1280]) text_encoder_2-item4.clip_prompt_embeds: torch.Size([77, 1280])->torch.Size([1, 1, 77, 1280]) text_encoder_3-item5.t5_prompt_embeds: torch.Size([77, 4096])->torch.Size([1, 1, 77, 4096]) vae.encoder_f0: torch.Size([32, 128, 128])->torch.Size([1, 1, 512, 1024]) vae.encoder_f1: torch.Size([32, 128, 128])->torch.Size([1, 1, 512, 1024]) vae.decoder: torch.Size([16, 128, 128])->torch.Size([1, 1, 512, 512]) Using per-key quantization points (text_encoder-item0.clip_pooled_prompt_embeds: torch.Size([256])) for text_encoder-item0.clip_pooled_prompt_embeds text_encoder-item0.clip_pooled_prompt_embeds: 732B, BPFP=7.6250 Using per-key quantization points (text_encoder-item0.clip_prompt_embeds: torch.Size([256])) for text_encoder-item0.clip_prompt_embeds text_encoder-item0.clip_prompt_embeds: 5,940B, BPFP=0.8036 Using per-key quantization points (text_encoder_2-item1.clip_pooled_prompt_embeds: torch.Size([256])) for text_encoder_2-item1.clip_pooled_prompt_embeds text_encoder_2-item1.clip_pooled_prompt_embeds: 1,192B, BPFP=7.4500 Using per-key quantization points (text_encoder_2-item1.clip_prompt_embeds: torch.Size([256])) for text_encoder_2-item1.clip_prompt_embeds text_encoder_2-item1.clip_prompt_embeds: 10,844B, BPFP=0.8802 Using per-key quantization points (text_encoder_3-item2.t5_prompt_embeds: torch.Size([256])) for text_encoder_3-item2.t5_prompt_embeds text_encoder_3-item2.t5_prompt_embeds: 27,252B, BPFP=0.6913 Using per-key quantization points (text_encoder-item3.clip_pooled_prompt_embeds: torch.Size([256])) for text_encoder-item3.clip_pooled_prompt_embeds text_encoder-item3.clip_pooled_prompt_embeds: 760B, BPFP=7.9167 Using per-key quantization points (text_encoder-item3.clip_prompt_embeds: torch.Size([256])) for text_encoder-item3.clip_prompt_embeds text_encoder-item3.clip_prompt_embeds: 4,024B, BPFP=0.5444 Using per-key quantization points (text_encoder_2-item4.clip_pooled: torch.Size([256])) for text_encoder_2-item4.clip_pooled_prompt_embeds text_encoder_2-item4.clip_pooled_prompt_embeds: 1,268B, BPFP=7.9250 Using per-key quantization points (text_encoder_2-item4.clip_prompt: torch.Size([256])) for text_encoder_2-item4.clip_prompt_embeds text_encoder_2-item4.clip_prompt_embeds: 6,996B, BPFP=0.5679 Using per-key quantization points (text_encoder_3-item5.t5_prompt_embeds: torch.Size([256])) for text_encoder_3-item5.t5_prompt_embeds text_encoder_3-item5.t5_prompt_embeds: 18,416B, BPFP=0.4671 Using per-key quantization points (vae.encoder_f0: torch.Size([256])) for vae.encoder_f0 vae.encoder_f0: 13,704B, BPFP=0.2091 Using per-key quantization points (vae.encoder_f1: torch.Size([256])) for vae.encoder_f1 vae.encoder_f1: 13,704B, BPFP=0.2091 Using per-key quantization points (vae.decoder: torch.Size([256])) for vae.decoder vae.decoder: 5,724B, BPFP=0.1747 ⌛️ [2/4] FRONTEND: Frontend time: 0.293s (Pack+Encode) [3/4] BACKEND: Decode + Unpack... Using per-key quantization points (text_encoder-item0.clip_pooled_prompt_embeds: torch.Size([256])) for text_encoder-item0.clip_pooled_prompt_embeds Using per-key quantization points (text_encoder-item0.clip_prompt_embeds: torch.Size([256])) for text_encoder-item0.clip_prompt_embeds Using per-key quantization points (text_encoder_2-item1.clip_pooled_prompt_embeds: torch.Size([256])) for text_encoder_2-item1.clip_pooled_prompt_embeds Using per-key quantization points (text_encoder_2-item1.clip_prompt_embeds: torch.Size([256])) for text_encoder_2-item1.clip_prompt_embeds Using per-key quantization points (text_encoder_3-item2.t5_prompt_embeds: torch.Size([256])) for text_encoder_3-item2.t5_prompt_embeds Using per-key quantization points (text_encoder-item3.clip_pooled_prompt_embeds: torch.Size([256])) for text_encoder-item3.clip_pooled_prompt_embeds Using per-key quantization points (text_encoder-item3.clip_prompt_embeds: torch.Size([256])) for text_encoder-item3.clip_prompt_embeds Using per-key quantization points (text_encoder_2-item4.clip_pooled: torch.Size([256])) for text_encoder_2-item4.clip_pooled_prompt_embeds Using per-key quantization points (text_encoder_2-item4.clip_prompt: torch.Size([256])) for text_encoder_2-item4.clip_prompt_embeds Using per-key quantization points (text_encoder_3-item5.t5_prompt_embeds: torch.Size([256])) for text_encoder_3-item5.t5_prompt_embeds Using per-key quantization points (vae.encoder_f0: torch.Size([256])) for vae.encoder_f0 Using per-key quantization points (vae.encoder_f1: torch.Size([256])) for vae.encoder_f1 Using per-key quantization points (vae.decoder: torch.Size([256])) for vae.decoder IndividualUnPacker: text_encoder-item0.clip_pooled_prompt_embeds: torch.Size([768]) text_encoder-item0.clip_prompt_embeds: torch.Size([77, 768]) text_encoder_2-item1.clip_pooled_prompt_embeds: torch.Size([1280]) text_encoder_2-item1.clip_prompt_embeds: torch.Size([77, 1280]) text_encoder_3-item2.t5_prompt_embeds: torch.Size([77, 4096]) text_encoder-item3.clip_pooled_prompt_embeds: torch.Size([768]) text_encoder-item3.clip_prompt_embeds: torch.Size([77, 768]) text_encoder_2-item4.clip_pooled_prompt_embeds: torch.Size([1280]) text_encoder_2-item4.clip_prompt_embeds: torch.Size([77, 1280]) text_encoder_3-item5.t5_prompt_embeds: torch.Size([77, 4096]) vae.decoder: torch.Size([16, 128, 128]) vae.encoder_f0: torch.Size([32, 128, 128]) vae.encoder_f1: torch.Size([32, 128, 128]) ⌛️ [3/4] BACKEND: Backend time: 0.447s [4/4] METRICS: Computing MSE Breakdown... Using per-key quantization points (text_encoder-item0.clip_pooled_prompt_embeds: torch.Size([256])) for text_encoder-item0.clip_pooled_prompt_embeds Using per-key quantization points (text_encoder-item0.clip_prompt_embeds: torch.Size([256])) for text_encoder-item0.clip_prompt_embeds Using per-key quantization points (text_encoder_2-item1.clip_pooled_prompt_embeds: torch.Size([256])) for text_encoder_2-item1.clip_pooled_prompt_embeds Using per-key quantization points (text_encoder_2-item1.clip_prompt_embeds: torch.Size([256])) for text_encoder_2-item1.clip_prompt_embeds Using per-key quantization points (text_encoder_3-item2.t5_prompt_embeds: torch.Size([256])) for text_encoder_3-item2.t5_prompt_embeds Using per-key quantization points (text_encoder-item3.clip_pooled_prompt_embeds: torch.Size([256])) for text_encoder-item3.clip_pooled_prompt_embeds Using per-key quantization points (text_encoder-item3.clip_prompt_embeds: torch.Size([256])) for text_encoder-item3.clip_prompt_embeds Using per-key quantization points (text_encoder_2-item4.clip_pooled: torch.Size([256])) for text_encoder_2-item4.clip_pooled_prompt_embeds Using per-key quantization points (text_encoder_2-item4.clip_prompt: torch.Size([256])) for text_encoder_2-item4.clip_prompt_embeds Using per-key quantization points (text_encoder_3-item5.t5_prompt_embeds: torch.Size([256])) for text_encoder_3-item5.t5_prompt_embeds Using per-key quantization points (vae.encoder_f0: torch.Size([256])) for vae.encoder_f0 Using per-key quantization points (vae.encoder_f1: torch.Size([256])) for vae.encoder_f1 Using per-key quantization points (vae.decoder: torch.Size([256])) for vae.decoder IndividualUnPacker: text_encoder-item0.clip_pooled_prompt_embeds: torch.Size([768]) text_encoder-item0.clip_prompt_embeds: torch.Size([77, 768]) text_encoder_2-item1.clip_pooled_prompt_embeds: torch.Size([1280]) text_encoder_2-item1.clip_prompt_embeds: torch.Size([77, 1280]) text_encoder_3-item2.t5_prompt_embeds: torch.Size([77, 4096]) text_encoder-item3.clip_pooled_prompt_embeds: torch.Size([768]) text_encoder-item3.clip_prompt_embeds: torch.Size([77, 768]) text_encoder_2-item4.clip_pooled_prompt_embeds: torch.Size([1280]) text_encoder_2-item4.clip_prompt_embeds: torch.Size([77, 1280]) text_encoder_3-item5.t5_prompt_embeds: torch.Size([77, 4096]) vae.decoder: torch.Size([16, 128, 128]) vae.encoder_f0: torch.Size([32, 128, 128]) vae.encoder_f1: torch.Size([32, 128, 128]) Per-key MSE Breakdown (Quant=Truncation+Quantization, Codec=Compression): Key Quant-MSE Total-MSE ------------------------------------------------------------------------------------- text_encoder-item0.clip_pooled_prompt_embeds 0.00265765 0.95577192 text_encoder-item0.clip_prompt_embeds 0.00025784 23.93944044 text_encoder_2-item1.clip_pooled_prompt_embeds 0.00017733 0.76037216 text_encoder_2-item1.clip_prompt_embeds 0.00015430 0.12135769 text_encoder_3-item2.t5_prompt_embeds 0.00000823 0.00308562 text_encoder-item3.clip_pooled_prompt_embeds 0.00841799 1.01420029 text_encoder-item3.clip_prompt_embeds 0.00023247 23.90570887 text_encoder_2-item4.clip_pooled_prompt_embeds 0.00016751 0.67051430 text_encoder_2-item4.clip_prompt_embeds 0.00009830 0.47049568 text_encoder_3-item5.t5_prompt_embeds 0.00001263 0.00227117 vae.encoder_f0 0.00734802 2.81556892 vae.encoder_f1 0.00734987 2.81593871 vae.decoder 0.00018093 0.03928421 ------------------------------------------------------------------------------------- TOTAL 0.00345989 2.58982242 (elements=2,260,992) ---------------------- -------------------------------------------------------- SAMPLE-WISE STATISTICS ---------------------- -------------------------------------------------------- Handler sd35 Strategy individual Architecture hyperprior-featurecoding ---------------------- -------------------------------------------------------- Total Elements 2260992 Total Bytes 110556 BPFP 0.3912 bits/point EBPFP 0.7824 equivalent bits/point MSE 2.589822 ---------------------- -------------------------------------------------------- Time: 0.748s Load: 0.008s, Pack+Encode: 0.293s, Decode+Unpack: 0.447s ---------------------- -------------------------------------------------------- Restored Feature Format: [Dict] with 8 keys key['text_encoder-item0']: [Dict] with 2 keys key['clip_pooled_prompt_embeds']: [Tensor] shape=torch.Size([768]), dtype=torch.float16, device=cpu key['clip_prompt_embeds']: [Tensor] shape=torch.Size([77, 768]), dtype=torch.float16, device=cpu key['text_encoder_2-item1']: [Dict] with 2 keys key['clip_pooled_prompt_embeds']: [Tensor] shape=torch.Size([1280]), dtype=torch.float16, device=cpu key['clip_prompt_embeds']: [Tensor] shape=torch.Size([77, 1280]), dtype=torch.float16, device=cpu key['text_encoder_3-item2']: [Dict] with 1 keys key['t5_prompt_embeds']: [Tensor] shape=torch.Size([77, 4096]), dtype=torch.float16, device=cpu key['text_encoder-item3']: [Dict] with 2 keys key['clip_pooled_prompt_embeds']: [Tensor] shape=torch.Size([768]), dtype=torch.float16, device=cpu key['clip_prompt_embeds']: [Tensor] shape=torch.Size([77, 768]), dtype=torch.float16, device=cpu key['text_encoder_2-item4']: [Dict] with 2 keys key['clip_pooled_prompt_embeds']: [Tensor] shape=torch.Size([1280]), dtype=torch.float16, device=cpu key['clip_prompt_embeds']: [Tensor] shape=torch.Size([77, 1280]), dtype=torch.float16, device=cpu key['text_encoder_3-item5']: [Dict] with 1 keys key['t5_prompt_embeds']: [Tensor] shape=torch.Size([77, 4096]), dtype=torch.float16, device=cpu key['vae.decoder']: [Tensor] shape=torch.Size([16, 128, 128]), dtype=torch.float16, device=cpu key['vae.encoder']: [Dict] with 2 keys key['vae.encoder_f0']: [Tensor] shape=torch.Size([32, 128, 128]), dtype=torch.float16, device=cpu key['vae.encoder_f1']: [Tensor] shape=torch.Size([32, 128, 128]), dtype=torch.float16, device=cpu 💾 Converting with 2.5898 MSE: from ../datasets/FeatureCoding-StableDiffusion3.5/100samples_features/000000449996.zst to output-fixed/sd35/lambda0.004/hyperprior-featurecoding-8bit-individual/sd35cond/000000449996.zst 💪 Processing: ../datasets/FeatureCoding-StableDiffusion3.5/100samples_features/000000451714.zst (80/100) [1/4] FRONTEND: Loading features from ../datasets/FeatureCoding-StableDiffusion3.5/100samples_features/000000451714.zst... Original data structure: root: [Dict] with 8 keys key['text_encoder-item0']: [Dict] with 2 keys key['clip_pooled_prompt_embeds']: [Tensor] shape=torch.Size([768]), dtype=torch.float16, device=cpu key['clip_prompt_embeds']: [Tensor] shape=torch.Size([77, 768]), dtype=torch.float16, device=cpu key['text_encoder_2-item1']: [Dict] with 2 keys key['clip_pooled_prompt_embeds']: [Tensor] shape=torch.Size([1280]), dtype=torch.float16, device=cpu key['clip_prompt_embeds']: [Tensor] shape=torch.Size([77, 1280]), dtype=torch.float16, device=cpu key['text_encoder_3-item2']: [Dict] with 1 keys key['t5_prompt_embeds']: [Tensor] shape=torch.Size([77, 4096]), dtype=torch.float16, device=cpu key['text_encoder-item3']: [Dict] with 2 keys key['clip_pooled_prompt_embeds']: [Tensor] shape=torch.Size([768]), dtype=torch.float16, device=cpu key['clip_prompt_embeds']: [Tensor] shape=torch.Size([77, 768]), dtype=torch.float16, device=cpu key['text_encoder_2-item4']: [Dict] with 2 keys key['clip_pooled_prompt_embeds']: [Tensor] shape=torch.Size([1280]), dtype=torch.float16, device=cpu key['clip_prompt_embeds']: [Tensor] shape=torch.Size([77, 1280]), dtype=torch.float16, device=cpu key['text_encoder_3-item5']: [Dict] with 1 keys key['t5_prompt_embeds']: [Tensor] shape=torch.Size([77, 4096]), dtype=torch.float16, device=cpu key['vae.encoder-item6']: [Dict] with 2 keys key['vae.encoder_f0']: [Tensor] shape=torch.Size([32, 128, 128]), dtype=torch.float16, device=cpu key['vae.encoder_f1']: [Tensor] shape=torch.Size([32, 128, 128]), dtype=torch.float16, device=cpu key['vae.decoder']: [Tensor] shape=torch.Size([16, 128, 128]), dtype=torch.float16, device=cpu ⌛️ [1/4] FRONTEND: Load time: 0.008s ------------------------------------------------------------ SD3.5 Features Summary ------------------------------------------------------------ Number of text encoders: 3 Encoder names: ['text_encoder', 'text_encoder_2', 'text_encoder_3'] Has VAE latents: True Has VAE encoder features: True Data type: torch.float16 text_encoder-item0: clip_pooled_prompt_embeds: torch.Size([768]), torch.float16 clip_prompt_embeds: torch.Size([77, 768]), torch.float16 text_encoder_2-item1: clip_pooled_prompt_embeds: torch.Size([1280]), torch.float16 clip_prompt_embeds: torch.Size([77, 1280]), torch.float16 text_encoder_3-item2: t5_prompt_embeds: torch.Size([77, 4096]), torch.float16 text_encoder-item3: clip_pooled_prompt_embeds: torch.Size([768]), torch.float16 clip_prompt_embeds: torch.Size([77, 768]), torch.float16 text_encoder_2-item4: clip_pooled_prompt_embeds: torch.Size([1280]), torch.float16 clip_prompt_embeds: torch.Size([77, 1280]), torch.float16 text_encoder_3-item5: t5_prompt_embeds: torch.Size([77, 4096]), torch.float16 vae.decoder latents: torch.Size([16, 128, 128]) vae.encoder features: vae.encoder_f0: torch.Size([32, 128, 128]), torch.float16 vae.encoder_f1: torch.Size([32, 128, 128]), torch.float16 ------------------------------------------------------------ [2/4] FRONTEND: Pack + Encode (strategy: individual)... IndividualPacker: text_encoder-item0.clip_pooled_prompt_embeds: torch.Size([768])->torch.Size([1, 1, 1, 768]) text_encoder-item0.clip_prompt_embeds: torch.Size([77, 768])->torch.Size([1, 1, 77, 768]) text_encoder_2-item1.clip_pooled_prompt_embeds: torch.Size([1280])->torch.Size([1, 1, 1, 1280]) text_encoder_2-item1.clip_prompt_embeds: torch.Size([77, 1280])->torch.Size([1, 1, 77, 1280]) text_encoder_3-item2.t5_prompt_embeds: torch.Size([77, 4096])->torch.Size([1, 1, 77, 4096]) text_encoder-item3.clip_pooled_prompt_embeds: torch.Size([768])->torch.Size([1, 1, 1, 768]) text_encoder-item3.clip_prompt_embeds: torch.Size([77, 768])->torch.Size([1, 1, 77, 768]) text_encoder_2-item4.clip_pooled_prompt_embeds: torch.Size([1280])->torch.Size([1, 1, 1, 1280]) text_encoder_2-item4.clip_prompt_embeds: torch.Size([77, 1280])->torch.Size([1, 1, 77, 1280]) text_encoder_3-item5.t5_prompt_embeds: torch.Size([77, 4096])->torch.Size([1, 1, 77, 4096]) vae.encoder_f0: torch.Size([32, 128, 128])->torch.Size([1, 1, 512, 1024]) vae.encoder_f1: torch.Size([32, 128, 128])->torch.Size([1, 1, 512, 1024]) vae.decoder: torch.Size([16, 128, 128])->torch.Size([1, 1, 512, 512]) Using per-key quantization points (text_encoder-item0.clip_pooled_prompt_embeds: torch.Size([256])) for text_encoder-item0.clip_pooled_prompt_embeds text_encoder-item0.clip_pooled_prompt_embeds: 728B, BPFP=7.5833 Using per-key quantization points (text_encoder-item0.clip_prompt_embeds: torch.Size([256])) for text_encoder-item0.clip_prompt_embeds text_encoder-item0.clip_prompt_embeds: 6,696B, BPFP=0.9058 Using per-key quantization points (text_encoder_2-item1.clip_pooled_prompt_embeds: torch.Size([256])) for text_encoder_2-item1.clip_pooled_prompt_embeds text_encoder_2-item1.clip_pooled_prompt_embeds: 1,200B, BPFP=7.5000 Using per-key quantization points (text_encoder_2-item1.clip_prompt_embeds: torch.Size([256])) for text_encoder_2-item1.clip_prompt_embeds text_encoder_2-item1.clip_prompt_embeds: 11,444B, BPFP=0.9289 Using per-key quantization points (text_encoder_3-item2.t5_prompt_embeds: torch.Size([256])) for text_encoder_3-item2.t5_prompt_embeds text_encoder_3-item2.t5_prompt_embeds: 28,276B, BPFP=0.7172 Using per-key quantization points (text_encoder-item3.clip_pooled_prompt_embeds: torch.Size([256])) for text_encoder-item3.clip_pooled_prompt_embeds text_encoder-item3.clip_pooled_prompt_embeds: 760B, BPFP=7.9167 Using per-key quantization points (text_encoder-item3.clip_prompt_embeds: torch.Size([256])) for text_encoder-item3.clip_prompt_embeds text_encoder-item3.clip_prompt_embeds: 4,024B, BPFP=0.5444 Using per-key quantization points (text_encoder_2-item4.clip_pooled: torch.Size([256])) for text_encoder_2-item4.clip_pooled_prompt_embeds text_encoder_2-item4.clip_pooled_prompt_embeds: 1,268B, BPFP=7.9250 Using per-key quantization points (text_encoder_2-item4.clip_prompt: torch.Size([256])) for text_encoder_2-item4.clip_prompt_embeds text_encoder_2-item4.clip_prompt_embeds: 6,996B, BPFP=0.5679 Using per-key quantization points (text_encoder_3-item5.t5_prompt_embeds: torch.Size([256])) for text_encoder_3-item5.t5_prompt_embeds text_encoder_3-item5.t5_prompt_embeds: 18,416B, BPFP=0.4671 Using per-key quantization points (vae.encoder_f0: torch.Size([256])) for vae.encoder_f0 vae.encoder_f0: 21,908B, BPFP=0.3343 Using per-key quantization points (vae.encoder_f1: torch.Size([256])) for vae.encoder_f1 vae.encoder_f1: 21,908B, BPFP=0.3343 Using per-key quantization points (vae.decoder: torch.Size([256])) for vae.decoder vae.decoder: 6,892B, BPFP=0.2103 ⌛️ [2/4] FRONTEND: Frontend time: 0.295s (Pack+Encode) [3/4] BACKEND: Decode + Unpack... Using per-key quantization points (text_encoder-item0.clip_pooled_prompt_embeds: torch.Size([256])) for text_encoder-item0.clip_pooled_prompt_embeds Using per-key quantization points (text_encoder-item0.clip_prompt_embeds: torch.Size([256])) for text_encoder-item0.clip_prompt_embeds Using per-key quantization points (text_encoder_2-item1.clip_pooled_prompt_embeds: torch.Size([256])) for text_encoder_2-item1.clip_pooled_prompt_embeds Using per-key quantization points (text_encoder_2-item1.clip_prompt_embeds: torch.Size([256])) for text_encoder_2-item1.clip_prompt_embeds Using per-key quantization points (text_encoder_3-item2.t5_prompt_embeds: torch.Size([256])) for text_encoder_3-item2.t5_prompt_embeds Using per-key quantization points (text_encoder-item3.clip_pooled_prompt_embeds: torch.Size([256])) for text_encoder-item3.clip_pooled_prompt_embeds Using per-key quantization points (text_encoder-item3.clip_prompt_embeds: torch.Size([256])) for text_encoder-item3.clip_prompt_embeds Using per-key quantization points (text_encoder_2-item4.clip_pooled: torch.Size([256])) for text_encoder_2-item4.clip_pooled_prompt_embeds Using per-key quantization points (text_encoder_2-item4.clip_prompt: torch.Size([256])) for text_encoder_2-item4.clip_prompt_embeds Using per-key quantization points (text_encoder_3-item5.t5_prompt_embeds: torch.Size([256])) for text_encoder_3-item5.t5_prompt_embeds Using per-key quantization points (vae.encoder_f0: torch.Size([256])) for vae.encoder_f0 Using per-key quantization points (vae.encoder_f1: torch.Size([256])) for vae.encoder_f1 Using per-key quantization points (vae.decoder: torch.Size([256])) for vae.decoder IndividualUnPacker: text_encoder-item0.clip_pooled_prompt_embeds: torch.Size([768]) text_encoder-item0.clip_prompt_embeds: torch.Size([77, 768]) text_encoder_2-item1.clip_pooled_prompt_embeds: torch.Size([1280]) text_encoder_2-item1.clip_prompt_embeds: torch.Size([77, 1280]) text_encoder_3-item2.t5_prompt_embeds: torch.Size([77, 4096]) text_encoder-item3.clip_pooled_prompt_embeds: torch.Size([768]) text_encoder-item3.clip_prompt_embeds: torch.Size([77, 768]) text_encoder_2-item4.clip_pooled_prompt_embeds: torch.Size([1280]) text_encoder_2-item4.clip_prompt_embeds: torch.Size([77, 1280]) text_encoder_3-item5.t5_prompt_embeds: torch.Size([77, 4096]) vae.decoder: torch.Size([16, 128, 128]) vae.encoder_f0: torch.Size([32, 128, 128]) vae.encoder_f1: torch.Size([32, 128, 128]) ⌛️ [3/4] BACKEND: Backend time: 0.446s [4/4] METRICS: Computing MSE Breakdown... Using per-key quantization points (text_encoder-item0.clip_pooled_prompt_embeds: torch.Size([256])) for text_encoder-item0.clip_pooled_prompt_embeds Using per-key quantization points (text_encoder-item0.clip_prompt_embeds: torch.Size([256])) for text_encoder-item0.clip_prompt_embeds Using per-key quantization points (text_encoder_2-item1.clip_pooled_prompt_embeds: torch.Size([256])) for text_encoder_2-item1.clip_pooled_prompt_embeds Using per-key quantization points (text_encoder_2-item1.clip_prompt_embeds: torch.Size([256])) for text_encoder_2-item1.clip_prompt_embeds Using per-key quantization points (text_encoder_3-item2.t5_prompt_embeds: torch.Size([256])) for text_encoder_3-item2.t5_prompt_embeds Using per-key quantization points (text_encoder-item3.clip_pooled_prompt_embeds: torch.Size([256])) for text_encoder-item3.clip_pooled_prompt_embeds Using per-key quantization points (text_encoder-item3.clip_prompt_embeds: torch.Size([256])) for text_encoder-item3.clip_prompt_embeds Using per-key quantization points (text_encoder_2-item4.clip_pooled: torch.Size([256])) for text_encoder_2-item4.clip_pooled_prompt_embeds Using per-key quantization points (text_encoder_2-item4.clip_prompt: torch.Size([256])) for text_encoder_2-item4.clip_prompt_embeds Using per-key quantization points (text_encoder_3-item5.t5_prompt_embeds: torch.Size([256])) for text_encoder_3-item5.t5_prompt_embeds Using per-key quantization points (vae.encoder_f0: torch.Size([256])) for vae.encoder_f0 Using per-key quantization points (vae.encoder_f1: torch.Size([256])) for vae.encoder_f1 Using per-key quantization points (vae.decoder: torch.Size([256])) for vae.decoder IndividualUnPacker: text_encoder-item0.clip_pooled_prompt_embeds: torch.Size([768]) text_encoder-item0.clip_prompt_embeds: torch.Size([77, 768]) text_encoder_2-item1.clip_pooled_prompt_embeds: torch.Size([1280]) text_encoder_2-item1.clip_prompt_embeds: torch.Size([77, 1280]) text_encoder_3-item2.t5_prompt_embeds: torch.Size([77, 4096]) text_encoder-item3.clip_pooled_prompt_embeds: torch.Size([768]) text_encoder-item3.clip_prompt_embeds: torch.Size([77, 768]) text_encoder_2-item4.clip_pooled_prompt_embeds: torch.Size([1280]) text_encoder_2-item4.clip_prompt_embeds: torch.Size([77, 1280]) text_encoder_3-item5.t5_prompt_embeds: torch.Size([77, 4096]) vae.decoder: torch.Size([16, 128, 128]) vae.encoder_f0: torch.Size([32, 128, 128]) vae.encoder_f1: torch.Size([32, 128, 128]) Per-key MSE Breakdown (Quant=Truncation+Quantization, Codec=Compression): Key Quant-MSE Total-MSE ------------------------------------------------------------------------------------- text_encoder-item0.clip_pooled_prompt_embeds 0.00019649 0.94335349 text_encoder-item0.clip_prompt_embeds 0.00023510 156.96306818 text_encoder_2-item1.clip_pooled_prompt_embeds 0.00023039 0.87551193 text_encoder_2-item1.clip_prompt_embeds 0.00019044 0.14941153 text_encoder_3-item2.t5_prompt_embeds 0.00000826 0.00310181 text_encoder-item3.clip_pooled_prompt_embeds 0.00841799 1.01420029 text_encoder-item3.clip_prompt_embeds 0.00023247 23.90570887 text_encoder_2-item4.clip_pooled_prompt_embeds 0.00016751 0.67051430 text_encoder_2-item4.clip_prompt_embeds 0.00009830 0.47049568 text_encoder_3-item5.t5_prompt_embeds 0.00001263 0.00227117 vae.encoder_f0 0.00637359 2.93842912 vae.encoder_f1 0.00637830 2.93750906 vae.decoder 0.00018566 0.06401458 ------------------------------------------------------------------------------------- TOTAL 0.00300937 6.12987391 (elements=2,260,992) ---------------------- -------------------------------------------------------- SAMPLE-WISE STATISTICS ---------------------- -------------------------------------------------------- Handler sd35 Strategy individual Architecture hyperprior-featurecoding ---------------------- -------------------------------------------------------- Total Elements 2260992 Total Bytes 130516 BPFP 0.4618 bits/point EBPFP 0.9236 equivalent bits/point MSE 6.129874 ---------------------- -------------------------------------------------------- Time: 0.749s Load: 0.008s, Pack+Encode: 0.295s, Decode+Unpack: 0.446s ---------------------- -------------------------------------------------------- Restored Feature Format: [Dict] with 8 keys key['text_encoder-item0']: [Dict] with 2 keys key['clip_pooled_prompt_embeds']: [Tensor] shape=torch.Size([768]), dtype=torch.float16, device=cpu key['clip_prompt_embeds']: [Tensor] shape=torch.Size([77, 768]), dtype=torch.float16, device=cpu key['text_encoder_2-item1']: [Dict] with 2 keys key['clip_pooled_prompt_embeds']: [Tensor] shape=torch.Size([1280]), dtype=torch.float16, device=cpu key['clip_prompt_embeds']: [Tensor] shape=torch.Size([77, 1280]), dtype=torch.float16, device=cpu key['text_encoder_3-item2']: [Dict] with 1 keys key['t5_prompt_embeds']: [Tensor] shape=torch.Size([77, 4096]), dtype=torch.float16, device=cpu key['text_encoder-item3']: [Dict] with 2 keys key['clip_pooled_prompt_embeds']: [Tensor] shape=torch.Size([768]), dtype=torch.float16, device=cpu key['clip_prompt_embeds']: [Tensor] shape=torch.Size([77, 768]), dtype=torch.float16, device=cpu key['text_encoder_2-item4']: [Dict] with 2 keys key['clip_pooled_prompt_embeds']: [Tensor] shape=torch.Size([1280]), dtype=torch.float16, device=cpu key['clip_prompt_embeds']: [Tensor] shape=torch.Size([77, 1280]), dtype=torch.float16, device=cpu key['text_encoder_3-item5']: [Dict] with 1 keys key['t5_prompt_embeds']: [Tensor] shape=torch.Size([77, 4096]), dtype=torch.float16, device=cpu key['vae.decoder']: [Tensor] shape=torch.Size([16, 128, 128]), dtype=torch.float16, device=cpu key['vae.encoder']: [Dict] with 2 keys key['vae.encoder_f0']: [Tensor] shape=torch.Size([32, 128, 128]), dtype=torch.float16, device=cpu key['vae.encoder_f1']: [Tensor] shape=torch.Size([32, 128, 128]), dtype=torch.float16, device=cpu 💾 Converting with 6.1299 MSE: from ../datasets/FeatureCoding-StableDiffusion3.5/100samples_features/000000451714.zst to output-fixed/sd35/lambda0.004/hyperprior-featurecoding-8bit-individual/sd35cond/000000451714.zst 💪 Processing: ../datasets/FeatureCoding-StableDiffusion3.5/100samples_features/000000464358.zst (81/100) [1/4] FRONTEND: Loading features from ../datasets/FeatureCoding-StableDiffusion3.5/100samples_features/000000464358.zst... Original data structure: root: [Dict] with 8 keys key['text_encoder-item0']: [Dict] with 2 keys key['clip_pooled_prompt_embeds']: [Tensor] shape=torch.Size([768]), dtype=torch.float16, device=cpu key['clip_prompt_embeds']: [Tensor] shape=torch.Size([77, 768]), dtype=torch.float16, device=cpu key['text_encoder_2-item1']: [Dict] with 2 keys key['clip_pooled_prompt_embeds']: [Tensor] shape=torch.Size([1280]), dtype=torch.float16, device=cpu key['clip_prompt_embeds']: [Tensor] shape=torch.Size([77, 1280]), dtype=torch.float16, device=cpu key['text_encoder_3-item2']: [Dict] with 1 keys key['t5_prompt_embeds']: [Tensor] shape=torch.Size([77, 4096]), dtype=torch.float16, device=cpu key['text_encoder-item3']: [Dict] with 2 keys key['clip_pooled_prompt_embeds']: [Tensor] shape=torch.Size([768]), dtype=torch.float16, device=cpu key['clip_prompt_embeds']: [Tensor] shape=torch.Size([77, 768]), dtype=torch.float16, device=cpu key['text_encoder_2-item4']: [Dict] with 2 keys key['clip_pooled_prompt_embeds']: [Tensor] shape=torch.Size([1280]), dtype=torch.float16, device=cpu key['clip_prompt_embeds']: [Tensor] shape=torch.Size([77, 1280]), dtype=torch.float16, device=cpu key['text_encoder_3-item5']: [Dict] with 1 keys key['t5_prompt_embeds']: [Tensor] shape=torch.Size([77, 4096]), dtype=torch.float16, device=cpu key['vae.encoder-item6']: [Dict] with 2 keys key['vae.encoder_f0']: [Tensor] shape=torch.Size([32, 128, 128]), dtype=torch.float16, device=cpu key['vae.encoder_f1']: [Tensor] shape=torch.Size([32, 128, 128]), dtype=torch.float16, device=cpu key['vae.decoder']: [Tensor] shape=torch.Size([16, 128, 128]), dtype=torch.float16, device=cpu ⌛️ [1/4] FRONTEND: Load time: 0.008s ------------------------------------------------------------ SD3.5 Features Summary ------------------------------------------------------------ Number of text encoders: 3 Encoder names: ['text_encoder', 'text_encoder_2', 'text_encoder_3'] Has VAE latents: True Has VAE encoder features: True Data type: torch.float16 text_encoder-item0: clip_pooled_prompt_embeds: torch.Size([768]), torch.float16 clip_prompt_embeds: torch.Size([77, 768]), torch.float16 text_encoder_2-item1: clip_pooled_prompt_embeds: torch.Size([1280]), torch.float16 clip_prompt_embeds: torch.Size([77, 1280]), torch.float16 text_encoder_3-item2: t5_prompt_embeds: torch.Size([77, 4096]), torch.float16 text_encoder-item3: clip_pooled_prompt_embeds: torch.Size([768]), torch.float16 clip_prompt_embeds: torch.Size([77, 768]), torch.float16 text_encoder_2-item4: clip_pooled_prompt_embeds: torch.Size([1280]), torch.float16 clip_prompt_embeds: torch.Size([77, 1280]), torch.float16 text_encoder_3-item5: t5_prompt_embeds: torch.Size([77, 4096]), torch.float16 vae.decoder latents: torch.Size([16, 128, 128]) vae.encoder features: vae.encoder_f0: torch.Size([32, 128, 128]), torch.float16 vae.encoder_f1: torch.Size([32, 128, 128]), torch.float16 ------------------------------------------------------------ [2/4] FRONTEND: Pack + Encode (strategy: individual)... IndividualPacker: text_encoder-item0.clip_pooled_prompt_embeds: torch.Size([768])->torch.Size([1, 1, 1, 768]) text_encoder-item0.clip_prompt_embeds: torch.Size([77, 768])->torch.Size([1, 1, 77, 768]) text_encoder_2-item1.clip_pooled_prompt_embeds: torch.Size([1280])->torch.Size([1, 1, 1, 1280]) text_encoder_2-item1.clip_prompt_embeds: torch.Size([77, 1280])->torch.Size([1, 1, 77, 1280]) text_encoder_3-item2.t5_prompt_embeds: torch.Size([77, 4096])->torch.Size([1, 1, 77, 4096]) text_encoder-item3.clip_pooled_prompt_embeds: torch.Size([768])->torch.Size([1, 1, 1, 768]) text_encoder-item3.clip_prompt_embeds: torch.Size([77, 768])->torch.Size([1, 1, 77, 768]) text_encoder_2-item4.clip_pooled_prompt_embeds: torch.Size([1280])->torch.Size([1, 1, 1, 1280]) text_encoder_2-item4.clip_prompt_embeds: torch.Size([77, 1280])->torch.Size([1, 1, 77, 1280]) text_encoder_3-item5.t5_prompt_embeds: torch.Size([77, 4096])->torch.Size([1, 1, 77, 4096]) vae.encoder_f0: torch.Size([32, 128, 128])->torch.Size([1, 1, 512, 1024]) vae.encoder_f1: torch.Size([32, 128, 128])->torch.Size([1, 1, 512, 1024]) vae.decoder: torch.Size([16, 128, 128])->torch.Size([1, 1, 512, 512]) Using per-key quantization points (text_encoder-item0.clip_pooled_prompt_embeds: torch.Size([256])) for text_encoder-item0.clip_pooled_prompt_embeds text_encoder-item0.clip_pooled_prompt_embeds: 736B, BPFP=7.6667 Using per-key quantization points (text_encoder-item0.clip_prompt_embeds: torch.Size([256])) for text_encoder-item0.clip_prompt_embeds text_encoder-item0.clip_prompt_embeds: 6,024B, BPFP=0.8149 Using per-key quantization points (text_encoder_2-item1.clip_pooled_prompt_embeds: torch.Size([256])) for text_encoder_2-item1.clip_pooled_prompt_embeds text_encoder_2-item1.clip_pooled_prompt_embeds: 1,200B, BPFP=7.5000 Using per-key quantization points (text_encoder_2-item1.clip_prompt_embeds: torch.Size([256])) for text_encoder_2-item1.clip_prompt_embeds text_encoder_2-item1.clip_prompt_embeds: 11,296B, BPFP=0.9169 Using per-key quantization points (text_encoder_3-item2.t5_prompt_embeds: torch.Size([256])) for text_encoder_3-item2.t5_prompt_embeds text_encoder_3-item2.t5_prompt_embeds: 28,664B, BPFP=0.7271 Using per-key quantization points (text_encoder-item3.clip_pooled_prompt_embeds: torch.Size([256])) for text_encoder-item3.clip_pooled_prompt_embeds text_encoder-item3.clip_pooled_prompt_embeds: 760B, BPFP=7.9167 Using per-key quantization points (text_encoder-item3.clip_prompt_embeds: torch.Size([256])) for text_encoder-item3.clip_prompt_embeds text_encoder-item3.clip_prompt_embeds: 4,024B, BPFP=0.5444 Using per-key quantization points (text_encoder_2-item4.clip_pooled: torch.Size([256])) for text_encoder_2-item4.clip_pooled_prompt_embeds text_encoder_2-item4.clip_pooled_prompt_embeds: 1,268B, BPFP=7.9250 Using per-key quantization points (text_encoder_2-item4.clip_prompt: torch.Size([256])) for text_encoder_2-item4.clip_prompt_embeds text_encoder_2-item4.clip_prompt_embeds: 6,996B, BPFP=0.5679 Using per-key quantization points (text_encoder_3-item5.t5_prompt_embeds: torch.Size([256])) for text_encoder_3-item5.t5_prompt_embeds text_encoder_3-item5.t5_prompt_embeds: 18,416B, BPFP=0.4671 Using per-key quantization points (vae.encoder_f0: torch.Size([256])) for vae.encoder_f0 vae.encoder_f0: 16,424B, BPFP=0.2506 Using per-key quantization points (vae.encoder_f1: torch.Size([256])) for vae.encoder_f1 vae.encoder_f1: 16,424B, BPFP=0.2506 Using per-key quantization points (vae.decoder: torch.Size([256])) for vae.decoder vae.decoder: 4,880B, BPFP=0.1489 ⌛️ [2/4] FRONTEND: Frontend time: 0.293s (Pack+Encode) [3/4] BACKEND: Decode + Unpack... Using per-key quantization points (text_encoder-item0.clip_pooled_prompt_embeds: torch.Size([256])) for text_encoder-item0.clip_pooled_prompt_embeds Using per-key quantization points (text_encoder-item0.clip_prompt_embeds: torch.Size([256])) for text_encoder-item0.clip_prompt_embeds Using per-key quantization points (text_encoder_2-item1.clip_pooled_prompt_embeds: torch.Size([256])) for text_encoder_2-item1.clip_pooled_prompt_embeds Using per-key quantization points (text_encoder_2-item1.clip_prompt_embeds: torch.Size([256])) for text_encoder_2-item1.clip_prompt_embeds Using per-key quantization points (text_encoder_3-item2.t5_prompt_embeds: torch.Size([256])) for text_encoder_3-item2.t5_prompt_embeds Using per-key quantization points (text_encoder-item3.clip_pooled_prompt_embeds: torch.Size([256])) for text_encoder-item3.clip_pooled_prompt_embeds Using per-key quantization points (text_encoder-item3.clip_prompt_embeds: torch.Size([256])) for text_encoder-item3.clip_prompt_embeds Using per-key quantization points (text_encoder_2-item4.clip_pooled: torch.Size([256])) for text_encoder_2-item4.clip_pooled_prompt_embeds Using per-key quantization points (text_encoder_2-item4.clip_prompt: torch.Size([256])) for text_encoder_2-item4.clip_prompt_embeds Using per-key quantization points (text_encoder_3-item5.t5_prompt_embeds: torch.Size([256])) for text_encoder_3-item5.t5_prompt_embeds Using per-key quantization points (vae.encoder_f0: torch.Size([256])) for vae.encoder_f0 Using per-key quantization points (vae.encoder_f1: torch.Size([256])) for vae.encoder_f1 Using per-key quantization points (vae.decoder: torch.Size([256])) for vae.decoder IndividualUnPacker: text_encoder-item0.clip_pooled_prompt_embeds: torch.Size([768]) text_encoder-item0.clip_prompt_embeds: torch.Size([77, 768]) text_encoder_2-item1.clip_pooled_prompt_embeds: torch.Size([1280]) text_encoder_2-item1.clip_prompt_embeds: torch.Size([77, 1280]) text_encoder_3-item2.t5_prompt_embeds: torch.Size([77, 4096]) text_encoder-item3.clip_pooled_prompt_embeds: torch.Size([768]) text_encoder-item3.clip_prompt_embeds: torch.Size([77, 768]) text_encoder_2-item4.clip_pooled_prompt_embeds: torch.Size([1280]) text_encoder_2-item4.clip_prompt_embeds: torch.Size([77, 1280]) text_encoder_3-item5.t5_prompt_embeds: torch.Size([77, 4096]) vae.decoder: torch.Size([16, 128, 128]) vae.encoder_f0: torch.Size([32, 128, 128]) vae.encoder_f1: torch.Size([32, 128, 128]) ⌛️ [3/4] BACKEND: Backend time: 0.449s [4/4] METRICS: Computing MSE Breakdown... Using per-key quantization points (text_encoder-item0.clip_pooled_prompt_embeds: torch.Size([256])) for text_encoder-item0.clip_pooled_prompt_embeds Using per-key quantization points (text_encoder-item0.clip_prompt_embeds: torch.Size([256])) for text_encoder-item0.clip_prompt_embeds Using per-key quantization points (text_encoder_2-item1.clip_pooled_prompt_embeds: torch.Size([256])) for text_encoder_2-item1.clip_pooled_prompt_embeds Using per-key quantization points (text_encoder_2-item1.clip_prompt_embeds: torch.Size([256])) for text_encoder_2-item1.clip_prompt_embeds Using per-key quantization points (text_encoder_3-item2.t5_prompt_embeds: torch.Size([256])) for text_encoder_3-item2.t5_prompt_embeds Using per-key quantization points (text_encoder-item3.clip_pooled_prompt_embeds: torch.Size([256])) for text_encoder-item3.clip_pooled_prompt_embeds Using per-key quantization points (text_encoder-item3.clip_prompt_embeds: torch.Size([256])) for text_encoder-item3.clip_prompt_embeds Using per-key quantization points (text_encoder_2-item4.clip_pooled: torch.Size([256])) for text_encoder_2-item4.clip_pooled_prompt_embeds Using per-key quantization points (text_encoder_2-item4.clip_prompt: torch.Size([256])) for text_encoder_2-item4.clip_prompt_embeds Using per-key quantization points (text_encoder_3-item5.t5_prompt_embeds: torch.Size([256])) for text_encoder_3-item5.t5_prompt_embeds Using per-key quantization points (vae.encoder_f0: torch.Size([256])) for vae.encoder_f0 Using per-key quantization points (vae.encoder_f1: torch.Size([256])) for vae.encoder_f1 Using per-key quantization points (vae.decoder: torch.Size([256])) for vae.decoder IndividualUnPacker: text_encoder-item0.clip_pooled_prompt_embeds: torch.Size([768]) text_encoder-item0.clip_prompt_embeds: torch.Size([77, 768]) text_encoder_2-item1.clip_pooled_prompt_embeds: torch.Size([1280]) text_encoder_2-item1.clip_prompt_embeds: torch.Size([77, 1280]) text_encoder_3-item2.t5_prompt_embeds: torch.Size([77, 4096]) text_encoder-item3.clip_pooled_prompt_embeds: torch.Size([768]) text_encoder-item3.clip_prompt_embeds: torch.Size([77, 768]) text_encoder_2-item4.clip_pooled_prompt_embeds: torch.Size([1280]) text_encoder_2-item4.clip_prompt_embeds: torch.Size([77, 1280]) text_encoder_3-item5.t5_prompt_embeds: torch.Size([77, 4096]) vae.decoder: torch.Size([16, 128, 128]) vae.encoder_f0: torch.Size([32, 128, 128]) vae.encoder_f1: torch.Size([32, 128, 128]) Per-key MSE Breakdown (Quant=Truncation+Quantization, Codec=Compression): Key Quant-MSE Total-MSE ------------------------------------------------------------------------------------- text_encoder-item0.clip_pooled_prompt_embeds 0.00018476 0.92882562 text_encoder-item0.clip_prompt_embeds 0.00026418 59.46081067 text_encoder_2-item1.clip_pooled_prompt_embeds 0.00018200 0.89317408 text_encoder_2-item1.clip_prompt_embeds 0.00017999 0.13005736 text_encoder_3-item2.t5_prompt_embeds 0.00000755 0.00308635 text_encoder-item3.clip_pooled_prompt_embeds 0.00841799 1.01420029 text_encoder-item3.clip_prompt_embeds 0.00023247 23.90570887 text_encoder_2-item4.clip_pooled_prompt_embeds 0.00016751 0.67051430 text_encoder_2-item4.clip_prompt_embeds 0.00009830 0.47049568 text_encoder_3-item5.t5_prompt_embeds 0.00001263 0.00227117 vae.encoder_f0 0.01530954 3.37827373 vae.encoder_f1 0.01531230 3.38023615 vae.decoder 0.00017892 0.03727905 ------------------------------------------------------------------------------------- TOTAL 0.00715252 3.78042673 (elements=2,260,992) ---------------------- -------------------------------------------------------- SAMPLE-WISE STATISTICS ---------------------- -------------------------------------------------------- Handler sd35 Strategy individual Architecture hyperprior-featurecoding ---------------------- -------------------------------------------------------- Total Elements 2260992 Total Bytes 117112 BPFP 0.4144 bits/point EBPFP 0.8287 equivalent bits/point MSE 3.780427 ---------------------- -------------------------------------------------------- Time: 0.750s Load: 0.008s, Pack+Encode: 0.293s, Decode+Unpack: 0.449s ---------------------- -------------------------------------------------------- Restored Feature Format: [Dict] with 8 keys key['text_encoder-item0']: [Dict] with 2 keys key['clip_pooled_prompt_embeds']: [Tensor] shape=torch.Size([768]), dtype=torch.float16, device=cpu key['clip_prompt_embeds']: [Tensor] shape=torch.Size([77, 768]), dtype=torch.float16, device=cpu key['text_encoder_2-item1']: [Dict] with 2 keys key['clip_pooled_prompt_embeds']: [Tensor] shape=torch.Size([1280]), dtype=torch.float16, device=cpu key['clip_prompt_embeds']: [Tensor] shape=torch.Size([77, 1280]), dtype=torch.float16, device=cpu key['text_encoder_3-item2']: [Dict] with 1 keys key['t5_prompt_embeds']: [Tensor] shape=torch.Size([77, 4096]), dtype=torch.float16, device=cpu key['text_encoder-item3']: [Dict] with 2 keys key['clip_pooled_prompt_embeds']: [Tensor] shape=torch.Size([768]), dtype=torch.float16, device=cpu key['clip_prompt_embeds']: [Tensor] shape=torch.Size([77, 768]), dtype=torch.float16, device=cpu key['text_encoder_2-item4']: [Dict] with 2 keys key['clip_pooled_prompt_embeds']: [Tensor] shape=torch.Size([1280]), dtype=torch.float16, device=cpu key['clip_prompt_embeds']: [Tensor] shape=torch.Size([77, 1280]), dtype=torch.float16, device=cpu key['text_encoder_3-item5']: [Dict] with 1 keys key['t5_prompt_embeds']: [Tensor] shape=torch.Size([77, 4096]), dtype=torch.float16, device=cpu key['vae.decoder']: [Tensor] shape=torch.Size([16, 128, 128]), dtype=torch.float16, device=cpu key['vae.encoder']: [Dict] with 2 keys key['vae.encoder_f0']: [Tensor] shape=torch.Size([32, 128, 128]), dtype=torch.float16, device=cpu key['vae.encoder_f1']: [Tensor] shape=torch.Size([32, 128, 128]), dtype=torch.float16, device=cpu 💾 Converting with 3.7804 MSE: from ../datasets/FeatureCoding-StableDiffusion3.5/100samples_features/000000464358.zst to output-fixed/sd35/lambda0.004/hyperprior-featurecoding-8bit-individual/sd35cond/000000464358.zst 💪 Processing: ../datasets/FeatureCoding-StableDiffusion3.5/100samples_features/000000466256.zst (82/100) [1/4] FRONTEND: Loading features from ../datasets/FeatureCoding-StableDiffusion3.5/100samples_features/000000466256.zst... Original data structure: root: [Dict] with 8 keys key['text_encoder-item0']: [Dict] with 2 keys key['clip_pooled_prompt_embeds']: [Tensor] shape=torch.Size([768]), dtype=torch.float16, device=cpu key['clip_prompt_embeds']: [Tensor] shape=torch.Size([77, 768]), dtype=torch.float16, device=cpu key['text_encoder_2-item1']: [Dict] with 2 keys key['clip_pooled_prompt_embeds']: [Tensor] shape=torch.Size([1280]), dtype=torch.float16, device=cpu key['clip_prompt_embeds']: [Tensor] shape=torch.Size([77, 1280]), dtype=torch.float16, device=cpu key['text_encoder_3-item2']: [Dict] with 1 keys key['t5_prompt_embeds']: [Tensor] shape=torch.Size([77, 4096]), dtype=torch.float16, device=cpu key['text_encoder-item3']: [Dict] with 2 keys key['clip_pooled_prompt_embeds']: [Tensor] shape=torch.Size([768]), dtype=torch.float16, device=cpu key['clip_prompt_embeds']: [Tensor] shape=torch.Size([77, 768]), dtype=torch.float16, device=cpu key['text_encoder_2-item4']: [Dict] with 2 keys key['clip_pooled_prompt_embeds']: [Tensor] shape=torch.Size([1280]), dtype=torch.float16, device=cpu key['clip_prompt_embeds']: [Tensor] shape=torch.Size([77, 1280]), dtype=torch.float16, device=cpu key['text_encoder_3-item5']: [Dict] with 1 keys key['t5_prompt_embeds']: [Tensor] shape=torch.Size([77, 4096]), dtype=torch.float16, device=cpu key['vae.encoder-item6']: [Dict] with 2 keys key['vae.encoder_f0']: [Tensor] shape=torch.Size([32, 128, 128]), dtype=torch.float16, device=cpu key['vae.encoder_f1']: [Tensor] shape=torch.Size([32, 128, 128]), dtype=torch.float16, device=cpu key['vae.decoder']: [Tensor] shape=torch.Size([16, 128, 128]), dtype=torch.float16, device=cpu ⌛️ [1/4] FRONTEND: Load time: 0.009s ------------------------------------------------------------ SD3.5 Features Summary ------------------------------------------------------------ Number of text encoders: 3 Encoder names: ['text_encoder', 'text_encoder_2', 'text_encoder_3'] Has VAE latents: True Has VAE encoder features: True Data type: torch.float16 text_encoder-item0: clip_pooled_prompt_embeds: torch.Size([768]), torch.float16 clip_prompt_embeds: torch.Size([77, 768]), torch.float16 text_encoder_2-item1: clip_pooled_prompt_embeds: torch.Size([1280]), torch.float16 clip_prompt_embeds: torch.Size([77, 1280]), torch.float16 text_encoder_3-item2: t5_prompt_embeds: torch.Size([77, 4096]), torch.float16 text_encoder-item3: clip_pooled_prompt_embeds: torch.Size([768]), torch.float16 clip_prompt_embeds: torch.Size([77, 768]), torch.float16 text_encoder_2-item4: clip_pooled_prompt_embeds: torch.Size([1280]), torch.float16 clip_prompt_embeds: torch.Size([77, 1280]), torch.float16 text_encoder_3-item5: t5_prompt_embeds: torch.Size([77, 4096]), torch.float16 vae.decoder latents: torch.Size([16, 128, 128]) vae.encoder features: vae.encoder_f0: torch.Size([32, 128, 128]), torch.float16 vae.encoder_f1: torch.Size([32, 128, 128]), torch.float16 ------------------------------------------------------------ [2/4] FRONTEND: Pack + Encode (strategy: individual)... IndividualPacker: text_encoder-item0.clip_pooled_prompt_embeds: torch.Size([768])->torch.Size([1, 1, 1, 768]) text_encoder-item0.clip_prompt_embeds: torch.Size([77, 768])->torch.Size([1, 1, 77, 768]) text_encoder_2-item1.clip_pooled_prompt_embeds: torch.Size([1280])->torch.Size([1, 1, 1, 1280]) text_encoder_2-item1.clip_prompt_embeds: torch.Size([77, 1280])->torch.Size([1, 1, 77, 1280]) text_encoder_3-item2.t5_prompt_embeds: torch.Size([77, 4096])->torch.Size([1, 1, 77, 4096]) text_encoder-item3.clip_pooled_prompt_embeds: torch.Size([768])->torch.Size([1, 1, 1, 768]) text_encoder-item3.clip_prompt_embeds: torch.Size([77, 768])->torch.Size([1, 1, 77, 768]) text_encoder_2-item4.clip_pooled_prompt_embeds: torch.Size([1280])->torch.Size([1, 1, 1, 1280]) text_encoder_2-item4.clip_prompt_embeds: torch.Size([77, 1280])->torch.Size([1, 1, 77, 1280]) text_encoder_3-item5.t5_prompt_embeds: torch.Size([77, 4096])->torch.Size([1, 1, 77, 4096]) vae.encoder_f0: torch.Size([32, 128, 128])->torch.Size([1, 1, 512, 1024]) vae.encoder_f1: torch.Size([32, 128, 128])->torch.Size([1, 1, 512, 1024]) vae.decoder: torch.Size([16, 128, 128])->torch.Size([1, 1, 512, 512]) Using per-key quantization points (text_encoder-item0.clip_pooled_prompt_embeds: torch.Size([256])) for text_encoder-item0.clip_pooled_prompt_embeds text_encoder-item0.clip_pooled_prompt_embeds: 720B, BPFP=7.5000 Using per-key quantization points (text_encoder-item0.clip_prompt_embeds: torch.Size([256])) for text_encoder-item0.clip_prompt_embeds text_encoder-item0.clip_prompt_embeds: 6,548B, BPFP=0.8858 Using per-key quantization points (text_encoder_2-item1.clip_pooled_prompt_embeds: torch.Size([256])) for text_encoder_2-item1.clip_pooled_prompt_embeds text_encoder_2-item1.clip_pooled_prompt_embeds: 1,196B, BPFP=7.4750 Using per-key quantization points (text_encoder_2-item1.clip_prompt_embeds: torch.Size([256])) for text_encoder_2-item1.clip_prompt_embeds text_encoder_2-item1.clip_prompt_embeds: 11,588B, BPFP=0.9406 Using per-key quantization points (text_encoder_3-item2.t5_prompt_embeds: torch.Size([256])) for text_encoder_3-item2.t5_prompt_embeds text_encoder_3-item2.t5_prompt_embeds: 28,180B, BPFP=0.7148 Using per-key quantization points (text_encoder-item3.clip_pooled_prompt_embeds: torch.Size([256])) for text_encoder-item3.clip_pooled_prompt_embeds text_encoder-item3.clip_pooled_prompt_embeds: 760B, BPFP=7.9167 Using per-key quantization points (text_encoder-item3.clip_prompt_embeds: torch.Size([256])) for text_encoder-item3.clip_prompt_embeds text_encoder-item3.clip_prompt_embeds: 4,024B, BPFP=0.5444 Using per-key quantization points (text_encoder_2-item4.clip_pooled: torch.Size([256])) for text_encoder_2-item4.clip_pooled_prompt_embeds text_encoder_2-item4.clip_pooled_prompt_embeds: 1,268B, BPFP=7.9250 Using per-key quantization points (text_encoder_2-item4.clip_prompt: torch.Size([256])) for text_encoder_2-item4.clip_prompt_embeds text_encoder_2-item4.clip_prompt_embeds: 6,996B, BPFP=0.5679 Using per-key quantization points (text_encoder_3-item5.t5_prompt_embeds: torch.Size([256])) for text_encoder_3-item5.t5_prompt_embeds text_encoder_3-item5.t5_prompt_embeds: 18,416B, BPFP=0.4671 Using per-key quantization points (vae.encoder_f0: torch.Size([256])) for vae.encoder_f0 vae.encoder_f0: 20,472B, BPFP=0.3124 Using per-key quantization points (vae.encoder_f1: torch.Size([256])) for vae.encoder_f1 vae.encoder_f1: 20,472B, BPFP=0.3124 Using per-key quantization points (vae.decoder: torch.Size([256])) for vae.decoder vae.decoder: 7,424B, BPFP=0.2266 ⌛️ [2/4] FRONTEND: Frontend time: 0.293s (Pack+Encode) [3/4] BACKEND: Decode + Unpack... Using per-key quantization points (text_encoder-item0.clip_pooled_prompt_embeds: torch.Size([256])) for text_encoder-item0.clip_pooled_prompt_embeds Using per-key quantization points (text_encoder-item0.clip_prompt_embeds: torch.Size([256])) for text_encoder-item0.clip_prompt_embeds Using per-key quantization points (text_encoder_2-item1.clip_pooled_prompt_embeds: torch.Size([256])) for text_encoder_2-item1.clip_pooled_prompt_embeds Using per-key quantization points (text_encoder_2-item1.clip_prompt_embeds: torch.Size([256])) for text_encoder_2-item1.clip_prompt_embeds Using per-key quantization points (text_encoder_3-item2.t5_prompt_embeds: torch.Size([256])) for text_encoder_3-item2.t5_prompt_embeds Using per-key quantization points (text_encoder-item3.clip_pooled_prompt_embeds: torch.Size([256])) for text_encoder-item3.clip_pooled_prompt_embeds Using per-key quantization points (text_encoder-item3.clip_prompt_embeds: torch.Size([256])) for text_encoder-item3.clip_prompt_embeds Using per-key quantization points (text_encoder_2-item4.clip_pooled: torch.Size([256])) for text_encoder_2-item4.clip_pooled_prompt_embeds Using per-key quantization points (text_encoder_2-item4.clip_prompt: torch.Size([256])) for text_encoder_2-item4.clip_prompt_embeds Using per-key quantization points (text_encoder_3-item5.t5_prompt_embeds: torch.Size([256])) for text_encoder_3-item5.t5_prompt_embeds Using per-key quantization points (vae.encoder_f0: torch.Size([256])) for vae.encoder_f0 Using per-key quantization points (vae.encoder_f1: torch.Size([256])) for vae.encoder_f1 Using per-key quantization points (vae.decoder: torch.Size([256])) for vae.decoder IndividualUnPacker: text_encoder-item0.clip_pooled_prompt_embeds: torch.Size([768]) text_encoder-item0.clip_prompt_embeds: torch.Size([77, 768]) text_encoder_2-item1.clip_pooled_prompt_embeds: torch.Size([1280]) text_encoder_2-item1.clip_prompt_embeds: torch.Size([77, 1280]) text_encoder_3-item2.t5_prompt_embeds: torch.Size([77, 4096]) text_encoder-item3.clip_pooled_prompt_embeds: torch.Size([768]) text_encoder-item3.clip_prompt_embeds: torch.Size([77, 768]) text_encoder_2-item4.clip_pooled_prompt_embeds: torch.Size([1280]) text_encoder_2-item4.clip_prompt_embeds: torch.Size([77, 1280]) text_encoder_3-item5.t5_prompt_embeds: torch.Size([77, 4096]) vae.decoder: torch.Size([16, 128, 128]) vae.encoder_f0: torch.Size([32, 128, 128]) vae.encoder_f1: torch.Size([32, 128, 128]) ⌛️ [3/4] BACKEND: Backend time: 0.449s [4/4] METRICS: Computing MSE Breakdown... Using per-key quantization points (text_encoder-item0.clip_pooled_prompt_embeds: torch.Size([256])) for text_encoder-item0.clip_pooled_prompt_embeds Using per-key quantization points (text_encoder-item0.clip_prompt_embeds: torch.Size([256])) for text_encoder-item0.clip_prompt_embeds Using per-key quantization points (text_encoder_2-item1.clip_pooled_prompt_embeds: torch.Size([256])) for text_encoder_2-item1.clip_pooled_prompt_embeds Using per-key quantization points (text_encoder_2-item1.clip_prompt_embeds: torch.Size([256])) for text_encoder_2-item1.clip_prompt_embeds Using per-key quantization points (text_encoder_3-item2.t5_prompt_embeds: torch.Size([256])) for text_encoder_3-item2.t5_prompt_embeds Using per-key quantization points (text_encoder-item3.clip_pooled_prompt_embeds: torch.Size([256])) for text_encoder-item3.clip_pooled_prompt_embeds Using per-key quantization points (text_encoder-item3.clip_prompt_embeds: torch.Size([256])) for text_encoder-item3.clip_prompt_embeds Using per-key quantization points (text_encoder_2-item4.clip_pooled: torch.Size([256])) for text_encoder_2-item4.clip_pooled_prompt_embeds Using per-key quantization points (text_encoder_2-item4.clip_prompt: torch.Size([256])) for text_encoder_2-item4.clip_prompt_embeds Using per-key quantization points (text_encoder_3-item5.t5_prompt_embeds: torch.Size([256])) for text_encoder_3-item5.t5_prompt_embeds Using per-key quantization points (vae.encoder_f0: torch.Size([256])) for vae.encoder_f0 Using per-key quantization points (vae.encoder_f1: torch.Size([256])) for vae.encoder_f1 Using per-key quantization points (vae.decoder: torch.Size([256])) for vae.decoder IndividualUnPacker: text_encoder-item0.clip_pooled_prompt_embeds: torch.Size([768]) text_encoder-item0.clip_prompt_embeds: torch.Size([77, 768]) text_encoder_2-item1.clip_pooled_prompt_embeds: torch.Size([1280]) text_encoder_2-item1.clip_prompt_embeds: torch.Size([77, 1280]) text_encoder_3-item2.t5_prompt_embeds: torch.Size([77, 4096]) text_encoder-item3.clip_pooled_prompt_embeds: torch.Size([768]) text_encoder-item3.clip_prompt_embeds: torch.Size([77, 768]) text_encoder_2-item4.clip_pooled_prompt_embeds: torch.Size([1280]) text_encoder_2-item4.clip_prompt_embeds: torch.Size([77, 1280]) text_encoder_3-item5.t5_prompt_embeds: torch.Size([77, 4096]) vae.decoder: torch.Size([16, 128, 128]) vae.encoder_f0: torch.Size([32, 128, 128]) vae.encoder_f1: torch.Size([32, 128, 128]) Per-key MSE Breakdown (Quant=Truncation+Quantization, Codec=Compression): Key Quant-MSE Total-MSE ------------------------------------------------------------------------------------- text_encoder-item0.clip_pooled_prompt_embeds 0.00018183 0.89459713 text_encoder-item0.clip_prompt_embeds 0.00021481 23.95045319 text_encoder_2-item1.clip_pooled_prompt_embeds 0.00019636 0.83880749 text_encoder_2-item1.clip_prompt_embeds 0.00020983 0.13542601 text_encoder_3-item2.t5_prompt_embeds 0.00000831 0.00373823 text_encoder-item3.clip_pooled_prompt_embeds 0.00841799 1.01420029 text_encoder-item3.clip_prompt_embeds 0.00023247 23.90570887 text_encoder_2-item4.clip_pooled_prompt_embeds 0.00016751 0.67051430 text_encoder_2-item4.clip_prompt_embeds 0.00009830 0.47049568 text_encoder_3-item5.t5_prompt_embeds 0.00001263 0.00227117 vae.encoder_f0 0.00591154 2.51572800 vae.encoder_f1 0.00591973 2.51556826 vae.decoder 0.00025286 0.06472340 ------------------------------------------------------------------------------------- TOTAL 0.00280398 2.45460840 (elements=2,260,992) ---------------------- -------------------------------------------------------- SAMPLE-WISE STATISTICS ---------------------- -------------------------------------------------------- Handler sd35 Strategy individual Architecture hyperprior-featurecoding ---------------------- -------------------------------------------------------- Total Elements 2260992 Total Bytes 128064 BPFP 0.4531 bits/point EBPFP 0.9062 equivalent bits/point MSE 2.454608 ---------------------- -------------------------------------------------------- Time: 0.750s Load: 0.009s, Pack+Encode: 0.293s, Decode+Unpack: 0.449s ---------------------- -------------------------------------------------------- Restored Feature Format: [Dict] with 8 keys key['text_encoder-item0']: [Dict] with 2 keys key['clip_pooled_prompt_embeds']: [Tensor] shape=torch.Size([768]), dtype=torch.float16, device=cpu key['clip_prompt_embeds']: [Tensor] shape=torch.Size([77, 768]), dtype=torch.float16, device=cpu key['text_encoder_2-item1']: [Dict] with 2 keys key['clip_pooled_prompt_embeds']: [Tensor] shape=torch.Size([1280]), dtype=torch.float16, device=cpu key['clip_prompt_embeds']: [Tensor] shape=torch.Size([77, 1280]), dtype=torch.float16, device=cpu key['text_encoder_3-item2']: [Dict] with 1 keys key['t5_prompt_embeds']: [Tensor] shape=torch.Size([77, 4096]), dtype=torch.float16, device=cpu key['text_encoder-item3']: [Dict] with 2 keys key['clip_pooled_prompt_embeds']: [Tensor] shape=torch.Size([768]), dtype=torch.float16, device=cpu key['clip_prompt_embeds']: [Tensor] shape=torch.Size([77, 768]), dtype=torch.float16, device=cpu key['text_encoder_2-item4']: [Dict] with 2 keys key['clip_pooled_prompt_embeds']: [Tensor] shape=torch.Size([1280]), dtype=torch.float16, device=cpu key['clip_prompt_embeds']: [Tensor] shape=torch.Size([77, 1280]), dtype=torch.float16, device=cpu key['text_encoder_3-item5']: [Dict] with 1 keys key['t5_prompt_embeds']: [Tensor] shape=torch.Size([77, 4096]), dtype=torch.float16, device=cpu key['vae.decoder']: [Tensor] shape=torch.Size([16, 128, 128]), dtype=torch.float16, device=cpu key['vae.encoder']: [Dict] with 2 keys key['vae.encoder_f0']: [Tensor] shape=torch.Size([32, 128, 128]), dtype=torch.float16, device=cpu key['vae.encoder_f1']: [Tensor] shape=torch.Size([32, 128, 128]), dtype=torch.float16, device=cpu 💾 Converting with 2.4546 MSE: from ../datasets/FeatureCoding-StableDiffusion3.5/100samples_features/000000466256.zst to output-fixed/sd35/lambda0.004/hyperprior-featurecoding-8bit-individual/sd35cond/000000466256.zst 💪 Processing: ../datasets/FeatureCoding-StableDiffusion3.5/100samples_features/000000467848.zst (83/100) [1/4] FRONTEND: Loading features from ../datasets/FeatureCoding-StableDiffusion3.5/100samples_features/000000467848.zst... Original data structure: root: [Dict] with 8 keys key['text_encoder-item0']: [Dict] with 2 keys key['clip_pooled_prompt_embeds']: [Tensor] shape=torch.Size([768]), dtype=torch.float16, device=cpu key['clip_prompt_embeds']: [Tensor] shape=torch.Size([77, 768]), dtype=torch.float16, device=cpu key['text_encoder_2-item1']: [Dict] with 2 keys key['clip_pooled_prompt_embeds']: [Tensor] shape=torch.Size([1280]), dtype=torch.float16, device=cpu key['clip_prompt_embeds']: [Tensor] shape=torch.Size([77, 1280]), dtype=torch.float16, device=cpu key['text_encoder_3-item2']: [Dict] with 1 keys key['t5_prompt_embeds']: [Tensor] shape=torch.Size([77, 4096]), dtype=torch.float16, device=cpu key['text_encoder-item3']: [Dict] with 2 keys key['clip_pooled_prompt_embeds']: [Tensor] shape=torch.Size([768]), dtype=torch.float16, device=cpu key['clip_prompt_embeds']: [Tensor] shape=torch.Size([77, 768]), dtype=torch.float16, device=cpu key['text_encoder_2-item4']: [Dict] with 2 keys key['clip_pooled_prompt_embeds']: [Tensor] shape=torch.Size([1280]), dtype=torch.float16, device=cpu key['clip_prompt_embeds']: [Tensor] shape=torch.Size([77, 1280]), dtype=torch.float16, device=cpu key['text_encoder_3-item5']: [Dict] with 1 keys key['t5_prompt_embeds']: [Tensor] shape=torch.Size([77, 4096]), dtype=torch.float16, device=cpu key['vae.encoder-item6']: [Dict] with 2 keys key['vae.encoder_f0']: [Tensor] shape=torch.Size([32, 128, 128]), dtype=torch.float16, device=cpu key['vae.encoder_f1']: [Tensor] shape=torch.Size([32, 128, 128]), dtype=torch.float16, device=cpu key['vae.decoder']: [Tensor] shape=torch.Size([16, 128, 128]), dtype=torch.float16, device=cpu ⌛️ [1/4] FRONTEND: Load time: 0.009s ------------------------------------------------------------ SD3.5 Features Summary ------------------------------------------------------------ Number of text encoders: 3 Encoder names: ['text_encoder', 'text_encoder_2', 'text_encoder_3'] Has VAE latents: True Has VAE encoder features: True Data type: torch.float16 text_encoder-item0: clip_pooled_prompt_embeds: torch.Size([768]), torch.float16 clip_prompt_embeds: torch.Size([77, 768]), torch.float16 text_encoder_2-item1: clip_pooled_prompt_embeds: torch.Size([1280]), torch.float16 clip_prompt_embeds: torch.Size([77, 1280]), torch.float16 text_encoder_3-item2: t5_prompt_embeds: torch.Size([77, 4096]), torch.float16 text_encoder-item3: clip_pooled_prompt_embeds: torch.Size([768]), torch.float16 clip_prompt_embeds: torch.Size([77, 768]), torch.float16 text_encoder_2-item4: clip_pooled_prompt_embeds: torch.Size([1280]), torch.float16 clip_prompt_embeds: torch.Size([77, 1280]), torch.float16 text_encoder_3-item5: t5_prompt_embeds: torch.Size([77, 4096]), torch.float16 vae.decoder latents: torch.Size([16, 128, 128]) vae.encoder features: vae.encoder_f0: torch.Size([32, 128, 128]), torch.float16 vae.encoder_f1: torch.Size([32, 128, 128]), torch.float16 ------------------------------------------------------------ [2/4] FRONTEND: Pack + Encode (strategy: individual)... IndividualPacker: text_encoder-item0.clip_pooled_prompt_embeds: torch.Size([768])->torch.Size([1, 1, 1, 768]) text_encoder-item0.clip_prompt_embeds: torch.Size([77, 768])->torch.Size([1, 1, 77, 768]) text_encoder_2-item1.clip_pooled_prompt_embeds: torch.Size([1280])->torch.Size([1, 1, 1, 1280]) text_encoder_2-item1.clip_prompt_embeds: torch.Size([77, 1280])->torch.Size([1, 1, 77, 1280]) text_encoder_3-item2.t5_prompt_embeds: torch.Size([77, 4096])->torch.Size([1, 1, 77, 4096]) text_encoder-item3.clip_pooled_prompt_embeds: torch.Size([768])->torch.Size([1, 1, 1, 768]) text_encoder-item3.clip_prompt_embeds: torch.Size([77, 768])->torch.Size([1, 1, 77, 768]) text_encoder_2-item4.clip_pooled_prompt_embeds: torch.Size([1280])->torch.Size([1, 1, 1, 1280]) text_encoder_2-item4.clip_prompt_embeds: torch.Size([77, 1280])->torch.Size([1, 1, 77, 1280]) text_encoder_3-item5.t5_prompt_embeds: torch.Size([77, 4096])->torch.Size([1, 1, 77, 4096]) vae.encoder_f0: torch.Size([32, 128, 128])->torch.Size([1, 1, 512, 1024]) vae.encoder_f1: torch.Size([32, 128, 128])->torch.Size([1, 1, 512, 1024]) vae.decoder: torch.Size([16, 128, 128])->torch.Size([1, 1, 512, 512]) Using per-key quantization points (text_encoder-item0.clip_pooled_prompt_embeds: torch.Size([256])) for text_encoder-item0.clip_pooled_prompt_embeds text_encoder-item0.clip_pooled_prompt_embeds: 736B, BPFP=7.6667 Using per-key quantization points (text_encoder-item0.clip_prompt_embeds: torch.Size([256])) for text_encoder-item0.clip_prompt_embeds text_encoder-item0.clip_prompt_embeds: 6,476B, BPFP=0.8761 Using per-key quantization points (text_encoder_2-item1.clip_pooled_prompt_embeds: torch.Size([256])) for text_encoder_2-item1.clip_pooled_prompt_embeds text_encoder_2-item1.clip_pooled_prompt_embeds: 1,184B, BPFP=7.4000 Using per-key quantization points (text_encoder_2-item1.clip_prompt_embeds: torch.Size([256])) for text_encoder_2-item1.clip_prompt_embeds text_encoder_2-item1.clip_prompt_embeds: 11,068B, BPFP=0.8984 Using per-key quantization points (text_encoder_3-item2.t5_prompt_embeds: torch.Size([256])) for text_encoder_3-item2.t5_prompt_embeds text_encoder_3-item2.t5_prompt_embeds: 30,564B, BPFP=0.7753 Using per-key quantization points (text_encoder-item3.clip_pooled_prompt_embeds: torch.Size([256])) for text_encoder-item3.clip_pooled_prompt_embeds text_encoder-item3.clip_pooled_prompt_embeds: 760B, BPFP=7.9167 Using per-key quantization points (text_encoder-item3.clip_prompt_embeds: torch.Size([256])) for text_encoder-item3.clip_prompt_embeds text_encoder-item3.clip_prompt_embeds: 4,024B, BPFP=0.5444 Using per-key quantization points (text_encoder_2-item4.clip_pooled: torch.Size([256])) for text_encoder_2-item4.clip_pooled_prompt_embeds text_encoder_2-item4.clip_pooled_prompt_embeds: 1,268B, BPFP=7.9250 Using per-key quantization points (text_encoder_2-item4.clip_prompt: torch.Size([256])) for text_encoder_2-item4.clip_prompt_embeds text_encoder_2-item4.clip_prompt_embeds: 6,996B, BPFP=0.5679 Using per-key quantization points (text_encoder_3-item5.t5_prompt_embeds: torch.Size([256])) for text_encoder_3-item5.t5_prompt_embeds text_encoder_3-item5.t5_prompt_embeds: 18,416B, BPFP=0.4671 Using per-key quantization points (vae.encoder_f0: torch.Size([256])) for vae.encoder_f0 vae.encoder_f0: 13,028B, BPFP=0.1988 Using per-key quantization points (vae.encoder_f1: torch.Size([256])) for vae.encoder_f1 vae.encoder_f1: 13,028B, BPFP=0.1988 Using per-key quantization points (vae.decoder: torch.Size([256])) for vae.decoder vae.decoder: 8,712B, BPFP=0.2659 ⌛️ [2/4] FRONTEND: Frontend time: 0.292s (Pack+Encode) [3/4] BACKEND: Decode + Unpack... Using per-key quantization points (text_encoder-item0.clip_pooled_prompt_embeds: torch.Size([256])) for text_encoder-item0.clip_pooled_prompt_embeds Using per-key quantization points (text_encoder-item0.clip_prompt_embeds: torch.Size([256])) for text_encoder-item0.clip_prompt_embeds Using per-key quantization points (text_encoder_2-item1.clip_pooled_prompt_embeds: torch.Size([256])) for text_encoder_2-item1.clip_pooled_prompt_embeds Using per-key quantization points (text_encoder_2-item1.clip_prompt_embeds: torch.Size([256])) for text_encoder_2-item1.clip_prompt_embeds Using per-key quantization points (text_encoder_3-item2.t5_prompt_embeds: torch.Size([256])) for text_encoder_3-item2.t5_prompt_embeds Using per-key quantization points (text_encoder-item3.clip_pooled_prompt_embeds: torch.Size([256])) for text_encoder-item3.clip_pooled_prompt_embeds Using per-key quantization points (text_encoder-item3.clip_prompt_embeds: torch.Size([256])) for text_encoder-item3.clip_prompt_embeds Using per-key quantization points (text_encoder_2-item4.clip_pooled: torch.Size([256])) for text_encoder_2-item4.clip_pooled_prompt_embeds Using per-key quantization points (text_encoder_2-item4.clip_prompt: torch.Size([256])) for text_encoder_2-item4.clip_prompt_embeds Using per-key quantization points (text_encoder_3-item5.t5_prompt_embeds: torch.Size([256])) for text_encoder_3-item5.t5_prompt_embeds Using per-key quantization points (vae.encoder_f0: torch.Size([256])) for vae.encoder_f0 Using per-key quantization points (vae.encoder_f1: torch.Size([256])) for vae.encoder_f1 Using per-key quantization points (vae.decoder: torch.Size([256])) for vae.decoder IndividualUnPacker: text_encoder-item0.clip_pooled_prompt_embeds: torch.Size([768]) text_encoder-item0.clip_prompt_embeds: torch.Size([77, 768]) text_encoder_2-item1.clip_pooled_prompt_embeds: torch.Size([1280]) text_encoder_2-item1.clip_prompt_embeds: torch.Size([77, 1280]) text_encoder_3-item2.t5_prompt_embeds: torch.Size([77, 4096]) text_encoder-item3.clip_pooled_prompt_embeds: torch.Size([768]) text_encoder-item3.clip_prompt_embeds: torch.Size([77, 768]) text_encoder_2-item4.clip_pooled_prompt_embeds: torch.Size([1280]) text_encoder_2-item4.clip_prompt_embeds: torch.Size([77, 1280]) text_encoder_3-item5.t5_prompt_embeds: torch.Size([77, 4096]) vae.decoder: torch.Size([16, 128, 128]) vae.encoder_f0: torch.Size([32, 128, 128]) vae.encoder_f1: torch.Size([32, 128, 128]) ⌛️ [3/4] BACKEND: Backend time: 0.447s [4/4] METRICS: Computing MSE Breakdown... Using per-key quantization points (text_encoder-item0.clip_pooled_prompt_embeds: torch.Size([256])) for text_encoder-item0.clip_pooled_prompt_embeds Using per-key quantization points (text_encoder-item0.clip_prompt_embeds: torch.Size([256])) for text_encoder-item0.clip_prompt_embeds Using per-key quantization points (text_encoder_2-item1.clip_pooled_prompt_embeds: torch.Size([256])) for text_encoder_2-item1.clip_pooled_prompt_embeds Using per-key quantization points (text_encoder_2-item1.clip_prompt_embeds: torch.Size([256])) for text_encoder_2-item1.clip_prompt_embeds Using per-key quantization points (text_encoder_3-item2.t5_prompt_embeds: torch.Size([256])) for text_encoder_3-item2.t5_prompt_embeds Using per-key quantization points (text_encoder-item3.clip_pooled_prompt_embeds: torch.Size([256])) for text_encoder-item3.clip_pooled_prompt_embeds Using per-key quantization points (text_encoder-item3.clip_prompt_embeds: torch.Size([256])) for text_encoder-item3.clip_prompt_embeds Using per-key quantization points (text_encoder_2-item4.clip_pooled: torch.Size([256])) for text_encoder_2-item4.clip_pooled_prompt_embeds Using per-key quantization points (text_encoder_2-item4.clip_prompt: torch.Size([256])) for text_encoder_2-item4.clip_prompt_embeds Using per-key quantization points (text_encoder_3-item5.t5_prompt_embeds: torch.Size([256])) for text_encoder_3-item5.t5_prompt_embeds Using per-key quantization points (vae.encoder_f0: torch.Size([256])) for vae.encoder_f0 Using per-key quantization points (vae.encoder_f1: torch.Size([256])) for vae.encoder_f1 Using per-key quantization points (vae.decoder: torch.Size([256])) for vae.decoder IndividualUnPacker: text_encoder-item0.clip_pooled_prompt_embeds: torch.Size([768]) text_encoder-item0.clip_prompt_embeds: torch.Size([77, 768]) text_encoder_2-item1.clip_pooled_prompt_embeds: torch.Size([1280]) text_encoder_2-item1.clip_prompt_embeds: torch.Size([77, 1280]) text_encoder_3-item2.t5_prompt_embeds: torch.Size([77, 4096]) text_encoder-item3.clip_pooled_prompt_embeds: torch.Size([768]) text_encoder-item3.clip_prompt_embeds: torch.Size([77, 768]) text_encoder_2-item4.clip_pooled_prompt_embeds: torch.Size([1280]) text_encoder_2-item4.clip_prompt_embeds: torch.Size([77, 1280]) text_encoder_3-item5.t5_prompt_embeds: torch.Size([77, 4096]) vae.decoder: torch.Size([16, 128, 128]) vae.encoder_f0: torch.Size([32, 128, 128]) vae.encoder_f1: torch.Size([32, 128, 128]) Per-key MSE Breakdown (Quant=Truncation+Quantization, Codec=Compression): Key Quant-MSE Total-MSE ------------------------------------------------------------------------------------- text_encoder-item0.clip_pooled_prompt_embeds 0.00017556 0.94081839 text_encoder-item0.clip_prompt_embeds 0.00023458 45.87281859 text_encoder_2-item1.clip_pooled_prompt_embeds 0.00219611 0.92754860 text_encoder_2-item1.clip_prompt_embeds 0.00186620 0.14551398 text_encoder_3-item2.t5_prompt_embeds 0.00000775 0.00321259 text_encoder-item3.clip_pooled_prompt_embeds 0.00841799 1.01420029 text_encoder-item3.clip_prompt_embeds 0.00023247 23.90570887 text_encoder_2-item4.clip_pooled_prompt_embeds 0.00016751 0.67051430 text_encoder_2-item4.clip_prompt_embeds 0.00009830 0.47049568 text_encoder_3-item5.t5_prompt_embeds 0.00001263 0.00227117 vae.encoder_f0 0.00588703 1.39957166 vae.encoder_f1 0.00589573 1.39970410 vae.decoder 0.00053402 0.07078428 ------------------------------------------------------------------------------------- TOTAL 0.00289910 2.51155059 (elements=2,260,992) ---------------------- -------------------------------------------------------- SAMPLE-WISE STATISTICS ---------------------- -------------------------------------------------------- Handler sd35 Strategy individual Architecture hyperprior-featurecoding ---------------------- -------------------------------------------------------- Total Elements 2260992 Total Bytes 116260 BPFP 0.4114 bits/point EBPFP 0.8227 equivalent bits/point MSE 2.511551 ---------------------- -------------------------------------------------------- Time: 0.748s Load: 0.009s, Pack+Encode: 0.292s, Decode+Unpack: 0.447s ---------------------- -------------------------------------------------------- Restored Feature Format: [Dict] with 8 keys key['text_encoder-item0']: [Dict] with 2 keys key['clip_pooled_prompt_embeds']: [Tensor] shape=torch.Size([768]), dtype=torch.float16, device=cpu key['clip_prompt_embeds']: [Tensor] shape=torch.Size([77, 768]), dtype=torch.float16, device=cpu key['text_encoder_2-item1']: [Dict] with 2 keys key['clip_pooled_prompt_embeds']: [Tensor] shape=torch.Size([1280]), dtype=torch.float16, device=cpu key['clip_prompt_embeds']: [Tensor] shape=torch.Size([77, 1280]), dtype=torch.float16, device=cpu key['text_encoder_3-item2']: [Dict] with 1 keys key['t5_prompt_embeds']: [Tensor] shape=torch.Size([77, 4096]), dtype=torch.float16, device=cpu key['text_encoder-item3']: [Dict] with 2 keys key['clip_pooled_prompt_embeds']: [Tensor] shape=torch.Size([768]), dtype=torch.float16, device=cpu key['clip_prompt_embeds']: [Tensor] shape=torch.Size([77, 768]), dtype=torch.float16, device=cpu key['text_encoder_2-item4']: [Dict] with 2 keys key['clip_pooled_prompt_embeds']: [Tensor] shape=torch.Size([1280]), dtype=torch.float16, device=cpu key['clip_prompt_embeds']: [Tensor] shape=torch.Size([77, 1280]), dtype=torch.float16, device=cpu key['text_encoder_3-item5']: [Dict] with 1 keys key['t5_prompt_embeds']: [Tensor] shape=torch.Size([77, 4096]), dtype=torch.float16, device=cpu key['vae.decoder']: [Tensor] shape=torch.Size([16, 128, 128]), dtype=torch.float16, device=cpu key['vae.encoder']: [Dict] with 2 keys key['vae.encoder_f0']: [Tensor] shape=torch.Size([32, 128, 128]), dtype=torch.float16, device=cpu key['vae.encoder_f1']: [Tensor] shape=torch.Size([32, 128, 128]), dtype=torch.float16, device=cpu 💾 Converting with 2.5116 MSE: from ../datasets/FeatureCoding-StableDiffusion3.5/100samples_features/000000467848.zst to output-fixed/sd35/lambda0.004/hyperprior-featurecoding-8bit-individual/sd35cond/000000467848.zst 💪 Processing: ../datasets/FeatureCoding-StableDiffusion3.5/100samples_features/000000468501.zst (84/100) [1/4] FRONTEND: Loading features from ../datasets/FeatureCoding-StableDiffusion3.5/100samples_features/000000468501.zst... Original data structure: root: [Dict] with 8 keys key['text_encoder-item0']: [Dict] with 2 keys key['clip_pooled_prompt_embeds']: [Tensor] shape=torch.Size([768]), dtype=torch.float16, device=cpu key['clip_prompt_embeds']: [Tensor] shape=torch.Size([77, 768]), dtype=torch.float16, device=cpu key['text_encoder_2-item1']: [Dict] with 2 keys key['clip_pooled_prompt_embeds']: [Tensor] shape=torch.Size([1280]), dtype=torch.float16, device=cpu key['clip_prompt_embeds']: [Tensor] shape=torch.Size([77, 1280]), dtype=torch.float16, device=cpu key['text_encoder_3-item2']: [Dict] with 1 keys key['t5_prompt_embeds']: [Tensor] shape=torch.Size([77, 4096]), dtype=torch.float16, device=cpu key['text_encoder-item3']: [Dict] with 2 keys key['clip_pooled_prompt_embeds']: [Tensor] shape=torch.Size([768]), dtype=torch.float16, device=cpu key['clip_prompt_embeds']: [Tensor] shape=torch.Size([77, 768]), dtype=torch.float16, device=cpu key['text_encoder_2-item4']: [Dict] with 2 keys key['clip_pooled_prompt_embeds']: [Tensor] shape=torch.Size([1280]), dtype=torch.float16, device=cpu key['clip_prompt_embeds']: [Tensor] shape=torch.Size([77, 1280]), dtype=torch.float16, device=cpu key['text_encoder_3-item5']: [Dict] with 1 keys key['t5_prompt_embeds']: [Tensor] shape=torch.Size([77, 4096]), dtype=torch.float16, device=cpu key['vae.encoder-item6']: [Dict] with 2 keys key['vae.encoder_f0']: [Tensor] shape=torch.Size([32, 128, 128]), dtype=torch.float16, device=cpu key['vae.encoder_f1']: [Tensor] shape=torch.Size([32, 128, 128]), dtype=torch.float16, device=cpu key['vae.decoder']: [Tensor] shape=torch.Size([16, 128, 128]), dtype=torch.float16, device=cpu ⌛️ [1/4] FRONTEND: Load time: 0.009s ------------------------------------------------------------ SD3.5 Features Summary ------------------------------------------------------------ Number of text encoders: 3 Encoder names: ['text_encoder', 'text_encoder_2', 'text_encoder_3'] Has VAE latents: True Has VAE encoder features: True Data type: torch.float16 text_encoder-item0: clip_pooled_prompt_embeds: torch.Size([768]), torch.float16 clip_prompt_embeds: torch.Size([77, 768]), torch.float16 text_encoder_2-item1: clip_pooled_prompt_embeds: torch.Size([1280]), torch.float16 clip_prompt_embeds: torch.Size([77, 1280]), torch.float16 text_encoder_3-item2: t5_prompt_embeds: torch.Size([77, 4096]), torch.float16 text_encoder-item3: clip_pooled_prompt_embeds: torch.Size([768]), torch.float16 clip_prompt_embeds: torch.Size([77, 768]), torch.float16 text_encoder_2-item4: clip_pooled_prompt_embeds: torch.Size([1280]), torch.float16 clip_prompt_embeds: torch.Size([77, 1280]), torch.float16 text_encoder_3-item5: t5_prompt_embeds: torch.Size([77, 4096]), torch.float16 vae.decoder latents: torch.Size([16, 128, 128]) vae.encoder features: vae.encoder_f0: torch.Size([32, 128, 128]), torch.float16 vae.encoder_f1: torch.Size([32, 128, 128]), torch.float16 ------------------------------------------------------------ [2/4] FRONTEND: Pack + Encode (strategy: individual)... IndividualPacker: text_encoder-item0.clip_pooled_prompt_embeds: torch.Size([768])->torch.Size([1, 1, 1, 768]) text_encoder-item0.clip_prompt_embeds: torch.Size([77, 768])->torch.Size([1, 1, 77, 768]) text_encoder_2-item1.clip_pooled_prompt_embeds: torch.Size([1280])->torch.Size([1, 1, 1, 1280]) text_encoder_2-item1.clip_prompt_embeds: torch.Size([77, 1280])->torch.Size([1, 1, 77, 1280]) text_encoder_3-item2.t5_prompt_embeds: torch.Size([77, 4096])->torch.Size([1, 1, 77, 4096]) text_encoder-item3.clip_pooled_prompt_embeds: torch.Size([768])->torch.Size([1, 1, 1, 768]) text_encoder-item3.clip_prompt_embeds: torch.Size([77, 768])->torch.Size([1, 1, 77, 768]) text_encoder_2-item4.clip_pooled_prompt_embeds: torch.Size([1280])->torch.Size([1, 1, 1, 1280]) text_encoder_2-item4.clip_prompt_embeds: torch.Size([77, 1280])->torch.Size([1, 1, 77, 1280]) text_encoder_3-item5.t5_prompt_embeds: torch.Size([77, 4096])->torch.Size([1, 1, 77, 4096]) vae.encoder_f0: torch.Size([32, 128, 128])->torch.Size([1, 1, 512, 1024]) vae.encoder_f1: torch.Size([32, 128, 128])->torch.Size([1, 1, 512, 1024]) vae.decoder: torch.Size([16, 128, 128])->torch.Size([1, 1, 512, 512]) Using per-key quantization points (text_encoder-item0.clip_pooled_prompt_embeds: torch.Size([256])) for text_encoder-item0.clip_pooled_prompt_embeds text_encoder-item0.clip_pooled_prompt_embeds: 732B, BPFP=7.6250 Using per-key quantization points (text_encoder-item0.clip_prompt_embeds: torch.Size([256])) for text_encoder-item0.clip_prompt_embeds text_encoder-item0.clip_prompt_embeds: 7,048B, BPFP=0.9535 Using per-key quantization points (text_encoder_2-item1.clip_pooled_prompt_embeds: torch.Size([256])) for text_encoder_2-item1.clip_pooled_prompt_embeds text_encoder_2-item1.clip_pooled_prompt_embeds: 1,204B, BPFP=7.5250 Using per-key quantization points (text_encoder_2-item1.clip_prompt_embeds: torch.Size([256])) for text_encoder_2-item1.clip_prompt_embeds text_encoder_2-item1.clip_prompt_embeds: 11,028B, BPFP=0.8951 Using per-key quantization points (text_encoder_3-item2.t5_prompt_embeds: torch.Size([256])) for text_encoder_3-item2.t5_prompt_embeds text_encoder_3-item2.t5_prompt_embeds: 30,096B, BPFP=0.7634 Using per-key quantization points (text_encoder-item3.clip_pooled_prompt_embeds: torch.Size([256])) for text_encoder-item3.clip_pooled_prompt_embeds text_encoder-item3.clip_pooled_prompt_embeds: 760B, BPFP=7.9167 Using per-key quantization points (text_encoder-item3.clip_prompt_embeds: torch.Size([256])) for text_encoder-item3.clip_prompt_embeds text_encoder-item3.clip_prompt_embeds: 4,024B, BPFP=0.5444 Using per-key quantization points (text_encoder_2-item4.clip_pooled: torch.Size([256])) for text_encoder_2-item4.clip_pooled_prompt_embeds text_encoder_2-item4.clip_pooled_prompt_embeds: 1,268B, BPFP=7.9250 Using per-key quantization points (text_encoder_2-item4.clip_prompt: torch.Size([256])) for text_encoder_2-item4.clip_prompt_embeds text_encoder_2-item4.clip_prompt_embeds: 6,996B, BPFP=0.5679 Using per-key quantization points (text_encoder_3-item5.t5_prompt_embeds: torch.Size([256])) for text_encoder_3-item5.t5_prompt_embeds text_encoder_3-item5.t5_prompt_embeds: 18,416B, BPFP=0.4671 Using per-key quantization points (vae.encoder_f0: torch.Size([256])) for vae.encoder_f0 vae.encoder_f0: 19,012B, BPFP=0.2901 Using per-key quantization points (vae.encoder_f1: torch.Size([256])) for vae.encoder_f1 vae.encoder_f1: 19,016B, BPFP=0.2902 Using per-key quantization points (vae.decoder: torch.Size([256])) for vae.decoder vae.decoder: 5,768B, BPFP=0.1760 ⌛️ [2/4] FRONTEND: Frontend time: 0.294s (Pack+Encode) [3/4] BACKEND: Decode + Unpack... Using per-key quantization points (text_encoder-item0.clip_pooled_prompt_embeds: torch.Size([256])) for text_encoder-item0.clip_pooled_prompt_embeds Using per-key quantization points (text_encoder-item0.clip_prompt_embeds: torch.Size([256])) for text_encoder-item0.clip_prompt_embeds Using per-key quantization points (text_encoder_2-item1.clip_pooled_prompt_embeds: torch.Size([256])) for text_encoder_2-item1.clip_pooled_prompt_embeds Using per-key quantization points (text_encoder_2-item1.clip_prompt_embeds: torch.Size([256])) for text_encoder_2-item1.clip_prompt_embeds Using per-key quantization points (text_encoder_3-item2.t5_prompt_embeds: torch.Size([256])) for text_encoder_3-item2.t5_prompt_embeds Using per-key quantization points (text_encoder-item3.clip_pooled_prompt_embeds: torch.Size([256])) for text_encoder-item3.clip_pooled_prompt_embeds Using per-key quantization points (text_encoder-item3.clip_prompt_embeds: torch.Size([256])) for text_encoder-item3.clip_prompt_embeds Using per-key quantization points (text_encoder_2-item4.clip_pooled: torch.Size([256])) for text_encoder_2-item4.clip_pooled_prompt_embeds Using per-key quantization points (text_encoder_2-item4.clip_prompt: torch.Size([256])) for text_encoder_2-item4.clip_prompt_embeds Using per-key quantization points (text_encoder_3-item5.t5_prompt_embeds: torch.Size([256])) for text_encoder_3-item5.t5_prompt_embeds Using per-key quantization points (vae.encoder_f0: torch.Size([256])) for vae.encoder_f0 Using per-key quantization points (vae.encoder_f1: torch.Size([256])) for vae.encoder_f1 Using per-key quantization points (vae.decoder: torch.Size([256])) for vae.decoder IndividualUnPacker: text_encoder-item0.clip_pooled_prompt_embeds: torch.Size([768]) text_encoder-item0.clip_prompt_embeds: torch.Size([77, 768]) text_encoder_2-item1.clip_pooled_prompt_embeds: torch.Size([1280]) text_encoder_2-item1.clip_prompt_embeds: torch.Size([77, 1280]) text_encoder_3-item2.t5_prompt_embeds: torch.Size([77, 4096]) text_encoder-item3.clip_pooled_prompt_embeds: torch.Size([768]) text_encoder-item3.clip_prompt_embeds: torch.Size([77, 768]) text_encoder_2-item4.clip_pooled_prompt_embeds: torch.Size([1280]) text_encoder_2-item4.clip_prompt_embeds: torch.Size([77, 1280]) text_encoder_3-item5.t5_prompt_embeds: torch.Size([77, 4096]) vae.decoder: torch.Size([16, 128, 128]) vae.encoder_f0: torch.Size([32, 128, 128]) vae.encoder_f1: torch.Size([32, 128, 128]) ⌛️ [3/4] BACKEND: Backend time: 0.450s [4/4] METRICS: Computing MSE Breakdown... Using per-key quantization points (text_encoder-item0.clip_pooled_prompt_embeds: torch.Size([256])) for text_encoder-item0.clip_pooled_prompt_embeds Using per-key quantization points (text_encoder-item0.clip_prompt_embeds: torch.Size([256])) for text_encoder-item0.clip_prompt_embeds Using per-key quantization points (text_encoder_2-item1.clip_pooled_prompt_embeds: torch.Size([256])) for text_encoder_2-item1.clip_pooled_prompt_embeds Using per-key quantization points (text_encoder_2-item1.clip_prompt_embeds: torch.Size([256])) for text_encoder_2-item1.clip_prompt_embeds Using per-key quantization points (text_encoder_3-item2.t5_prompt_embeds: torch.Size([256])) for text_encoder_3-item2.t5_prompt_embeds Using per-key quantization points (text_encoder-item3.clip_pooled_prompt_embeds: torch.Size([256])) for text_encoder-item3.clip_pooled_prompt_embeds Using per-key quantization points (text_encoder-item3.clip_prompt_embeds: torch.Size([256])) for text_encoder-item3.clip_prompt_embeds Using per-key quantization points (text_encoder_2-item4.clip_pooled: torch.Size([256])) for text_encoder_2-item4.clip_pooled_prompt_embeds Using per-key quantization points (text_encoder_2-item4.clip_prompt: torch.Size([256])) for text_encoder_2-item4.clip_prompt_embeds Using per-key quantization points (text_encoder_3-item5.t5_prompt_embeds: torch.Size([256])) for text_encoder_3-item5.t5_prompt_embeds Using per-key quantization points (vae.encoder_f0: torch.Size([256])) for vae.encoder_f0 Using per-key quantization points (vae.encoder_f1: torch.Size([256])) for vae.encoder_f1 Using per-key quantization points (vae.decoder: torch.Size([256])) for vae.decoder IndividualUnPacker: text_encoder-item0.clip_pooled_prompt_embeds: torch.Size([768]) text_encoder-item0.clip_prompt_embeds: torch.Size([77, 768]) text_encoder_2-item1.clip_pooled_prompt_embeds: torch.Size([1280]) text_encoder_2-item1.clip_prompt_embeds: torch.Size([77, 1280]) text_encoder_3-item2.t5_prompt_embeds: torch.Size([77, 4096]) text_encoder-item3.clip_pooled_prompt_embeds: torch.Size([768]) text_encoder-item3.clip_prompt_embeds: torch.Size([77, 768]) text_encoder_2-item4.clip_pooled_prompt_embeds: torch.Size([1280]) text_encoder_2-item4.clip_prompt_embeds: torch.Size([77, 1280]) text_encoder_3-item5.t5_prompt_embeds: torch.Size([77, 4096]) vae.decoder: torch.Size([16, 128, 128]) vae.encoder_f0: torch.Size([32, 128, 128]) vae.encoder_f1: torch.Size([32, 128, 128]) Per-key MSE Breakdown (Quant=Truncation+Quantization, Codec=Compression): Key Quant-MSE Total-MSE ------------------------------------------------------------------------------------- text_encoder-item0.clip_pooled_prompt_embeds 0.00027559 0.92706347 text_encoder-item0.clip_prompt_embeds 0.00022882 34.88478676 text_encoder_2-item1.clip_pooled_prompt_embeds 0.00110871 0.95120201 text_encoder_2-item1.clip_prompt_embeds 0.00019473 0.12463380 text_encoder_3-item2.t5_prompt_embeds 0.00000770 0.00295117 text_encoder-item3.clip_pooled_prompt_embeds 0.00841799 1.01420029 text_encoder-item3.clip_prompt_embeds 0.00023247 23.90570887 text_encoder_2-item4.clip_pooled_prompt_embeds 0.00016751 0.67051430 text_encoder_2-item4.clip_prompt_embeds 0.00009830 0.47049568 text_encoder_3-item5.t5_prompt_embeds 0.00001263 0.00227117 vae.encoder_f0 0.00659691 2.93343282 vae.encoder_f1 0.00660300 2.93207932 vae.decoder 0.00023739 0.04591537 ------------------------------------------------------------------------------------- TOTAL 0.00311972 2.93134984 (elements=2,260,992) ---------------------- -------------------------------------------------------- SAMPLE-WISE STATISTICS ---------------------- -------------------------------------------------------- Handler sd35 Strategy individual Architecture hyperprior-featurecoding ---------------------- -------------------------------------------------------- Total Elements 2260992 Total Bytes 125368 BPFP 0.4436 bits/point EBPFP 0.8872 equivalent bits/point MSE 2.931350 ---------------------- -------------------------------------------------------- Time: 0.753s Load: 0.009s, Pack+Encode: 0.294s, Decode+Unpack: 0.450s ---------------------- -------------------------------------------------------- Restored Feature Format: [Dict] with 8 keys key['text_encoder-item0']: [Dict] with 2 keys key['clip_pooled_prompt_embeds']: [Tensor] shape=torch.Size([768]), dtype=torch.float16, device=cpu key['clip_prompt_embeds']: [Tensor] shape=torch.Size([77, 768]), dtype=torch.float16, device=cpu key['text_encoder_2-item1']: [Dict] with 2 keys key['clip_pooled_prompt_embeds']: [Tensor] shape=torch.Size([1280]), dtype=torch.float16, device=cpu key['clip_prompt_embeds']: [Tensor] shape=torch.Size([77, 1280]), dtype=torch.float16, device=cpu key['text_encoder_3-item2']: [Dict] with 1 keys key['t5_prompt_embeds']: [Tensor] shape=torch.Size([77, 4096]), dtype=torch.float16, device=cpu key['text_encoder-item3']: [Dict] with 2 keys key['clip_pooled_prompt_embeds']: [Tensor] shape=torch.Size([768]), dtype=torch.float16, device=cpu key['clip_prompt_embeds']: [Tensor] shape=torch.Size([77, 768]), dtype=torch.float16, device=cpu key['text_encoder_2-item4']: [Dict] with 2 keys key['clip_pooled_prompt_embeds']: [Tensor] shape=torch.Size([1280]), dtype=torch.float16, device=cpu key['clip_prompt_embeds']: [Tensor] shape=torch.Size([77, 1280]), dtype=torch.float16, device=cpu key['text_encoder_3-item5']: [Dict] with 1 keys key['t5_prompt_embeds']: [Tensor] shape=torch.Size([77, 4096]), dtype=torch.float16, device=cpu key['vae.decoder']: [Tensor] shape=torch.Size([16, 128, 128]), dtype=torch.float16, device=cpu key['vae.encoder']: [Dict] with 2 keys key['vae.encoder_f0']: [Tensor] shape=torch.Size([32, 128, 128]), dtype=torch.float16, device=cpu key['vae.encoder_f1']: [Tensor] shape=torch.Size([32, 128, 128]), dtype=torch.float16, device=cpu 💾 Converting with 2.9313 MSE: from ../datasets/FeatureCoding-StableDiffusion3.5/100samples_features/000000468501.zst to output-fixed/sd35/lambda0.004/hyperprior-featurecoding-8bit-individual/sd35cond/000000468501.zst 💪 Processing: ../datasets/FeatureCoding-StableDiffusion3.5/100samples_features/000000468632.zst (85/100) [1/4] FRONTEND: Loading features from ../datasets/FeatureCoding-StableDiffusion3.5/100samples_features/000000468632.zst... Original data structure: root: [Dict] with 8 keys key['text_encoder-item0']: [Dict] with 2 keys key['clip_pooled_prompt_embeds']: [Tensor] shape=torch.Size([768]), dtype=torch.float16, device=cpu key['clip_prompt_embeds']: [Tensor] shape=torch.Size([77, 768]), dtype=torch.float16, device=cpu key['text_encoder_2-item1']: [Dict] with 2 keys key['clip_pooled_prompt_embeds']: [Tensor] shape=torch.Size([1280]), dtype=torch.float16, device=cpu key['clip_prompt_embeds']: [Tensor] shape=torch.Size([77, 1280]), dtype=torch.float16, device=cpu key['text_encoder_3-item2']: [Dict] with 1 keys key['t5_prompt_embeds']: [Tensor] shape=torch.Size([77, 4096]), dtype=torch.float16, device=cpu key['text_encoder-item3']: [Dict] with 2 keys key['clip_pooled_prompt_embeds']: [Tensor] shape=torch.Size([768]), dtype=torch.float16, device=cpu key['clip_prompt_embeds']: [Tensor] shape=torch.Size([77, 768]), dtype=torch.float16, device=cpu key['text_encoder_2-item4']: [Dict] with 2 keys key['clip_pooled_prompt_embeds']: [Tensor] shape=torch.Size([1280]), dtype=torch.float16, device=cpu key['clip_prompt_embeds']: [Tensor] shape=torch.Size([77, 1280]), dtype=torch.float16, device=cpu key['text_encoder_3-item5']: [Dict] with 1 keys key['t5_prompt_embeds']: [Tensor] shape=torch.Size([77, 4096]), dtype=torch.float16, device=cpu key['vae.encoder-item6']: [Dict] with 2 keys key['vae.encoder_f0']: [Tensor] shape=torch.Size([32, 128, 128]), dtype=torch.float16, device=cpu key['vae.encoder_f1']: [Tensor] shape=torch.Size([32, 128, 128]), dtype=torch.float16, device=cpu key['vae.decoder']: [Tensor] shape=torch.Size([16, 128, 128]), dtype=torch.float16, device=cpu ⌛️ [1/4] FRONTEND: Load time: 0.008s ------------------------------------------------------------ SD3.5 Features Summary ------------------------------------------------------------ Number of text encoders: 3 Encoder names: ['text_encoder', 'text_encoder_2', 'text_encoder_3'] Has VAE latents: True Has VAE encoder features: True Data type: torch.float16 text_encoder-item0: clip_pooled_prompt_embeds: torch.Size([768]), torch.float16 clip_prompt_embeds: torch.Size([77, 768]), torch.float16 text_encoder_2-item1: clip_pooled_prompt_embeds: torch.Size([1280]), torch.float16 clip_prompt_embeds: torch.Size([77, 1280]), torch.float16 text_encoder_3-item2: t5_prompt_embeds: torch.Size([77, 4096]), torch.float16 text_encoder-item3: clip_pooled_prompt_embeds: torch.Size([768]), torch.float16 clip_prompt_embeds: torch.Size([77, 768]), torch.float16 text_encoder_2-item4: clip_pooled_prompt_embeds: torch.Size([1280]), torch.float16 clip_prompt_embeds: torch.Size([77, 1280]), torch.float16 text_encoder_3-item5: t5_prompt_embeds: torch.Size([77, 4096]), torch.float16 vae.decoder latents: torch.Size([16, 128, 128]) vae.encoder features: vae.encoder_f0: torch.Size([32, 128, 128]), torch.float16 vae.encoder_f1: torch.Size([32, 128, 128]), torch.float16 ------------------------------------------------------------ [2/4] FRONTEND: Pack + Encode (strategy: individual)... IndividualPacker: text_encoder-item0.clip_pooled_prompt_embeds: torch.Size([768])->torch.Size([1, 1, 1, 768]) text_encoder-item0.clip_prompt_embeds: torch.Size([77, 768])->torch.Size([1, 1, 77, 768]) text_encoder_2-item1.clip_pooled_prompt_embeds: torch.Size([1280])->torch.Size([1, 1, 1, 1280]) text_encoder_2-item1.clip_prompt_embeds: torch.Size([77, 1280])->torch.Size([1, 1, 77, 1280]) text_encoder_3-item2.t5_prompt_embeds: torch.Size([77, 4096])->torch.Size([1, 1, 77, 4096]) text_encoder-item3.clip_pooled_prompt_embeds: torch.Size([768])->torch.Size([1, 1, 1, 768]) text_encoder-item3.clip_prompt_embeds: torch.Size([77, 768])->torch.Size([1, 1, 77, 768]) text_encoder_2-item4.clip_pooled_prompt_embeds: torch.Size([1280])->torch.Size([1, 1, 1, 1280]) text_encoder_2-item4.clip_prompt_embeds: torch.Size([77, 1280])->torch.Size([1, 1, 77, 1280]) text_encoder_3-item5.t5_prompt_embeds: torch.Size([77, 4096])->torch.Size([1, 1, 77, 4096]) vae.encoder_f0: torch.Size([32, 128, 128])->torch.Size([1, 1, 512, 1024]) vae.encoder_f1: torch.Size([32, 128, 128])->torch.Size([1, 1, 512, 1024]) vae.decoder: torch.Size([16, 128, 128])->torch.Size([1, 1, 512, 512]) Using per-key quantization points (text_encoder-item0.clip_pooled_prompt_embeds: torch.Size([256])) for text_encoder-item0.clip_pooled_prompt_embeds text_encoder-item0.clip_pooled_prompt_embeds: 732B, BPFP=7.6250 Using per-key quantization points (text_encoder-item0.clip_prompt_embeds: torch.Size([256])) for text_encoder-item0.clip_prompt_embeds text_encoder-item0.clip_prompt_embeds: 6,288B, BPFP=0.8506 Using per-key quantization points (text_encoder_2-item1.clip_pooled_prompt_embeds: torch.Size([256])) for text_encoder_2-item1.clip_pooled_prompt_embeds text_encoder_2-item1.clip_pooled_prompt_embeds: 1,204B, BPFP=7.5250 Using per-key quantization points (text_encoder_2-item1.clip_prompt_embeds: torch.Size([256])) for text_encoder_2-item1.clip_prompt_embeds text_encoder_2-item1.clip_prompt_embeds: 10,868B, BPFP=0.8821 Using per-key quantization points (text_encoder_3-item2.t5_prompt_embeds: torch.Size([256])) for text_encoder_3-item2.t5_prompt_embeds text_encoder_3-item2.t5_prompt_embeds: 26,300B, BPFP=0.6671 Using per-key quantization points (text_encoder-item3.clip_pooled_prompt_embeds: torch.Size([256])) for text_encoder-item3.clip_pooled_prompt_embeds text_encoder-item3.clip_pooled_prompt_embeds: 760B, BPFP=7.9167 Using per-key quantization points (text_encoder-item3.clip_prompt_embeds: torch.Size([256])) for text_encoder-item3.clip_prompt_embeds text_encoder-item3.clip_prompt_embeds: 4,024B, BPFP=0.5444 Using per-key quantization points (text_encoder_2-item4.clip_pooled: torch.Size([256])) for text_encoder_2-item4.clip_pooled_prompt_embeds text_encoder_2-item4.clip_pooled_prompt_embeds: 1,268B, BPFP=7.9250 Using per-key quantization points (text_encoder_2-item4.clip_prompt: torch.Size([256])) for text_encoder_2-item4.clip_prompt_embeds text_encoder_2-item4.clip_prompt_embeds: 6,996B, BPFP=0.5679 Using per-key quantization points (text_encoder_3-item5.t5_prompt_embeds: torch.Size([256])) for text_encoder_3-item5.t5_prompt_embeds text_encoder_3-item5.t5_prompt_embeds: 18,416B, BPFP=0.4671 Using per-key quantization points (vae.encoder_f0: torch.Size([256])) for vae.encoder_f0 vae.encoder_f0: 12,068B, BPFP=0.1841 Using per-key quantization points (vae.encoder_f1: torch.Size([256])) for vae.encoder_f1 vae.encoder_f1: 12,068B, BPFP=0.1841 Using per-key quantization points (vae.decoder: torch.Size([256])) for vae.decoder vae.decoder: 9,360B, BPFP=0.2856 ⌛️ [2/4] FRONTEND: Frontend time: 0.299s (Pack+Encode) [3/4] BACKEND: Decode + Unpack... Using per-key quantization points (text_encoder-item0.clip_pooled_prompt_embeds: torch.Size([256])) for text_encoder-item0.clip_pooled_prompt_embeds Using per-key quantization points (text_encoder-item0.clip_prompt_embeds: torch.Size([256])) for text_encoder-item0.clip_prompt_embeds Using per-key quantization points (text_encoder_2-item1.clip_pooled_prompt_embeds: torch.Size([256])) for text_encoder_2-item1.clip_pooled_prompt_embeds Using per-key quantization points (text_encoder_2-item1.clip_prompt_embeds: torch.Size([256])) for text_encoder_2-item1.clip_prompt_embeds Using per-key quantization points (text_encoder_3-item2.t5_prompt_embeds: torch.Size([256])) for text_encoder_3-item2.t5_prompt_embeds Using per-key quantization points (text_encoder-item3.clip_pooled_prompt_embeds: torch.Size([256])) for text_encoder-item3.clip_pooled_prompt_embeds Using per-key quantization points (text_encoder-item3.clip_prompt_embeds: torch.Size([256])) for text_encoder-item3.clip_prompt_embeds Using per-key quantization points (text_encoder_2-item4.clip_pooled: torch.Size([256])) for text_encoder_2-item4.clip_pooled_prompt_embeds Using per-key quantization points (text_encoder_2-item4.clip_prompt: torch.Size([256])) for text_encoder_2-item4.clip_prompt_embeds Using per-key quantization points (text_encoder_3-item5.t5_prompt_embeds: torch.Size([256])) for text_encoder_3-item5.t5_prompt_embeds Using per-key quantization points (vae.encoder_f0: torch.Size([256])) for vae.encoder_f0 Using per-key quantization points (vae.encoder_f1: torch.Size([256])) for vae.encoder_f1 Using per-key quantization points (vae.decoder: torch.Size([256])) for vae.decoder IndividualUnPacker: text_encoder-item0.clip_pooled_prompt_embeds: torch.Size([768]) text_encoder-item0.clip_prompt_embeds: torch.Size([77, 768]) text_encoder_2-item1.clip_pooled_prompt_embeds: torch.Size([1280]) text_encoder_2-item1.clip_prompt_embeds: torch.Size([77, 1280]) text_encoder_3-item2.t5_prompt_embeds: torch.Size([77, 4096]) text_encoder-item3.clip_pooled_prompt_embeds: torch.Size([768]) text_encoder-item3.clip_prompt_embeds: torch.Size([77, 768]) text_encoder_2-item4.clip_pooled_prompt_embeds: torch.Size([1280]) text_encoder_2-item4.clip_prompt_embeds: torch.Size([77, 1280]) text_encoder_3-item5.t5_prompt_embeds: torch.Size([77, 4096]) vae.decoder: torch.Size([16, 128, 128]) vae.encoder_f0: torch.Size([32, 128, 128]) vae.encoder_f1: torch.Size([32, 128, 128]) ⌛️ [3/4] BACKEND: Backend time: 0.452s [4/4] METRICS: Computing MSE Breakdown... Using per-key quantization points (text_encoder-item0.clip_pooled_prompt_embeds: torch.Size([256])) for text_encoder-item0.clip_pooled_prompt_embeds Using per-key quantization points (text_encoder-item0.clip_prompt_embeds: torch.Size([256])) for text_encoder-item0.clip_prompt_embeds Using per-key quantization points (text_encoder_2-item1.clip_pooled_prompt_embeds: torch.Size([256])) for text_encoder_2-item1.clip_pooled_prompt_embeds Using per-key quantization points (text_encoder_2-item1.clip_prompt_embeds: torch.Size([256])) for text_encoder_2-item1.clip_prompt_embeds Using per-key quantization points (text_encoder_3-item2.t5_prompt_embeds: torch.Size([256])) for text_encoder_3-item2.t5_prompt_embeds Using per-key quantization points (text_encoder-item3.clip_pooled_prompt_embeds: torch.Size([256])) for text_encoder-item3.clip_pooled_prompt_embeds Using per-key quantization points (text_encoder-item3.clip_prompt_embeds: torch.Size([256])) for text_encoder-item3.clip_prompt_embeds Using per-key quantization points (text_encoder_2-item4.clip_pooled: torch.Size([256])) for text_encoder_2-item4.clip_pooled_prompt_embeds Using per-key quantization points (text_encoder_2-item4.clip_prompt: torch.Size([256])) for text_encoder_2-item4.clip_prompt_embeds Using per-key quantization points (text_encoder_3-item5.t5_prompt_embeds: torch.Size([256])) for text_encoder_3-item5.t5_prompt_embeds Using per-key quantization points (vae.encoder_f0: torch.Size([256])) for vae.encoder_f0 Using per-key quantization points (vae.encoder_f1: torch.Size([256])) for vae.encoder_f1 Using per-key quantization points (vae.decoder: torch.Size([256])) for vae.decoder IndividualUnPacker: text_encoder-item0.clip_pooled_prompt_embeds: torch.Size([768]) text_encoder-item0.clip_prompt_embeds: torch.Size([77, 768]) text_encoder_2-item1.clip_pooled_prompt_embeds: torch.Size([1280]) text_encoder_2-item1.clip_prompt_embeds: torch.Size([77, 1280]) text_encoder_3-item2.t5_prompt_embeds: torch.Size([77, 4096]) text_encoder-item3.clip_pooled_prompt_embeds: torch.Size([768]) text_encoder-item3.clip_prompt_embeds: torch.Size([77, 768]) text_encoder_2-item4.clip_pooled_prompt_embeds: torch.Size([1280]) text_encoder_2-item4.clip_prompt_embeds: torch.Size([77, 1280]) text_encoder_3-item5.t5_prompt_embeds: torch.Size([77, 4096]) vae.decoder: torch.Size([16, 128, 128]) vae.encoder_f0: torch.Size([32, 128, 128]) vae.encoder_f1: torch.Size([32, 128, 128]) Per-key MSE Breakdown (Quant=Truncation+Quantization, Codec=Compression): Key Quant-MSE Total-MSE ------------------------------------------------------------------------------------- text_encoder-item0.clip_pooled_prompt_embeds 0.00098754 0.94744118 text_encoder-item0.clip_prompt_embeds 0.00023928 23.94288800 text_encoder_2-item1.clip_pooled_prompt_embeds 0.00022734 0.93944969 text_encoder_2-item1.clip_prompt_embeds 0.00018899 0.12624249 text_encoder_3-item2.t5_prompt_embeds 0.00000832 0.00322803 text_encoder-item3.clip_pooled_prompt_embeds 0.00841799 1.01420029 text_encoder-item3.clip_prompt_embeds 0.00023247 23.90570887 text_encoder_2-item4.clip_pooled_prompt_embeds 0.00016751 0.67051430 text_encoder_2-item4.clip_prompt_embeds 0.00009830 0.47049568 text_encoder_3-item5.t5_prompt_embeds 0.00001263 0.00227117 vae.encoder_f0 0.00583864 1.41783857 vae.encoder_f1 0.00583800 1.41783047 vae.decoder 0.00018889 0.07847898 ------------------------------------------------------------------------------------- TOTAL 0.00276073 1.94647786 (elements=2,260,992) ---------------------- -------------------------------------------------------- SAMPLE-WISE STATISTICS ---------------------- -------------------------------------------------------- Handler sd35 Strategy individual Architecture hyperprior-featurecoding ---------------------- -------------------------------------------------------- Total Elements 2260992 Total Bytes 110352 BPFP 0.3905 bits/point EBPFP 0.7809 equivalent bits/point MSE 1.946478 ---------------------- -------------------------------------------------------- Time: 0.760s Load: 0.008s, Pack+Encode: 0.299s, Decode+Unpack: 0.452s ---------------------- -------------------------------------------------------- Restored Feature Format: [Dict] with 8 keys key['text_encoder-item0']: [Dict] with 2 keys key['clip_pooled_prompt_embeds']: [Tensor] shape=torch.Size([768]), dtype=torch.float16, device=cpu key['clip_prompt_embeds']: [Tensor] shape=torch.Size([77, 768]), dtype=torch.float16, device=cpu key['text_encoder_2-item1']: [Dict] with 2 keys key['clip_pooled_prompt_embeds']: [Tensor] shape=torch.Size([1280]), dtype=torch.float16, device=cpu key['clip_prompt_embeds']: [Tensor] shape=torch.Size([77, 1280]), dtype=torch.float16, device=cpu key['text_encoder_3-item2']: [Dict] with 1 keys key['t5_prompt_embeds']: [Tensor] shape=torch.Size([77, 4096]), dtype=torch.float16, device=cpu key['text_encoder-item3']: [Dict] with 2 keys key['clip_pooled_prompt_embeds']: [Tensor] shape=torch.Size([768]), dtype=torch.float16, device=cpu key['clip_prompt_embeds']: [Tensor] shape=torch.Size([77, 768]), dtype=torch.float16, device=cpu key['text_encoder_2-item4']: [Dict] with 2 keys key['clip_pooled_prompt_embeds']: [Tensor] shape=torch.Size([1280]), dtype=torch.float16, device=cpu key['clip_prompt_embeds']: [Tensor] shape=torch.Size([77, 1280]), dtype=torch.float16, device=cpu key['text_encoder_3-item5']: [Dict] with 1 keys key['t5_prompt_embeds']: [Tensor] shape=torch.Size([77, 4096]), dtype=torch.float16, device=cpu key['vae.decoder']: [Tensor] shape=torch.Size([16, 128, 128]), dtype=torch.float16, device=cpu key['vae.encoder']: [Dict] with 2 keys key['vae.encoder_f0']: [Tensor] shape=torch.Size([32, 128, 128]), dtype=torch.float16, device=cpu key['vae.encoder_f1']: [Tensor] shape=torch.Size([32, 128, 128]), dtype=torch.float16, device=cpu 💾 Converting with 1.9465 MSE: from ../datasets/FeatureCoding-StableDiffusion3.5/100samples_features/000000468632.zst to output-fixed/sd35/lambda0.004/hyperprior-featurecoding-8bit-individual/sd35cond/000000468632.zst 💪 Processing: ../datasets/FeatureCoding-StableDiffusion3.5/100samples_features/000000471087.zst (86/100) [1/4] FRONTEND: Loading features from ../datasets/FeatureCoding-StableDiffusion3.5/100samples_features/000000471087.zst... Original data structure: root: [Dict] with 8 keys key['text_encoder-item0']: [Dict] with 2 keys key['clip_pooled_prompt_embeds']: [Tensor] shape=torch.Size([768]), dtype=torch.float16, device=cpu key['clip_prompt_embeds']: [Tensor] shape=torch.Size([77, 768]), dtype=torch.float16, device=cpu key['text_encoder_2-item1']: [Dict] with 2 keys key['clip_pooled_prompt_embeds']: [Tensor] shape=torch.Size([1280]), dtype=torch.float16, device=cpu key['clip_prompt_embeds']: [Tensor] shape=torch.Size([77, 1280]), dtype=torch.float16, device=cpu key['text_encoder_3-item2']: [Dict] with 1 keys key['t5_prompt_embeds']: [Tensor] shape=torch.Size([77, 4096]), dtype=torch.float16, device=cpu key['text_encoder-item3']: [Dict] with 2 keys key['clip_pooled_prompt_embeds']: [Tensor] shape=torch.Size([768]), dtype=torch.float16, device=cpu key['clip_prompt_embeds']: [Tensor] shape=torch.Size([77, 768]), dtype=torch.float16, device=cpu key['text_encoder_2-item4']: [Dict] with 2 keys key['clip_pooled_prompt_embeds']: [Tensor] shape=torch.Size([1280]), dtype=torch.float16, device=cpu key['clip_prompt_embeds']: [Tensor] shape=torch.Size([77, 1280]), dtype=torch.float16, device=cpu key['text_encoder_3-item5']: [Dict] with 1 keys key['t5_prompt_embeds']: [Tensor] shape=torch.Size([77, 4096]), dtype=torch.float16, device=cpu key['vae.encoder-item6']: [Dict] with 2 keys key['vae.encoder_f0']: [Tensor] shape=torch.Size([32, 128, 128]), dtype=torch.float16, device=cpu key['vae.encoder_f1']: [Tensor] shape=torch.Size([32, 128, 128]), dtype=torch.float16, device=cpu key['vae.decoder']: [Tensor] shape=torch.Size([16, 128, 128]), dtype=torch.float16, device=cpu ⌛️ [1/4] FRONTEND: Load time: 0.009s ------------------------------------------------------------ SD3.5 Features Summary ------------------------------------------------------------ Number of text encoders: 3 Encoder names: ['text_encoder', 'text_encoder_2', 'text_encoder_3'] Has VAE latents: True Has VAE encoder features: True Data type: torch.float16 text_encoder-item0: clip_pooled_prompt_embeds: torch.Size([768]), torch.float16 clip_prompt_embeds: torch.Size([77, 768]), torch.float16 text_encoder_2-item1: clip_pooled_prompt_embeds: torch.Size([1280]), torch.float16 clip_prompt_embeds: torch.Size([77, 1280]), torch.float16 text_encoder_3-item2: t5_prompt_embeds: torch.Size([77, 4096]), torch.float16 text_encoder-item3: clip_pooled_prompt_embeds: torch.Size([768]), torch.float16 clip_prompt_embeds: torch.Size([77, 768]), torch.float16 text_encoder_2-item4: clip_pooled_prompt_embeds: torch.Size([1280]), torch.float16 clip_prompt_embeds: torch.Size([77, 1280]), torch.float16 text_encoder_3-item5: t5_prompt_embeds: torch.Size([77, 4096]), torch.float16 vae.decoder latents: torch.Size([16, 128, 128]) vae.encoder features: vae.encoder_f0: torch.Size([32, 128, 128]), torch.float16 vae.encoder_f1: torch.Size([32, 128, 128]), torch.float16 ------------------------------------------------------------ [2/4] FRONTEND: Pack + Encode (strategy: individual)... IndividualPacker: text_encoder-item0.clip_pooled_prompt_embeds: torch.Size([768])->torch.Size([1, 1, 1, 768]) text_encoder-item0.clip_prompt_embeds: torch.Size([77, 768])->torch.Size([1, 1, 77, 768]) text_encoder_2-item1.clip_pooled_prompt_embeds: torch.Size([1280])->torch.Size([1, 1, 1, 1280]) text_encoder_2-item1.clip_prompt_embeds: torch.Size([77, 1280])->torch.Size([1, 1, 77, 1280]) text_encoder_3-item2.t5_prompt_embeds: torch.Size([77, 4096])->torch.Size([1, 1, 77, 4096]) text_encoder-item3.clip_pooled_prompt_embeds: torch.Size([768])->torch.Size([1, 1, 1, 768]) text_encoder-item3.clip_prompt_embeds: torch.Size([77, 768])->torch.Size([1, 1, 77, 768]) text_encoder_2-item4.clip_pooled_prompt_embeds: torch.Size([1280])->torch.Size([1, 1, 1, 1280]) text_encoder_2-item4.clip_prompt_embeds: torch.Size([77, 1280])->torch.Size([1, 1, 77, 1280]) text_encoder_3-item5.t5_prompt_embeds: torch.Size([77, 4096])->torch.Size([1, 1, 77, 4096]) vae.encoder_f0: torch.Size([32, 128, 128])->torch.Size([1, 1, 512, 1024]) vae.encoder_f1: torch.Size([32, 128, 128])->torch.Size([1, 1, 512, 1024]) vae.decoder: torch.Size([16, 128, 128])->torch.Size([1, 1, 512, 512]) Using per-key quantization points (text_encoder-item0.clip_pooled_prompt_embeds: torch.Size([256])) for text_encoder-item0.clip_pooled_prompt_embeds text_encoder-item0.clip_pooled_prompt_embeds: 736B, BPFP=7.6667 Using per-key quantization points (text_encoder-item0.clip_prompt_embeds: torch.Size([256])) for text_encoder-item0.clip_prompt_embeds text_encoder-item0.clip_prompt_embeds: 6,788B, BPFP=0.9183 Using per-key quantization points (text_encoder_2-item1.clip_pooled_prompt_embeds: torch.Size([256])) for text_encoder_2-item1.clip_pooled_prompt_embeds text_encoder_2-item1.clip_pooled_prompt_embeds: 1,196B, BPFP=7.4750 Using per-key quantization points (text_encoder_2-item1.clip_prompt_embeds: torch.Size([256])) for text_encoder_2-item1.clip_prompt_embeds text_encoder_2-item1.clip_prompt_embeds: 11,040B, BPFP=0.8961 Using per-key quantization points (text_encoder_3-item2.t5_prompt_embeds: torch.Size([256])) for text_encoder_3-item2.t5_prompt_embeds text_encoder_3-item2.t5_prompt_embeds: 31,080B, BPFP=0.7884 Using per-key quantization points (text_encoder-item3.clip_pooled_prompt_embeds: torch.Size([256])) for text_encoder-item3.clip_pooled_prompt_embeds text_encoder-item3.clip_pooled_prompt_embeds: 760B, BPFP=7.9167 Using per-key quantization points (text_encoder-item3.clip_prompt_embeds: torch.Size([256])) for text_encoder-item3.clip_prompt_embeds text_encoder-item3.clip_prompt_embeds: 4,024B, BPFP=0.5444 Using per-key quantization points (text_encoder_2-item4.clip_pooled: torch.Size([256])) for text_encoder_2-item4.clip_pooled_prompt_embeds text_encoder_2-item4.clip_pooled_prompt_embeds: 1,268B, BPFP=7.9250 Using per-key quantization points (text_encoder_2-item4.clip_prompt: torch.Size([256])) for text_encoder_2-item4.clip_prompt_embeds text_encoder_2-item4.clip_prompt_embeds: 6,996B, BPFP=0.5679 Using per-key quantization points (text_encoder_3-item5.t5_prompt_embeds: torch.Size([256])) for text_encoder_3-item5.t5_prompt_embeds text_encoder_3-item5.t5_prompt_embeds: 18,416B, BPFP=0.4671 Using per-key quantization points (vae.encoder_f0: torch.Size([256])) for vae.encoder_f0 vae.encoder_f0: 10,872B, BPFP=0.1659 Using per-key quantization points (vae.encoder_f1: torch.Size([256])) for vae.encoder_f1 vae.encoder_f1: 10,872B, BPFP=0.1659 Using per-key quantization points (vae.decoder: torch.Size([256])) for vae.decoder vae.decoder: 7,416B, BPFP=0.2263 ⌛️ [2/4] FRONTEND: Frontend time: 0.297s (Pack+Encode) [3/4] BACKEND: Decode + Unpack... Using per-key quantization points (text_encoder-item0.clip_pooled_prompt_embeds: torch.Size([256])) for text_encoder-item0.clip_pooled_prompt_embeds Using per-key quantization points (text_encoder-item0.clip_prompt_embeds: torch.Size([256])) for text_encoder-item0.clip_prompt_embeds Using per-key quantization points (text_encoder_2-item1.clip_pooled_prompt_embeds: torch.Size([256])) for text_encoder_2-item1.clip_pooled_prompt_embeds Using per-key quantization points (text_encoder_2-item1.clip_prompt_embeds: torch.Size([256])) for text_encoder_2-item1.clip_prompt_embeds Using per-key quantization points (text_encoder_3-item2.t5_prompt_embeds: torch.Size([256])) for text_encoder_3-item2.t5_prompt_embeds Using per-key quantization points (text_encoder-item3.clip_pooled_prompt_embeds: torch.Size([256])) for text_encoder-item3.clip_pooled_prompt_embeds Using per-key quantization points (text_encoder-item3.clip_prompt_embeds: torch.Size([256])) for text_encoder-item3.clip_prompt_embeds Using per-key quantization points (text_encoder_2-item4.clip_pooled: torch.Size([256])) for text_encoder_2-item4.clip_pooled_prompt_embeds Using per-key quantization points (text_encoder_2-item4.clip_prompt: torch.Size([256])) for text_encoder_2-item4.clip_prompt_embeds Using per-key quantization points (text_encoder_3-item5.t5_prompt_embeds: torch.Size([256])) for text_encoder_3-item5.t5_prompt_embeds Using per-key quantization points (vae.encoder_f0: torch.Size([256])) for vae.encoder_f0 Using per-key quantization points (vae.encoder_f1: torch.Size([256])) for vae.encoder_f1 Using per-key quantization points (vae.decoder: torch.Size([256])) for vae.decoder IndividualUnPacker: text_encoder-item0.clip_pooled_prompt_embeds: torch.Size([768]) text_encoder-item0.clip_prompt_embeds: torch.Size([77, 768]) text_encoder_2-item1.clip_pooled_prompt_embeds: torch.Size([1280]) text_encoder_2-item1.clip_prompt_embeds: torch.Size([77, 1280]) text_encoder_3-item2.t5_prompt_embeds: torch.Size([77, 4096]) text_encoder-item3.clip_pooled_prompt_embeds: torch.Size([768]) text_encoder-item3.clip_prompt_embeds: torch.Size([77, 768]) text_encoder_2-item4.clip_pooled_prompt_embeds: torch.Size([1280]) text_encoder_2-item4.clip_prompt_embeds: torch.Size([77, 1280]) text_encoder_3-item5.t5_prompt_embeds: torch.Size([77, 4096]) vae.decoder: torch.Size([16, 128, 128]) vae.encoder_f0: torch.Size([32, 128, 128]) vae.encoder_f1: torch.Size([32, 128, 128]) ⌛️ [3/4] BACKEND: Backend time: 0.447s [4/4] METRICS: Computing MSE Breakdown... Using per-key quantization points (text_encoder-item0.clip_pooled_prompt_embeds: torch.Size([256])) for text_encoder-item0.clip_pooled_prompt_embeds Using per-key quantization points (text_encoder-item0.clip_prompt_embeds: torch.Size([256])) for text_encoder-item0.clip_prompt_embeds Using per-key quantization points (text_encoder_2-item1.clip_pooled_prompt_embeds: torch.Size([256])) for text_encoder_2-item1.clip_pooled_prompt_embeds Using per-key quantization points (text_encoder_2-item1.clip_prompt_embeds: torch.Size([256])) for text_encoder_2-item1.clip_prompt_embeds Using per-key quantization points (text_encoder_3-item2.t5_prompt_embeds: torch.Size([256])) for text_encoder_3-item2.t5_prompt_embeds Using per-key quantization points (text_encoder-item3.clip_pooled_prompt_embeds: torch.Size([256])) for text_encoder-item3.clip_pooled_prompt_embeds Using per-key quantization points (text_encoder-item3.clip_prompt_embeds: torch.Size([256])) for text_encoder-item3.clip_prompt_embeds Using per-key quantization points (text_encoder_2-item4.clip_pooled: torch.Size([256])) for text_encoder_2-item4.clip_pooled_prompt_embeds Using per-key quantization points (text_encoder_2-item4.clip_prompt: torch.Size([256])) for text_encoder_2-item4.clip_prompt_embeds Using per-key quantization points (text_encoder_3-item5.t5_prompt_embeds: torch.Size([256])) for text_encoder_3-item5.t5_prompt_embeds Using per-key quantization points (vae.encoder_f0: torch.Size([256])) for vae.encoder_f0 Using per-key quantization points (vae.encoder_f1: torch.Size([256])) for vae.encoder_f1 Using per-key quantization points (vae.decoder: torch.Size([256])) for vae.decoder IndividualUnPacker: text_encoder-item0.clip_pooled_prompt_embeds: torch.Size([768]) text_encoder-item0.clip_prompt_embeds: torch.Size([77, 768]) text_encoder_2-item1.clip_pooled_prompt_embeds: torch.Size([1280]) text_encoder_2-item1.clip_prompt_embeds: torch.Size([77, 1280]) text_encoder_3-item2.t5_prompt_embeds: torch.Size([77, 4096]) text_encoder-item3.clip_pooled_prompt_embeds: torch.Size([768]) text_encoder-item3.clip_prompt_embeds: torch.Size([77, 768]) text_encoder_2-item4.clip_pooled_prompt_embeds: torch.Size([1280]) text_encoder_2-item4.clip_prompt_embeds: torch.Size([77, 1280]) text_encoder_3-item5.t5_prompt_embeds: torch.Size([77, 4096]) vae.decoder: torch.Size([16, 128, 128]) vae.encoder_f0: torch.Size([32, 128, 128]) vae.encoder_f1: torch.Size([32, 128, 128]) Per-key MSE Breakdown (Quant=Truncation+Quantization, Codec=Compression): Key Quant-MSE Total-MSE ------------------------------------------------------------------------------------- text_encoder-item0.clip_pooled_prompt_embeds 0.00032508 0.92683331 text_encoder-item0.clip_prompt_embeds 0.00024821 23.94847259 text_encoder_2-item1.clip_pooled_prompt_embeds 0.00060829 0.73711042 text_encoder_2-item1.clip_prompt_embeds 0.00018297 0.12326753 text_encoder_3-item2.t5_prompt_embeds 0.00002546 0.00445593 text_encoder-item3.clip_pooled_prompt_embeds 0.00841799 1.01420029 text_encoder-item3.clip_prompt_embeds 0.00023247 23.90570887 text_encoder_2-item4.clip_pooled_prompt_embeds 0.00016751 0.67051430 text_encoder_2-item4.clip_prompt_embeds 0.00009830 0.47049568 text_encoder_3-item5.t5_prompt_embeds 0.00001263 0.00227117 vae.encoder_f0 0.00570467 1.13493145 vae.encoder_f1 0.00570488 1.13488328 vae.decoder 0.00017302 0.05981145 ------------------------------------------------------------------------------------- TOTAL 0.00269931 1.81316704 (elements=2,260,992) ---------------------- -------------------------------------------------------- SAMPLE-WISE STATISTICS ---------------------- -------------------------------------------------------- Handler sd35 Strategy individual Architecture hyperprior-featurecoding ---------------------- -------------------------------------------------------- Total Elements 2260992 Total Bytes 111464 BPFP 0.3944 bits/point EBPFP 0.7888 equivalent bits/point MSE 1.813167 ---------------------- -------------------------------------------------------- Time: 0.753s Load: 0.009s, Pack+Encode: 0.297s, Decode+Unpack: 0.447s ---------------------- -------------------------------------------------------- Restored Feature Format: [Dict] with 8 keys key['text_encoder-item0']: [Dict] with 2 keys key['clip_pooled_prompt_embeds']: [Tensor] shape=torch.Size([768]), dtype=torch.float16, device=cpu key['clip_prompt_embeds']: [Tensor] shape=torch.Size([77, 768]), dtype=torch.float16, device=cpu key['text_encoder_2-item1']: [Dict] with 2 keys key['clip_pooled_prompt_embeds']: [Tensor] shape=torch.Size([1280]), dtype=torch.float16, device=cpu key['clip_prompt_embeds']: [Tensor] shape=torch.Size([77, 1280]), dtype=torch.float16, device=cpu key['text_encoder_3-item2']: [Dict] with 1 keys key['t5_prompt_embeds']: [Tensor] shape=torch.Size([77, 4096]), dtype=torch.float16, device=cpu key['text_encoder-item3']: [Dict] with 2 keys key['clip_pooled_prompt_embeds']: [Tensor] shape=torch.Size([768]), dtype=torch.float16, device=cpu key['clip_prompt_embeds']: [Tensor] shape=torch.Size([77, 768]), dtype=torch.float16, device=cpu key['text_encoder_2-item4']: [Dict] with 2 keys key['clip_pooled_prompt_embeds']: [Tensor] shape=torch.Size([1280]), dtype=torch.float16, device=cpu key['clip_prompt_embeds']: [Tensor] shape=torch.Size([77, 1280]), dtype=torch.float16, device=cpu key['text_encoder_3-item5']: [Dict] with 1 keys key['t5_prompt_embeds']: [Tensor] shape=torch.Size([77, 4096]), dtype=torch.float16, device=cpu key['vae.decoder']: [Tensor] shape=torch.Size([16, 128, 128]), dtype=torch.float16, device=cpu key['vae.encoder']: [Dict] with 2 keys key['vae.encoder_f0']: [Tensor] shape=torch.Size([32, 128, 128]), dtype=torch.float16, device=cpu key['vae.encoder_f1']: [Tensor] shape=torch.Size([32, 128, 128]), dtype=torch.float16, device=cpu 💾 Converting with 1.8132 MSE: from ../datasets/FeatureCoding-StableDiffusion3.5/100samples_features/000000471087.zst to output-fixed/sd35/lambda0.004/hyperprior-featurecoding-8bit-individual/sd35cond/000000471087.zst 💪 Processing: ../datasets/FeatureCoding-StableDiffusion3.5/100samples_features/000000482477.zst (87/100) [1/4] FRONTEND: Loading features from ../datasets/FeatureCoding-StableDiffusion3.5/100samples_features/000000482477.zst... Original data structure: root: [Dict] with 8 keys key['text_encoder-item0']: [Dict] with 2 keys key['clip_pooled_prompt_embeds']: [Tensor] shape=torch.Size([768]), dtype=torch.float16, device=cpu key['clip_prompt_embeds']: [Tensor] shape=torch.Size([77, 768]), dtype=torch.float16, device=cpu key['text_encoder_2-item1']: [Dict] with 2 keys key['clip_pooled_prompt_embeds']: [Tensor] shape=torch.Size([1280]), dtype=torch.float16, device=cpu key['clip_prompt_embeds']: [Tensor] shape=torch.Size([77, 1280]), dtype=torch.float16, device=cpu key['text_encoder_3-item2']: [Dict] with 1 keys key['t5_prompt_embeds']: [Tensor] shape=torch.Size([77, 4096]), dtype=torch.float16, device=cpu key['text_encoder-item3']: [Dict] with 2 keys key['clip_pooled_prompt_embeds']: [Tensor] shape=torch.Size([768]), dtype=torch.float16, device=cpu key['clip_prompt_embeds']: [Tensor] shape=torch.Size([77, 768]), dtype=torch.float16, device=cpu key['text_encoder_2-item4']: [Dict] with 2 keys key['clip_pooled_prompt_embeds']: [Tensor] shape=torch.Size([1280]), dtype=torch.float16, device=cpu key['clip_prompt_embeds']: [Tensor] shape=torch.Size([77, 1280]), dtype=torch.float16, device=cpu key['text_encoder_3-item5']: [Dict] with 1 keys key['t5_prompt_embeds']: [Tensor] shape=torch.Size([77, 4096]), dtype=torch.float16, device=cpu key['vae.encoder-item6']: [Dict] with 2 keys key['vae.encoder_f0']: [Tensor] shape=torch.Size([32, 128, 128]), dtype=torch.float16, device=cpu key['vae.encoder_f1']: [Tensor] shape=torch.Size([32, 128, 128]), dtype=torch.float16, device=cpu key['vae.decoder']: [Tensor] shape=torch.Size([16, 128, 128]), dtype=torch.float16, device=cpu ⌛️ [1/4] FRONTEND: Load time: 0.008s ------------------------------------------------------------ SD3.5 Features Summary ------------------------------------------------------------ Number of text encoders: 3 Encoder names: ['text_encoder', 'text_encoder_2', 'text_encoder_3'] Has VAE latents: True Has VAE encoder features: True Data type: torch.float16 text_encoder-item0: clip_pooled_prompt_embeds: torch.Size([768]), torch.float16 clip_prompt_embeds: torch.Size([77, 768]), torch.float16 text_encoder_2-item1: clip_pooled_prompt_embeds: torch.Size([1280]), torch.float16 clip_prompt_embeds: torch.Size([77, 1280]), torch.float16 text_encoder_3-item2: t5_prompt_embeds: torch.Size([77, 4096]), torch.float16 text_encoder-item3: clip_pooled_prompt_embeds: torch.Size([768]), torch.float16 clip_prompt_embeds: torch.Size([77, 768]), torch.float16 text_encoder_2-item4: clip_pooled_prompt_embeds: torch.Size([1280]), torch.float16 clip_prompt_embeds: torch.Size([77, 1280]), torch.float16 text_encoder_3-item5: t5_prompt_embeds: torch.Size([77, 4096]), torch.float16 vae.decoder latents: torch.Size([16, 128, 128]) vae.encoder features: vae.encoder_f0: torch.Size([32, 128, 128]), torch.float16 vae.encoder_f1: torch.Size([32, 128, 128]), torch.float16 ------------------------------------------------------------ [2/4] FRONTEND: Pack + Encode (strategy: individual)... IndividualPacker: text_encoder-item0.clip_pooled_prompt_embeds: torch.Size([768])->torch.Size([1, 1, 1, 768]) text_encoder-item0.clip_prompt_embeds: torch.Size([77, 768])->torch.Size([1, 1, 77, 768]) text_encoder_2-item1.clip_pooled_prompt_embeds: torch.Size([1280])->torch.Size([1, 1, 1, 1280]) text_encoder_2-item1.clip_prompt_embeds: torch.Size([77, 1280])->torch.Size([1, 1, 77, 1280]) text_encoder_3-item2.t5_prompt_embeds: torch.Size([77, 4096])->torch.Size([1, 1, 77, 4096]) text_encoder-item3.clip_pooled_prompt_embeds: torch.Size([768])->torch.Size([1, 1, 1, 768]) text_encoder-item3.clip_prompt_embeds: torch.Size([77, 768])->torch.Size([1, 1, 77, 768]) text_encoder_2-item4.clip_pooled_prompt_embeds: torch.Size([1280])->torch.Size([1, 1, 1, 1280]) text_encoder_2-item4.clip_prompt_embeds: torch.Size([77, 1280])->torch.Size([1, 1, 77, 1280]) text_encoder_3-item5.t5_prompt_embeds: torch.Size([77, 4096])->torch.Size([1, 1, 77, 4096]) vae.encoder_f0: torch.Size([32, 128, 128])->torch.Size([1, 1, 512, 1024]) vae.encoder_f1: torch.Size([32, 128, 128])->torch.Size([1, 1, 512, 1024]) vae.decoder: torch.Size([16, 128, 128])->torch.Size([1, 1, 512, 512]) Using per-key quantization points (text_encoder-item0.clip_pooled_prompt_embeds: torch.Size([256])) for text_encoder-item0.clip_pooled_prompt_embeds text_encoder-item0.clip_pooled_prompt_embeds: 728B, BPFP=7.5833 Using per-key quantization points (text_encoder-item0.clip_prompt_embeds: torch.Size([256])) for text_encoder-item0.clip_prompt_embeds text_encoder-item0.clip_prompt_embeds: 6,436B, BPFP=0.8707 Using per-key quantization points (text_encoder_2-item1.clip_pooled_prompt_embeds: torch.Size([256])) for text_encoder_2-item1.clip_pooled_prompt_embeds text_encoder_2-item1.clip_pooled_prompt_embeds: 1,196B, BPFP=7.4750 Using per-key quantization points (text_encoder_2-item1.clip_prompt_embeds: torch.Size([256])) for text_encoder_2-item1.clip_prompt_embeds text_encoder_2-item1.clip_prompt_embeds: 11,060B, BPFP=0.8977 Using per-key quantization points (text_encoder_3-item2.t5_prompt_embeds: torch.Size([256])) for text_encoder_3-item2.t5_prompt_embeds text_encoder_3-item2.t5_prompt_embeds: 27,892B, BPFP=0.7075 Using per-key quantization points (text_encoder-item3.clip_pooled_prompt_embeds: torch.Size([256])) for text_encoder-item3.clip_pooled_prompt_embeds text_encoder-item3.clip_pooled_prompt_embeds: 760B, BPFP=7.9167 Using per-key quantization points (text_encoder-item3.clip_prompt_embeds: torch.Size([256])) for text_encoder-item3.clip_prompt_embeds text_encoder-item3.clip_prompt_embeds: 4,024B, BPFP=0.5444 Using per-key quantization points (text_encoder_2-item4.clip_pooled: torch.Size([256])) for text_encoder_2-item4.clip_pooled_prompt_embeds text_encoder_2-item4.clip_pooled_prompt_embeds: 1,268B, BPFP=7.9250 Using per-key quantization points (text_encoder_2-item4.clip_prompt: torch.Size([256])) for text_encoder_2-item4.clip_prompt_embeds text_encoder_2-item4.clip_prompt_embeds: 6,996B, BPFP=0.5679 Using per-key quantization points (text_encoder_3-item5.t5_prompt_embeds: torch.Size([256])) for text_encoder_3-item5.t5_prompt_embeds text_encoder_3-item5.t5_prompt_embeds: 18,416B, BPFP=0.4671 Using per-key quantization points (vae.encoder_f0: torch.Size([256])) for vae.encoder_f0 vae.encoder_f0: 12,624B, BPFP=0.1926 Using per-key quantization points (vae.encoder_f1: torch.Size([256])) for vae.encoder_f1 vae.encoder_f1: 12,624B, BPFP=0.1926 Using per-key quantization points (vae.decoder: torch.Size([256])) for vae.decoder vae.decoder: 4,508B, BPFP=0.1376 ⌛️ [2/4] FRONTEND: Frontend time: 0.295s (Pack+Encode) [3/4] BACKEND: Decode + Unpack... Using per-key quantization points (text_encoder-item0.clip_pooled_prompt_embeds: torch.Size([256])) for text_encoder-item0.clip_pooled_prompt_embeds Using per-key quantization points (text_encoder-item0.clip_prompt_embeds: torch.Size([256])) for text_encoder-item0.clip_prompt_embeds Using per-key quantization points (text_encoder_2-item1.clip_pooled_prompt_embeds: torch.Size([256])) for text_encoder_2-item1.clip_pooled_prompt_embeds Using per-key quantization points (text_encoder_2-item1.clip_prompt_embeds: torch.Size([256])) for text_encoder_2-item1.clip_prompt_embeds Using per-key quantization points (text_encoder_3-item2.t5_prompt_embeds: torch.Size([256])) for text_encoder_3-item2.t5_prompt_embeds Using per-key quantization points (text_encoder-item3.clip_pooled_prompt_embeds: torch.Size([256])) for text_encoder-item3.clip_pooled_prompt_embeds Using per-key quantization points (text_encoder-item3.clip_prompt_embeds: torch.Size([256])) for text_encoder-item3.clip_prompt_embeds Using per-key quantization points (text_encoder_2-item4.clip_pooled: torch.Size([256])) for text_encoder_2-item4.clip_pooled_prompt_embeds Using per-key quantization points (text_encoder_2-item4.clip_prompt: torch.Size([256])) for text_encoder_2-item4.clip_prompt_embeds Using per-key quantization points (text_encoder_3-item5.t5_prompt_embeds: torch.Size([256])) for text_encoder_3-item5.t5_prompt_embeds Using per-key quantization points (vae.encoder_f0: torch.Size([256])) for vae.encoder_f0 Using per-key quantization points (vae.encoder_f1: torch.Size([256])) for vae.encoder_f1 Using per-key quantization points (vae.decoder: torch.Size([256])) for vae.decoder IndividualUnPacker: text_encoder-item0.clip_pooled_prompt_embeds: torch.Size([768]) text_encoder-item0.clip_prompt_embeds: torch.Size([77, 768]) text_encoder_2-item1.clip_pooled_prompt_embeds: torch.Size([1280]) text_encoder_2-item1.clip_prompt_embeds: torch.Size([77, 1280]) text_encoder_3-item2.t5_prompt_embeds: torch.Size([77, 4096]) text_encoder-item3.clip_pooled_prompt_embeds: torch.Size([768]) text_encoder-item3.clip_prompt_embeds: torch.Size([77, 768]) text_encoder_2-item4.clip_pooled_prompt_embeds: torch.Size([1280]) text_encoder_2-item4.clip_prompt_embeds: torch.Size([77, 1280]) text_encoder_3-item5.t5_prompt_embeds: torch.Size([77, 4096]) vae.decoder: torch.Size([16, 128, 128]) vae.encoder_f0: torch.Size([32, 128, 128]) vae.encoder_f1: torch.Size([32, 128, 128]) ⌛️ [3/4] BACKEND: Backend time: 0.456s [4/4] METRICS: Computing MSE Breakdown... Using per-key quantization points (text_encoder-item0.clip_pooled_prompt_embeds: torch.Size([256])) for text_encoder-item0.clip_pooled_prompt_embeds Using per-key quantization points (text_encoder-item0.clip_prompt_embeds: torch.Size([256])) for text_encoder-item0.clip_prompt_embeds Using per-key quantization points (text_encoder_2-item1.clip_pooled_prompt_embeds: torch.Size([256])) for text_encoder_2-item1.clip_pooled_prompt_embeds Using per-key quantization points (text_encoder_2-item1.clip_prompt_embeds: torch.Size([256])) for text_encoder_2-item1.clip_prompt_embeds Using per-key quantization points (text_encoder_3-item2.t5_prompt_embeds: torch.Size([256])) for text_encoder_3-item2.t5_prompt_embeds Using per-key quantization points (text_encoder-item3.clip_pooled_prompt_embeds: torch.Size([256])) for text_encoder-item3.clip_pooled_prompt_embeds Using per-key quantization points (text_encoder-item3.clip_prompt_embeds: torch.Size([256])) for text_encoder-item3.clip_prompt_embeds Using per-key quantization points (text_encoder_2-item4.clip_pooled: torch.Size([256])) for text_encoder_2-item4.clip_pooled_prompt_embeds Using per-key quantization points (text_encoder_2-item4.clip_prompt: torch.Size([256])) for text_encoder_2-item4.clip_prompt_embeds Using per-key quantization points (text_encoder_3-item5.t5_prompt_embeds: torch.Size([256])) for text_encoder_3-item5.t5_prompt_embeds Using per-key quantization points (vae.encoder_f0: torch.Size([256])) for vae.encoder_f0 Using per-key quantization points (vae.encoder_f1: torch.Size([256])) for vae.encoder_f1 Using per-key quantization points (vae.decoder: torch.Size([256])) for vae.decoder IndividualUnPacker: text_encoder-item0.clip_pooled_prompt_embeds: torch.Size([768]) text_encoder-item0.clip_prompt_embeds: torch.Size([77, 768]) text_encoder_2-item1.clip_pooled_prompt_embeds: torch.Size([1280]) text_encoder_2-item1.clip_prompt_embeds: torch.Size([77, 1280]) text_encoder_3-item2.t5_prompt_embeds: torch.Size([77, 4096]) text_encoder-item3.clip_pooled_prompt_embeds: torch.Size([768]) text_encoder-item3.clip_prompt_embeds: torch.Size([77, 768]) text_encoder_2-item4.clip_pooled_prompt_embeds: torch.Size([1280]) text_encoder_2-item4.clip_prompt_embeds: torch.Size([77, 1280]) text_encoder_3-item5.t5_prompt_embeds: torch.Size([77, 4096]) vae.decoder: torch.Size([16, 128, 128]) vae.encoder_f0: torch.Size([32, 128, 128]) vae.encoder_f1: torch.Size([32, 128, 128]) Per-key MSE Breakdown (Quant=Truncation+Quantization, Codec=Compression): Key Quant-MSE Total-MSE ------------------------------------------------------------------------------------- text_encoder-item0.clip_pooled_prompt_embeds 0.00022393 0.92735100 text_encoder-item0.clip_prompt_embeds 0.00021458 24.22180651 text_encoder_2-item1.clip_pooled_prompt_embeds 0.00020115 0.80404949 text_encoder_2-item1.clip_prompt_embeds 0.00017334 0.12917483 text_encoder_3-item2.t5_prompt_embeds 0.00000867 0.00327194 text_encoder-item3.clip_pooled_prompt_embeds 0.00841799 1.01420029 text_encoder-item3.clip_prompt_embeds 0.00023247 23.90570887 text_encoder_2-item4.clip_pooled_prompt_embeds 0.00016751 0.67051430 text_encoder_2-item4.clip_prompt_embeds 0.00009830 0.47049568 text_encoder_3-item5.t5_prompt_embeds 0.00001263 0.00227117 vae.encoder_f0 0.00914783 2.77752328 vae.encoder_f1 0.00914958 2.77746820 vae.decoder 0.00017527 0.03746323 ------------------------------------------------------------------------------------- TOTAL 0.00429285 2.57963550 (elements=2,260,992) ---------------------- -------------------------------------------------------- SAMPLE-WISE STATISTICS ---------------------- -------------------------------------------------------- Handler sd35 Strategy individual Architecture hyperprior-featurecoding ---------------------- -------------------------------------------------------- Total Elements 2260992 Total Bytes 108532 BPFP 0.3840 bits/point EBPFP 0.7680 equivalent bits/point MSE 2.579635 ---------------------- -------------------------------------------------------- Time: 0.759s Load: 0.008s, Pack+Encode: 0.295s, Decode+Unpack: 0.456s ---------------------- -------------------------------------------------------- Restored Feature Format: [Dict] with 8 keys key['text_encoder-item0']: [Dict] with 2 keys key['clip_pooled_prompt_embeds']: [Tensor] shape=torch.Size([768]), dtype=torch.float16, device=cpu key['clip_prompt_embeds']: [Tensor] shape=torch.Size([77, 768]), dtype=torch.float16, device=cpu key['text_encoder_2-item1']: [Dict] with 2 keys key['clip_pooled_prompt_embeds']: [Tensor] shape=torch.Size([1280]), dtype=torch.float16, device=cpu key['clip_prompt_embeds']: [Tensor] shape=torch.Size([77, 1280]), dtype=torch.float16, device=cpu key['text_encoder_3-item2']: [Dict] with 1 keys key['t5_prompt_embeds']: [Tensor] shape=torch.Size([77, 4096]), dtype=torch.float16, device=cpu key['text_encoder-item3']: [Dict] with 2 keys key['clip_pooled_prompt_embeds']: [Tensor] shape=torch.Size([768]), dtype=torch.float16, device=cpu key['clip_prompt_embeds']: [Tensor] shape=torch.Size([77, 768]), dtype=torch.float16, device=cpu key['text_encoder_2-item4']: [Dict] with 2 keys key['clip_pooled_prompt_embeds']: [Tensor] shape=torch.Size([1280]), dtype=torch.float16, device=cpu key['clip_prompt_embeds']: [Tensor] shape=torch.Size([77, 1280]), dtype=torch.float16, device=cpu key['text_encoder_3-item5']: [Dict] with 1 keys key['t5_prompt_embeds']: [Tensor] shape=torch.Size([77, 4096]), dtype=torch.float16, device=cpu key['vae.decoder']: [Tensor] shape=torch.Size([16, 128, 128]), dtype=torch.float16, device=cpu key['vae.encoder']: [Dict] with 2 keys key['vae.encoder_f0']: [Tensor] shape=torch.Size([32, 128, 128]), dtype=torch.float16, device=cpu key['vae.encoder_f1']: [Tensor] shape=torch.Size([32, 128, 128]), dtype=torch.float16, device=cpu 💾 Converting with 2.5796 MSE: from ../datasets/FeatureCoding-StableDiffusion3.5/100samples_features/000000482477.zst to output-fixed/sd35/lambda0.004/hyperprior-featurecoding-8bit-individual/sd35cond/000000482477.zst 💪 Processing: ../datasets/FeatureCoding-StableDiffusion3.5/100samples_features/000000499768.zst (88/100) [1/4] FRONTEND: Loading features from ../datasets/FeatureCoding-StableDiffusion3.5/100samples_features/000000499768.zst... Original data structure: root: [Dict] with 8 keys key['text_encoder-item0']: [Dict] with 2 keys key['clip_pooled_prompt_embeds']: [Tensor] shape=torch.Size([768]), dtype=torch.float16, device=cpu key['clip_prompt_embeds']: [Tensor] shape=torch.Size([77, 768]), dtype=torch.float16, device=cpu key['text_encoder_2-item1']: [Dict] with 2 keys key['clip_pooled_prompt_embeds']: [Tensor] shape=torch.Size([1280]), dtype=torch.float16, device=cpu key['clip_prompt_embeds']: [Tensor] shape=torch.Size([77, 1280]), dtype=torch.float16, device=cpu key['text_encoder_3-item2']: [Dict] with 1 keys key['t5_prompt_embeds']: [Tensor] shape=torch.Size([77, 4096]), dtype=torch.float16, device=cpu key['text_encoder-item3']: [Dict] with 2 keys key['clip_pooled_prompt_embeds']: [Tensor] shape=torch.Size([768]), dtype=torch.float16, device=cpu key['clip_prompt_embeds']: [Tensor] shape=torch.Size([77, 768]), dtype=torch.float16, device=cpu key['text_encoder_2-item4']: [Dict] with 2 keys key['clip_pooled_prompt_embeds']: [Tensor] shape=torch.Size([1280]), dtype=torch.float16, device=cpu key['clip_prompt_embeds']: [Tensor] shape=torch.Size([77, 1280]), dtype=torch.float16, device=cpu key['text_encoder_3-item5']: [Dict] with 1 keys key['t5_prompt_embeds']: [Tensor] shape=torch.Size([77, 4096]), dtype=torch.float16, device=cpu key['vae.encoder-item6']: [Dict] with 2 keys key['vae.encoder_f0']: [Tensor] shape=torch.Size([32, 128, 128]), dtype=torch.float16, device=cpu key['vae.encoder_f1']: [Tensor] shape=torch.Size([32, 128, 128]), dtype=torch.float16, device=cpu key['vae.decoder']: [Tensor] shape=torch.Size([16, 128, 128]), dtype=torch.float16, device=cpu ⌛️ [1/4] FRONTEND: Load time: 0.009s ------------------------------------------------------------ SD3.5 Features Summary ------------------------------------------------------------ Number of text encoders: 3 Encoder names: ['text_encoder', 'text_encoder_2', 'text_encoder_3'] Has VAE latents: True Has VAE encoder features: True Data type: torch.float16 text_encoder-item0: clip_pooled_prompt_embeds: torch.Size([768]), torch.float16 clip_prompt_embeds: torch.Size([77, 768]), torch.float16 text_encoder_2-item1: clip_pooled_prompt_embeds: torch.Size([1280]), torch.float16 clip_prompt_embeds: torch.Size([77, 1280]), torch.float16 text_encoder_3-item2: t5_prompt_embeds: torch.Size([77, 4096]), torch.float16 text_encoder-item3: clip_pooled_prompt_embeds: torch.Size([768]), torch.float16 clip_prompt_embeds: torch.Size([77, 768]), torch.float16 text_encoder_2-item4: clip_pooled_prompt_embeds: torch.Size([1280]), torch.float16 clip_prompt_embeds: torch.Size([77, 1280]), torch.float16 text_encoder_3-item5: t5_prompt_embeds: torch.Size([77, 4096]), torch.float16 vae.decoder latents: torch.Size([16, 128, 128]) vae.encoder features: vae.encoder_f0: torch.Size([32, 128, 128]), torch.float16 vae.encoder_f1: torch.Size([32, 128, 128]), torch.float16 ------------------------------------------------------------ [2/4] FRONTEND: Pack + Encode (strategy: individual)... IndividualPacker: text_encoder-item0.clip_pooled_prompt_embeds: torch.Size([768])->torch.Size([1, 1, 1, 768]) text_encoder-item0.clip_prompt_embeds: torch.Size([77, 768])->torch.Size([1, 1, 77, 768]) text_encoder_2-item1.clip_pooled_prompt_embeds: torch.Size([1280])->torch.Size([1, 1, 1, 1280]) text_encoder_2-item1.clip_prompt_embeds: torch.Size([77, 1280])->torch.Size([1, 1, 77, 1280]) text_encoder_3-item2.t5_prompt_embeds: torch.Size([77, 4096])->torch.Size([1, 1, 77, 4096]) text_encoder-item3.clip_pooled_prompt_embeds: torch.Size([768])->torch.Size([1, 1, 1, 768]) text_encoder-item3.clip_prompt_embeds: torch.Size([77, 768])->torch.Size([1, 1, 77, 768]) text_encoder_2-item4.clip_pooled_prompt_embeds: torch.Size([1280])->torch.Size([1, 1, 1, 1280]) text_encoder_2-item4.clip_prompt_embeds: torch.Size([77, 1280])->torch.Size([1, 1, 77, 1280]) text_encoder_3-item5.t5_prompt_embeds: torch.Size([77, 4096])->torch.Size([1, 1, 77, 4096]) vae.encoder_f0: torch.Size([32, 128, 128])->torch.Size([1, 1, 512, 1024]) vae.encoder_f1: torch.Size([32, 128, 128])->torch.Size([1, 1, 512, 1024]) vae.decoder: torch.Size([16, 128, 128])->torch.Size([1, 1, 512, 512]) Using per-key quantization points (text_encoder-item0.clip_pooled_prompt_embeds: torch.Size([256])) for text_encoder-item0.clip_pooled_prompt_embeds text_encoder-item0.clip_pooled_prompt_embeds: 732B, BPFP=7.6250 Using per-key quantization points (text_encoder-item0.clip_prompt_embeds: torch.Size([256])) for text_encoder-item0.clip_prompt_embeds text_encoder-item0.clip_prompt_embeds: 6,992B, BPFP=0.9459 Using per-key quantization points (text_encoder_2-item1.clip_pooled_prompt_embeds: torch.Size([256])) for text_encoder_2-item1.clip_pooled_prompt_embeds text_encoder_2-item1.clip_pooled_prompt_embeds: 1,208B, BPFP=7.5500 Using per-key quantization points (text_encoder_2-item1.clip_prompt_embeds: torch.Size([256])) for text_encoder_2-item1.clip_prompt_embeds text_encoder_2-item1.clip_prompt_embeds: 11,040B, BPFP=0.8961 Using per-key quantization points (text_encoder_3-item2.t5_prompt_embeds: torch.Size([256])) for text_encoder_3-item2.t5_prompt_embeds text_encoder_3-item2.t5_prompt_embeds: 29,836B, BPFP=0.7568 Using per-key quantization points (text_encoder-item3.clip_pooled_prompt_embeds: torch.Size([256])) for text_encoder-item3.clip_pooled_prompt_embeds text_encoder-item3.clip_pooled_prompt_embeds: 760B, BPFP=7.9167 Using per-key quantization points (text_encoder-item3.clip_prompt_embeds: torch.Size([256])) for text_encoder-item3.clip_prompt_embeds text_encoder-item3.clip_prompt_embeds: 4,024B, BPFP=0.5444 Using per-key quantization points (text_encoder_2-item4.clip_pooled: torch.Size([256])) for text_encoder_2-item4.clip_pooled_prompt_embeds text_encoder_2-item4.clip_pooled_prompt_embeds: 1,268B, BPFP=7.9250 Using per-key quantization points (text_encoder_2-item4.clip_prompt: torch.Size([256])) for text_encoder_2-item4.clip_prompt_embeds text_encoder_2-item4.clip_prompt_embeds: 6,996B, BPFP=0.5679 Using per-key quantization points (text_encoder_3-item5.t5_prompt_embeds: torch.Size([256])) for text_encoder_3-item5.t5_prompt_embeds text_encoder_3-item5.t5_prompt_embeds: 18,416B, BPFP=0.4671 Using per-key quantization points (vae.encoder_f0: torch.Size([256])) for vae.encoder_f0 vae.encoder_f0: 12,616B, BPFP=0.1925 Using per-key quantization points (vae.encoder_f1: torch.Size([256])) for vae.encoder_f1 vae.encoder_f1: 12,616B, BPFP=0.1925 Using per-key quantization points (vae.decoder: torch.Size([256])) for vae.decoder vae.decoder: 10,252B, BPFP=0.3129 ⌛️ [2/4] FRONTEND: Frontend time: 0.296s (Pack+Encode) [3/4] BACKEND: Decode + Unpack... Using per-key quantization points (text_encoder-item0.clip_pooled_prompt_embeds: torch.Size([256])) for text_encoder-item0.clip_pooled_prompt_embeds Using per-key quantization points (text_encoder-item0.clip_prompt_embeds: torch.Size([256])) for text_encoder-item0.clip_prompt_embeds Using per-key quantization points (text_encoder_2-item1.clip_pooled_prompt_embeds: torch.Size([256])) for text_encoder_2-item1.clip_pooled_prompt_embeds Using per-key quantization points (text_encoder_2-item1.clip_prompt_embeds: torch.Size([256])) for text_encoder_2-item1.clip_prompt_embeds Using per-key quantization points (text_encoder_3-item2.t5_prompt_embeds: torch.Size([256])) for text_encoder_3-item2.t5_prompt_embeds Using per-key quantization points (text_encoder-item3.clip_pooled_prompt_embeds: torch.Size([256])) for text_encoder-item3.clip_pooled_prompt_embeds Using per-key quantization points (text_encoder-item3.clip_prompt_embeds: torch.Size([256])) for text_encoder-item3.clip_prompt_embeds Using per-key quantization points (text_encoder_2-item4.clip_pooled: torch.Size([256])) for text_encoder_2-item4.clip_pooled_prompt_embeds Using per-key quantization points (text_encoder_2-item4.clip_prompt: torch.Size([256])) for text_encoder_2-item4.clip_prompt_embeds Using per-key quantization points (text_encoder_3-item5.t5_prompt_embeds: torch.Size([256])) for text_encoder_3-item5.t5_prompt_embeds Using per-key quantization points (vae.encoder_f0: torch.Size([256])) for vae.encoder_f0 Using per-key quantization points (vae.encoder_f1: torch.Size([256])) for vae.encoder_f1 Using per-key quantization points (vae.decoder: torch.Size([256])) for vae.decoder IndividualUnPacker: text_encoder-item0.clip_pooled_prompt_embeds: torch.Size([768]) text_encoder-item0.clip_prompt_embeds: torch.Size([77, 768]) text_encoder_2-item1.clip_pooled_prompt_embeds: torch.Size([1280]) text_encoder_2-item1.clip_prompt_embeds: torch.Size([77, 1280]) text_encoder_3-item2.t5_prompt_embeds: torch.Size([77, 4096]) text_encoder-item3.clip_pooled_prompt_embeds: torch.Size([768]) text_encoder-item3.clip_prompt_embeds: torch.Size([77, 768]) text_encoder_2-item4.clip_pooled_prompt_embeds: torch.Size([1280]) text_encoder_2-item4.clip_prompt_embeds: torch.Size([77, 1280]) text_encoder_3-item5.t5_prompt_embeds: torch.Size([77, 4096]) vae.decoder: torch.Size([16, 128, 128]) vae.encoder_f0: torch.Size([32, 128, 128]) vae.encoder_f1: torch.Size([32, 128, 128]) ⌛️ [3/4] BACKEND: Backend time: 0.456s [4/4] METRICS: Computing MSE Breakdown... Using per-key quantization points (text_encoder-item0.clip_pooled_prompt_embeds: torch.Size([256])) for text_encoder-item0.clip_pooled_prompt_embeds Using per-key quantization points (text_encoder-item0.clip_prompt_embeds: torch.Size([256])) for text_encoder-item0.clip_prompt_embeds Using per-key quantization points (text_encoder_2-item1.clip_pooled_prompt_embeds: torch.Size([256])) for text_encoder_2-item1.clip_pooled_prompt_embeds Using per-key quantization points (text_encoder_2-item1.clip_prompt_embeds: torch.Size([256])) for text_encoder_2-item1.clip_prompt_embeds Using per-key quantization points (text_encoder_3-item2.t5_prompt_embeds: torch.Size([256])) for text_encoder_3-item2.t5_prompt_embeds Using per-key quantization points (text_encoder-item3.clip_pooled_prompt_embeds: torch.Size([256])) for text_encoder-item3.clip_pooled_prompt_embeds Using per-key quantization points (text_encoder-item3.clip_prompt_embeds: torch.Size([256])) for text_encoder-item3.clip_prompt_embeds Using per-key quantization points (text_encoder_2-item4.clip_pooled: torch.Size([256])) for text_encoder_2-item4.clip_pooled_prompt_embeds Using per-key quantization points (text_encoder_2-item4.clip_prompt: torch.Size([256])) for text_encoder_2-item4.clip_prompt_embeds Using per-key quantization points (text_encoder_3-item5.t5_prompt_embeds: torch.Size([256])) for text_encoder_3-item5.t5_prompt_embeds Using per-key quantization points (vae.encoder_f0: torch.Size([256])) for vae.encoder_f0 Using per-key quantization points (vae.encoder_f1: torch.Size([256])) for vae.encoder_f1 Using per-key quantization points (vae.decoder: torch.Size([256])) for vae.decoder IndividualUnPacker: text_encoder-item0.clip_pooled_prompt_embeds: torch.Size([768]) text_encoder-item0.clip_prompt_embeds: torch.Size([77, 768]) text_encoder_2-item1.clip_pooled_prompt_embeds: torch.Size([1280]) text_encoder_2-item1.clip_prompt_embeds: torch.Size([77, 1280]) text_encoder_3-item2.t5_prompt_embeds: torch.Size([77, 4096]) text_encoder-item3.clip_pooled_prompt_embeds: torch.Size([768]) text_encoder-item3.clip_prompt_embeds: torch.Size([77, 768]) text_encoder_2-item4.clip_pooled_prompt_embeds: torch.Size([1280]) text_encoder_2-item4.clip_prompt_embeds: torch.Size([77, 1280]) text_encoder_3-item5.t5_prompt_embeds: torch.Size([77, 4096]) vae.decoder: torch.Size([16, 128, 128]) vae.encoder_f0: torch.Size([32, 128, 128]) vae.encoder_f1: torch.Size([32, 128, 128]) Per-key MSE Breakdown (Quant=Truncation+Quantization, Codec=Compression): Key Quant-MSE Total-MSE ------------------------------------------------------------------------------------- text_encoder-item0.clip_pooled_prompt_embeds 0.00029464 0.95068192 text_encoder-item0.clip_prompt_embeds 0.00022150 23.94016124 text_encoder_2-item1.clip_pooled_prompt_embeds 0.00048959 0.94726162 text_encoder_2-item1.clip_prompt_embeds 0.00016852 0.14475061 text_encoder_3-item2.t5_prompt_embeds 0.00000767 0.00336107 text_encoder-item3.clip_pooled_prompt_embeds 0.00841799 1.01420029 text_encoder-item3.clip_prompt_embeds 0.00023247 23.90570887 text_encoder_2-item4.clip_pooled_prompt_embeds 0.00016751 0.67051430 text_encoder_2-item4.clip_prompt_embeds 0.00009830 0.47049568 text_encoder_3-item5.t5_prompt_embeds 0.00001263 0.00227117 vae.encoder_f0 0.00578482 1.43715274 vae.encoder_f1 0.00579739 1.43684483 vae.decoder 0.00017668 0.08250174 ------------------------------------------------------------------------------------- TOTAL 0.00273588 1.95659161 (elements=2,260,992) ---------------------- -------------------------------------------------------- SAMPLE-WISE STATISTICS ---------------------- -------------------------------------------------------- Handler sd35 Strategy individual Architecture hyperprior-featurecoding ---------------------- -------------------------------------------------------- Total Elements 2260992 Total Bytes 116756 BPFP 0.4131 bits/point EBPFP 0.8262 equivalent bits/point MSE 1.956592 ---------------------- -------------------------------------------------------- Time: 0.761s Load: 0.009s, Pack+Encode: 0.296s, Decode+Unpack: 0.456s ---------------------- -------------------------------------------------------- Restored Feature Format: [Dict] with 8 keys key['text_encoder-item0']: [Dict] with 2 keys key['clip_pooled_prompt_embeds']: [Tensor] shape=torch.Size([768]), dtype=torch.float16, device=cpu key['clip_prompt_embeds']: [Tensor] shape=torch.Size([77, 768]), dtype=torch.float16, device=cpu key['text_encoder_2-item1']: [Dict] with 2 keys key['clip_pooled_prompt_embeds']: [Tensor] shape=torch.Size([1280]), dtype=torch.float16, device=cpu key['clip_prompt_embeds']: [Tensor] shape=torch.Size([77, 1280]), dtype=torch.float16, device=cpu key['text_encoder_3-item2']: [Dict] with 1 keys key['t5_prompt_embeds']: [Tensor] shape=torch.Size([77, 4096]), dtype=torch.float16, device=cpu key['text_encoder-item3']: [Dict] with 2 keys key['clip_pooled_prompt_embeds']: [Tensor] shape=torch.Size([768]), dtype=torch.float16, device=cpu key['clip_prompt_embeds']: [Tensor] shape=torch.Size([77, 768]), dtype=torch.float16, device=cpu key['text_encoder_2-item4']: [Dict] with 2 keys key['clip_pooled_prompt_embeds']: [Tensor] shape=torch.Size([1280]), dtype=torch.float16, device=cpu key['clip_prompt_embeds']: [Tensor] shape=torch.Size([77, 1280]), dtype=torch.float16, device=cpu key['text_encoder_3-item5']: [Dict] with 1 keys key['t5_prompt_embeds']: [Tensor] shape=torch.Size([77, 4096]), dtype=torch.float16, device=cpu key['vae.decoder']: [Tensor] shape=torch.Size([16, 128, 128]), dtype=torch.float16, device=cpu key['vae.encoder']: [Dict] with 2 keys key['vae.encoder_f0']: [Tensor] shape=torch.Size([32, 128, 128]), dtype=torch.float16, device=cpu key['vae.encoder_f1']: [Tensor] shape=torch.Size([32, 128, 128]), dtype=torch.float16, device=cpu 💾 Converting with 1.9566 MSE: from ../datasets/FeatureCoding-StableDiffusion3.5/100samples_features/000000499768.zst to output-fixed/sd35/lambda0.004/hyperprior-featurecoding-8bit-individual/sd35cond/000000499768.zst 💪 Processing: ../datasets/FeatureCoding-StableDiffusion3.5/100samples_features/000000499775.zst (89/100) [1/4] FRONTEND: Loading features from ../datasets/FeatureCoding-StableDiffusion3.5/100samples_features/000000499775.zst... Original data structure: root: [Dict] with 8 keys key['text_encoder-item0']: [Dict] with 2 keys key['clip_pooled_prompt_embeds']: [Tensor] shape=torch.Size([768]), dtype=torch.float16, device=cpu key['clip_prompt_embeds']: [Tensor] shape=torch.Size([77, 768]), dtype=torch.float16, device=cpu key['text_encoder_2-item1']: [Dict] with 2 keys key['clip_pooled_prompt_embeds']: [Tensor] shape=torch.Size([1280]), dtype=torch.float16, device=cpu key['clip_prompt_embeds']: [Tensor] shape=torch.Size([77, 1280]), dtype=torch.float16, device=cpu key['text_encoder_3-item2']: [Dict] with 1 keys key['t5_prompt_embeds']: [Tensor] shape=torch.Size([77, 4096]), dtype=torch.float16, device=cpu key['text_encoder-item3']: [Dict] with 2 keys key['clip_pooled_prompt_embeds']: [Tensor] shape=torch.Size([768]), dtype=torch.float16, device=cpu key['clip_prompt_embeds']: [Tensor] shape=torch.Size([77, 768]), dtype=torch.float16, device=cpu key['text_encoder_2-item4']: [Dict] with 2 keys key['clip_pooled_prompt_embeds']: [Tensor] shape=torch.Size([1280]), dtype=torch.float16, device=cpu key['clip_prompt_embeds']: [Tensor] shape=torch.Size([77, 1280]), dtype=torch.float16, device=cpu key['text_encoder_3-item5']: [Dict] with 1 keys key['t5_prompt_embeds']: [Tensor] shape=torch.Size([77, 4096]), dtype=torch.float16, device=cpu key['vae.encoder-item6']: [Dict] with 2 keys key['vae.encoder_f0']: [Tensor] shape=torch.Size([32, 128, 128]), dtype=torch.float16, device=cpu key['vae.encoder_f1']: [Tensor] shape=torch.Size([32, 128, 128]), dtype=torch.float16, device=cpu key['vae.decoder']: [Tensor] shape=torch.Size([16, 128, 128]), dtype=torch.float16, device=cpu ⌛️ [1/4] FRONTEND: Load time: 0.009s ------------------------------------------------------------ SD3.5 Features Summary ------------------------------------------------------------ Number of text encoders: 3 Encoder names: ['text_encoder', 'text_encoder_2', 'text_encoder_3'] Has VAE latents: True Has VAE encoder features: True Data type: torch.float16 text_encoder-item0: clip_pooled_prompt_embeds: torch.Size([768]), torch.float16 clip_prompt_embeds: torch.Size([77, 768]), torch.float16 text_encoder_2-item1: clip_pooled_prompt_embeds: torch.Size([1280]), torch.float16 clip_prompt_embeds: torch.Size([77, 1280]), torch.float16 text_encoder_3-item2: t5_prompt_embeds: torch.Size([77, 4096]), torch.float16 text_encoder-item3: clip_pooled_prompt_embeds: torch.Size([768]), torch.float16 clip_prompt_embeds: torch.Size([77, 768]), torch.float16 text_encoder_2-item4: clip_pooled_prompt_embeds: torch.Size([1280]), torch.float16 clip_prompt_embeds: torch.Size([77, 1280]), torch.float16 text_encoder_3-item5: t5_prompt_embeds: torch.Size([77, 4096]), torch.float16 vae.decoder latents: torch.Size([16, 128, 128]) vae.encoder features: vae.encoder_f0: torch.Size([32, 128, 128]), torch.float16 vae.encoder_f1: torch.Size([32, 128, 128]), torch.float16 ------------------------------------------------------------ [2/4] FRONTEND: Pack + Encode (strategy: individual)... IndividualPacker: text_encoder-item0.clip_pooled_prompt_embeds: torch.Size([768])->torch.Size([1, 1, 1, 768]) text_encoder-item0.clip_prompt_embeds: torch.Size([77, 768])->torch.Size([1, 1, 77, 768]) text_encoder_2-item1.clip_pooled_prompt_embeds: torch.Size([1280])->torch.Size([1, 1, 1, 1280]) text_encoder_2-item1.clip_prompt_embeds: torch.Size([77, 1280])->torch.Size([1, 1, 77, 1280]) text_encoder_3-item2.t5_prompt_embeds: torch.Size([77, 4096])->torch.Size([1, 1, 77, 4096]) text_encoder-item3.clip_pooled_prompt_embeds: torch.Size([768])->torch.Size([1, 1, 1, 768]) text_encoder-item3.clip_prompt_embeds: torch.Size([77, 768])->torch.Size([1, 1, 77, 768]) text_encoder_2-item4.clip_pooled_prompt_embeds: torch.Size([1280])->torch.Size([1, 1, 1, 1280]) text_encoder_2-item4.clip_prompt_embeds: torch.Size([77, 1280])->torch.Size([1, 1, 77, 1280]) text_encoder_3-item5.t5_prompt_embeds: torch.Size([77, 4096])->torch.Size([1, 1, 77, 4096]) vae.encoder_f0: torch.Size([32, 128, 128])->torch.Size([1, 1, 512, 1024]) vae.encoder_f1: torch.Size([32, 128, 128])->torch.Size([1, 1, 512, 1024]) vae.decoder: torch.Size([16, 128, 128])->torch.Size([1, 1, 512, 512]) Using per-key quantization points (text_encoder-item0.clip_pooled_prompt_embeds: torch.Size([256])) for text_encoder-item0.clip_pooled_prompt_embeds text_encoder-item0.clip_pooled_prompt_embeds: 732B, BPFP=7.6250 Using per-key quantization points (text_encoder-item0.clip_prompt_embeds: torch.Size([256])) for text_encoder-item0.clip_prompt_embeds text_encoder-item0.clip_prompt_embeds: 6,144B, BPFP=0.8312 Using per-key quantization points (text_encoder_2-item1.clip_pooled_prompt_embeds: torch.Size([256])) for text_encoder_2-item1.clip_pooled_prompt_embeds text_encoder_2-item1.clip_pooled_prompt_embeds: 1,196B, BPFP=7.4750 Using per-key quantization points (text_encoder_2-item1.clip_prompt_embeds: torch.Size([256])) for text_encoder_2-item1.clip_prompt_embeds text_encoder_2-item1.clip_prompt_embeds: 10,628B, BPFP=0.8627 Using per-key quantization points (text_encoder_3-item2.t5_prompt_embeds: torch.Size([256])) for text_encoder_3-item2.t5_prompt_embeds text_encoder_3-item2.t5_prompt_embeds: 27,056B, BPFP=0.6863 Using per-key quantization points (text_encoder-item3.clip_pooled_prompt_embeds: torch.Size([256])) for text_encoder-item3.clip_pooled_prompt_embeds text_encoder-item3.clip_pooled_prompt_embeds: 760B, BPFP=7.9167 Using per-key quantization points (text_encoder-item3.clip_prompt_embeds: torch.Size([256])) for text_encoder-item3.clip_prompt_embeds text_encoder-item3.clip_prompt_embeds: 4,024B, BPFP=0.5444 Using per-key quantization points (text_encoder_2-item4.clip_pooled: torch.Size([256])) for text_encoder_2-item4.clip_pooled_prompt_embeds text_encoder_2-item4.clip_pooled_prompt_embeds: 1,268B, BPFP=7.9250 Using per-key quantization points (text_encoder_2-item4.clip_prompt: torch.Size([256])) for text_encoder_2-item4.clip_prompt_embeds text_encoder_2-item4.clip_prompt_embeds: 6,996B, BPFP=0.5679 Using per-key quantization points (text_encoder_3-item5.t5_prompt_embeds: torch.Size([256])) for text_encoder_3-item5.t5_prompt_embeds text_encoder_3-item5.t5_prompt_embeds: 18,416B, BPFP=0.4671 Using per-key quantization points (vae.encoder_f0: torch.Size([256])) for vae.encoder_f0 vae.encoder_f0: 14,608B, BPFP=0.2229 Using per-key quantization points (vae.encoder_f1: torch.Size([256])) for vae.encoder_f1 vae.encoder_f1: 14,604B, BPFP=0.2228 Using per-key quantization points (vae.decoder: torch.Size([256])) for vae.decoder vae.decoder: 7,404B, BPFP=0.2260 ⌛️ [2/4] FRONTEND: Frontend time: 0.298s (Pack+Encode) [3/4] BACKEND: Decode + Unpack... Using per-key quantization points (text_encoder-item0.clip_pooled_prompt_embeds: torch.Size([256])) for text_encoder-item0.clip_pooled_prompt_embeds Using per-key quantization points (text_encoder-item0.clip_prompt_embeds: torch.Size([256])) for text_encoder-item0.clip_prompt_embeds Using per-key quantization points (text_encoder_2-item1.clip_pooled_prompt_embeds: torch.Size([256])) for text_encoder_2-item1.clip_pooled_prompt_embeds Using per-key quantization points (text_encoder_2-item1.clip_prompt_embeds: torch.Size([256])) for text_encoder_2-item1.clip_prompt_embeds Using per-key quantization points (text_encoder_3-item2.t5_prompt_embeds: torch.Size([256])) for text_encoder_3-item2.t5_prompt_embeds Using per-key quantization points (text_encoder-item3.clip_pooled_prompt_embeds: torch.Size([256])) for text_encoder-item3.clip_pooled_prompt_embeds Using per-key quantization points (text_encoder-item3.clip_prompt_embeds: torch.Size([256])) for text_encoder-item3.clip_prompt_embeds Using per-key quantization points (text_encoder_2-item4.clip_pooled: torch.Size([256])) for text_encoder_2-item4.clip_pooled_prompt_embeds Using per-key quantization points (text_encoder_2-item4.clip_prompt: torch.Size([256])) for text_encoder_2-item4.clip_prompt_embeds Using per-key quantization points (text_encoder_3-item5.t5_prompt_embeds: torch.Size([256])) for text_encoder_3-item5.t5_prompt_embeds Using per-key quantization points (vae.encoder_f0: torch.Size([256])) for vae.encoder_f0 Using per-key quantization points (vae.encoder_f1: torch.Size([256])) for vae.encoder_f1 Using per-key quantization points (vae.decoder: torch.Size([256])) for vae.decoder IndividualUnPacker: text_encoder-item0.clip_pooled_prompt_embeds: torch.Size([768]) text_encoder-item0.clip_prompt_embeds: torch.Size([77, 768]) text_encoder_2-item1.clip_pooled_prompt_embeds: torch.Size([1280]) text_encoder_2-item1.clip_prompt_embeds: torch.Size([77, 1280]) text_encoder_3-item2.t5_prompt_embeds: torch.Size([77, 4096]) text_encoder-item3.clip_pooled_prompt_embeds: torch.Size([768]) text_encoder-item3.clip_prompt_embeds: torch.Size([77, 768]) text_encoder_2-item4.clip_pooled_prompt_embeds: torch.Size([1280]) text_encoder_2-item4.clip_prompt_embeds: torch.Size([77, 1280]) text_encoder_3-item5.t5_prompt_embeds: torch.Size([77, 4096]) vae.decoder: torch.Size([16, 128, 128]) vae.encoder_f0: torch.Size([32, 128, 128]) vae.encoder_f1: torch.Size([32, 128, 128]) ⌛️ [3/4] BACKEND: Backend time: 0.439s [4/4] METRICS: Computing MSE Breakdown... Using per-key quantization points (text_encoder-item0.clip_pooled_prompt_embeds: torch.Size([256])) for text_encoder-item0.clip_pooled_prompt_embeds Using per-key quantization points (text_encoder-item0.clip_prompt_embeds: torch.Size([256])) for text_encoder-item0.clip_prompt_embeds Using per-key quantization points (text_encoder_2-item1.clip_pooled_prompt_embeds: torch.Size([256])) for text_encoder_2-item1.clip_pooled_prompt_embeds Using per-key quantization points (text_encoder_2-item1.clip_prompt_embeds: torch.Size([256])) for text_encoder_2-item1.clip_prompt_embeds Using per-key quantization points (text_encoder_3-item2.t5_prompt_embeds: torch.Size([256])) for text_encoder_3-item2.t5_prompt_embeds Using per-key quantization points (text_encoder-item3.clip_pooled_prompt_embeds: torch.Size([256])) for text_encoder-item3.clip_pooled_prompt_embeds Using per-key quantization points (text_encoder-item3.clip_prompt_embeds: torch.Size([256])) for text_encoder-item3.clip_prompt_embeds Using per-key quantization points (text_encoder_2-item4.clip_pooled: torch.Size([256])) for text_encoder_2-item4.clip_pooled_prompt_embeds Using per-key quantization points (text_encoder_2-item4.clip_prompt: torch.Size([256])) for text_encoder_2-item4.clip_prompt_embeds Using per-key quantization points (text_encoder_3-item5.t5_prompt_embeds: torch.Size([256])) for text_encoder_3-item5.t5_prompt_embeds Using per-key quantization points (vae.encoder_f0: torch.Size([256])) for vae.encoder_f0 Using per-key quantization points (vae.encoder_f1: torch.Size([256])) for vae.encoder_f1 Using per-key quantization points (vae.decoder: torch.Size([256])) for vae.decoder IndividualUnPacker: text_encoder-item0.clip_pooled_prompt_embeds: torch.Size([768]) text_encoder-item0.clip_prompt_embeds: torch.Size([77, 768]) text_encoder_2-item1.clip_pooled_prompt_embeds: torch.Size([1280]) text_encoder_2-item1.clip_prompt_embeds: torch.Size([77, 1280]) text_encoder_3-item2.t5_prompt_embeds: torch.Size([77, 4096]) text_encoder-item3.clip_pooled_prompt_embeds: torch.Size([768]) text_encoder-item3.clip_prompt_embeds: torch.Size([77, 768]) text_encoder_2-item4.clip_pooled_prompt_embeds: torch.Size([1280]) text_encoder_2-item4.clip_prompt_embeds: torch.Size([77, 1280]) text_encoder_3-item5.t5_prompt_embeds: torch.Size([77, 4096]) vae.decoder: torch.Size([16, 128, 128]) vae.encoder_f0: torch.Size([32, 128, 128]) vae.encoder_f1: torch.Size([32, 128, 128]) Per-key MSE Breakdown (Quant=Truncation+Quantization, Codec=Compression): Key Quant-MSE Total-MSE ------------------------------------------------------------------------------------- text_encoder-item0.clip_pooled_prompt_embeds 0.00085811 0.95121233 text_encoder-item0.clip_prompt_embeds 0.00023894 23.94528925 text_encoder_2-item1.clip_pooled_prompt_embeds 0.00033417 0.92296820 text_encoder_2-item1.clip_prompt_embeds 0.00016768 0.12001479 text_encoder_3-item2.t5_prompt_embeds 0.00000784 0.00337827 text_encoder-item3.clip_pooled_prompt_embeds 0.00841799 1.01420029 text_encoder-item3.clip_prompt_embeds 0.00023247 23.90570887 text_encoder_2-item4.clip_pooled_prompt_embeds 0.00016751 0.67051430 text_encoder_2-item4.clip_prompt_embeds 0.00009830 0.47049568 text_encoder_3-item5.t5_prompt_embeds 0.00001263 0.00227117 vae.encoder_f0 0.00958025 3.00654221 vae.encoder_f1 0.00958229 3.00660229 vae.decoder 0.00019995 0.06026506 ------------------------------------------------------------------------------------- TOTAL 0.00449688 2.68097625 (elements=2,260,992) ---------------------- -------------------------------------------------------- SAMPLE-WISE STATISTICS ---------------------- -------------------------------------------------------- Handler sd35 Strategy individual Architecture hyperprior-featurecoding ---------------------- -------------------------------------------------------- Total Elements 2260992 Total Bytes 113836 BPFP 0.4028 bits/point EBPFP 0.8056 equivalent bits/point MSE 2.680976 ---------------------- -------------------------------------------------------- Time: 0.746s Load: 0.009s, Pack+Encode: 0.298s, Decode+Unpack: 0.439s ---------------------- -------------------------------------------------------- Restored Feature Format: [Dict] with 8 keys key['text_encoder-item0']: [Dict] with 2 keys key['clip_pooled_prompt_embeds']: [Tensor] shape=torch.Size([768]), dtype=torch.float16, device=cpu key['clip_prompt_embeds']: [Tensor] shape=torch.Size([77, 768]), dtype=torch.float16, device=cpu key['text_encoder_2-item1']: [Dict] with 2 keys key['clip_pooled_prompt_embeds']: [Tensor] shape=torch.Size([1280]), dtype=torch.float16, device=cpu key['clip_prompt_embeds']: [Tensor] shape=torch.Size([77, 1280]), dtype=torch.float16, device=cpu key['text_encoder_3-item2']: [Dict] with 1 keys key['t5_prompt_embeds']: [Tensor] shape=torch.Size([77, 4096]), dtype=torch.float16, device=cpu key['text_encoder-item3']: [Dict] with 2 keys key['clip_pooled_prompt_embeds']: [Tensor] shape=torch.Size([768]), dtype=torch.float16, device=cpu key['clip_prompt_embeds']: [Tensor] shape=torch.Size([77, 768]), dtype=torch.float16, device=cpu key['text_encoder_2-item4']: [Dict] with 2 keys key['clip_pooled_prompt_embeds']: [Tensor] shape=torch.Size([1280]), dtype=torch.float16, device=cpu key['clip_prompt_embeds']: [Tensor] shape=torch.Size([77, 1280]), dtype=torch.float16, device=cpu key['text_encoder_3-item5']: [Dict] with 1 keys key['t5_prompt_embeds']: [Tensor] shape=torch.Size([77, 4096]), dtype=torch.float16, device=cpu key['vae.decoder']: [Tensor] shape=torch.Size([16, 128, 128]), dtype=torch.float16, device=cpu key['vae.encoder']: [Dict] with 2 keys key['vae.encoder_f0']: [Tensor] shape=torch.Size([32, 128, 128]), dtype=torch.float16, device=cpu key['vae.encoder_f1']: [Tensor] shape=torch.Size([32, 128, 128]), dtype=torch.float16, device=cpu 💾 Converting with 2.6810 MSE: from ../datasets/FeatureCoding-StableDiffusion3.5/100samples_features/000000499775.zst to output-fixed/sd35/lambda0.004/hyperprior-featurecoding-8bit-individual/sd35cond/000000499775.zst 💪 Processing: ../datasets/FeatureCoding-StableDiffusion3.5/100samples_features/000000506454.zst (90/100) [1/4] FRONTEND: Loading features from ../datasets/FeatureCoding-StableDiffusion3.5/100samples_features/000000506454.zst... Original data structure: root: [Dict] with 8 keys key['text_encoder-item0']: [Dict] with 2 keys key['clip_pooled_prompt_embeds']: [Tensor] shape=torch.Size([768]), dtype=torch.float16, device=cpu key['clip_prompt_embeds']: [Tensor] shape=torch.Size([77, 768]), dtype=torch.float16, device=cpu key['text_encoder_2-item1']: [Dict] with 2 keys key['clip_pooled_prompt_embeds']: [Tensor] shape=torch.Size([1280]), dtype=torch.float16, device=cpu key['clip_prompt_embeds']: [Tensor] shape=torch.Size([77, 1280]), dtype=torch.float16, device=cpu key['text_encoder_3-item2']: [Dict] with 1 keys key['t5_prompt_embeds']: [Tensor] shape=torch.Size([77, 4096]), dtype=torch.float16, device=cpu key['text_encoder-item3']: [Dict] with 2 keys key['clip_pooled_prompt_embeds']: [Tensor] shape=torch.Size([768]), dtype=torch.float16, device=cpu key['clip_prompt_embeds']: [Tensor] shape=torch.Size([77, 768]), dtype=torch.float16, device=cpu key['text_encoder_2-item4']: [Dict] with 2 keys key['clip_pooled_prompt_embeds']: [Tensor] shape=torch.Size([1280]), dtype=torch.float16, device=cpu key['clip_prompt_embeds']: [Tensor] shape=torch.Size([77, 1280]), dtype=torch.float16, device=cpu key['text_encoder_3-item5']: [Dict] with 1 keys key['t5_prompt_embeds']: [Tensor] shape=torch.Size([77, 4096]), dtype=torch.float16, device=cpu key['vae.encoder-item6']: [Dict] with 2 keys key['vae.encoder_f0']: [Tensor] shape=torch.Size([32, 128, 128]), dtype=torch.float16, device=cpu key['vae.encoder_f1']: [Tensor] shape=torch.Size([32, 128, 128]), dtype=torch.float16, device=cpu key['vae.decoder']: [Tensor] shape=torch.Size([16, 128, 128]), dtype=torch.float16, device=cpu ⌛️ [1/4] FRONTEND: Load time: 0.009s ------------------------------------------------------------ SD3.5 Features Summary ------------------------------------------------------------ Number of text encoders: 3 Encoder names: ['text_encoder', 'text_encoder_2', 'text_encoder_3'] Has VAE latents: True Has VAE encoder features: True Data type: torch.float16 text_encoder-item0: clip_pooled_prompt_embeds: torch.Size([768]), torch.float16 clip_prompt_embeds: torch.Size([77, 768]), torch.float16 text_encoder_2-item1: clip_pooled_prompt_embeds: torch.Size([1280]), torch.float16 clip_prompt_embeds: torch.Size([77, 1280]), torch.float16 text_encoder_3-item2: t5_prompt_embeds: torch.Size([77, 4096]), torch.float16 text_encoder-item3: clip_pooled_prompt_embeds: torch.Size([768]), torch.float16 clip_prompt_embeds: torch.Size([77, 768]), torch.float16 text_encoder_2-item4: clip_pooled_prompt_embeds: torch.Size([1280]), torch.float16 clip_prompt_embeds: torch.Size([77, 1280]), torch.float16 text_encoder_3-item5: t5_prompt_embeds: torch.Size([77, 4096]), torch.float16 vae.decoder latents: torch.Size([16, 128, 128]) vae.encoder features: vae.encoder_f0: torch.Size([32, 128, 128]), torch.float16 vae.encoder_f1: torch.Size([32, 128, 128]), torch.float16 ------------------------------------------------------------ [2/4] FRONTEND: Pack + Encode (strategy: individual)... IndividualPacker: text_encoder-item0.clip_pooled_prompt_embeds: torch.Size([768])->torch.Size([1, 1, 1, 768]) text_encoder-item0.clip_prompt_embeds: torch.Size([77, 768])->torch.Size([1, 1, 77, 768]) text_encoder_2-item1.clip_pooled_prompt_embeds: torch.Size([1280])->torch.Size([1, 1, 1, 1280]) text_encoder_2-item1.clip_prompt_embeds: torch.Size([77, 1280])->torch.Size([1, 1, 77, 1280]) text_encoder_3-item2.t5_prompt_embeds: torch.Size([77, 4096])->torch.Size([1, 1, 77, 4096]) text_encoder-item3.clip_pooled_prompt_embeds: torch.Size([768])->torch.Size([1, 1, 1, 768]) text_encoder-item3.clip_prompt_embeds: torch.Size([77, 768])->torch.Size([1, 1, 77, 768]) text_encoder_2-item4.clip_pooled_prompt_embeds: torch.Size([1280])->torch.Size([1, 1, 1, 1280]) text_encoder_2-item4.clip_prompt_embeds: torch.Size([77, 1280])->torch.Size([1, 1, 77, 1280]) text_encoder_3-item5.t5_prompt_embeds: torch.Size([77, 4096])->torch.Size([1, 1, 77, 4096]) vae.encoder_f0: torch.Size([32, 128, 128])->torch.Size([1, 1, 512, 1024]) vae.encoder_f1: torch.Size([32, 128, 128])->torch.Size([1, 1, 512, 1024]) vae.decoder: torch.Size([16, 128, 128])->torch.Size([1, 1, 512, 512]) Using per-key quantization points (text_encoder-item0.clip_pooled_prompt_embeds: torch.Size([256])) for text_encoder-item0.clip_pooled_prompt_embeds text_encoder-item0.clip_pooled_prompt_embeds: 724B, BPFP=7.5417 Using per-key quantization points (text_encoder-item0.clip_prompt_embeds: torch.Size([256])) for text_encoder-item0.clip_prompt_embeds text_encoder-item0.clip_prompt_embeds: 7,040B, BPFP=0.9524 Using per-key quantization points (text_encoder_2-item1.clip_pooled_prompt_embeds: torch.Size([256])) for text_encoder_2-item1.clip_pooled_prompt_embeds text_encoder_2-item1.clip_pooled_prompt_embeds: 1,212B, BPFP=7.5750 Using per-key quantization points (text_encoder_2-item1.clip_prompt_embeds: torch.Size([256])) for text_encoder_2-item1.clip_prompt_embeds text_encoder_2-item1.clip_prompt_embeds: 10,968B, BPFP=0.8903 Using per-key quantization points (text_encoder_3-item2.t5_prompt_embeds: torch.Size([256])) for text_encoder_3-item2.t5_prompt_embeds text_encoder_3-item2.t5_prompt_embeds: 30,596B, BPFP=0.7761 Using per-key quantization points (text_encoder-item3.clip_pooled_prompt_embeds: torch.Size([256])) for text_encoder-item3.clip_pooled_prompt_embeds text_encoder-item3.clip_pooled_prompt_embeds: 760B, BPFP=7.9167 Using per-key quantization points (text_encoder-item3.clip_prompt_embeds: torch.Size([256])) for text_encoder-item3.clip_prompt_embeds text_encoder-item3.clip_prompt_embeds: 4,024B, BPFP=0.5444 Using per-key quantization points (text_encoder_2-item4.clip_pooled: torch.Size([256])) for text_encoder_2-item4.clip_pooled_prompt_embeds text_encoder_2-item4.clip_pooled_prompt_embeds: 1,268B, BPFP=7.9250 Using per-key quantization points (text_encoder_2-item4.clip_prompt: torch.Size([256])) for text_encoder_2-item4.clip_prompt_embeds text_encoder_2-item4.clip_prompt_embeds: 6,996B, BPFP=0.5679 Using per-key quantization points (text_encoder_3-item5.t5_prompt_embeds: torch.Size([256])) for text_encoder_3-item5.t5_prompt_embeds text_encoder_3-item5.t5_prompt_embeds: 18,416B, BPFP=0.4671 Using per-key quantization points (vae.encoder_f0: torch.Size([256])) for vae.encoder_f0 vae.encoder_f0: 9,804B, BPFP=0.1496 Using per-key quantization points (vae.encoder_f1: torch.Size([256])) for vae.encoder_f1 vae.encoder_f1: 9,804B, BPFP=0.1496 Using per-key quantization points (vae.decoder: torch.Size([256])) for vae.decoder vae.decoder: 9,456B, BPFP=0.2886 ⌛️ [2/4] FRONTEND: Frontend time: 0.290s (Pack+Encode) [3/4] BACKEND: Decode + Unpack... Using per-key quantization points (text_encoder-item0.clip_pooled_prompt_embeds: torch.Size([256])) for text_encoder-item0.clip_pooled_prompt_embeds Using per-key quantization points (text_encoder-item0.clip_prompt_embeds: torch.Size([256])) for text_encoder-item0.clip_prompt_embeds Using per-key quantization points (text_encoder_2-item1.clip_pooled_prompt_embeds: torch.Size([256])) for text_encoder_2-item1.clip_pooled_prompt_embeds Using per-key quantization points (text_encoder_2-item1.clip_prompt_embeds: torch.Size([256])) for text_encoder_2-item1.clip_prompt_embeds Using per-key quantization points (text_encoder_3-item2.t5_prompt_embeds: torch.Size([256])) for text_encoder_3-item2.t5_prompt_embeds Using per-key quantization points (text_encoder-item3.clip_pooled_prompt_embeds: torch.Size([256])) for text_encoder-item3.clip_pooled_prompt_embeds Using per-key quantization points (text_encoder-item3.clip_prompt_embeds: torch.Size([256])) for text_encoder-item3.clip_prompt_embeds Using per-key quantization points (text_encoder_2-item4.clip_pooled: torch.Size([256])) for text_encoder_2-item4.clip_pooled_prompt_embeds Using per-key quantization points (text_encoder_2-item4.clip_prompt: torch.Size([256])) for text_encoder_2-item4.clip_prompt_embeds Using per-key quantization points (text_encoder_3-item5.t5_prompt_embeds: torch.Size([256])) for text_encoder_3-item5.t5_prompt_embeds Using per-key quantization points (vae.encoder_f0: torch.Size([256])) for vae.encoder_f0 Using per-key quantization points (vae.encoder_f1: torch.Size([256])) for vae.encoder_f1 Using per-key quantization points (vae.decoder: torch.Size([256])) for vae.decoder IndividualUnPacker: text_encoder-item0.clip_pooled_prompt_embeds: torch.Size([768]) text_encoder-item0.clip_prompt_embeds: torch.Size([77, 768]) text_encoder_2-item1.clip_pooled_prompt_embeds: torch.Size([1280]) text_encoder_2-item1.clip_prompt_embeds: torch.Size([77, 1280]) text_encoder_3-item2.t5_prompt_embeds: torch.Size([77, 4096]) text_encoder-item3.clip_pooled_prompt_embeds: torch.Size([768]) text_encoder-item3.clip_prompt_embeds: torch.Size([77, 768]) text_encoder_2-item4.clip_pooled_prompt_embeds: torch.Size([1280]) text_encoder_2-item4.clip_prompt_embeds: torch.Size([77, 1280]) text_encoder_3-item5.t5_prompt_embeds: torch.Size([77, 4096]) vae.decoder: torch.Size([16, 128, 128]) vae.encoder_f0: torch.Size([32, 128, 128]) vae.encoder_f1: torch.Size([32, 128, 128]) ⌛️ [3/4] BACKEND: Backend time: 0.440s [4/4] METRICS: Computing MSE Breakdown... Using per-key quantization points (text_encoder-item0.clip_pooled_prompt_embeds: torch.Size([256])) for text_encoder-item0.clip_pooled_prompt_embeds Using per-key quantization points (text_encoder-item0.clip_prompt_embeds: torch.Size([256])) for text_encoder-item0.clip_prompt_embeds Using per-key quantization points (text_encoder_2-item1.clip_pooled_prompt_embeds: torch.Size([256])) for text_encoder_2-item1.clip_pooled_prompt_embeds Using per-key quantization points (text_encoder_2-item1.clip_prompt_embeds: torch.Size([256])) for text_encoder_2-item1.clip_prompt_embeds Using per-key quantization points (text_encoder_3-item2.t5_prompt_embeds: torch.Size([256])) for text_encoder_3-item2.t5_prompt_embeds Using per-key quantization points (text_encoder-item3.clip_pooled_prompt_embeds: torch.Size([256])) for text_encoder-item3.clip_pooled_prompt_embeds Using per-key quantization points (text_encoder-item3.clip_prompt_embeds: torch.Size([256])) for text_encoder-item3.clip_prompt_embeds Using per-key quantization points (text_encoder_2-item4.clip_pooled: torch.Size([256])) for text_encoder_2-item4.clip_pooled_prompt_embeds Using per-key quantization points (text_encoder_2-item4.clip_prompt: torch.Size([256])) for text_encoder_2-item4.clip_prompt_embeds Using per-key quantization points (text_encoder_3-item5.t5_prompt_embeds: torch.Size([256])) for text_encoder_3-item5.t5_prompt_embeds Using per-key quantization points (vae.encoder_f0: torch.Size([256])) for vae.encoder_f0 Using per-key quantization points (vae.encoder_f1: torch.Size([256])) for vae.encoder_f1 Using per-key quantization points (vae.decoder: torch.Size([256])) for vae.decoder IndividualUnPacker: text_encoder-item0.clip_pooled_prompt_embeds: torch.Size([768]) text_encoder-item0.clip_prompt_embeds: torch.Size([77, 768]) text_encoder_2-item1.clip_pooled_prompt_embeds: torch.Size([1280]) text_encoder_2-item1.clip_prompt_embeds: torch.Size([77, 1280]) text_encoder_3-item2.t5_prompt_embeds: torch.Size([77, 4096]) text_encoder-item3.clip_pooled_prompt_embeds: torch.Size([768]) text_encoder-item3.clip_prompt_embeds: torch.Size([77, 768]) text_encoder_2-item4.clip_pooled_prompt_embeds: torch.Size([1280]) text_encoder_2-item4.clip_prompt_embeds: torch.Size([77, 1280]) text_encoder_3-item5.t5_prompt_embeds: torch.Size([77, 4096]) vae.decoder: torch.Size([16, 128, 128]) vae.encoder_f0: torch.Size([32, 128, 128]) vae.encoder_f1: torch.Size([32, 128, 128]) Per-key MSE Breakdown (Quant=Truncation+Quantization, Codec=Compression): Key Quant-MSE Total-MSE ------------------------------------------------------------------------------------- text_encoder-item0.clip_pooled_prompt_embeds 0.00017781 0.92901889 text_encoder-item0.clip_prompt_embeds 0.00023387 34.93811934 text_encoder_2-item1.clip_pooled_prompt_embeds 0.00060859 0.92658672 text_encoder_2-item1.clip_prompt_embeds 0.00021718 0.15473623 text_encoder_3-item2.t5_prompt_embeds 0.00000840 0.00436072 text_encoder-item3.clip_pooled_prompt_embeds 0.00841799 1.01420029 text_encoder-item3.clip_prompt_embeds 0.00023247 23.90570887 text_encoder_2-item4.clip_pooled_prompt_embeds 0.00016751 0.67051430 text_encoder_2-item4.clip_prompt_embeds 0.00009830 0.47049568 text_encoder_3-item5.t5_prompt_embeds 0.00001263 0.00227117 vae.encoder_f0 0.00567713 1.16603839 vae.encoder_f1 0.00567905 1.16603017 vae.decoder 0.00019376 0.09078687 ------------------------------------------------------------------------------------- TOTAL 0.00268802 2.12009357 (elements=2,260,992) ---------------------- -------------------------------------------------------- SAMPLE-WISE STATISTICS ---------------------- -------------------------------------------------------- Handler sd35 Strategy individual Architecture hyperprior-featurecoding ---------------------- -------------------------------------------------------- Total Elements 2260992 Total Bytes 111068 BPFP 0.3930 bits/point EBPFP 0.7860 equivalent bits/point MSE 2.120094 ---------------------- -------------------------------------------------------- Time: 0.738s Load: 0.009s, Pack+Encode: 0.290s, Decode+Unpack: 0.440s ---------------------- -------------------------------------------------------- Restored Feature Format: [Dict] with 8 keys key['text_encoder-item0']: [Dict] with 2 keys key['clip_pooled_prompt_embeds']: [Tensor] shape=torch.Size([768]), dtype=torch.float16, device=cpu key['clip_prompt_embeds']: [Tensor] shape=torch.Size([77, 768]), dtype=torch.float16, device=cpu key['text_encoder_2-item1']: [Dict] with 2 keys key['clip_pooled_prompt_embeds']: [Tensor] shape=torch.Size([1280]), dtype=torch.float16, device=cpu key['clip_prompt_embeds']: [Tensor] shape=torch.Size([77, 1280]), dtype=torch.float16, device=cpu key['text_encoder_3-item2']: [Dict] with 1 keys key['t5_prompt_embeds']: [Tensor] shape=torch.Size([77, 4096]), dtype=torch.float16, device=cpu key['text_encoder-item3']: [Dict] with 2 keys key['clip_pooled_prompt_embeds']: [Tensor] shape=torch.Size([768]), dtype=torch.float16, device=cpu key['clip_prompt_embeds']: [Tensor] shape=torch.Size([77, 768]), dtype=torch.float16, device=cpu key['text_encoder_2-item4']: [Dict] with 2 keys key['clip_pooled_prompt_embeds']: [Tensor] shape=torch.Size([1280]), dtype=torch.float16, device=cpu key['clip_prompt_embeds']: [Tensor] shape=torch.Size([77, 1280]), dtype=torch.float16, device=cpu key['text_encoder_3-item5']: [Dict] with 1 keys key['t5_prompt_embeds']: [Tensor] shape=torch.Size([77, 4096]), dtype=torch.float16, device=cpu key['vae.decoder']: [Tensor] shape=torch.Size([16, 128, 128]), dtype=torch.float16, device=cpu key['vae.encoder']: [Dict] with 2 keys key['vae.encoder_f0']: [Tensor] shape=torch.Size([32, 128, 128]), dtype=torch.float16, device=cpu key['vae.encoder_f1']: [Tensor] shape=torch.Size([32, 128, 128]), dtype=torch.float16, device=cpu 💾 Converting with 2.1201 MSE: from ../datasets/FeatureCoding-StableDiffusion3.5/100samples_features/000000506454.zst to output-fixed/sd35/lambda0.004/hyperprior-featurecoding-8bit-individual/sd35cond/000000506454.zst 💪 Processing: ../datasets/FeatureCoding-StableDiffusion3.5/100samples_features/000000515828.zst (91/100) [1/4] FRONTEND: Loading features from ../datasets/FeatureCoding-StableDiffusion3.5/100samples_features/000000515828.zst... Original data structure: root: [Dict] with 8 keys key['text_encoder-item0']: [Dict] with 2 keys key['clip_pooled_prompt_embeds']: [Tensor] shape=torch.Size([768]), dtype=torch.float16, device=cpu key['clip_prompt_embeds']: [Tensor] shape=torch.Size([77, 768]), dtype=torch.float16, device=cpu key['text_encoder_2-item1']: [Dict] with 2 keys key['clip_pooled_prompt_embeds']: [Tensor] shape=torch.Size([1280]), dtype=torch.float16, device=cpu key['clip_prompt_embeds']: [Tensor] shape=torch.Size([77, 1280]), dtype=torch.float16, device=cpu key['text_encoder_3-item2']: [Dict] with 1 keys key['t5_prompt_embeds']: [Tensor] shape=torch.Size([77, 4096]), dtype=torch.float16, device=cpu key['text_encoder-item3']: [Dict] with 2 keys key['clip_pooled_prompt_embeds']: [Tensor] shape=torch.Size([768]), dtype=torch.float16, device=cpu key['clip_prompt_embeds']: [Tensor] shape=torch.Size([77, 768]), dtype=torch.float16, device=cpu key['text_encoder_2-item4']: [Dict] with 2 keys key['clip_pooled_prompt_embeds']: [Tensor] shape=torch.Size([1280]), dtype=torch.float16, device=cpu key['clip_prompt_embeds']: [Tensor] shape=torch.Size([77, 1280]), dtype=torch.float16, device=cpu key['text_encoder_3-item5']: [Dict] with 1 keys key['t5_prompt_embeds']: [Tensor] shape=torch.Size([77, 4096]), dtype=torch.float16, device=cpu key['vae.encoder-item6']: [Dict] with 2 keys key['vae.encoder_f0']: [Tensor] shape=torch.Size([32, 128, 128]), dtype=torch.float16, device=cpu key['vae.encoder_f1']: [Tensor] shape=torch.Size([32, 128, 128]), dtype=torch.float16, device=cpu key['vae.decoder']: [Tensor] shape=torch.Size([16, 128, 128]), dtype=torch.float16, device=cpu ⌛️ [1/4] FRONTEND: Load time: 0.009s ------------------------------------------------------------ SD3.5 Features Summary ------------------------------------------------------------ Number of text encoders: 3 Encoder names: ['text_encoder', 'text_encoder_2', 'text_encoder_3'] Has VAE latents: True Has VAE encoder features: True Data type: torch.float16 text_encoder-item0: clip_pooled_prompt_embeds: torch.Size([768]), torch.float16 clip_prompt_embeds: torch.Size([77, 768]), torch.float16 text_encoder_2-item1: clip_pooled_prompt_embeds: torch.Size([1280]), torch.float16 clip_prompt_embeds: torch.Size([77, 1280]), torch.float16 text_encoder_3-item2: t5_prompt_embeds: torch.Size([77, 4096]), torch.float16 text_encoder-item3: clip_pooled_prompt_embeds: torch.Size([768]), torch.float16 clip_prompt_embeds: torch.Size([77, 768]), torch.float16 text_encoder_2-item4: clip_pooled_prompt_embeds: torch.Size([1280]), torch.float16 clip_prompt_embeds: torch.Size([77, 1280]), torch.float16 text_encoder_3-item5: t5_prompt_embeds: torch.Size([77, 4096]), torch.float16 vae.decoder latents: torch.Size([16, 128, 128]) vae.encoder features: vae.encoder_f0: torch.Size([32, 128, 128]), torch.float16 vae.encoder_f1: torch.Size([32, 128, 128]), torch.float16 ------------------------------------------------------------ [2/4] FRONTEND: Pack + Encode (strategy: individual)... IndividualPacker: text_encoder-item0.clip_pooled_prompt_embeds: torch.Size([768])->torch.Size([1, 1, 1, 768]) text_encoder-item0.clip_prompt_embeds: torch.Size([77, 768])->torch.Size([1, 1, 77, 768]) text_encoder_2-item1.clip_pooled_prompt_embeds: torch.Size([1280])->torch.Size([1, 1, 1, 1280]) text_encoder_2-item1.clip_prompt_embeds: torch.Size([77, 1280])->torch.Size([1, 1, 77, 1280]) text_encoder_3-item2.t5_prompt_embeds: torch.Size([77, 4096])->torch.Size([1, 1, 77, 4096]) text_encoder-item3.clip_pooled_prompt_embeds: torch.Size([768])->torch.Size([1, 1, 1, 768]) text_encoder-item3.clip_prompt_embeds: torch.Size([77, 768])->torch.Size([1, 1, 77, 768]) text_encoder_2-item4.clip_pooled_prompt_embeds: torch.Size([1280])->torch.Size([1, 1, 1, 1280]) text_encoder_2-item4.clip_prompt_embeds: torch.Size([77, 1280])->torch.Size([1, 1, 77, 1280]) text_encoder_3-item5.t5_prompt_embeds: torch.Size([77, 4096])->torch.Size([1, 1, 77, 4096]) vae.encoder_f0: torch.Size([32, 128, 128])->torch.Size([1, 1, 512, 1024]) vae.encoder_f1: torch.Size([32, 128, 128])->torch.Size([1, 1, 512, 1024]) vae.decoder: torch.Size([16, 128, 128])->torch.Size([1, 1, 512, 512]) Using per-key quantization points (text_encoder-item0.clip_pooled_prompt_embeds: torch.Size([256])) for text_encoder-item0.clip_pooled_prompt_embeds text_encoder-item0.clip_pooled_prompt_embeds: 736B, BPFP=7.6667 Using per-key quantization points (text_encoder-item0.clip_prompt_embeds: torch.Size([256])) for text_encoder-item0.clip_prompt_embeds text_encoder-item0.clip_prompt_embeds: 6,676B, BPFP=0.9031 Using per-key quantization points (text_encoder_2-item1.clip_pooled_prompt_embeds: torch.Size([256])) for text_encoder_2-item1.clip_pooled_prompt_embeds text_encoder_2-item1.clip_pooled_prompt_embeds: 1,208B, BPFP=7.5500 Using per-key quantization points (text_encoder_2-item1.clip_prompt_embeds: torch.Size([256])) for text_encoder_2-item1.clip_prompt_embeds text_encoder_2-item1.clip_prompt_embeds: 11,796B, BPFP=0.9575 Using per-key quantization points (text_encoder_3-item2.t5_prompt_embeds: torch.Size([256])) for text_encoder_3-item2.t5_prompt_embeds text_encoder_3-item2.t5_prompt_embeds: 29,352B, BPFP=0.7445 Using per-key quantization points (text_encoder-item3.clip_pooled_prompt_embeds: torch.Size([256])) for text_encoder-item3.clip_pooled_prompt_embeds text_encoder-item3.clip_pooled_prompt_embeds: 760B, BPFP=7.9167 Using per-key quantization points (text_encoder-item3.clip_prompt_embeds: torch.Size([256])) for text_encoder-item3.clip_prompt_embeds text_encoder-item3.clip_prompt_embeds: 4,024B, BPFP=0.5444 Using per-key quantization points (text_encoder_2-item4.clip_pooled: torch.Size([256])) for text_encoder_2-item4.clip_pooled_prompt_embeds text_encoder_2-item4.clip_pooled_prompt_embeds: 1,268B, BPFP=7.9250 Using per-key quantization points (text_encoder_2-item4.clip_prompt: torch.Size([256])) for text_encoder_2-item4.clip_prompt_embeds text_encoder_2-item4.clip_prompt_embeds: 6,996B, BPFP=0.5679 Using per-key quantization points (text_encoder_3-item5.t5_prompt_embeds: torch.Size([256])) for text_encoder_3-item5.t5_prompt_embeds text_encoder_3-item5.t5_prompt_embeds: 18,416B, BPFP=0.4671 Using per-key quantization points (vae.encoder_f0: torch.Size([256])) for vae.encoder_f0 vae.encoder_f0: 15,016B, BPFP=0.2291 Using per-key quantization points (vae.encoder_f1: torch.Size([256])) for vae.encoder_f1 vae.encoder_f1: 15,012B, BPFP=0.2291 Using per-key quantization points (vae.decoder: torch.Size([256])) for vae.decoder vae.decoder: 6,664B, BPFP=0.2034 ⌛️ [2/4] FRONTEND: Frontend time: 0.290s (Pack+Encode) [3/4] BACKEND: Decode + Unpack... Using per-key quantization points (text_encoder-item0.clip_pooled_prompt_embeds: torch.Size([256])) for text_encoder-item0.clip_pooled_prompt_embeds Using per-key quantization points (text_encoder-item0.clip_prompt_embeds: torch.Size([256])) for text_encoder-item0.clip_prompt_embeds Using per-key quantization points (text_encoder_2-item1.clip_pooled_prompt_embeds: torch.Size([256])) for text_encoder_2-item1.clip_pooled_prompt_embeds Using per-key quantization points (text_encoder_2-item1.clip_prompt_embeds: torch.Size([256])) for text_encoder_2-item1.clip_prompt_embeds Using per-key quantization points (text_encoder_3-item2.t5_prompt_embeds: torch.Size([256])) for text_encoder_3-item2.t5_prompt_embeds Using per-key quantization points (text_encoder-item3.clip_pooled_prompt_embeds: torch.Size([256])) for text_encoder-item3.clip_pooled_prompt_embeds Using per-key quantization points (text_encoder-item3.clip_prompt_embeds: torch.Size([256])) for text_encoder-item3.clip_prompt_embeds Using per-key quantization points (text_encoder_2-item4.clip_pooled: torch.Size([256])) for text_encoder_2-item4.clip_pooled_prompt_embeds Using per-key quantization points (text_encoder_2-item4.clip_prompt: torch.Size([256])) for text_encoder_2-item4.clip_prompt_embeds Using per-key quantization points (text_encoder_3-item5.t5_prompt_embeds: torch.Size([256])) for text_encoder_3-item5.t5_prompt_embeds Using per-key quantization points (vae.encoder_f0: torch.Size([256])) for vae.encoder_f0 Using per-key quantization points (vae.encoder_f1: torch.Size([256])) for vae.encoder_f1 Using per-key quantization points (vae.decoder: torch.Size([256])) for vae.decoder IndividualUnPacker: text_encoder-item0.clip_pooled_prompt_embeds: torch.Size([768]) text_encoder-item0.clip_prompt_embeds: torch.Size([77, 768]) text_encoder_2-item1.clip_pooled_prompt_embeds: torch.Size([1280]) text_encoder_2-item1.clip_prompt_embeds: torch.Size([77, 1280]) text_encoder_3-item2.t5_prompt_embeds: torch.Size([77, 4096]) text_encoder-item3.clip_pooled_prompt_embeds: torch.Size([768]) text_encoder-item3.clip_prompt_embeds: torch.Size([77, 768]) text_encoder_2-item4.clip_pooled_prompt_embeds: torch.Size([1280]) text_encoder_2-item4.clip_prompt_embeds: torch.Size([77, 1280]) text_encoder_3-item5.t5_prompt_embeds: torch.Size([77, 4096]) vae.decoder: torch.Size([16, 128, 128]) vae.encoder_f0: torch.Size([32, 128, 128]) vae.encoder_f1: torch.Size([32, 128, 128]) ⌛️ [3/4] BACKEND: Backend time: 0.440s [4/4] METRICS: Computing MSE Breakdown... Using per-key quantization points (text_encoder-item0.clip_pooled_prompt_embeds: torch.Size([256])) for text_encoder-item0.clip_pooled_prompt_embeds Using per-key quantization points (text_encoder-item0.clip_prompt_embeds: torch.Size([256])) for text_encoder-item0.clip_prompt_embeds Using per-key quantization points (text_encoder_2-item1.clip_pooled_prompt_embeds: torch.Size([256])) for text_encoder_2-item1.clip_pooled_prompt_embeds Using per-key quantization points (text_encoder_2-item1.clip_prompt_embeds: torch.Size([256])) for text_encoder_2-item1.clip_prompt_embeds Using per-key quantization points (text_encoder_3-item2.t5_prompt_embeds: torch.Size([256])) for text_encoder_3-item2.t5_prompt_embeds Using per-key quantization points (text_encoder-item3.clip_pooled_prompt_embeds: torch.Size([256])) for text_encoder-item3.clip_pooled_prompt_embeds Using per-key quantization points (text_encoder-item3.clip_prompt_embeds: torch.Size([256])) for text_encoder-item3.clip_prompt_embeds Using per-key quantization points (text_encoder_2-item4.clip_pooled: torch.Size([256])) for text_encoder_2-item4.clip_pooled_prompt_embeds Using per-key quantization points (text_encoder_2-item4.clip_prompt: torch.Size([256])) for text_encoder_2-item4.clip_prompt_embeds Using per-key quantization points (text_encoder_3-item5.t5_prompt_embeds: torch.Size([256])) for text_encoder_3-item5.t5_prompt_embeds Using per-key quantization points (vae.encoder_f0: torch.Size([256])) for vae.encoder_f0 Using per-key quantization points (vae.encoder_f1: torch.Size([256])) for vae.encoder_f1 Using per-key quantization points (vae.decoder: torch.Size([256])) for vae.decoder IndividualUnPacker: text_encoder-item0.clip_pooled_prompt_embeds: torch.Size([768]) text_encoder-item0.clip_prompt_embeds: torch.Size([77, 768]) text_encoder_2-item1.clip_pooled_prompt_embeds: torch.Size([1280]) text_encoder_2-item1.clip_prompt_embeds: torch.Size([77, 1280]) text_encoder_3-item2.t5_prompt_embeds: torch.Size([77, 4096]) text_encoder-item3.clip_pooled_prompt_embeds: torch.Size([768]) text_encoder-item3.clip_prompt_embeds: torch.Size([77, 768]) text_encoder_2-item4.clip_pooled_prompt_embeds: torch.Size([1280]) text_encoder_2-item4.clip_prompt_embeds: torch.Size([77, 1280]) text_encoder_3-item5.t5_prompt_embeds: torch.Size([77, 4096]) vae.decoder: torch.Size([16, 128, 128]) vae.encoder_f0: torch.Size([32, 128, 128]) vae.encoder_f1: torch.Size([32, 128, 128]) Per-key MSE Breakdown (Quant=Truncation+Quantization, Codec=Compression): Key Quant-MSE Total-MSE ------------------------------------------------------------------------------------- text_encoder-item0.clip_pooled_prompt_embeds 0.00020194 0.91278513 text_encoder-item0.clip_prompt_embeds 0.00024281 23.95470821 text_encoder_2-item1.clip_pooled_prompt_embeds 0.00020758 0.88318129 text_encoder_2-item1.clip_prompt_embeds 0.00017819 0.12257521 text_encoder_3-item2.t5_prompt_embeds 0.00000960 0.00348998 text_encoder-item3.clip_pooled_prompt_embeds 0.00841799 1.01420029 text_encoder-item3.clip_prompt_embeds 0.00023247 23.90570887 text_encoder_2-item4.clip_pooled_prompt_embeds 0.00016751 0.67051430 text_encoder_2-item4.clip_prompt_embeds 0.00009830 0.47049568 text_encoder_3-item5.t5_prompt_embeds 0.00001263 0.00227117 vae.encoder_f0 0.02387581 2.59121561 vae.encoder_f1 0.02387858 2.59078813 vae.decoder 0.00018648 0.05001713 ------------------------------------------------------------------------------------- TOTAL 0.01112583 2.48739776 (elements=2,260,992) ---------------------- -------------------------------------------------------- SAMPLE-WISE STATISTICS ---------------------- -------------------------------------------------------- Handler sd35 Strategy individual Architecture hyperprior-featurecoding ---------------------- -------------------------------------------------------- Total Elements 2260992 Total Bytes 117924 BPFP 0.4172 bits/point EBPFP 0.8345 equivalent bits/point MSE 2.487398 ---------------------- -------------------------------------------------------- Time: 0.738s Load: 0.009s, Pack+Encode: 0.290s, Decode+Unpack: 0.440s ---------------------- -------------------------------------------------------- Restored Feature Format: [Dict] with 8 keys key['text_encoder-item0']: [Dict] with 2 keys key['clip_pooled_prompt_embeds']: [Tensor] shape=torch.Size([768]), dtype=torch.float16, device=cpu key['clip_prompt_embeds']: [Tensor] shape=torch.Size([77, 768]), dtype=torch.float16, device=cpu key['text_encoder_2-item1']: [Dict] with 2 keys key['clip_pooled_prompt_embeds']: [Tensor] shape=torch.Size([1280]), dtype=torch.float16, device=cpu key['clip_prompt_embeds']: [Tensor] shape=torch.Size([77, 1280]), dtype=torch.float16, device=cpu key['text_encoder_3-item2']: [Dict] with 1 keys key['t5_prompt_embeds']: [Tensor] shape=torch.Size([77, 4096]), dtype=torch.float16, device=cpu key['text_encoder-item3']: [Dict] with 2 keys key['clip_pooled_prompt_embeds']: [Tensor] shape=torch.Size([768]), dtype=torch.float16, device=cpu key['clip_prompt_embeds']: [Tensor] shape=torch.Size([77, 768]), dtype=torch.float16, device=cpu key['text_encoder_2-item4']: [Dict] with 2 keys key['clip_pooled_prompt_embeds']: [Tensor] shape=torch.Size([1280]), dtype=torch.float16, device=cpu key['clip_prompt_embeds']: [Tensor] shape=torch.Size([77, 1280]), dtype=torch.float16, device=cpu key['text_encoder_3-item5']: [Dict] with 1 keys key['t5_prompt_embeds']: [Tensor] shape=torch.Size([77, 4096]), dtype=torch.float16, device=cpu key['vae.decoder']: [Tensor] shape=torch.Size([16, 128, 128]), dtype=torch.float16, device=cpu key['vae.encoder']: [Dict] with 2 keys key['vae.encoder_f0']: [Tensor] shape=torch.Size([32, 128, 128]), dtype=torch.float16, device=cpu key['vae.encoder_f1']: [Tensor] shape=torch.Size([32, 128, 128]), dtype=torch.float16, device=cpu 💾 Converting with 2.4874 MSE: from ../datasets/FeatureCoding-StableDiffusion3.5/100samples_features/000000515828.zst to output-fixed/sd35/lambda0.004/hyperprior-featurecoding-8bit-individual/sd35cond/000000515828.zst 💪 Processing: ../datasets/FeatureCoding-StableDiffusion3.5/100samples_features/000000517056.zst (92/100) [1/4] FRONTEND: Loading features from ../datasets/FeatureCoding-StableDiffusion3.5/100samples_features/000000517056.zst... Original data structure: root: [Dict] with 8 keys key['text_encoder-item0']: [Dict] with 2 keys key['clip_pooled_prompt_embeds']: [Tensor] shape=torch.Size([768]), dtype=torch.float16, device=cpu key['clip_prompt_embeds']: [Tensor] shape=torch.Size([77, 768]), dtype=torch.float16, device=cpu key['text_encoder_2-item1']: [Dict] with 2 keys key['clip_pooled_prompt_embeds']: [Tensor] shape=torch.Size([1280]), dtype=torch.float16, device=cpu key['clip_prompt_embeds']: [Tensor] shape=torch.Size([77, 1280]), dtype=torch.float16, device=cpu key['text_encoder_3-item2']: [Dict] with 1 keys key['t5_prompt_embeds']: [Tensor] shape=torch.Size([77, 4096]), dtype=torch.float16, device=cpu key['text_encoder-item3']: [Dict] with 2 keys key['clip_pooled_prompt_embeds']: [Tensor] shape=torch.Size([768]), dtype=torch.float16, device=cpu key['clip_prompt_embeds']: [Tensor] shape=torch.Size([77, 768]), dtype=torch.float16, device=cpu key['text_encoder_2-item4']: [Dict] with 2 keys key['clip_pooled_prompt_embeds']: [Tensor] shape=torch.Size([1280]), dtype=torch.float16, device=cpu key['clip_prompt_embeds']: [Tensor] shape=torch.Size([77, 1280]), dtype=torch.float16, device=cpu key['text_encoder_3-item5']: [Dict] with 1 keys key['t5_prompt_embeds']: [Tensor] shape=torch.Size([77, 4096]), dtype=torch.float16, device=cpu key['vae.encoder-item6']: [Dict] with 2 keys key['vae.encoder_f0']: [Tensor] shape=torch.Size([32, 128, 128]), dtype=torch.float16, device=cpu key['vae.encoder_f1']: [Tensor] shape=torch.Size([32, 128, 128]), dtype=torch.float16, device=cpu key['vae.decoder']: [Tensor] shape=torch.Size([16, 128, 128]), dtype=torch.float16, device=cpu ⌛️ [1/4] FRONTEND: Load time: 0.009s ------------------------------------------------------------ SD3.5 Features Summary ------------------------------------------------------------ Number of text encoders: 3 Encoder names: ['text_encoder', 'text_encoder_2', 'text_encoder_3'] Has VAE latents: True Has VAE encoder features: True Data type: torch.float16 text_encoder-item0: clip_pooled_prompt_embeds: torch.Size([768]), torch.float16 clip_prompt_embeds: torch.Size([77, 768]), torch.float16 text_encoder_2-item1: clip_pooled_prompt_embeds: torch.Size([1280]), torch.float16 clip_prompt_embeds: torch.Size([77, 1280]), torch.float16 text_encoder_3-item2: t5_prompt_embeds: torch.Size([77, 4096]), torch.float16 text_encoder-item3: clip_pooled_prompt_embeds: torch.Size([768]), torch.float16 clip_prompt_embeds: torch.Size([77, 768]), torch.float16 text_encoder_2-item4: clip_pooled_prompt_embeds: torch.Size([1280]), torch.float16 clip_prompt_embeds: torch.Size([77, 1280]), torch.float16 text_encoder_3-item5: t5_prompt_embeds: torch.Size([77, 4096]), torch.float16 vae.decoder latents: torch.Size([16, 128, 128]) vae.encoder features: vae.encoder_f0: torch.Size([32, 128, 128]), torch.float16 vae.encoder_f1: torch.Size([32, 128, 128]), torch.float16 ------------------------------------------------------------ [2/4] FRONTEND: Pack + Encode (strategy: individual)... IndividualPacker: text_encoder-item0.clip_pooled_prompt_embeds: torch.Size([768])->torch.Size([1, 1, 1, 768]) text_encoder-item0.clip_prompt_embeds: torch.Size([77, 768])->torch.Size([1, 1, 77, 768]) text_encoder_2-item1.clip_pooled_prompt_embeds: torch.Size([1280])->torch.Size([1, 1, 1, 1280]) text_encoder_2-item1.clip_prompt_embeds: torch.Size([77, 1280])->torch.Size([1, 1, 77, 1280]) text_encoder_3-item2.t5_prompt_embeds: torch.Size([77, 4096])->torch.Size([1, 1, 77, 4096]) text_encoder-item3.clip_pooled_prompt_embeds: torch.Size([768])->torch.Size([1, 1, 1, 768]) text_encoder-item3.clip_prompt_embeds: torch.Size([77, 768])->torch.Size([1, 1, 77, 768]) text_encoder_2-item4.clip_pooled_prompt_embeds: torch.Size([1280])->torch.Size([1, 1, 1, 1280]) text_encoder_2-item4.clip_prompt_embeds: torch.Size([77, 1280])->torch.Size([1, 1, 77, 1280]) text_encoder_3-item5.t5_prompt_embeds: torch.Size([77, 4096])->torch.Size([1, 1, 77, 4096]) vae.encoder_f0: torch.Size([32, 128, 128])->torch.Size([1, 1, 512, 1024]) vae.encoder_f1: torch.Size([32, 128, 128])->torch.Size([1, 1, 512, 1024]) vae.decoder: torch.Size([16, 128, 128])->torch.Size([1, 1, 512, 512]) Using per-key quantization points (text_encoder-item0.clip_pooled_prompt_embeds: torch.Size([256])) for text_encoder-item0.clip_pooled_prompt_embeds text_encoder-item0.clip_pooled_prompt_embeds: 732B, BPFP=7.6250 Using per-key quantization points (text_encoder-item0.clip_prompt_embeds: torch.Size([256])) for text_encoder-item0.clip_prompt_embeds text_encoder-item0.clip_prompt_embeds: 6,248B, BPFP=0.8452 Using per-key quantization points (text_encoder_2-item1.clip_pooled_prompt_embeds: torch.Size([256])) for text_encoder_2-item1.clip_pooled_prompt_embeds text_encoder_2-item1.clip_pooled_prompt_embeds: 1,200B, BPFP=7.5000 Using per-key quantization points (text_encoder_2-item1.clip_prompt_embeds: torch.Size([256])) for text_encoder_2-item1.clip_prompt_embeds text_encoder_2-item1.clip_prompt_embeds: 10,668B, BPFP=0.8659 Using per-key quantization points (text_encoder_3-item2.t5_prompt_embeds: torch.Size([256])) for text_encoder_3-item2.t5_prompt_embeds text_encoder_3-item2.t5_prompt_embeds: 28,596B, BPFP=0.7253 Using per-key quantization points (text_encoder-item3.clip_pooled_prompt_embeds: torch.Size([256])) for text_encoder-item3.clip_pooled_prompt_embeds text_encoder-item3.clip_pooled_prompt_embeds: 760B, BPFP=7.9167 Using per-key quantization points (text_encoder-item3.clip_prompt_embeds: torch.Size([256])) for text_encoder-item3.clip_prompt_embeds text_encoder-item3.clip_prompt_embeds: 4,024B, BPFP=0.5444 Using per-key quantization points (text_encoder_2-item4.clip_pooled: torch.Size([256])) for text_encoder_2-item4.clip_pooled_prompt_embeds text_encoder_2-item4.clip_pooled_prompt_embeds: 1,268B, BPFP=7.9250 Using per-key quantization points (text_encoder_2-item4.clip_prompt: torch.Size([256])) for text_encoder_2-item4.clip_prompt_embeds text_encoder_2-item4.clip_prompt_embeds: 6,996B, BPFP=0.5679 Using per-key quantization points (text_encoder_3-item5.t5_prompt_embeds: torch.Size([256])) for text_encoder_3-item5.t5_prompt_embeds text_encoder_3-item5.t5_prompt_embeds: 18,416B, BPFP=0.4671 Using per-key quantization points (vae.encoder_f0: torch.Size([256])) for vae.encoder_f0 vae.encoder_f0: 23,012B, BPFP=0.3511 Using per-key quantization points (vae.encoder_f1: torch.Size([256])) for vae.encoder_f1 vae.encoder_f1: 23,016B, BPFP=0.3512 Using per-key quantization points (vae.decoder: torch.Size([256])) for vae.decoder vae.decoder: 5,520B, BPFP=0.1685 ⌛️ [2/4] FRONTEND: Frontend time: 0.290s (Pack+Encode) [3/4] BACKEND: Decode + Unpack... Using per-key quantization points (text_encoder-item0.clip_pooled_prompt_embeds: torch.Size([256])) for text_encoder-item0.clip_pooled_prompt_embeds Using per-key quantization points (text_encoder-item0.clip_prompt_embeds: torch.Size([256])) for text_encoder-item0.clip_prompt_embeds Using per-key quantization points (text_encoder_2-item1.clip_pooled_prompt_embeds: torch.Size([256])) for text_encoder_2-item1.clip_pooled_prompt_embeds Using per-key quantization points (text_encoder_2-item1.clip_prompt_embeds: torch.Size([256])) for text_encoder_2-item1.clip_prompt_embeds Using per-key quantization points (text_encoder_3-item2.t5_prompt_embeds: torch.Size([256])) for text_encoder_3-item2.t5_prompt_embeds Using per-key quantization points (text_encoder-item3.clip_pooled_prompt_embeds: torch.Size([256])) for text_encoder-item3.clip_pooled_prompt_embeds Using per-key quantization points (text_encoder-item3.clip_prompt_embeds: torch.Size([256])) for text_encoder-item3.clip_prompt_embeds Using per-key quantization points (text_encoder_2-item4.clip_pooled: torch.Size([256])) for text_encoder_2-item4.clip_pooled_prompt_embeds Using per-key quantization points (text_encoder_2-item4.clip_prompt: torch.Size([256])) for text_encoder_2-item4.clip_prompt_embeds Using per-key quantization points (text_encoder_3-item5.t5_prompt_embeds: torch.Size([256])) for text_encoder_3-item5.t5_prompt_embeds Using per-key quantization points (vae.encoder_f0: torch.Size([256])) for vae.encoder_f0 Using per-key quantization points (vae.encoder_f1: torch.Size([256])) for vae.encoder_f1 Using per-key quantization points (vae.decoder: torch.Size([256])) for vae.decoder IndividualUnPacker: text_encoder-item0.clip_pooled_prompt_embeds: torch.Size([768]) text_encoder-item0.clip_prompt_embeds: torch.Size([77, 768]) text_encoder_2-item1.clip_pooled_prompt_embeds: torch.Size([1280]) text_encoder_2-item1.clip_prompt_embeds: torch.Size([77, 1280]) text_encoder_3-item2.t5_prompt_embeds: torch.Size([77, 4096]) text_encoder-item3.clip_pooled_prompt_embeds: torch.Size([768]) text_encoder-item3.clip_prompt_embeds: torch.Size([77, 768]) text_encoder_2-item4.clip_pooled_prompt_embeds: torch.Size([1280]) text_encoder_2-item4.clip_prompt_embeds: torch.Size([77, 1280]) text_encoder_3-item5.t5_prompt_embeds: torch.Size([77, 4096]) vae.decoder: torch.Size([16, 128, 128]) vae.encoder_f0: torch.Size([32, 128, 128]) vae.encoder_f1: torch.Size([32, 128, 128]) ⌛️ [3/4] BACKEND: Backend time: 0.442s [4/4] METRICS: Computing MSE Breakdown... Using per-key quantization points (text_encoder-item0.clip_pooled_prompt_embeds: torch.Size([256])) for text_encoder-item0.clip_pooled_prompt_embeds Using per-key quantization points (text_encoder-item0.clip_prompt_embeds: torch.Size([256])) for text_encoder-item0.clip_prompt_embeds Using per-key quantization points (text_encoder_2-item1.clip_pooled_prompt_embeds: torch.Size([256])) for text_encoder_2-item1.clip_pooled_prompt_embeds Using per-key quantization points (text_encoder_2-item1.clip_prompt_embeds: torch.Size([256])) for text_encoder_2-item1.clip_prompt_embeds Using per-key quantization points (text_encoder_3-item2.t5_prompt_embeds: torch.Size([256])) for text_encoder_3-item2.t5_prompt_embeds Using per-key quantization points (text_encoder-item3.clip_pooled_prompt_embeds: torch.Size([256])) for text_encoder-item3.clip_pooled_prompt_embeds Using per-key quantization points (text_encoder-item3.clip_prompt_embeds: torch.Size([256])) for text_encoder-item3.clip_prompt_embeds Using per-key quantization points (text_encoder_2-item4.clip_pooled: torch.Size([256])) for text_encoder_2-item4.clip_pooled_prompt_embeds Using per-key quantization points (text_encoder_2-item4.clip_prompt: torch.Size([256])) for text_encoder_2-item4.clip_prompt_embeds Using per-key quantization points (text_encoder_3-item5.t5_prompt_embeds: torch.Size([256])) for text_encoder_3-item5.t5_prompt_embeds Using per-key quantization points (vae.encoder_f0: torch.Size([256])) for vae.encoder_f0 Using per-key quantization points (vae.encoder_f1: torch.Size([256])) for vae.encoder_f1 Using per-key quantization points (vae.decoder: torch.Size([256])) for vae.decoder IndividualUnPacker: text_encoder-item0.clip_pooled_prompt_embeds: torch.Size([768]) text_encoder-item0.clip_prompt_embeds: torch.Size([77, 768]) text_encoder_2-item1.clip_pooled_prompt_embeds: torch.Size([1280]) text_encoder_2-item1.clip_prompt_embeds: torch.Size([77, 1280]) text_encoder_3-item2.t5_prompt_embeds: torch.Size([77, 4096]) text_encoder-item3.clip_pooled_prompt_embeds: torch.Size([768]) text_encoder-item3.clip_prompt_embeds: torch.Size([77, 768]) text_encoder_2-item4.clip_pooled_prompt_embeds: torch.Size([1280]) text_encoder_2-item4.clip_prompt_embeds: torch.Size([77, 1280]) text_encoder_3-item5.t5_prompt_embeds: torch.Size([77, 4096]) vae.decoder: torch.Size([16, 128, 128]) vae.encoder_f0: torch.Size([32, 128, 128]) vae.encoder_f1: torch.Size([32, 128, 128]) Per-key MSE Breakdown (Quant=Truncation+Quantization, Codec=Compression): Key Quant-MSE Total-MSE ------------------------------------------------------------------------------------- text_encoder-item0.clip_pooled_prompt_embeds 0.00018118 0.94917250 text_encoder-item0.clip_prompt_embeds 0.00022399 23.94595509 text_encoder_2-item1.clip_pooled_prompt_embeds 0.00031391 0.87171583 text_encoder_2-item1.clip_prompt_embeds 0.00020480 0.12988346 text_encoder_3-item2.t5_prompt_embeds 0.00000727 0.00341682 text_encoder-item3.clip_pooled_prompt_embeds 0.00841799 1.01420029 text_encoder-item3.clip_prompt_embeds 0.00023247 23.90570887 text_encoder_2-item4.clip_pooled_prompt_embeds 0.00016751 0.67051430 text_encoder_2-item4.clip_prompt_embeds 0.00009830 0.47049568 text_encoder_3-item5.t5_prompt_embeds 0.00001263 0.00227117 vae.encoder_f0 0.01169517 3.83196354 vae.encoder_f1 0.01169969 3.83244538 vae.decoder 0.00021186 0.03621769 ------------------------------------------------------------------------------------- TOTAL 0.00548058 3.06151332 (elements=2,260,992) ---------------------- -------------------------------------------------------- SAMPLE-WISE STATISTICS ---------------------- -------------------------------------------------------- Handler sd35 Strategy individual Architecture hyperprior-featurecoding ---------------------- -------------------------------------------------------- Total Elements 2260992 Total Bytes 130456 BPFP 0.4616 bits/point EBPFP 0.9232 equivalent bits/point MSE 3.061513 ---------------------- -------------------------------------------------------- Time: 0.741s Load: 0.009s, Pack+Encode: 0.290s, Decode+Unpack: 0.442s ---------------------- -------------------------------------------------------- Restored Feature Format: [Dict] with 8 keys key['text_encoder-item0']: [Dict] with 2 keys key['clip_pooled_prompt_embeds']: [Tensor] shape=torch.Size([768]), dtype=torch.float16, device=cpu key['clip_prompt_embeds']: [Tensor] shape=torch.Size([77, 768]), dtype=torch.float16, device=cpu key['text_encoder_2-item1']: [Dict] with 2 keys key['clip_pooled_prompt_embeds']: [Tensor] shape=torch.Size([1280]), dtype=torch.float16, device=cpu key['clip_prompt_embeds']: [Tensor] shape=torch.Size([77, 1280]), dtype=torch.float16, device=cpu key['text_encoder_3-item2']: [Dict] with 1 keys key['t5_prompt_embeds']: [Tensor] shape=torch.Size([77, 4096]), dtype=torch.float16, device=cpu key['text_encoder-item3']: [Dict] with 2 keys key['clip_pooled_prompt_embeds']: [Tensor] shape=torch.Size([768]), dtype=torch.float16, device=cpu key['clip_prompt_embeds']: [Tensor] shape=torch.Size([77, 768]), dtype=torch.float16, device=cpu key['text_encoder_2-item4']: [Dict] with 2 keys key['clip_pooled_prompt_embeds']: [Tensor] shape=torch.Size([1280]), dtype=torch.float16, device=cpu key['clip_prompt_embeds']: [Tensor] shape=torch.Size([77, 1280]), dtype=torch.float16, device=cpu key['text_encoder_3-item5']: [Dict] with 1 keys key['t5_prompt_embeds']: [Tensor] shape=torch.Size([77, 4096]), dtype=torch.float16, device=cpu key['vae.decoder']: [Tensor] shape=torch.Size([16, 128, 128]), dtype=torch.float16, device=cpu key['vae.encoder']: [Dict] with 2 keys key['vae.encoder_f0']: [Tensor] shape=torch.Size([32, 128, 128]), dtype=torch.float16, device=cpu key['vae.encoder_f1']: [Tensor] shape=torch.Size([32, 128, 128]), dtype=torch.float16, device=cpu 💾 Converting with 3.0615 MSE: from ../datasets/FeatureCoding-StableDiffusion3.5/100samples_features/000000517056.zst to output-fixed/sd35/lambda0.004/hyperprior-featurecoding-8bit-individual/sd35cond/000000517056.zst 💪 Processing: ../datasets/FeatureCoding-StableDiffusion3.5/100samples_features/000000523100.zst (93/100) [1/4] FRONTEND: Loading features from ../datasets/FeatureCoding-StableDiffusion3.5/100samples_features/000000523100.zst... Original data structure: root: [Dict] with 8 keys key['text_encoder-item0']: [Dict] with 2 keys key['clip_pooled_prompt_embeds']: [Tensor] shape=torch.Size([768]), dtype=torch.float16, device=cpu key['clip_prompt_embeds']: [Tensor] shape=torch.Size([77, 768]), dtype=torch.float16, device=cpu key['text_encoder_2-item1']: [Dict] with 2 keys key['clip_pooled_prompt_embeds']: [Tensor] shape=torch.Size([1280]), dtype=torch.float16, device=cpu key['clip_prompt_embeds']: [Tensor] shape=torch.Size([77, 1280]), dtype=torch.float16, device=cpu key['text_encoder_3-item2']: [Dict] with 1 keys key['t5_prompt_embeds']: [Tensor] shape=torch.Size([77, 4096]), dtype=torch.float16, device=cpu key['text_encoder-item3']: [Dict] with 2 keys key['clip_pooled_prompt_embeds']: [Tensor] shape=torch.Size([768]), dtype=torch.float16, device=cpu key['clip_prompt_embeds']: [Tensor] shape=torch.Size([77, 768]), dtype=torch.float16, device=cpu key['text_encoder_2-item4']: [Dict] with 2 keys key['clip_pooled_prompt_embeds']: [Tensor] shape=torch.Size([1280]), dtype=torch.float16, device=cpu key['clip_prompt_embeds']: [Tensor] shape=torch.Size([77, 1280]), dtype=torch.float16, device=cpu key['text_encoder_3-item5']: [Dict] with 1 keys key['t5_prompt_embeds']: [Tensor] shape=torch.Size([77, 4096]), dtype=torch.float16, device=cpu key['vae.encoder-item6']: [Dict] with 2 keys key['vae.encoder_f0']: [Tensor] shape=torch.Size([32, 128, 128]), dtype=torch.float16, device=cpu key['vae.encoder_f1']: [Tensor] shape=torch.Size([32, 128, 128]), dtype=torch.float16, device=cpu key['vae.decoder']: [Tensor] shape=torch.Size([16, 128, 128]), dtype=torch.float16, device=cpu ⌛️ [1/4] FRONTEND: Load time: 0.009s ------------------------------------------------------------ SD3.5 Features Summary ------------------------------------------------------------ Number of text encoders: 3 Encoder names: ['text_encoder', 'text_encoder_2', 'text_encoder_3'] Has VAE latents: True Has VAE encoder features: True Data type: torch.float16 text_encoder-item0: clip_pooled_prompt_embeds: torch.Size([768]), torch.float16 clip_prompt_embeds: torch.Size([77, 768]), torch.float16 text_encoder_2-item1: clip_pooled_prompt_embeds: torch.Size([1280]), torch.float16 clip_prompt_embeds: torch.Size([77, 1280]), torch.float16 text_encoder_3-item2: t5_prompt_embeds: torch.Size([77, 4096]), torch.float16 text_encoder-item3: clip_pooled_prompt_embeds: torch.Size([768]), torch.float16 clip_prompt_embeds: torch.Size([77, 768]), torch.float16 text_encoder_2-item4: clip_pooled_prompt_embeds: torch.Size([1280]), torch.float16 clip_prompt_embeds: torch.Size([77, 1280]), torch.float16 text_encoder_3-item5: t5_prompt_embeds: torch.Size([77, 4096]), torch.float16 vae.decoder latents: torch.Size([16, 128, 128]) vae.encoder features: vae.encoder_f0: torch.Size([32, 128, 128]), torch.float16 vae.encoder_f1: torch.Size([32, 128, 128]), torch.float16 ------------------------------------------------------------ [2/4] FRONTEND: Pack + Encode (strategy: individual)... IndividualPacker: text_encoder-item0.clip_pooled_prompt_embeds: torch.Size([768])->torch.Size([1, 1, 1, 768]) text_encoder-item0.clip_prompt_embeds: torch.Size([77, 768])->torch.Size([1, 1, 77, 768]) text_encoder_2-item1.clip_pooled_prompt_embeds: torch.Size([1280])->torch.Size([1, 1, 1, 1280]) text_encoder_2-item1.clip_prompt_embeds: torch.Size([77, 1280])->torch.Size([1, 1, 77, 1280]) text_encoder_3-item2.t5_prompt_embeds: torch.Size([77, 4096])->torch.Size([1, 1, 77, 4096]) text_encoder-item3.clip_pooled_prompt_embeds: torch.Size([768])->torch.Size([1, 1, 1, 768]) text_encoder-item3.clip_prompt_embeds: torch.Size([77, 768])->torch.Size([1, 1, 77, 768]) text_encoder_2-item4.clip_pooled_prompt_embeds: torch.Size([1280])->torch.Size([1, 1, 1, 1280]) text_encoder_2-item4.clip_prompt_embeds: torch.Size([77, 1280])->torch.Size([1, 1, 77, 1280]) text_encoder_3-item5.t5_prompt_embeds: torch.Size([77, 4096])->torch.Size([1, 1, 77, 4096]) vae.encoder_f0: torch.Size([32, 128, 128])->torch.Size([1, 1, 512, 1024]) vae.encoder_f1: torch.Size([32, 128, 128])->torch.Size([1, 1, 512, 1024]) vae.decoder: torch.Size([16, 128, 128])->torch.Size([1, 1, 512, 512]) Using per-key quantization points (text_encoder-item0.clip_pooled_prompt_embeds: torch.Size([256])) for text_encoder-item0.clip_pooled_prompt_embeds text_encoder-item0.clip_pooled_prompt_embeds: 736B, BPFP=7.6667 Using per-key quantization points (text_encoder-item0.clip_prompt_embeds: torch.Size([256])) for text_encoder-item0.clip_prompt_embeds text_encoder-item0.clip_prompt_embeds: 6,280B, BPFP=0.8496 Using per-key quantization points (text_encoder_2-item1.clip_pooled_prompt_embeds: torch.Size([256])) for text_encoder_2-item1.clip_pooled_prompt_embeds text_encoder_2-item1.clip_pooled_prompt_embeds: 1,188B, BPFP=7.4250 Using per-key quantization points (text_encoder_2-item1.clip_prompt_embeds: torch.Size([256])) for text_encoder_2-item1.clip_prompt_embeds text_encoder_2-item1.clip_prompt_embeds: 10,356B, BPFP=0.8406 Using per-key quantization points (text_encoder_3-item2.t5_prompt_embeds: torch.Size([256])) for text_encoder_3-item2.t5_prompt_embeds text_encoder_3-item2.t5_prompt_embeds: 31,140B, BPFP=0.7899 Using per-key quantization points (text_encoder-item3.clip_pooled_prompt_embeds: torch.Size([256])) for text_encoder-item3.clip_pooled_prompt_embeds text_encoder-item3.clip_pooled_prompt_embeds: 760B, BPFP=7.9167 Using per-key quantization points (text_encoder-item3.clip_prompt_embeds: torch.Size([256])) for text_encoder-item3.clip_prompt_embeds text_encoder-item3.clip_prompt_embeds: 4,024B, BPFP=0.5444 Using per-key quantization points (text_encoder_2-item4.clip_pooled: torch.Size([256])) for text_encoder_2-item4.clip_pooled_prompt_embeds text_encoder_2-item4.clip_pooled_prompt_embeds: 1,268B, BPFP=7.9250 Using per-key quantization points (text_encoder_2-item4.clip_prompt: torch.Size([256])) for text_encoder_2-item4.clip_prompt_embeds text_encoder_2-item4.clip_prompt_embeds: 6,996B, BPFP=0.5679 Using per-key quantization points (text_encoder_3-item5.t5_prompt_embeds: torch.Size([256])) for text_encoder_3-item5.t5_prompt_embeds text_encoder_3-item5.t5_prompt_embeds: 18,416B, BPFP=0.4671 Using per-key quantization points (vae.encoder_f0: torch.Size([256])) for vae.encoder_f0 vae.encoder_f0: 16,856B, BPFP=0.2572 Using per-key quantization points (vae.encoder_f1: torch.Size([256])) for vae.encoder_f1 vae.encoder_f1: 16,856B, BPFP=0.2572 Using per-key quantization points (vae.decoder: torch.Size([256])) for vae.decoder vae.decoder: 9,208B, BPFP=0.2810 ⌛️ [2/4] FRONTEND: Frontend time: 0.290s (Pack+Encode) [3/4] BACKEND: Decode + Unpack... Using per-key quantization points (text_encoder-item0.clip_pooled_prompt_embeds: torch.Size([256])) for text_encoder-item0.clip_pooled_prompt_embeds Using per-key quantization points (text_encoder-item0.clip_prompt_embeds: torch.Size([256])) for text_encoder-item0.clip_prompt_embeds Using per-key quantization points (text_encoder_2-item1.clip_pooled_prompt_embeds: torch.Size([256])) for text_encoder_2-item1.clip_pooled_prompt_embeds Using per-key quantization points (text_encoder_2-item1.clip_prompt_embeds: torch.Size([256])) for text_encoder_2-item1.clip_prompt_embeds Using per-key quantization points (text_encoder_3-item2.t5_prompt_embeds: torch.Size([256])) for text_encoder_3-item2.t5_prompt_embeds Using per-key quantization points (text_encoder-item3.clip_pooled_prompt_embeds: torch.Size([256])) for text_encoder-item3.clip_pooled_prompt_embeds Using per-key quantization points (text_encoder-item3.clip_prompt_embeds: torch.Size([256])) for text_encoder-item3.clip_prompt_embeds Using per-key quantization points (text_encoder_2-item4.clip_pooled: torch.Size([256])) for text_encoder_2-item4.clip_pooled_prompt_embeds Using per-key quantization points (text_encoder_2-item4.clip_prompt: torch.Size([256])) for text_encoder_2-item4.clip_prompt_embeds Using per-key quantization points (text_encoder_3-item5.t5_prompt_embeds: torch.Size([256])) for text_encoder_3-item5.t5_prompt_embeds Using per-key quantization points (vae.encoder_f0: torch.Size([256])) for vae.encoder_f0 Using per-key quantization points (vae.encoder_f1: torch.Size([256])) for vae.encoder_f1 Using per-key quantization points (vae.decoder: torch.Size([256])) for vae.decoder IndividualUnPacker: text_encoder-item0.clip_pooled_prompt_embeds: torch.Size([768]) text_encoder-item0.clip_prompt_embeds: torch.Size([77, 768]) text_encoder_2-item1.clip_pooled_prompt_embeds: torch.Size([1280]) text_encoder_2-item1.clip_prompt_embeds: torch.Size([77, 1280]) text_encoder_3-item2.t5_prompt_embeds: torch.Size([77, 4096]) text_encoder-item3.clip_pooled_prompt_embeds: torch.Size([768]) text_encoder-item3.clip_prompt_embeds: torch.Size([77, 768]) text_encoder_2-item4.clip_pooled_prompt_embeds: torch.Size([1280]) text_encoder_2-item4.clip_prompt_embeds: torch.Size([77, 1280]) text_encoder_3-item5.t5_prompt_embeds: torch.Size([77, 4096]) vae.decoder: torch.Size([16, 128, 128]) vae.encoder_f0: torch.Size([32, 128, 128]) vae.encoder_f1: torch.Size([32, 128, 128]) ⌛️ [3/4] BACKEND: Backend time: 0.443s [4/4] METRICS: Computing MSE Breakdown... Using per-key quantization points (text_encoder-item0.clip_pooled_prompt_embeds: torch.Size([256])) for text_encoder-item0.clip_pooled_prompt_embeds Using per-key quantization points (text_encoder-item0.clip_prompt_embeds: torch.Size([256])) for text_encoder-item0.clip_prompt_embeds Using per-key quantization points (text_encoder_2-item1.clip_pooled_prompt_embeds: torch.Size([256])) for text_encoder_2-item1.clip_pooled_prompt_embeds Using per-key quantization points (text_encoder_2-item1.clip_prompt_embeds: torch.Size([256])) for text_encoder_2-item1.clip_prompt_embeds Using per-key quantization points (text_encoder_3-item2.t5_prompt_embeds: torch.Size([256])) for text_encoder_3-item2.t5_prompt_embeds Using per-key quantization points (text_encoder-item3.clip_pooled_prompt_embeds: torch.Size([256])) for text_encoder-item3.clip_pooled_prompt_embeds Using per-key quantization points (text_encoder-item3.clip_prompt_embeds: torch.Size([256])) for text_encoder-item3.clip_prompt_embeds Using per-key quantization points (text_encoder_2-item4.clip_pooled: torch.Size([256])) for text_encoder_2-item4.clip_pooled_prompt_embeds Using per-key quantization points (text_encoder_2-item4.clip_prompt: torch.Size([256])) for text_encoder_2-item4.clip_prompt_embeds Using per-key quantization points (text_encoder_3-item5.t5_prompt_embeds: torch.Size([256])) for text_encoder_3-item5.t5_prompt_embeds Using per-key quantization points (vae.encoder_f0: torch.Size([256])) for vae.encoder_f0 Using per-key quantization points (vae.encoder_f1: torch.Size([256])) for vae.encoder_f1 Using per-key quantization points (vae.decoder: torch.Size([256])) for vae.decoder IndividualUnPacker: text_encoder-item0.clip_pooled_prompt_embeds: torch.Size([768]) text_encoder-item0.clip_prompt_embeds: torch.Size([77, 768]) text_encoder_2-item1.clip_pooled_prompt_embeds: torch.Size([1280]) text_encoder_2-item1.clip_prompt_embeds: torch.Size([77, 1280]) text_encoder_3-item2.t5_prompt_embeds: torch.Size([77, 4096]) text_encoder-item3.clip_pooled_prompt_embeds: torch.Size([768]) text_encoder-item3.clip_prompt_embeds: torch.Size([77, 768]) text_encoder_2-item4.clip_pooled_prompt_embeds: torch.Size([1280]) text_encoder_2-item4.clip_prompt_embeds: torch.Size([77, 1280]) text_encoder_3-item5.t5_prompt_embeds: torch.Size([77, 4096]) vae.decoder: torch.Size([16, 128, 128]) vae.encoder_f0: torch.Size([32, 128, 128]) vae.encoder_f1: torch.Size([32, 128, 128]) Per-key MSE Breakdown (Quant=Truncation+Quantization, Codec=Compression): Key Quant-MSE Total-MSE ------------------------------------------------------------------------------------- text_encoder-item0.clip_pooled_prompt_embeds 0.00018108 0.91457836 text_encoder-item0.clip_prompt_embeds 0.00022123 48.35028155 text_encoder_2-item1.clip_pooled_prompt_embeds 0.00020346 0.86974726 text_encoder_2-item1.clip_prompt_embeds 0.00016509 0.12093570 text_encoder_3-item2.t5_prompt_embeds 0.00000793 0.00382086 text_encoder-item3.clip_pooled_prompt_embeds 0.00841799 1.01420029 text_encoder-item3.clip_prompt_embeds 0.00023247 23.90570887 text_encoder_2-item4.clip_pooled_prompt_embeds 0.00016751 0.67051430 text_encoder_2-item4.clip_prompt_embeds 0.00009830 0.47049568 text_encoder_3-item5.t5_prompt_embeds 0.00001263 0.00227117 vae.encoder_f0 0.32749966 7.18941593 vae.encoder_f1 0.32750070 7.18985605 vae.decoder 0.00039956 0.06474409 ------------------------------------------------------------------------------------- TOTAL 0.15195981 5.25983638 (elements=2,260,992) ---------------------- -------------------------------------------------------- SAMPLE-WISE STATISTICS ---------------------- -------------------------------------------------------- Handler sd35 Strategy individual Architecture hyperprior-featurecoding ---------------------- -------------------------------------------------------- Total Elements 2260992 Total Bytes 124084 BPFP 0.4390 bits/point EBPFP 0.8781 equivalent bits/point MSE 5.259836 ---------------------- -------------------------------------------------------- Time: 0.742s Load: 0.009s, Pack+Encode: 0.290s, Decode+Unpack: 0.443s ---------------------- -------------------------------------------------------- Restored Feature Format: [Dict] with 8 keys key['text_encoder-item0']: [Dict] with 2 keys key['clip_pooled_prompt_embeds']: [Tensor] shape=torch.Size([768]), dtype=torch.float16, device=cpu key['clip_prompt_embeds']: [Tensor] shape=torch.Size([77, 768]), dtype=torch.float16, device=cpu key['text_encoder_2-item1']: [Dict] with 2 keys key['clip_pooled_prompt_embeds']: [Tensor] shape=torch.Size([1280]), dtype=torch.float16, device=cpu key['clip_prompt_embeds']: [Tensor] shape=torch.Size([77, 1280]), dtype=torch.float16, device=cpu key['text_encoder_3-item2']: [Dict] with 1 keys key['t5_prompt_embeds']: [Tensor] shape=torch.Size([77, 4096]), dtype=torch.float16, device=cpu key['text_encoder-item3']: [Dict] with 2 keys key['clip_pooled_prompt_embeds']: [Tensor] shape=torch.Size([768]), dtype=torch.float16, device=cpu key['clip_prompt_embeds']: [Tensor] shape=torch.Size([77, 768]), dtype=torch.float16, device=cpu key['text_encoder_2-item4']: [Dict] with 2 keys key['clip_pooled_prompt_embeds']: [Tensor] shape=torch.Size([1280]), dtype=torch.float16, device=cpu key['clip_prompt_embeds']: [Tensor] shape=torch.Size([77, 1280]), dtype=torch.float16, device=cpu key['text_encoder_3-item5']: [Dict] with 1 keys key['t5_prompt_embeds']: [Tensor] shape=torch.Size([77, 4096]), dtype=torch.float16, device=cpu key['vae.decoder']: [Tensor] shape=torch.Size([16, 128, 128]), dtype=torch.float16, device=cpu key['vae.encoder']: [Dict] with 2 keys key['vae.encoder_f0']: [Tensor] shape=torch.Size([32, 128, 128]), dtype=torch.float16, device=cpu key['vae.encoder_f1']: [Tensor] shape=torch.Size([32, 128, 128]), dtype=torch.float16, device=cpu 💾 Converting with 5.2598 MSE: from ../datasets/FeatureCoding-StableDiffusion3.5/100samples_features/000000523100.zst to output-fixed/sd35/lambda0.004/hyperprior-featurecoding-8bit-individual/sd35cond/000000523100.zst 💪 Processing: ../datasets/FeatureCoding-StableDiffusion3.5/100samples_features/000000526751.zst (94/100) [1/4] FRONTEND: Loading features from ../datasets/FeatureCoding-StableDiffusion3.5/100samples_features/000000526751.zst... Original data structure: root: [Dict] with 8 keys key['text_encoder-item0']: [Dict] with 2 keys key['clip_pooled_prompt_embeds']: [Tensor] shape=torch.Size([768]), dtype=torch.float16, device=cpu key['clip_prompt_embeds']: [Tensor] shape=torch.Size([77, 768]), dtype=torch.float16, device=cpu key['text_encoder_2-item1']: [Dict] with 2 keys key['clip_pooled_prompt_embeds']: [Tensor] shape=torch.Size([1280]), dtype=torch.float16, device=cpu key['clip_prompt_embeds']: [Tensor] shape=torch.Size([77, 1280]), dtype=torch.float16, device=cpu key['text_encoder_3-item2']: [Dict] with 1 keys key['t5_prompt_embeds']: [Tensor] shape=torch.Size([77, 4096]), dtype=torch.float16, device=cpu key['text_encoder-item3']: [Dict] with 2 keys key['clip_pooled_prompt_embeds']: [Tensor] shape=torch.Size([768]), dtype=torch.float16, device=cpu key['clip_prompt_embeds']: [Tensor] shape=torch.Size([77, 768]), dtype=torch.float16, device=cpu key['text_encoder_2-item4']: [Dict] with 2 keys key['clip_pooled_prompt_embeds']: [Tensor] shape=torch.Size([1280]), dtype=torch.float16, device=cpu key['clip_prompt_embeds']: [Tensor] shape=torch.Size([77, 1280]), dtype=torch.float16, device=cpu key['text_encoder_3-item5']: [Dict] with 1 keys key['t5_prompt_embeds']: [Tensor] shape=torch.Size([77, 4096]), dtype=torch.float16, device=cpu key['vae.encoder-item6']: [Dict] with 2 keys key['vae.encoder_f0']: [Tensor] shape=torch.Size([32, 128, 128]), dtype=torch.float16, device=cpu key['vae.encoder_f1']: [Tensor] shape=torch.Size([32, 128, 128]), dtype=torch.float16, device=cpu key['vae.decoder']: [Tensor] shape=torch.Size([16, 128, 128]), dtype=torch.float16, device=cpu ⌛️ [1/4] FRONTEND: Load time: 0.009s ------------------------------------------------------------ SD3.5 Features Summary ------------------------------------------------------------ Number of text encoders: 3 Encoder names: ['text_encoder', 'text_encoder_2', 'text_encoder_3'] Has VAE latents: True Has VAE encoder features: True Data type: torch.float16 text_encoder-item0: clip_pooled_prompt_embeds: torch.Size([768]), torch.float16 clip_prompt_embeds: torch.Size([77, 768]), torch.float16 text_encoder_2-item1: clip_pooled_prompt_embeds: torch.Size([1280]), torch.float16 clip_prompt_embeds: torch.Size([77, 1280]), torch.float16 text_encoder_3-item2: t5_prompt_embeds: torch.Size([77, 4096]), torch.float16 text_encoder-item3: clip_pooled_prompt_embeds: torch.Size([768]), torch.float16 clip_prompt_embeds: torch.Size([77, 768]), torch.float16 text_encoder_2-item4: clip_pooled_prompt_embeds: torch.Size([1280]), torch.float16 clip_prompt_embeds: torch.Size([77, 1280]), torch.float16 text_encoder_3-item5: t5_prompt_embeds: torch.Size([77, 4096]), torch.float16 vae.decoder latents: torch.Size([16, 128, 128]) vae.encoder features: vae.encoder_f0: torch.Size([32, 128, 128]), torch.float16 vae.encoder_f1: torch.Size([32, 128, 128]), torch.float16 ------------------------------------------------------------ [2/4] FRONTEND: Pack + Encode (strategy: individual)... IndividualPacker: text_encoder-item0.clip_pooled_prompt_embeds: torch.Size([768])->torch.Size([1, 1, 1, 768]) text_encoder-item0.clip_prompt_embeds: torch.Size([77, 768])->torch.Size([1, 1, 77, 768]) text_encoder_2-item1.clip_pooled_prompt_embeds: torch.Size([1280])->torch.Size([1, 1, 1, 1280]) text_encoder_2-item1.clip_prompt_embeds: torch.Size([77, 1280])->torch.Size([1, 1, 77, 1280]) text_encoder_3-item2.t5_prompt_embeds: torch.Size([77, 4096])->torch.Size([1, 1, 77, 4096]) text_encoder-item3.clip_pooled_prompt_embeds: torch.Size([768])->torch.Size([1, 1, 1, 768]) text_encoder-item3.clip_prompt_embeds: torch.Size([77, 768])->torch.Size([1, 1, 77, 768]) text_encoder_2-item4.clip_pooled_prompt_embeds: torch.Size([1280])->torch.Size([1, 1, 1, 1280]) text_encoder_2-item4.clip_prompt_embeds: torch.Size([77, 1280])->torch.Size([1, 1, 77, 1280]) text_encoder_3-item5.t5_prompt_embeds: torch.Size([77, 4096])->torch.Size([1, 1, 77, 4096]) vae.encoder_f0: torch.Size([32, 128, 128])->torch.Size([1, 1, 512, 1024]) vae.encoder_f1: torch.Size([32, 128, 128])->torch.Size([1, 1, 512, 1024]) vae.decoder: torch.Size([16, 128, 128])->torch.Size([1, 1, 512, 512]) Using per-key quantization points (text_encoder-item0.clip_pooled_prompt_embeds: torch.Size([256])) for text_encoder-item0.clip_pooled_prompt_embeds text_encoder-item0.clip_pooled_prompt_embeds: 724B, BPFP=7.5417 Using per-key quantization points (text_encoder-item0.clip_prompt_embeds: torch.Size([256])) for text_encoder-item0.clip_prompt_embeds text_encoder-item0.clip_prompt_embeds: 6,356B, BPFP=0.8598 Using per-key quantization points (text_encoder_2-item1.clip_pooled_prompt_embeds: torch.Size([256])) for text_encoder_2-item1.clip_pooled_prompt_embeds text_encoder_2-item1.clip_pooled_prompt_embeds: 1,200B, BPFP=7.5000 Using per-key quantization points (text_encoder_2-item1.clip_prompt_embeds: torch.Size([256])) for text_encoder_2-item1.clip_prompt_embeds text_encoder_2-item1.clip_prompt_embeds: 11,392B, BPFP=0.9247 Using per-key quantization points (text_encoder_3-item2.t5_prompt_embeds: torch.Size([256])) for text_encoder_3-item2.t5_prompt_embeds text_encoder_3-item2.t5_prompt_embeds: 26,668B, BPFP=0.6764 Using per-key quantization points (text_encoder-item3.clip_pooled_prompt_embeds: torch.Size([256])) for text_encoder-item3.clip_pooled_prompt_embeds text_encoder-item3.clip_pooled_prompt_embeds: 760B, BPFP=7.9167 Using per-key quantization points (text_encoder-item3.clip_prompt_embeds: torch.Size([256])) for text_encoder-item3.clip_prompt_embeds text_encoder-item3.clip_prompt_embeds: 4,024B, BPFP=0.5444 Using per-key quantization points (text_encoder_2-item4.clip_pooled: torch.Size([256])) for text_encoder_2-item4.clip_pooled_prompt_embeds text_encoder_2-item4.clip_pooled_prompt_embeds: 1,268B, BPFP=7.9250 Using per-key quantization points (text_encoder_2-item4.clip_prompt: torch.Size([256])) for text_encoder_2-item4.clip_prompt_embeds text_encoder_2-item4.clip_prompt_embeds: 6,996B, BPFP=0.5679 Using per-key quantization points (text_encoder_3-item5.t5_prompt_embeds: torch.Size([256])) for text_encoder_3-item5.t5_prompt_embeds text_encoder_3-item5.t5_prompt_embeds: 18,416B, BPFP=0.4671 Using per-key quantization points (vae.encoder_f0: torch.Size([256])) for vae.encoder_f0 vae.encoder_f0: 13,384B, BPFP=0.2042 Using per-key quantization points (vae.encoder_f1: torch.Size([256])) for vae.encoder_f1 vae.encoder_f1: 13,384B, BPFP=0.2042 Using per-key quantization points (vae.decoder: torch.Size([256])) for vae.decoder vae.decoder: 8,072B, BPFP=0.2463 ⌛️ [2/4] FRONTEND: Frontend time: 0.298s (Pack+Encode) [3/4] BACKEND: Decode + Unpack... Using per-key quantization points (text_encoder-item0.clip_pooled_prompt_embeds: torch.Size([256])) for text_encoder-item0.clip_pooled_prompt_embeds Using per-key quantization points (text_encoder-item0.clip_prompt_embeds: torch.Size([256])) for text_encoder-item0.clip_prompt_embeds Using per-key quantization points (text_encoder_2-item1.clip_pooled_prompt_embeds: torch.Size([256])) for text_encoder_2-item1.clip_pooled_prompt_embeds Using per-key quantization points (text_encoder_2-item1.clip_prompt_embeds: torch.Size([256])) for text_encoder_2-item1.clip_prompt_embeds Using per-key quantization points (text_encoder_3-item2.t5_prompt_embeds: torch.Size([256])) for text_encoder_3-item2.t5_prompt_embeds Using per-key quantization points (text_encoder-item3.clip_pooled_prompt_embeds: torch.Size([256])) for text_encoder-item3.clip_pooled_prompt_embeds Using per-key quantization points (text_encoder-item3.clip_prompt_embeds: torch.Size([256])) for text_encoder-item3.clip_prompt_embeds Using per-key quantization points (text_encoder_2-item4.clip_pooled: torch.Size([256])) for text_encoder_2-item4.clip_pooled_prompt_embeds Using per-key quantization points (text_encoder_2-item4.clip_prompt: torch.Size([256])) for text_encoder_2-item4.clip_prompt_embeds Using per-key quantization points (text_encoder_3-item5.t5_prompt_embeds: torch.Size([256])) for text_encoder_3-item5.t5_prompt_embeds Using per-key quantization points (vae.encoder_f0: torch.Size([256])) for vae.encoder_f0 Using per-key quantization points (vae.encoder_f1: torch.Size([256])) for vae.encoder_f1 Using per-key quantization points (vae.decoder: torch.Size([256])) for vae.decoder IndividualUnPacker: text_encoder-item0.clip_pooled_prompt_embeds: torch.Size([768]) text_encoder-item0.clip_prompt_embeds: torch.Size([77, 768]) text_encoder_2-item1.clip_pooled_prompt_embeds: torch.Size([1280]) text_encoder_2-item1.clip_prompt_embeds: torch.Size([77, 1280]) text_encoder_3-item2.t5_prompt_embeds: torch.Size([77, 4096]) text_encoder-item3.clip_pooled_prompt_embeds: torch.Size([768]) text_encoder-item3.clip_prompt_embeds: torch.Size([77, 768]) text_encoder_2-item4.clip_pooled_prompt_embeds: torch.Size([1280]) text_encoder_2-item4.clip_prompt_embeds: torch.Size([77, 1280]) text_encoder_3-item5.t5_prompt_embeds: torch.Size([77, 4096]) vae.decoder: torch.Size([16, 128, 128]) vae.encoder_f0: torch.Size([32, 128, 128]) vae.encoder_f1: torch.Size([32, 128, 128]) ⌛️ [3/4] BACKEND: Backend time: 0.448s [4/4] METRICS: Computing MSE Breakdown... Using per-key quantization points (text_encoder-item0.clip_pooled_prompt_embeds: torch.Size([256])) for text_encoder-item0.clip_pooled_prompt_embeds Using per-key quantization points (text_encoder-item0.clip_prompt_embeds: torch.Size([256])) for text_encoder-item0.clip_prompt_embeds Using per-key quantization points (text_encoder_2-item1.clip_pooled_prompt_embeds: torch.Size([256])) for text_encoder_2-item1.clip_pooled_prompt_embeds Using per-key quantization points (text_encoder_2-item1.clip_prompt_embeds: torch.Size([256])) for text_encoder_2-item1.clip_prompt_embeds Using per-key quantization points (text_encoder_3-item2.t5_prompt_embeds: torch.Size([256])) for text_encoder_3-item2.t5_prompt_embeds Using per-key quantization points (text_encoder-item3.clip_pooled_prompt_embeds: torch.Size([256])) for text_encoder-item3.clip_pooled_prompt_embeds Using per-key quantization points (text_encoder-item3.clip_prompt_embeds: torch.Size([256])) for text_encoder-item3.clip_prompt_embeds Using per-key quantization points (text_encoder_2-item4.clip_pooled: torch.Size([256])) for text_encoder_2-item4.clip_pooled_prompt_embeds Using per-key quantization points (text_encoder_2-item4.clip_prompt: torch.Size([256])) for text_encoder_2-item4.clip_prompt_embeds Using per-key quantization points (text_encoder_3-item5.t5_prompt_embeds: torch.Size([256])) for text_encoder_3-item5.t5_prompt_embeds Using per-key quantization points (vae.encoder_f0: torch.Size([256])) for vae.encoder_f0 Using per-key quantization points (vae.encoder_f1: torch.Size([256])) for vae.encoder_f1 Using per-key quantization points (vae.decoder: torch.Size([256])) for vae.decoder IndividualUnPacker: text_encoder-item0.clip_pooled_prompt_embeds: torch.Size([768]) text_encoder-item0.clip_prompt_embeds: torch.Size([77, 768]) text_encoder_2-item1.clip_pooled_prompt_embeds: torch.Size([1280]) text_encoder_2-item1.clip_prompt_embeds: torch.Size([77, 1280]) text_encoder_3-item2.t5_prompt_embeds: torch.Size([77, 4096]) text_encoder-item3.clip_pooled_prompt_embeds: torch.Size([768]) text_encoder-item3.clip_prompt_embeds: torch.Size([77, 768]) text_encoder_2-item4.clip_pooled_prompt_embeds: torch.Size([1280]) text_encoder_2-item4.clip_prompt_embeds: torch.Size([77, 1280]) text_encoder_3-item5.t5_prompt_embeds: torch.Size([77, 4096]) vae.decoder: torch.Size([16, 128, 128]) vae.encoder_f0: torch.Size([32, 128, 128]) vae.encoder_f1: torch.Size([32, 128, 128]) Per-key MSE Breakdown (Quant=Truncation+Quantization, Codec=Compression): Key Quant-MSE Total-MSE ------------------------------------------------------------------------------------- text_encoder-item0.clip_pooled_prompt_embeds 0.00109564 0.89397510 text_encoder-item0.clip_prompt_embeds 0.00024675 23.94587054 text_encoder_2-item1.clip_pooled_prompt_embeds 0.00084628 0.85555744 text_encoder_2-item1.clip_prompt_embeds 0.00016730 0.12648292 text_encoder_3-item2.t5_prompt_embeds 0.00000841 0.00287454 text_encoder-item3.clip_pooled_prompt_embeds 0.00841799 1.01420029 text_encoder-item3.clip_prompt_embeds 0.00023247 23.90570887 text_encoder_2-item4.clip_pooled_prompt_embeds 0.00016751 0.67051430 text_encoder_2-item4.clip_prompt_embeds 0.00009830 0.47049568 text_encoder_3-item5.t5_prompt_embeds 0.00001263 0.00227117 vae.encoder_f0 0.00566967 1.69204879 vae.encoder_f1 0.00567867 1.69186091 vae.decoder 0.00017839 0.06483991 ------------------------------------------------------------------------------------- TOTAL 0.00268303 2.07199832 (elements=2,260,992) ---------------------- -------------------------------------------------------- SAMPLE-WISE STATISTICS ---------------------- -------------------------------------------------------- Handler sd35 Strategy individual Architecture hyperprior-featurecoding ---------------------- -------------------------------------------------------- Total Elements 2260992 Total Bytes 112644 BPFP 0.3986 bits/point EBPFP 0.7971 equivalent bits/point MSE 2.071998 ---------------------- -------------------------------------------------------- Time: 0.755s Load: 0.009s, Pack+Encode: 0.298s, Decode+Unpack: 0.448s ---------------------- -------------------------------------------------------- Restored Feature Format: [Dict] with 8 keys key['text_encoder-item0']: [Dict] with 2 keys key['clip_pooled_prompt_embeds']: [Tensor] shape=torch.Size([768]), dtype=torch.float16, device=cpu key['clip_prompt_embeds']: [Tensor] shape=torch.Size([77, 768]), dtype=torch.float16, device=cpu key['text_encoder_2-item1']: [Dict] with 2 keys key['clip_pooled_prompt_embeds']: [Tensor] shape=torch.Size([1280]), dtype=torch.float16, device=cpu key['clip_prompt_embeds']: [Tensor] shape=torch.Size([77, 1280]), dtype=torch.float16, device=cpu key['text_encoder_3-item2']: [Dict] with 1 keys key['t5_prompt_embeds']: [Tensor] shape=torch.Size([77, 4096]), dtype=torch.float16, device=cpu key['text_encoder-item3']: [Dict] with 2 keys key['clip_pooled_prompt_embeds']: [Tensor] shape=torch.Size([768]), dtype=torch.float16, device=cpu key['clip_prompt_embeds']: [Tensor] shape=torch.Size([77, 768]), dtype=torch.float16, device=cpu key['text_encoder_2-item4']: [Dict] with 2 keys key['clip_pooled_prompt_embeds']: [Tensor] shape=torch.Size([1280]), dtype=torch.float16, device=cpu key['clip_prompt_embeds']: [Tensor] shape=torch.Size([77, 1280]), dtype=torch.float16, device=cpu key['text_encoder_3-item5']: [Dict] with 1 keys key['t5_prompt_embeds']: [Tensor] shape=torch.Size([77, 4096]), dtype=torch.float16, device=cpu key['vae.decoder']: [Tensor] shape=torch.Size([16, 128, 128]), dtype=torch.float16, device=cpu key['vae.encoder']: [Dict] with 2 keys key['vae.encoder_f0']: [Tensor] shape=torch.Size([32, 128, 128]), dtype=torch.float16, device=cpu key['vae.encoder_f1']: [Tensor] shape=torch.Size([32, 128, 128]), dtype=torch.float16, device=cpu 💾 Converting with 2.0720 MSE: from ../datasets/FeatureCoding-StableDiffusion3.5/100samples_features/000000526751.zst to output-fixed/sd35/lambda0.004/hyperprior-featurecoding-8bit-individual/sd35cond/000000526751.zst 💪 Processing: ../datasets/FeatureCoding-StableDiffusion3.5/100samples_features/000000535578.zst (95/100) [1/4] FRONTEND: Loading features from ../datasets/FeatureCoding-StableDiffusion3.5/100samples_features/000000535578.zst... Original data structure: root: [Dict] with 8 keys key['text_encoder-item0']: [Dict] with 2 keys key['clip_pooled_prompt_embeds']: [Tensor] shape=torch.Size([768]), dtype=torch.float16, device=cpu key['clip_prompt_embeds']: [Tensor] shape=torch.Size([77, 768]), dtype=torch.float16, device=cpu key['text_encoder_2-item1']: [Dict] with 2 keys key['clip_pooled_prompt_embeds']: [Tensor] shape=torch.Size([1280]), dtype=torch.float16, device=cpu key['clip_prompt_embeds']: [Tensor] shape=torch.Size([77, 1280]), dtype=torch.float16, device=cpu key['text_encoder_3-item2']: [Dict] with 1 keys key['t5_prompt_embeds']: [Tensor] shape=torch.Size([77, 4096]), dtype=torch.float16, device=cpu key['text_encoder-item3']: [Dict] with 2 keys key['clip_pooled_prompt_embeds']: [Tensor] shape=torch.Size([768]), dtype=torch.float16, device=cpu key['clip_prompt_embeds']: [Tensor] shape=torch.Size([77, 768]), dtype=torch.float16, device=cpu key['text_encoder_2-item4']: [Dict] with 2 keys key['clip_pooled_prompt_embeds']: [Tensor] shape=torch.Size([1280]), dtype=torch.float16, device=cpu key['clip_prompt_embeds']: [Tensor] shape=torch.Size([77, 1280]), dtype=torch.float16, device=cpu key['text_encoder_3-item5']: [Dict] with 1 keys key['t5_prompt_embeds']: [Tensor] shape=torch.Size([77, 4096]), dtype=torch.float16, device=cpu key['vae.encoder-item6']: [Dict] with 2 keys key['vae.encoder_f0']: [Tensor] shape=torch.Size([32, 128, 128]), dtype=torch.float16, device=cpu key['vae.encoder_f1']: [Tensor] shape=torch.Size([32, 128, 128]), dtype=torch.float16, device=cpu key['vae.decoder']: [Tensor] shape=torch.Size([16, 128, 128]), dtype=torch.float16, device=cpu ⌛️ [1/4] FRONTEND: Load time: 0.009s ------------------------------------------------------------ SD3.5 Features Summary ------------------------------------------------------------ Number of text encoders: 3 Encoder names: ['text_encoder', 'text_encoder_2', 'text_encoder_3'] Has VAE latents: True Has VAE encoder features: True Data type: torch.float16 text_encoder-item0: clip_pooled_prompt_embeds: torch.Size([768]), torch.float16 clip_prompt_embeds: torch.Size([77, 768]), torch.float16 text_encoder_2-item1: clip_pooled_prompt_embeds: torch.Size([1280]), torch.float16 clip_prompt_embeds: torch.Size([77, 1280]), torch.float16 text_encoder_3-item2: t5_prompt_embeds: torch.Size([77, 4096]), torch.float16 text_encoder-item3: clip_pooled_prompt_embeds: torch.Size([768]), torch.float16 clip_prompt_embeds: torch.Size([77, 768]), torch.float16 text_encoder_2-item4: clip_pooled_prompt_embeds: torch.Size([1280]), torch.float16 clip_prompt_embeds: torch.Size([77, 1280]), torch.float16 text_encoder_3-item5: t5_prompt_embeds: torch.Size([77, 4096]), torch.float16 vae.decoder latents: torch.Size([16, 128, 128]) vae.encoder features: vae.encoder_f0: torch.Size([32, 128, 128]), torch.float16 vae.encoder_f1: torch.Size([32, 128, 128]), torch.float16 ------------------------------------------------------------ [2/4] FRONTEND: Pack + Encode (strategy: individual)... IndividualPacker: text_encoder-item0.clip_pooled_prompt_embeds: torch.Size([768])->torch.Size([1, 1, 1, 768]) text_encoder-item0.clip_prompt_embeds: torch.Size([77, 768])->torch.Size([1, 1, 77, 768]) text_encoder_2-item1.clip_pooled_prompt_embeds: torch.Size([1280])->torch.Size([1, 1, 1, 1280]) text_encoder_2-item1.clip_prompt_embeds: torch.Size([77, 1280])->torch.Size([1, 1, 77, 1280]) text_encoder_3-item2.t5_prompt_embeds: torch.Size([77, 4096])->torch.Size([1, 1, 77, 4096]) text_encoder-item3.clip_pooled_prompt_embeds: torch.Size([768])->torch.Size([1, 1, 1, 768]) text_encoder-item3.clip_prompt_embeds: torch.Size([77, 768])->torch.Size([1, 1, 77, 768]) text_encoder_2-item4.clip_pooled_prompt_embeds: torch.Size([1280])->torch.Size([1, 1, 1, 1280]) text_encoder_2-item4.clip_prompt_embeds: torch.Size([77, 1280])->torch.Size([1, 1, 77, 1280]) text_encoder_3-item5.t5_prompt_embeds: torch.Size([77, 4096])->torch.Size([1, 1, 77, 4096]) vae.encoder_f0: torch.Size([32, 128, 128])->torch.Size([1, 1, 512, 1024]) vae.encoder_f1: torch.Size([32, 128, 128])->torch.Size([1, 1, 512, 1024]) vae.decoder: torch.Size([16, 128, 128])->torch.Size([1, 1, 512, 512]) Using per-key quantization points (text_encoder-item0.clip_pooled_prompt_embeds: torch.Size([256])) for text_encoder-item0.clip_pooled_prompt_embeds text_encoder-item0.clip_pooled_prompt_embeds: 748B, BPFP=7.7917 Using per-key quantization points (text_encoder-item0.clip_prompt_embeds: torch.Size([256])) for text_encoder-item0.clip_prompt_embeds text_encoder-item0.clip_prompt_embeds: 5,308B, BPFP=0.7181 Using per-key quantization points (text_encoder_2-item1.clip_pooled_prompt_embeds: torch.Size([256])) for text_encoder_2-item1.clip_pooled_prompt_embeds text_encoder_2-item1.clip_pooled_prompt_embeds: 1,212B, BPFP=7.5750 Using per-key quantization points (text_encoder_2-item1.clip_prompt_embeds: torch.Size([256])) for text_encoder_2-item1.clip_prompt_embeds text_encoder_2-item1.clip_prompt_embeds: 10,064B, BPFP=0.8169 Using per-key quantization points (text_encoder_3-item2.t5_prompt_embeds: torch.Size([256])) for text_encoder_3-item2.t5_prompt_embeds text_encoder_3-item2.t5_prompt_embeds: 27,476B, BPFP=0.6969 Using per-key quantization points (text_encoder-item3.clip_pooled_prompt_embeds: torch.Size([256])) for text_encoder-item3.clip_pooled_prompt_embeds text_encoder-item3.clip_pooled_prompt_embeds: 760B, BPFP=7.9167 Using per-key quantization points (text_encoder-item3.clip_prompt_embeds: torch.Size([256])) for text_encoder-item3.clip_prompt_embeds text_encoder-item3.clip_prompt_embeds: 4,024B, BPFP=0.5444 Using per-key quantization points (text_encoder_2-item4.clip_pooled: torch.Size([256])) for text_encoder_2-item4.clip_pooled_prompt_embeds text_encoder_2-item4.clip_pooled_prompt_embeds: 1,268B, BPFP=7.9250 Using per-key quantization points (text_encoder_2-item4.clip_prompt: torch.Size([256])) for text_encoder_2-item4.clip_prompt_embeds text_encoder_2-item4.clip_prompt_embeds: 6,996B, BPFP=0.5679 Using per-key quantization points (text_encoder_3-item5.t5_prompt_embeds: torch.Size([256])) for text_encoder_3-item5.t5_prompt_embeds text_encoder_3-item5.t5_prompt_embeds: 18,416B, BPFP=0.4671 Using per-key quantization points (vae.encoder_f0: torch.Size([256])) for vae.encoder_f0 vae.encoder_f0: 11,000B, BPFP=0.1678 Using per-key quantization points (vae.encoder_f1: torch.Size([256])) for vae.encoder_f1 vae.encoder_f1: 11,000B, BPFP=0.1678 Using per-key quantization points (vae.decoder: torch.Size([256])) for vae.decoder vae.decoder: 10,192B, BPFP=0.3110 ⌛️ [2/4] FRONTEND: Frontend time: 0.293s (Pack+Encode) [3/4] BACKEND: Decode + Unpack... Using per-key quantization points (text_encoder-item0.clip_pooled_prompt_embeds: torch.Size([256])) for text_encoder-item0.clip_pooled_prompt_embeds Using per-key quantization points (text_encoder-item0.clip_prompt_embeds: torch.Size([256])) for text_encoder-item0.clip_prompt_embeds Using per-key quantization points (text_encoder_2-item1.clip_pooled_prompt_embeds: torch.Size([256])) for text_encoder_2-item1.clip_pooled_prompt_embeds Using per-key quantization points (text_encoder_2-item1.clip_prompt_embeds: torch.Size([256])) for text_encoder_2-item1.clip_prompt_embeds Using per-key quantization points (text_encoder_3-item2.t5_prompt_embeds: torch.Size([256])) for text_encoder_3-item2.t5_prompt_embeds Using per-key quantization points (text_encoder-item3.clip_pooled_prompt_embeds: torch.Size([256])) for text_encoder-item3.clip_pooled_prompt_embeds Using per-key quantization points (text_encoder-item3.clip_prompt_embeds: torch.Size([256])) for text_encoder-item3.clip_prompt_embeds Using per-key quantization points (text_encoder_2-item4.clip_pooled: torch.Size([256])) for text_encoder_2-item4.clip_pooled_prompt_embeds Using per-key quantization points (text_encoder_2-item4.clip_prompt: torch.Size([256])) for text_encoder_2-item4.clip_prompt_embeds Using per-key quantization points (text_encoder_3-item5.t5_prompt_embeds: torch.Size([256])) for text_encoder_3-item5.t5_prompt_embeds Using per-key quantization points (vae.encoder_f0: torch.Size([256])) for vae.encoder_f0 Using per-key quantization points (vae.encoder_f1: torch.Size([256])) for vae.encoder_f1 Using per-key quantization points (vae.decoder: torch.Size([256])) for vae.decoder IndividualUnPacker: text_encoder-item0.clip_pooled_prompt_embeds: torch.Size([768]) text_encoder-item0.clip_prompt_embeds: torch.Size([77, 768]) text_encoder_2-item1.clip_pooled_prompt_embeds: torch.Size([1280]) text_encoder_2-item1.clip_prompt_embeds: torch.Size([77, 1280]) text_encoder_3-item2.t5_prompt_embeds: torch.Size([77, 4096]) text_encoder-item3.clip_pooled_prompt_embeds: torch.Size([768]) text_encoder-item3.clip_prompt_embeds: torch.Size([77, 768]) text_encoder_2-item4.clip_pooled_prompt_embeds: torch.Size([1280]) text_encoder_2-item4.clip_prompt_embeds: torch.Size([77, 1280]) text_encoder_3-item5.t5_prompt_embeds: torch.Size([77, 4096]) vae.decoder: torch.Size([16, 128, 128]) vae.encoder_f0: torch.Size([32, 128, 128]) vae.encoder_f1: torch.Size([32, 128, 128]) ⌛️ [3/4] BACKEND: Backend time: 0.446s [4/4] METRICS: Computing MSE Breakdown... Using per-key quantization points (text_encoder-item0.clip_pooled_prompt_embeds: torch.Size([256])) for text_encoder-item0.clip_pooled_prompt_embeds Using per-key quantization points (text_encoder-item0.clip_prompt_embeds: torch.Size([256])) for text_encoder-item0.clip_prompt_embeds Using per-key quantization points (text_encoder_2-item1.clip_pooled_prompt_embeds: torch.Size([256])) for text_encoder_2-item1.clip_pooled_prompt_embeds Using per-key quantization points (text_encoder_2-item1.clip_prompt_embeds: torch.Size([256])) for text_encoder_2-item1.clip_prompt_embeds Using per-key quantization points (text_encoder_3-item2.t5_prompt_embeds: torch.Size([256])) for text_encoder_3-item2.t5_prompt_embeds Using per-key quantization points (text_encoder-item3.clip_pooled_prompt_embeds: torch.Size([256])) for text_encoder-item3.clip_pooled_prompt_embeds Using per-key quantization points (text_encoder-item3.clip_prompt_embeds: torch.Size([256])) for text_encoder-item3.clip_prompt_embeds Using per-key quantization points (text_encoder_2-item4.clip_pooled: torch.Size([256])) for text_encoder_2-item4.clip_pooled_prompt_embeds Using per-key quantization points (text_encoder_2-item4.clip_prompt: torch.Size([256])) for text_encoder_2-item4.clip_prompt_embeds Using per-key quantization points (text_encoder_3-item5.t5_prompt_embeds: torch.Size([256])) for text_encoder_3-item5.t5_prompt_embeds Using per-key quantization points (vae.encoder_f0: torch.Size([256])) for vae.encoder_f0 Using per-key quantization points (vae.encoder_f1: torch.Size([256])) for vae.encoder_f1 Using per-key quantization points (vae.decoder: torch.Size([256])) for vae.decoder IndividualUnPacker: text_encoder-item0.clip_pooled_prompt_embeds: torch.Size([768]) text_encoder-item0.clip_prompt_embeds: torch.Size([77, 768]) text_encoder_2-item1.clip_pooled_prompt_embeds: torch.Size([1280]) text_encoder_2-item1.clip_prompt_embeds: torch.Size([77, 1280]) text_encoder_3-item2.t5_prompt_embeds: torch.Size([77, 4096]) text_encoder-item3.clip_pooled_prompt_embeds: torch.Size([768]) text_encoder-item3.clip_prompt_embeds: torch.Size([77, 768]) text_encoder_2-item4.clip_pooled_prompt_embeds: torch.Size([1280]) text_encoder_2-item4.clip_prompt_embeds: torch.Size([77, 1280]) text_encoder_3-item5.t5_prompt_embeds: torch.Size([77, 4096]) vae.decoder: torch.Size([16, 128, 128]) vae.encoder_f0: torch.Size([32, 128, 128]) vae.encoder_f1: torch.Size([32, 128, 128]) Per-key MSE Breakdown (Quant=Truncation+Quantization, Codec=Compression): Key Quant-MSE Total-MSE ------------------------------------------------------------------------------------- text_encoder-item0.clip_pooled_prompt_embeds 0.00017308 0.88045343 text_encoder-item0.clip_prompt_embeds 0.00022364 192.69277597 text_encoder_2-item1.clip_pooled_prompt_embeds 0.00036756 1.00277996 text_encoder_2-item1.clip_prompt_embeds 0.00015289 0.11633131 text_encoder_3-item2.t5_prompt_embeds 0.00000784 0.00303855 text_encoder-item3.clip_pooled_prompt_embeds 0.00841799 1.01420029 text_encoder-item3.clip_prompt_embeds 0.00023247 23.90570887 text_encoder_2-item4.clip_pooled_prompt_embeds 0.00016751 0.67051430 text_encoder_2-item4.clip_prompt_embeds 0.00009830 0.47049568 text_encoder_3-item5.t5_prompt_embeds 0.00001263 0.00227117 vae.encoder_f0 0.00580750 1.36961448 vae.encoder_f1 0.00580664 1.36960614 vae.decoder 0.00018044 0.09161040 ------------------------------------------------------------------------------------- TOTAL 0.00274301 6.33882510 (elements=2,260,992) ---------------------- -------------------------------------------------------- SAMPLE-WISE STATISTICS ---------------------- -------------------------------------------------------- Handler sd35 Strategy individual Architecture hyperprior-featurecoding ---------------------- -------------------------------------------------------- Total Elements 2260992 Total Bytes 108464 BPFP 0.3838 bits/point EBPFP 0.7675 equivalent bits/point MSE 6.338825 ---------------------- -------------------------------------------------------- Time: 0.748s Load: 0.009s, Pack+Encode: 0.293s, Decode+Unpack: 0.446s ---------------------- -------------------------------------------------------- Restored Feature Format: [Dict] with 8 keys key['text_encoder-item0']: [Dict] with 2 keys key['clip_pooled_prompt_embeds']: [Tensor] shape=torch.Size([768]), dtype=torch.float16, device=cpu key['clip_prompt_embeds']: [Tensor] shape=torch.Size([77, 768]), dtype=torch.float16, device=cpu key['text_encoder_2-item1']: [Dict] with 2 keys key['clip_pooled_prompt_embeds']: [Tensor] shape=torch.Size([1280]), dtype=torch.float16, device=cpu key['clip_prompt_embeds']: [Tensor] shape=torch.Size([77, 1280]), dtype=torch.float16, device=cpu key['text_encoder_3-item2']: [Dict] with 1 keys key['t5_prompt_embeds']: [Tensor] shape=torch.Size([77, 4096]), dtype=torch.float16, device=cpu key['text_encoder-item3']: [Dict] with 2 keys key['clip_pooled_prompt_embeds']: [Tensor] shape=torch.Size([768]), dtype=torch.float16, device=cpu key['clip_prompt_embeds']: [Tensor] shape=torch.Size([77, 768]), dtype=torch.float16, device=cpu key['text_encoder_2-item4']: [Dict] with 2 keys key['clip_pooled_prompt_embeds']: [Tensor] shape=torch.Size([1280]), dtype=torch.float16, device=cpu key['clip_prompt_embeds']: [Tensor] shape=torch.Size([77, 1280]), dtype=torch.float16, device=cpu key['text_encoder_3-item5']: [Dict] with 1 keys key['t5_prompt_embeds']: [Tensor] shape=torch.Size([77, 4096]), dtype=torch.float16, device=cpu key['vae.decoder']: [Tensor] shape=torch.Size([16, 128, 128]), dtype=torch.float16, device=cpu key['vae.encoder']: [Dict] with 2 keys key['vae.encoder_f0']: [Tensor] shape=torch.Size([32, 128, 128]), dtype=torch.float16, device=cpu key['vae.encoder_f1']: [Tensor] shape=torch.Size([32, 128, 128]), dtype=torch.float16, device=cpu 💾 Converting with 6.3388 MSE: from ../datasets/FeatureCoding-StableDiffusion3.5/100samples_features/000000535578.zst to output-fixed/sd35/lambda0.004/hyperprior-featurecoding-8bit-individual/sd35cond/000000535578.zst 💪 Processing: ../datasets/FeatureCoding-StableDiffusion3.5/100samples_features/000000546325.zst (96/100) [1/4] FRONTEND: Loading features from ../datasets/FeatureCoding-StableDiffusion3.5/100samples_features/000000546325.zst... Original data structure: root: [Dict] with 8 keys key['text_encoder-item0']: [Dict] with 2 keys key['clip_pooled_prompt_embeds']: [Tensor] shape=torch.Size([768]), dtype=torch.float16, device=cpu key['clip_prompt_embeds']: [Tensor] shape=torch.Size([77, 768]), dtype=torch.float16, device=cpu key['text_encoder_2-item1']: [Dict] with 2 keys key['clip_pooled_prompt_embeds']: [Tensor] shape=torch.Size([1280]), dtype=torch.float16, device=cpu key['clip_prompt_embeds']: [Tensor] shape=torch.Size([77, 1280]), dtype=torch.float16, device=cpu key['text_encoder_3-item2']: [Dict] with 1 keys key['t5_prompt_embeds']: [Tensor] shape=torch.Size([77, 4096]), dtype=torch.float16, device=cpu key['text_encoder-item3']: [Dict] with 2 keys key['clip_pooled_prompt_embeds']: [Tensor] shape=torch.Size([768]), dtype=torch.float16, device=cpu key['clip_prompt_embeds']: [Tensor] shape=torch.Size([77, 768]), dtype=torch.float16, device=cpu key['text_encoder_2-item4']: [Dict] with 2 keys key['clip_pooled_prompt_embeds']: [Tensor] shape=torch.Size([1280]), dtype=torch.float16, device=cpu key['clip_prompt_embeds']: [Tensor] shape=torch.Size([77, 1280]), dtype=torch.float16, device=cpu key['text_encoder_3-item5']: [Dict] with 1 keys key['t5_prompt_embeds']: [Tensor] shape=torch.Size([77, 4096]), dtype=torch.float16, device=cpu key['vae.encoder-item6']: [Dict] with 2 keys key['vae.encoder_f0']: [Tensor] shape=torch.Size([32, 128, 128]), dtype=torch.float16, device=cpu key['vae.encoder_f1']: [Tensor] shape=torch.Size([32, 128, 128]), dtype=torch.float16, device=cpu key['vae.decoder']: [Tensor] shape=torch.Size([16, 128, 128]), dtype=torch.float16, device=cpu ⌛️ [1/4] FRONTEND: Load time: 0.009s ------------------------------------------------------------ SD3.5 Features Summary ------------------------------------------------------------ Number of text encoders: 3 Encoder names: ['text_encoder', 'text_encoder_2', 'text_encoder_3'] Has VAE latents: True Has VAE encoder features: True Data type: torch.float16 text_encoder-item0: clip_pooled_prompt_embeds: torch.Size([768]), torch.float16 clip_prompt_embeds: torch.Size([77, 768]), torch.float16 text_encoder_2-item1: clip_pooled_prompt_embeds: torch.Size([1280]), torch.float16 clip_prompt_embeds: torch.Size([77, 1280]), torch.float16 text_encoder_3-item2: t5_prompt_embeds: torch.Size([77, 4096]), torch.float16 text_encoder-item3: clip_pooled_prompt_embeds: torch.Size([768]), torch.float16 clip_prompt_embeds: torch.Size([77, 768]), torch.float16 text_encoder_2-item4: clip_pooled_prompt_embeds: torch.Size([1280]), torch.float16 clip_prompt_embeds: torch.Size([77, 1280]), torch.float16 text_encoder_3-item5: t5_prompt_embeds: torch.Size([77, 4096]), torch.float16 vae.decoder latents: torch.Size([16, 128, 128]) vae.encoder features: vae.encoder_f0: torch.Size([32, 128, 128]), torch.float16 vae.encoder_f1: torch.Size([32, 128, 128]), torch.float16 ------------------------------------------------------------ [2/4] FRONTEND: Pack + Encode (strategy: individual)... IndividualPacker: text_encoder-item0.clip_pooled_prompt_embeds: torch.Size([768])->torch.Size([1, 1, 1, 768]) text_encoder-item0.clip_prompt_embeds: torch.Size([77, 768])->torch.Size([1, 1, 77, 768]) text_encoder_2-item1.clip_pooled_prompt_embeds: torch.Size([1280])->torch.Size([1, 1, 1, 1280]) text_encoder_2-item1.clip_prompt_embeds: torch.Size([77, 1280])->torch.Size([1, 1, 77, 1280]) text_encoder_3-item2.t5_prompt_embeds: torch.Size([77, 4096])->torch.Size([1, 1, 77, 4096]) text_encoder-item3.clip_pooled_prompt_embeds: torch.Size([768])->torch.Size([1, 1, 1, 768]) text_encoder-item3.clip_prompt_embeds: torch.Size([77, 768])->torch.Size([1, 1, 77, 768]) text_encoder_2-item4.clip_pooled_prompt_embeds: torch.Size([1280])->torch.Size([1, 1, 1, 1280]) text_encoder_2-item4.clip_prompt_embeds: torch.Size([77, 1280])->torch.Size([1, 1, 77, 1280]) text_encoder_3-item5.t5_prompt_embeds: torch.Size([77, 4096])->torch.Size([1, 1, 77, 4096]) vae.encoder_f0: torch.Size([32, 128, 128])->torch.Size([1, 1, 512, 1024]) vae.encoder_f1: torch.Size([32, 128, 128])->torch.Size([1, 1, 512, 1024]) vae.decoder: torch.Size([16, 128, 128])->torch.Size([1, 1, 512, 512]) Using per-key quantization points (text_encoder-item0.clip_pooled_prompt_embeds: torch.Size([256])) for text_encoder-item0.clip_pooled_prompt_embeds text_encoder-item0.clip_pooled_prompt_embeds: 736B, BPFP=7.6667 Using per-key quantization points (text_encoder-item0.clip_prompt_embeds: torch.Size([256])) for text_encoder-item0.clip_prompt_embeds text_encoder-item0.clip_prompt_embeds: 6,212B, BPFP=0.8404 Using per-key quantization points (text_encoder_2-item1.clip_pooled_prompt_embeds: torch.Size([256])) for text_encoder_2-item1.clip_pooled_prompt_embeds text_encoder_2-item1.clip_pooled_prompt_embeds: 1,200B, BPFP=7.5000 Using per-key quantization points (text_encoder_2-item1.clip_prompt_embeds: torch.Size([256])) for text_encoder_2-item1.clip_prompt_embeds text_encoder_2-item1.clip_prompt_embeds: 11,524B, BPFP=0.9354 Using per-key quantization points (text_encoder_3-item2.t5_prompt_embeds: torch.Size([256])) for text_encoder_3-item2.t5_prompt_embeds text_encoder_3-item2.t5_prompt_embeds: 26,148B, BPFP=0.6633 Using per-key quantization points (text_encoder-item3.clip_pooled_prompt_embeds: torch.Size([256])) for text_encoder-item3.clip_pooled_prompt_embeds text_encoder-item3.clip_pooled_prompt_embeds: 760B, BPFP=7.9167 Using per-key quantization points (text_encoder-item3.clip_prompt_embeds: torch.Size([256])) for text_encoder-item3.clip_prompt_embeds text_encoder-item3.clip_prompt_embeds: 4,024B, BPFP=0.5444 Using per-key quantization points (text_encoder_2-item4.clip_pooled: torch.Size([256])) for text_encoder_2-item4.clip_pooled_prompt_embeds text_encoder_2-item4.clip_pooled_prompt_embeds: 1,268B, BPFP=7.9250 Using per-key quantization points (text_encoder_2-item4.clip_prompt: torch.Size([256])) for text_encoder_2-item4.clip_prompt_embeds text_encoder_2-item4.clip_prompt_embeds: 6,996B, BPFP=0.5679 Using per-key quantization points (text_encoder_3-item5.t5_prompt_embeds: torch.Size([256])) for text_encoder_3-item5.t5_prompt_embeds text_encoder_3-item5.t5_prompt_embeds: 18,416B, BPFP=0.4671 Using per-key quantization points (vae.encoder_f0: torch.Size([256])) for vae.encoder_f0 vae.encoder_f0: 15,860B, BPFP=0.2420 Using per-key quantization points (vae.encoder_f1: torch.Size([256])) for vae.encoder_f1 vae.encoder_f1: 15,860B, BPFP=0.2420 Using per-key quantization points (vae.decoder: torch.Size([256])) for vae.decoder vae.decoder: 5,436B, BPFP=0.1659 ⌛️ [2/4] FRONTEND: Frontend time: 0.294s (Pack+Encode) [3/4] BACKEND: Decode + Unpack... Using per-key quantization points (text_encoder-item0.clip_pooled_prompt_embeds: torch.Size([256])) for text_encoder-item0.clip_pooled_prompt_embeds Using per-key quantization points (text_encoder-item0.clip_prompt_embeds: torch.Size([256])) for text_encoder-item0.clip_prompt_embeds Using per-key quantization points (text_encoder_2-item1.clip_pooled_prompt_embeds: torch.Size([256])) for text_encoder_2-item1.clip_pooled_prompt_embeds Using per-key quantization points (text_encoder_2-item1.clip_prompt_embeds: torch.Size([256])) for text_encoder_2-item1.clip_prompt_embeds Using per-key quantization points (text_encoder_3-item2.t5_prompt_embeds: torch.Size([256])) for text_encoder_3-item2.t5_prompt_embeds Using per-key quantization points (text_encoder-item3.clip_pooled_prompt_embeds: torch.Size([256])) for text_encoder-item3.clip_pooled_prompt_embeds Using per-key quantization points (text_encoder-item3.clip_prompt_embeds: torch.Size([256])) for text_encoder-item3.clip_prompt_embeds Using per-key quantization points (text_encoder_2-item4.clip_pooled: torch.Size([256])) for text_encoder_2-item4.clip_pooled_prompt_embeds Using per-key quantization points (text_encoder_2-item4.clip_prompt: torch.Size([256])) for text_encoder_2-item4.clip_prompt_embeds Using per-key quantization points (text_encoder_3-item5.t5_prompt_embeds: torch.Size([256])) for text_encoder_3-item5.t5_prompt_embeds Using per-key quantization points (vae.encoder_f0: torch.Size([256])) for vae.encoder_f0 Using per-key quantization points (vae.encoder_f1: torch.Size([256])) for vae.encoder_f1 Using per-key quantization points (vae.decoder: torch.Size([256])) for vae.decoder IndividualUnPacker: text_encoder-item0.clip_pooled_prompt_embeds: torch.Size([768]) text_encoder-item0.clip_prompt_embeds: torch.Size([77, 768]) text_encoder_2-item1.clip_pooled_prompt_embeds: torch.Size([1280]) text_encoder_2-item1.clip_prompt_embeds: torch.Size([77, 1280]) text_encoder_3-item2.t5_prompt_embeds: torch.Size([77, 4096]) text_encoder-item3.clip_pooled_prompt_embeds: torch.Size([768]) text_encoder-item3.clip_prompt_embeds: torch.Size([77, 768]) text_encoder_2-item4.clip_pooled_prompt_embeds: torch.Size([1280]) text_encoder_2-item4.clip_prompt_embeds: torch.Size([77, 1280]) text_encoder_3-item5.t5_prompt_embeds: torch.Size([77, 4096]) vae.decoder: torch.Size([16, 128, 128]) vae.encoder_f0: torch.Size([32, 128, 128]) vae.encoder_f1: torch.Size([32, 128, 128]) ⌛️ [3/4] BACKEND: Backend time: 0.452s [4/4] METRICS: Computing MSE Breakdown... Using per-key quantization points (text_encoder-item0.clip_pooled_prompt_embeds: torch.Size([256])) for text_encoder-item0.clip_pooled_prompt_embeds Using per-key quantization points (text_encoder-item0.clip_prompt_embeds: torch.Size([256])) for text_encoder-item0.clip_prompt_embeds Using per-key quantization points (text_encoder_2-item1.clip_pooled_prompt_embeds: torch.Size([256])) for text_encoder_2-item1.clip_pooled_prompt_embeds Using per-key quantization points (text_encoder_2-item1.clip_prompt_embeds: torch.Size([256])) for text_encoder_2-item1.clip_prompt_embeds Using per-key quantization points (text_encoder_3-item2.t5_prompt_embeds: torch.Size([256])) for text_encoder_3-item2.t5_prompt_embeds Using per-key quantization points (text_encoder-item3.clip_pooled_prompt_embeds: torch.Size([256])) for text_encoder-item3.clip_pooled_prompt_embeds Using per-key quantization points (text_encoder-item3.clip_prompt_embeds: torch.Size([256])) for text_encoder-item3.clip_prompt_embeds Using per-key quantization points (text_encoder_2-item4.clip_pooled: torch.Size([256])) for text_encoder_2-item4.clip_pooled_prompt_embeds Using per-key quantization points (text_encoder_2-item4.clip_prompt: torch.Size([256])) for text_encoder_2-item4.clip_prompt_embeds Using per-key quantization points (text_encoder_3-item5.t5_prompt_embeds: torch.Size([256])) for text_encoder_3-item5.t5_prompt_embeds Using per-key quantization points (vae.encoder_f0: torch.Size([256])) for vae.encoder_f0 Using per-key quantization points (vae.encoder_f1: torch.Size([256])) for vae.encoder_f1 Using per-key quantization points (vae.decoder: torch.Size([256])) for vae.decoder IndividualUnPacker: text_encoder-item0.clip_pooled_prompt_embeds: torch.Size([768]) text_encoder-item0.clip_prompt_embeds: torch.Size([77, 768]) text_encoder_2-item1.clip_pooled_prompt_embeds: torch.Size([1280]) text_encoder_2-item1.clip_prompt_embeds: torch.Size([77, 1280]) text_encoder_3-item2.t5_prompt_embeds: torch.Size([77, 4096]) text_encoder-item3.clip_pooled_prompt_embeds: torch.Size([768]) text_encoder-item3.clip_prompt_embeds: torch.Size([77, 768]) text_encoder_2-item4.clip_pooled_prompt_embeds: torch.Size([1280]) text_encoder_2-item4.clip_prompt_embeds: torch.Size([77, 1280]) text_encoder_3-item5.t5_prompt_embeds: torch.Size([77, 4096]) vae.decoder: torch.Size([16, 128, 128]) vae.encoder_f0: torch.Size([32, 128, 128]) vae.encoder_f1: torch.Size([32, 128, 128]) Per-key MSE Breakdown (Quant=Truncation+Quantization, Codec=Compression): Key Quant-MSE Total-MSE ------------------------------------------------------------------------------------- text_encoder-item0.clip_pooled_prompt_embeds 0.00038620 0.92038218 text_encoder-item0.clip_prompt_embeds 0.00030118 108.40493101 text_encoder_2-item1.clip_pooled_prompt_embeds 0.00020381 0.82953463 text_encoder_2-item1.clip_prompt_embeds 0.00019649 0.15629459 text_encoder_3-item2.t5_prompt_embeds 0.00000770 0.00290913 text_encoder-item3.clip_pooled_prompt_embeds 0.00841799 1.01420029 text_encoder-item3.clip_prompt_embeds 0.00023247 23.90570887 text_encoder_2-item4.clip_pooled_prompt_embeds 0.00016751 0.67051430 text_encoder_2-item4.clip_prompt_embeds 0.00009830 0.47049568 text_encoder_3-item5.t5_prompt_embeds 0.00001263 0.00227117 vae.encoder_f0 0.03869025 3.87183452 vae.encoder_f1 0.03869358 3.87184238 vae.decoder 0.00021614 0.04693230 ------------------------------------------------------------------------------------- TOTAL 0.01800198 5.29119873 (elements=2,260,992) ---------------------- -------------------------------------------------------- SAMPLE-WISE STATISTICS ---------------------- -------------------------------------------------------- Handler sd35 Strategy individual Architecture hyperprior-featurecoding ---------------------- -------------------------------------------------------- Total Elements 2260992 Total Bytes 114440 BPFP 0.4049 bits/point EBPFP 0.8098 equivalent bits/point MSE 5.291199 ---------------------- -------------------------------------------------------- Time: 0.754s Load: 0.009s, Pack+Encode: 0.294s, Decode+Unpack: 0.452s ---------------------- -------------------------------------------------------- Restored Feature Format: [Dict] with 8 keys key['text_encoder-item0']: [Dict] with 2 keys key['clip_pooled_prompt_embeds']: [Tensor] shape=torch.Size([768]), dtype=torch.float16, device=cpu key['clip_prompt_embeds']: [Tensor] shape=torch.Size([77, 768]), dtype=torch.float16, device=cpu key['text_encoder_2-item1']: [Dict] with 2 keys key['clip_pooled_prompt_embeds']: [Tensor] shape=torch.Size([1280]), dtype=torch.float16, device=cpu key['clip_prompt_embeds']: [Tensor] shape=torch.Size([77, 1280]), dtype=torch.float16, device=cpu key['text_encoder_3-item2']: [Dict] with 1 keys key['t5_prompt_embeds']: [Tensor] shape=torch.Size([77, 4096]), dtype=torch.float16, device=cpu key['text_encoder-item3']: [Dict] with 2 keys key['clip_pooled_prompt_embeds']: [Tensor] shape=torch.Size([768]), dtype=torch.float16, device=cpu key['clip_prompt_embeds']: [Tensor] shape=torch.Size([77, 768]), dtype=torch.float16, device=cpu key['text_encoder_2-item4']: [Dict] with 2 keys key['clip_pooled_prompt_embeds']: [Tensor] shape=torch.Size([1280]), dtype=torch.float16, device=cpu key['clip_prompt_embeds']: [Tensor] shape=torch.Size([77, 1280]), dtype=torch.float16, device=cpu key['text_encoder_3-item5']: [Dict] with 1 keys key['t5_prompt_embeds']: [Tensor] shape=torch.Size([77, 4096]), dtype=torch.float16, device=cpu key['vae.decoder']: [Tensor] shape=torch.Size([16, 128, 128]), dtype=torch.float16, device=cpu key['vae.encoder']: [Dict] with 2 keys key['vae.encoder_f0']: [Tensor] shape=torch.Size([32, 128, 128]), dtype=torch.float16, device=cpu key['vae.encoder_f1']: [Tensor] shape=torch.Size([32, 128, 128]), dtype=torch.float16, device=cpu 💾 Converting with 5.2912 MSE: from ../datasets/FeatureCoding-StableDiffusion3.5/100samples_features/000000546325.zst to output-fixed/sd35/lambda0.004/hyperprior-featurecoding-8bit-individual/sd35cond/000000546325.zst 💪 Processing: ../datasets/FeatureCoding-StableDiffusion3.5/100samples_features/000000551780.zst (97/100) [1/4] FRONTEND: Loading features from ../datasets/FeatureCoding-StableDiffusion3.5/100samples_features/000000551780.zst... Original data structure: root: [Dict] with 8 keys key['text_encoder-item0']: [Dict] with 2 keys key['clip_pooled_prompt_embeds']: [Tensor] shape=torch.Size([768]), dtype=torch.float16, device=cpu key['clip_prompt_embeds']: [Tensor] shape=torch.Size([77, 768]), dtype=torch.float16, device=cpu key['text_encoder_2-item1']: [Dict] with 2 keys key['clip_pooled_prompt_embeds']: [Tensor] shape=torch.Size([1280]), dtype=torch.float16, device=cpu key['clip_prompt_embeds']: [Tensor] shape=torch.Size([77, 1280]), dtype=torch.float16, device=cpu key['text_encoder_3-item2']: [Dict] with 1 keys key['t5_prompt_embeds']: [Tensor] shape=torch.Size([77, 4096]), dtype=torch.float16, device=cpu key['text_encoder-item3']: [Dict] with 2 keys key['clip_pooled_prompt_embeds']: [Tensor] shape=torch.Size([768]), dtype=torch.float16, device=cpu key['clip_prompt_embeds']: [Tensor] shape=torch.Size([77, 768]), dtype=torch.float16, device=cpu key['text_encoder_2-item4']: [Dict] with 2 keys key['clip_pooled_prompt_embeds']: [Tensor] shape=torch.Size([1280]), dtype=torch.float16, device=cpu key['clip_prompt_embeds']: [Tensor] shape=torch.Size([77, 1280]), dtype=torch.float16, device=cpu key['text_encoder_3-item5']: [Dict] with 1 keys key['t5_prompt_embeds']: [Tensor] shape=torch.Size([77, 4096]), dtype=torch.float16, device=cpu key['vae.encoder-item6']: [Dict] with 2 keys key['vae.encoder_f0']: [Tensor] shape=torch.Size([32, 128, 128]), dtype=torch.float16, device=cpu key['vae.encoder_f1']: [Tensor] shape=torch.Size([32, 128, 128]), dtype=torch.float16, device=cpu key['vae.decoder']: [Tensor] shape=torch.Size([16, 128, 128]), dtype=torch.float16, device=cpu ⌛️ [1/4] FRONTEND: Load time: 0.009s ------------------------------------------------------------ SD3.5 Features Summary ------------------------------------------------------------ Number of text encoders: 3 Encoder names: ['text_encoder', 'text_encoder_2', 'text_encoder_3'] Has VAE latents: True Has VAE encoder features: True Data type: torch.float16 text_encoder-item0: clip_pooled_prompt_embeds: torch.Size([768]), torch.float16 clip_prompt_embeds: torch.Size([77, 768]), torch.float16 text_encoder_2-item1: clip_pooled_prompt_embeds: torch.Size([1280]), torch.float16 clip_prompt_embeds: torch.Size([77, 1280]), torch.float16 text_encoder_3-item2: t5_prompt_embeds: torch.Size([77, 4096]), torch.float16 text_encoder-item3: clip_pooled_prompt_embeds: torch.Size([768]), torch.float16 clip_prompt_embeds: torch.Size([77, 768]), torch.float16 text_encoder_2-item4: clip_pooled_prompt_embeds: torch.Size([1280]), torch.float16 clip_prompt_embeds: torch.Size([77, 1280]), torch.float16 text_encoder_3-item5: t5_prompt_embeds: torch.Size([77, 4096]), torch.float16 vae.decoder latents: torch.Size([16, 128, 128]) vae.encoder features: vae.encoder_f0: torch.Size([32, 128, 128]), torch.float16 vae.encoder_f1: torch.Size([32, 128, 128]), torch.float16 ------------------------------------------------------------ [2/4] FRONTEND: Pack + Encode (strategy: individual)... IndividualPacker: text_encoder-item0.clip_pooled_prompt_embeds: torch.Size([768])->torch.Size([1, 1, 1, 768]) text_encoder-item0.clip_prompt_embeds: torch.Size([77, 768])->torch.Size([1, 1, 77, 768]) text_encoder_2-item1.clip_pooled_prompt_embeds: torch.Size([1280])->torch.Size([1, 1, 1, 1280]) text_encoder_2-item1.clip_prompt_embeds: torch.Size([77, 1280])->torch.Size([1, 1, 77, 1280]) text_encoder_3-item2.t5_prompt_embeds: torch.Size([77, 4096])->torch.Size([1, 1, 77, 4096]) text_encoder-item3.clip_pooled_prompt_embeds: torch.Size([768])->torch.Size([1, 1, 1, 768]) text_encoder-item3.clip_prompt_embeds: torch.Size([77, 768])->torch.Size([1, 1, 77, 768]) text_encoder_2-item4.clip_pooled_prompt_embeds: torch.Size([1280])->torch.Size([1, 1, 1, 1280]) text_encoder_2-item4.clip_prompt_embeds: torch.Size([77, 1280])->torch.Size([1, 1, 77, 1280]) text_encoder_3-item5.t5_prompt_embeds: torch.Size([77, 4096])->torch.Size([1, 1, 77, 4096]) vae.encoder_f0: torch.Size([32, 128, 128])->torch.Size([1, 1, 512, 1024]) vae.encoder_f1: torch.Size([32, 128, 128])->torch.Size([1, 1, 512, 1024]) vae.decoder: torch.Size([16, 128, 128])->torch.Size([1, 1, 512, 512]) Using per-key quantization points (text_encoder-item0.clip_pooled_prompt_embeds: torch.Size([256])) for text_encoder-item0.clip_pooled_prompt_embeds text_encoder-item0.clip_pooled_prompt_embeds: 736B, BPFP=7.6667 Using per-key quantization points (text_encoder-item0.clip_prompt_embeds: torch.Size([256])) for text_encoder-item0.clip_prompt_embeds text_encoder-item0.clip_prompt_embeds: 5,460B, BPFP=0.7386 Using per-key quantization points (text_encoder_2-item1.clip_pooled_prompt_embeds: torch.Size([256])) for text_encoder_2-item1.clip_pooled_prompt_embeds text_encoder_2-item1.clip_pooled_prompt_embeds: 1,200B, BPFP=7.5000 Using per-key quantization points (text_encoder_2-item1.clip_prompt_embeds: torch.Size([256])) for text_encoder_2-item1.clip_prompt_embeds text_encoder_2-item1.clip_prompt_embeds: 10,624B, BPFP=0.8623 Using per-key quantization points (text_encoder_3-item2.t5_prompt_embeds: torch.Size([256])) for text_encoder_3-item2.t5_prompt_embeds text_encoder_3-item2.t5_prompt_embeds: 25,352B, BPFP=0.6431 Using per-key quantization points (text_encoder-item3.clip_pooled_prompt_embeds: torch.Size([256])) for text_encoder-item3.clip_pooled_prompt_embeds text_encoder-item3.clip_pooled_prompt_embeds: 760B, BPFP=7.9167 Using per-key quantization points (text_encoder-item3.clip_prompt_embeds: torch.Size([256])) for text_encoder-item3.clip_prompt_embeds text_encoder-item3.clip_prompt_embeds: 4,024B, BPFP=0.5444 Using per-key quantization points (text_encoder_2-item4.clip_pooled: torch.Size([256])) for text_encoder_2-item4.clip_pooled_prompt_embeds text_encoder_2-item4.clip_pooled_prompt_embeds: 1,268B, BPFP=7.9250 Using per-key quantization points (text_encoder_2-item4.clip_prompt: torch.Size([256])) for text_encoder_2-item4.clip_prompt_embeds text_encoder_2-item4.clip_prompt_embeds: 6,996B, BPFP=0.5679 Using per-key quantization points (text_encoder_3-item5.t5_prompt_embeds: torch.Size([256])) for text_encoder_3-item5.t5_prompt_embeds text_encoder_3-item5.t5_prompt_embeds: 18,416B, BPFP=0.4671 Using per-key quantization points (vae.encoder_f0: torch.Size([256])) for vae.encoder_f0 vae.encoder_f0: 22,816B, BPFP=0.3481 Using per-key quantization points (vae.encoder_f1: torch.Size([256])) for vae.encoder_f1 vae.encoder_f1: 22,812B, BPFP=0.3481 Using per-key quantization points (vae.decoder: torch.Size([256])) for vae.decoder vae.decoder: 5,024B, BPFP=0.1533 ⌛️ [2/4] FRONTEND: Frontend time: 0.296s (Pack+Encode) [3/4] BACKEND: Decode + Unpack... Using per-key quantization points (text_encoder-item0.clip_pooled_prompt_embeds: torch.Size([256])) for text_encoder-item0.clip_pooled_prompt_embeds Using per-key quantization points (text_encoder-item0.clip_prompt_embeds: torch.Size([256])) for text_encoder-item0.clip_prompt_embeds Using per-key quantization points (text_encoder_2-item1.clip_pooled_prompt_embeds: torch.Size([256])) for text_encoder_2-item1.clip_pooled_prompt_embeds Using per-key quantization points (text_encoder_2-item1.clip_prompt_embeds: torch.Size([256])) for text_encoder_2-item1.clip_prompt_embeds Using per-key quantization points (text_encoder_3-item2.t5_prompt_embeds: torch.Size([256])) for text_encoder_3-item2.t5_prompt_embeds Using per-key quantization points (text_encoder-item3.clip_pooled_prompt_embeds: torch.Size([256])) for text_encoder-item3.clip_pooled_prompt_embeds Using per-key quantization points (text_encoder-item3.clip_prompt_embeds: torch.Size([256])) for text_encoder-item3.clip_prompt_embeds Using per-key quantization points (text_encoder_2-item4.clip_pooled: torch.Size([256])) for text_encoder_2-item4.clip_pooled_prompt_embeds Using per-key quantization points (text_encoder_2-item4.clip_prompt: torch.Size([256])) for text_encoder_2-item4.clip_prompt_embeds Using per-key quantization points (text_encoder_3-item5.t5_prompt_embeds: torch.Size([256])) for text_encoder_3-item5.t5_prompt_embeds Using per-key quantization points (vae.encoder_f0: torch.Size([256])) for vae.encoder_f0 Using per-key quantization points (vae.encoder_f1: torch.Size([256])) for vae.encoder_f1 Using per-key quantization points (vae.decoder: torch.Size([256])) for vae.decoder IndividualUnPacker: text_encoder-item0.clip_pooled_prompt_embeds: torch.Size([768]) text_encoder-item0.clip_prompt_embeds: torch.Size([77, 768]) text_encoder_2-item1.clip_pooled_prompt_embeds: torch.Size([1280]) text_encoder_2-item1.clip_prompt_embeds: torch.Size([77, 1280]) text_encoder_3-item2.t5_prompt_embeds: torch.Size([77, 4096]) text_encoder-item3.clip_pooled_prompt_embeds: torch.Size([768]) text_encoder-item3.clip_prompt_embeds: torch.Size([77, 768]) text_encoder_2-item4.clip_pooled_prompt_embeds: torch.Size([1280]) text_encoder_2-item4.clip_prompt_embeds: torch.Size([77, 1280]) text_encoder_3-item5.t5_prompt_embeds: torch.Size([77, 4096]) vae.decoder: torch.Size([16, 128, 128]) vae.encoder_f0: torch.Size([32, 128, 128]) vae.encoder_f1: torch.Size([32, 128, 128]) ⌛️ [3/4] BACKEND: Backend time: 0.447s [4/4] METRICS: Computing MSE Breakdown... Using per-key quantization points (text_encoder-item0.clip_pooled_prompt_embeds: torch.Size([256])) for text_encoder-item0.clip_pooled_prompt_embeds Using per-key quantization points (text_encoder-item0.clip_prompt_embeds: torch.Size([256])) for text_encoder-item0.clip_prompt_embeds Using per-key quantization points (text_encoder_2-item1.clip_pooled_prompt_embeds: torch.Size([256])) for text_encoder_2-item1.clip_pooled_prompt_embeds Using per-key quantization points (text_encoder_2-item1.clip_prompt_embeds: torch.Size([256])) for text_encoder_2-item1.clip_prompt_embeds Using per-key quantization points (text_encoder_3-item2.t5_prompt_embeds: torch.Size([256])) for text_encoder_3-item2.t5_prompt_embeds Using per-key quantization points (text_encoder-item3.clip_pooled_prompt_embeds: torch.Size([256])) for text_encoder-item3.clip_pooled_prompt_embeds Using per-key quantization points (text_encoder-item3.clip_prompt_embeds: torch.Size([256])) for text_encoder-item3.clip_prompt_embeds Using per-key quantization points (text_encoder_2-item4.clip_pooled: torch.Size([256])) for text_encoder_2-item4.clip_pooled_prompt_embeds Using per-key quantization points (text_encoder_2-item4.clip_prompt: torch.Size([256])) for text_encoder_2-item4.clip_prompt_embeds Using per-key quantization points (text_encoder_3-item5.t5_prompt_embeds: torch.Size([256])) for text_encoder_3-item5.t5_prompt_embeds Using per-key quantization points (vae.encoder_f0: torch.Size([256])) for vae.encoder_f0 Using per-key quantization points (vae.encoder_f1: torch.Size([256])) for vae.encoder_f1 Using per-key quantization points (vae.decoder: torch.Size([256])) for vae.decoder IndividualUnPacker: text_encoder-item0.clip_pooled_prompt_embeds: torch.Size([768]) text_encoder-item0.clip_prompt_embeds: torch.Size([77, 768]) text_encoder_2-item1.clip_pooled_prompt_embeds: torch.Size([1280]) text_encoder_2-item1.clip_prompt_embeds: torch.Size([77, 1280]) text_encoder_3-item2.t5_prompt_embeds: torch.Size([77, 4096]) text_encoder-item3.clip_pooled_prompt_embeds: torch.Size([768]) text_encoder-item3.clip_prompt_embeds: torch.Size([77, 768]) text_encoder_2-item4.clip_pooled_prompt_embeds: torch.Size([1280]) text_encoder_2-item4.clip_prompt_embeds: torch.Size([77, 1280]) text_encoder_3-item5.t5_prompt_embeds: torch.Size([77, 4096]) vae.decoder: torch.Size([16, 128, 128]) vae.encoder_f0: torch.Size([32, 128, 128]) vae.encoder_f1: torch.Size([32, 128, 128]) Per-key MSE Breakdown (Quant=Truncation+Quantization, Codec=Compression): Key Quant-MSE Total-MSE ------------------------------------------------------------------------------------- text_encoder-item0.clip_pooled_prompt_embeds 0.00084877 0.95306961 text_encoder-item0.clip_prompt_embeds 0.00023260 23.94839227 text_encoder_2-item1.clip_pooled_prompt_embeds 0.00026409 0.97482805 text_encoder_2-item1.clip_prompt_embeds 0.00016683 0.12089771 text_encoder_3-item2.t5_prompt_embeds 0.00000828 0.00319252 text_encoder-item3.clip_pooled_prompt_embeds 0.00841799 1.01420029 text_encoder-item3.clip_prompt_embeds 0.00023247 23.90570887 text_encoder_2-item4.clip_pooled_prompt_embeds 0.00016751 0.67051430 text_encoder_2-item4.clip_prompt_embeds 0.00009830 0.47049568 text_encoder_3-item5.t5_prompt_embeds 0.00001263 0.00227117 vae.encoder_f0 0.00839879 3.81455112 vae.encoder_f1 0.00840224 3.81420183 vae.decoder 0.00019463 0.04420110 ------------------------------------------------------------------------------------- TOTAL 0.00394849 3.05387133 (elements=2,260,992) ---------------------- -------------------------------------------------------- SAMPLE-WISE STATISTICS ---------------------- -------------------------------------------------------- Handler sd35 Strategy individual Architecture hyperprior-featurecoding ---------------------- -------------------------------------------------------- Total Elements 2260992 Total Bytes 125488 BPFP 0.4440 bits/point EBPFP 0.8880 equivalent bits/point MSE 3.053871 ---------------------- -------------------------------------------------------- Time: 0.751s Load: 0.009s, Pack+Encode: 0.296s, Decode+Unpack: 0.447s ---------------------- -------------------------------------------------------- Restored Feature Format: [Dict] with 8 keys key['text_encoder-item0']: [Dict] with 2 keys key['clip_pooled_prompt_embeds']: [Tensor] shape=torch.Size([768]), dtype=torch.float16, device=cpu key['clip_prompt_embeds']: [Tensor] shape=torch.Size([77, 768]), dtype=torch.float16, device=cpu key['text_encoder_2-item1']: [Dict] with 2 keys key['clip_pooled_prompt_embeds']: [Tensor] shape=torch.Size([1280]), dtype=torch.float16, device=cpu key['clip_prompt_embeds']: [Tensor] shape=torch.Size([77, 1280]), dtype=torch.float16, device=cpu key['text_encoder_3-item2']: [Dict] with 1 keys key['t5_prompt_embeds']: [Tensor] shape=torch.Size([77, 4096]), dtype=torch.float16, device=cpu key['text_encoder-item3']: [Dict] with 2 keys key['clip_pooled_prompt_embeds']: [Tensor] shape=torch.Size([768]), dtype=torch.float16, device=cpu key['clip_prompt_embeds']: [Tensor] shape=torch.Size([77, 768]), dtype=torch.float16, device=cpu key['text_encoder_2-item4']: [Dict] with 2 keys key['clip_pooled_prompt_embeds']: [Tensor] shape=torch.Size([1280]), dtype=torch.float16, device=cpu key['clip_prompt_embeds']: [Tensor] shape=torch.Size([77, 1280]), dtype=torch.float16, device=cpu key['text_encoder_3-item5']: [Dict] with 1 keys key['t5_prompt_embeds']: [Tensor] shape=torch.Size([77, 4096]), dtype=torch.float16, device=cpu key['vae.decoder']: [Tensor] shape=torch.Size([16, 128, 128]), dtype=torch.float16, device=cpu key['vae.encoder']: [Dict] with 2 keys key['vae.encoder_f0']: [Tensor] shape=torch.Size([32, 128, 128]), dtype=torch.float16, device=cpu key['vae.encoder_f1']: [Tensor] shape=torch.Size([32, 128, 128]), dtype=torch.float16, device=cpu 💾 Converting with 3.0539 MSE: from ../datasets/FeatureCoding-StableDiffusion3.5/100samples_features/000000551780.zst to output-fixed/sd35/lambda0.004/hyperprior-featurecoding-8bit-individual/sd35cond/000000551780.zst 💪 Processing: ../datasets/FeatureCoding-StableDiffusion3.5/100samples_features/000000555009.zst (98/100) [1/4] FRONTEND: Loading features from ../datasets/FeatureCoding-StableDiffusion3.5/100samples_features/000000555009.zst... Original data structure: root: [Dict] with 8 keys key['text_encoder-item0']: [Dict] with 2 keys key['clip_pooled_prompt_embeds']: [Tensor] shape=torch.Size([768]), dtype=torch.float16, device=cpu key['clip_prompt_embeds']: [Tensor] shape=torch.Size([77, 768]), dtype=torch.float16, device=cpu key['text_encoder_2-item1']: [Dict] with 2 keys key['clip_pooled_prompt_embeds']: [Tensor] shape=torch.Size([1280]), dtype=torch.float16, device=cpu key['clip_prompt_embeds']: [Tensor] shape=torch.Size([77, 1280]), dtype=torch.float16, device=cpu key['text_encoder_3-item2']: [Dict] with 1 keys key['t5_prompt_embeds']: [Tensor] shape=torch.Size([77, 4096]), dtype=torch.float16, device=cpu key['text_encoder-item3']: [Dict] with 2 keys key['clip_pooled_prompt_embeds']: [Tensor] shape=torch.Size([768]), dtype=torch.float16, device=cpu key['clip_prompt_embeds']: [Tensor] shape=torch.Size([77, 768]), dtype=torch.float16, device=cpu key['text_encoder_2-item4']: [Dict] with 2 keys key['clip_pooled_prompt_embeds']: [Tensor] shape=torch.Size([1280]), dtype=torch.float16, device=cpu key['clip_prompt_embeds']: [Tensor] shape=torch.Size([77, 1280]), dtype=torch.float16, device=cpu key['text_encoder_3-item5']: [Dict] with 1 keys key['t5_prompt_embeds']: [Tensor] shape=torch.Size([77, 4096]), dtype=torch.float16, device=cpu key['vae.encoder-item6']: [Dict] with 2 keys key['vae.encoder_f0']: [Tensor] shape=torch.Size([32, 128, 128]), dtype=torch.float16, device=cpu key['vae.encoder_f1']: [Tensor] shape=torch.Size([32, 128, 128]), dtype=torch.float16, device=cpu key['vae.decoder']: [Tensor] shape=torch.Size([16, 128, 128]), dtype=torch.float16, device=cpu ⌛️ [1/4] FRONTEND: Load time: 0.009s ------------------------------------------------------------ SD3.5 Features Summary ------------------------------------------------------------ Number of text encoders: 3 Encoder names: ['text_encoder', 'text_encoder_2', 'text_encoder_3'] Has VAE latents: True Has VAE encoder features: True Data type: torch.float16 text_encoder-item0: clip_pooled_prompt_embeds: torch.Size([768]), torch.float16 clip_prompt_embeds: torch.Size([77, 768]), torch.float16 text_encoder_2-item1: clip_pooled_prompt_embeds: torch.Size([1280]), torch.float16 clip_prompt_embeds: torch.Size([77, 1280]), torch.float16 text_encoder_3-item2: t5_prompt_embeds: torch.Size([77, 4096]), torch.float16 text_encoder-item3: clip_pooled_prompt_embeds: torch.Size([768]), torch.float16 clip_prompt_embeds: torch.Size([77, 768]), torch.float16 text_encoder_2-item4: clip_pooled_prompt_embeds: torch.Size([1280]), torch.float16 clip_prompt_embeds: torch.Size([77, 1280]), torch.float16 text_encoder_3-item5: t5_prompt_embeds: torch.Size([77, 4096]), torch.float16 vae.decoder latents: torch.Size([16, 128, 128]) vae.encoder features: vae.encoder_f0: torch.Size([32, 128, 128]), torch.float16 vae.encoder_f1: torch.Size([32, 128, 128]), torch.float16 ------------------------------------------------------------ [2/4] FRONTEND: Pack + Encode (strategy: individual)... IndividualPacker: text_encoder-item0.clip_pooled_prompt_embeds: torch.Size([768])->torch.Size([1, 1, 1, 768]) text_encoder-item0.clip_prompt_embeds: torch.Size([77, 768])->torch.Size([1, 1, 77, 768]) text_encoder_2-item1.clip_pooled_prompt_embeds: torch.Size([1280])->torch.Size([1, 1, 1, 1280]) text_encoder_2-item1.clip_prompt_embeds: torch.Size([77, 1280])->torch.Size([1, 1, 77, 1280]) text_encoder_3-item2.t5_prompt_embeds: torch.Size([77, 4096])->torch.Size([1, 1, 77, 4096]) text_encoder-item3.clip_pooled_prompt_embeds: torch.Size([768])->torch.Size([1, 1, 1, 768]) text_encoder-item3.clip_prompt_embeds: torch.Size([77, 768])->torch.Size([1, 1, 77, 768]) text_encoder_2-item4.clip_pooled_prompt_embeds: torch.Size([1280])->torch.Size([1, 1, 1, 1280]) text_encoder_2-item4.clip_prompt_embeds: torch.Size([77, 1280])->torch.Size([1, 1, 77, 1280]) text_encoder_3-item5.t5_prompt_embeds: torch.Size([77, 4096])->torch.Size([1, 1, 77, 4096]) vae.encoder_f0: torch.Size([32, 128, 128])->torch.Size([1, 1, 512, 1024]) vae.encoder_f1: torch.Size([32, 128, 128])->torch.Size([1, 1, 512, 1024]) vae.decoder: torch.Size([16, 128, 128])->torch.Size([1, 1, 512, 512]) Using per-key quantization points (text_encoder-item0.clip_pooled_prompt_embeds: torch.Size([256])) for text_encoder-item0.clip_pooled_prompt_embeds text_encoder-item0.clip_pooled_prompt_embeds: 740B, BPFP=7.7083 Using per-key quantization points (text_encoder-item0.clip_prompt_embeds: torch.Size([256])) for text_encoder-item0.clip_prompt_embeds text_encoder-item0.clip_prompt_embeds: 7,412B, BPFP=1.0027 Using per-key quantization points (text_encoder_2-item1.clip_pooled_prompt_embeds: torch.Size([256])) for text_encoder_2-item1.clip_pooled_prompt_embeds text_encoder_2-item1.clip_pooled_prompt_embeds: 1,196B, BPFP=7.4750 Using per-key quantization points (text_encoder_2-item1.clip_prompt_embeds: torch.Size([256])) for text_encoder_2-item1.clip_prompt_embeds text_encoder_2-item1.clip_prompt_embeds: 11,240B, BPFP=0.9123 Using per-key quantization points (text_encoder_3-item2.t5_prompt_embeds: torch.Size([256])) for text_encoder_3-item2.t5_prompt_embeds text_encoder_3-item2.t5_prompt_embeds: 29,376B, BPFP=0.7451 Using per-key quantization points (text_encoder-item3.clip_pooled_prompt_embeds: torch.Size([256])) for text_encoder-item3.clip_pooled_prompt_embeds text_encoder-item3.clip_pooled_prompt_embeds: 760B, BPFP=7.9167 Using per-key quantization points (text_encoder-item3.clip_prompt_embeds: torch.Size([256])) for text_encoder-item3.clip_prompt_embeds text_encoder-item3.clip_prompt_embeds: 4,024B, BPFP=0.5444 Using per-key quantization points (text_encoder_2-item4.clip_pooled: torch.Size([256])) for text_encoder_2-item4.clip_pooled_prompt_embeds text_encoder_2-item4.clip_pooled_prompt_embeds: 1,268B, BPFP=7.9250 Using per-key quantization points (text_encoder_2-item4.clip_prompt: torch.Size([256])) for text_encoder_2-item4.clip_prompt_embeds text_encoder_2-item4.clip_prompt_embeds: 6,996B, BPFP=0.5679 Using per-key quantization points (text_encoder_3-item5.t5_prompt_embeds: torch.Size([256])) for text_encoder_3-item5.t5_prompt_embeds text_encoder_3-item5.t5_prompt_embeds: 18,416B, BPFP=0.4671 Using per-key quantization points (vae.encoder_f0: torch.Size([256])) for vae.encoder_f0 vae.encoder_f0: 19,888B, BPFP=0.3035 Using per-key quantization points (vae.encoder_f1: torch.Size([256])) for vae.encoder_f1 vae.encoder_f1: 19,884B, BPFP=0.3034 Using per-key quantization points (vae.decoder: torch.Size([256])) for vae.decoder vae.decoder: 6,012B, BPFP=0.1835 ⌛️ [2/4] FRONTEND: Frontend time: 0.292s (Pack+Encode) [3/4] BACKEND: Decode + Unpack... Using per-key quantization points (text_encoder-item0.clip_pooled_prompt_embeds: torch.Size([256])) for text_encoder-item0.clip_pooled_prompt_embeds Using per-key quantization points (text_encoder-item0.clip_prompt_embeds: torch.Size([256])) for text_encoder-item0.clip_prompt_embeds Using per-key quantization points (text_encoder_2-item1.clip_pooled_prompt_embeds: torch.Size([256])) for text_encoder_2-item1.clip_pooled_prompt_embeds Using per-key quantization points (text_encoder_2-item1.clip_prompt_embeds: torch.Size([256])) for text_encoder_2-item1.clip_prompt_embeds Using per-key quantization points (text_encoder_3-item2.t5_prompt_embeds: torch.Size([256])) for text_encoder_3-item2.t5_prompt_embeds Using per-key quantization points (text_encoder-item3.clip_pooled_prompt_embeds: torch.Size([256])) for text_encoder-item3.clip_pooled_prompt_embeds Using per-key quantization points (text_encoder-item3.clip_prompt_embeds: torch.Size([256])) for text_encoder-item3.clip_prompt_embeds Using per-key quantization points (text_encoder_2-item4.clip_pooled: torch.Size([256])) for text_encoder_2-item4.clip_pooled_prompt_embeds Using per-key quantization points (text_encoder_2-item4.clip_prompt: torch.Size([256])) for text_encoder_2-item4.clip_prompt_embeds Using per-key quantization points (text_encoder_3-item5.t5_prompt_embeds: torch.Size([256])) for text_encoder_3-item5.t5_prompt_embeds Using per-key quantization points (vae.encoder_f0: torch.Size([256])) for vae.encoder_f0 Using per-key quantization points (vae.encoder_f1: torch.Size([256])) for vae.encoder_f1 Using per-key quantization points (vae.decoder: torch.Size([256])) for vae.decoder IndividualUnPacker: text_encoder-item0.clip_pooled_prompt_embeds: torch.Size([768]) text_encoder-item0.clip_prompt_embeds: torch.Size([77, 768]) text_encoder_2-item1.clip_pooled_prompt_embeds: torch.Size([1280]) text_encoder_2-item1.clip_prompt_embeds: torch.Size([77, 1280]) text_encoder_3-item2.t5_prompt_embeds: torch.Size([77, 4096]) text_encoder-item3.clip_pooled_prompt_embeds: torch.Size([768]) text_encoder-item3.clip_prompt_embeds: torch.Size([77, 768]) text_encoder_2-item4.clip_pooled_prompt_embeds: torch.Size([1280]) text_encoder_2-item4.clip_prompt_embeds: torch.Size([77, 1280]) text_encoder_3-item5.t5_prompt_embeds: torch.Size([77, 4096]) vae.decoder: torch.Size([16, 128, 128]) vae.encoder_f0: torch.Size([32, 128, 128]) vae.encoder_f1: torch.Size([32, 128, 128]) ⌛️ [3/4] BACKEND: Backend time: 0.453s [4/4] METRICS: Computing MSE Breakdown... Using per-key quantization points (text_encoder-item0.clip_pooled_prompt_embeds: torch.Size([256])) for text_encoder-item0.clip_pooled_prompt_embeds Using per-key quantization points (text_encoder-item0.clip_prompt_embeds: torch.Size([256])) for text_encoder-item0.clip_prompt_embeds Using per-key quantization points (text_encoder_2-item1.clip_pooled_prompt_embeds: torch.Size([256])) for text_encoder_2-item1.clip_pooled_prompt_embeds Using per-key quantization points (text_encoder_2-item1.clip_prompt_embeds: torch.Size([256])) for text_encoder_2-item1.clip_prompt_embeds Using per-key quantization points (text_encoder_3-item2.t5_prompt_embeds: torch.Size([256])) for text_encoder_3-item2.t5_prompt_embeds Using per-key quantization points (text_encoder-item3.clip_pooled_prompt_embeds: torch.Size([256])) for text_encoder-item3.clip_pooled_prompt_embeds Using per-key quantization points (text_encoder-item3.clip_prompt_embeds: torch.Size([256])) for text_encoder-item3.clip_prompt_embeds Using per-key quantization points (text_encoder_2-item4.clip_pooled: torch.Size([256])) for text_encoder_2-item4.clip_pooled_prompt_embeds Using per-key quantization points (text_encoder_2-item4.clip_prompt: torch.Size([256])) for text_encoder_2-item4.clip_prompt_embeds Using per-key quantization points (text_encoder_3-item5.t5_prompt_embeds: torch.Size([256])) for text_encoder_3-item5.t5_prompt_embeds Using per-key quantization points (vae.encoder_f0: torch.Size([256])) for vae.encoder_f0 Using per-key quantization points (vae.encoder_f1: torch.Size([256])) for vae.encoder_f1 Using per-key quantization points (vae.decoder: torch.Size([256])) for vae.decoder IndividualUnPacker: text_encoder-item0.clip_pooled_prompt_embeds: torch.Size([768]) text_encoder-item0.clip_prompt_embeds: torch.Size([77, 768]) text_encoder_2-item1.clip_pooled_prompt_embeds: torch.Size([1280]) text_encoder_2-item1.clip_prompt_embeds: torch.Size([77, 1280]) text_encoder_3-item2.t5_prompt_embeds: torch.Size([77, 4096]) text_encoder-item3.clip_pooled_prompt_embeds: torch.Size([768]) text_encoder-item3.clip_prompt_embeds: torch.Size([77, 768]) text_encoder_2-item4.clip_pooled_prompt_embeds: torch.Size([1280]) text_encoder_2-item4.clip_prompt_embeds: torch.Size([77, 1280]) text_encoder_3-item5.t5_prompt_embeds: torch.Size([77, 4096]) vae.decoder: torch.Size([16, 128, 128]) vae.encoder_f0: torch.Size([32, 128, 128]) vae.encoder_f1: torch.Size([32, 128, 128]) Per-key MSE Breakdown (Quant=Truncation+Quantization, Codec=Compression): Key Quant-MSE Total-MSE ------------------------------------------------------------------------------------- text_encoder-item0.clip_pooled_prompt_embeds 0.00017723 0.93482288 text_encoder-item0.clip_prompt_embeds 0.00023544 23.93684262 text_encoder_2-item1.clip_pooled_prompt_embeds 0.00022156 0.94495420 text_encoder_2-item1.clip_prompt_embeds 0.00018986 0.15283854 text_encoder_3-item2.t5_prompt_embeds 0.00000832 0.00350402 text_encoder-item3.clip_pooled_prompt_embeds 0.00841799 1.01420029 text_encoder-item3.clip_prompt_embeds 0.00023247 23.90570887 text_encoder_2-item4.clip_pooled_prompt_embeds 0.00016751 0.67051430 text_encoder_2-item4.clip_prompt_embeds 0.00009830 0.47049568 text_encoder_3-item5.t5_prompt_embeds 0.00001263 0.00227117 vae.encoder_f0 0.01160815 3.49643040 vae.encoder_f1 0.01161249 3.49865580 vae.decoder 0.00021720 0.04436514 ------------------------------------------------------------------------------------- TOTAL 0.00544054 2.90806374 (elements=2,260,992) ---------------------- -------------------------------------------------------- SAMPLE-WISE STATISTICS ---------------------- -------------------------------------------------------- Handler sd35 Strategy individual Architecture hyperprior-featurecoding ---------------------- -------------------------------------------------------- Total Elements 2260992 Total Bytes 127212 BPFP 0.4501 bits/point EBPFP 0.9002 equivalent bits/point MSE 2.908064 ---------------------- -------------------------------------------------------- Time: 0.754s Load: 0.009s, Pack+Encode: 0.292s, Decode+Unpack: 0.453s ---------------------- -------------------------------------------------------- Restored Feature Format: [Dict] with 8 keys key['text_encoder-item0']: [Dict] with 2 keys key['clip_pooled_prompt_embeds']: [Tensor] shape=torch.Size([768]), dtype=torch.float16, device=cpu key['clip_prompt_embeds']: [Tensor] shape=torch.Size([77, 768]), dtype=torch.float16, device=cpu key['text_encoder_2-item1']: [Dict] with 2 keys key['clip_pooled_prompt_embeds']: [Tensor] shape=torch.Size([1280]), dtype=torch.float16, device=cpu key['clip_prompt_embeds']: [Tensor] shape=torch.Size([77, 1280]), dtype=torch.float16, device=cpu key['text_encoder_3-item2']: [Dict] with 1 keys key['t5_prompt_embeds']: [Tensor] shape=torch.Size([77, 4096]), dtype=torch.float16, device=cpu key['text_encoder-item3']: [Dict] with 2 keys key['clip_pooled_prompt_embeds']: [Tensor] shape=torch.Size([768]), dtype=torch.float16, device=cpu key['clip_prompt_embeds']: [Tensor] shape=torch.Size([77, 768]), dtype=torch.float16, device=cpu key['text_encoder_2-item4']: [Dict] with 2 keys key['clip_pooled_prompt_embeds']: [Tensor] shape=torch.Size([1280]), dtype=torch.float16, device=cpu key['clip_prompt_embeds']: [Tensor] shape=torch.Size([77, 1280]), dtype=torch.float16, device=cpu key['text_encoder_3-item5']: [Dict] with 1 keys key['t5_prompt_embeds']: [Tensor] shape=torch.Size([77, 4096]), dtype=torch.float16, device=cpu key['vae.decoder']: [Tensor] shape=torch.Size([16, 128, 128]), dtype=torch.float16, device=cpu key['vae.encoder']: [Dict] with 2 keys key['vae.encoder_f0']: [Tensor] shape=torch.Size([32, 128, 128]), dtype=torch.float16, device=cpu key['vae.encoder_f1']: [Tensor] shape=torch.Size([32, 128, 128]), dtype=torch.float16, device=cpu 💾 Converting with 2.9081 MSE: from ../datasets/FeatureCoding-StableDiffusion3.5/100samples_features/000000555009.zst to output-fixed/sd35/lambda0.004/hyperprior-featurecoding-8bit-individual/sd35cond/000000555009.zst 💪 Processing: ../datasets/FeatureCoding-StableDiffusion3.5/100samples_features/000000565469.zst (99/100) [1/4] FRONTEND: Loading features from ../datasets/FeatureCoding-StableDiffusion3.5/100samples_features/000000565469.zst... Original data structure: root: [Dict] with 8 keys key['text_encoder-item0']: [Dict] with 2 keys key['clip_pooled_prompt_embeds']: [Tensor] shape=torch.Size([768]), dtype=torch.float16, device=cpu key['clip_prompt_embeds']: [Tensor] shape=torch.Size([77, 768]), dtype=torch.float16, device=cpu key['text_encoder_2-item1']: [Dict] with 2 keys key['clip_pooled_prompt_embeds']: [Tensor] shape=torch.Size([1280]), dtype=torch.float16, device=cpu key['clip_prompt_embeds']: [Tensor] shape=torch.Size([77, 1280]), dtype=torch.float16, device=cpu key['text_encoder_3-item2']: [Dict] with 1 keys key['t5_prompt_embeds']: [Tensor] shape=torch.Size([77, 4096]), dtype=torch.float16, device=cpu key['text_encoder-item3']: [Dict] with 2 keys key['clip_pooled_prompt_embeds']: [Tensor] shape=torch.Size([768]), dtype=torch.float16, device=cpu key['clip_prompt_embeds']: [Tensor] shape=torch.Size([77, 768]), dtype=torch.float16, device=cpu key['text_encoder_2-item4']: [Dict] with 2 keys key['clip_pooled_prompt_embeds']: [Tensor] shape=torch.Size([1280]), dtype=torch.float16, device=cpu key['clip_prompt_embeds']: [Tensor] shape=torch.Size([77, 1280]), dtype=torch.float16, device=cpu key['text_encoder_3-item5']: [Dict] with 1 keys key['t5_prompt_embeds']: [Tensor] shape=torch.Size([77, 4096]), dtype=torch.float16, device=cpu key['vae.encoder-item6']: [Dict] with 2 keys key['vae.encoder_f0']: [Tensor] shape=torch.Size([32, 128, 128]), dtype=torch.float16, device=cpu key['vae.encoder_f1']: [Tensor] shape=torch.Size([32, 128, 128]), dtype=torch.float16, device=cpu key['vae.decoder']: [Tensor] shape=torch.Size([16, 128, 128]), dtype=torch.float16, device=cpu ⌛️ [1/4] FRONTEND: Load time: 0.008s ------------------------------------------------------------ SD3.5 Features Summary ------------------------------------------------------------ Number of text encoders: 3 Encoder names: ['text_encoder', 'text_encoder_2', 'text_encoder_3'] Has VAE latents: True Has VAE encoder features: True Data type: torch.float16 text_encoder-item0: clip_pooled_prompt_embeds: torch.Size([768]), torch.float16 clip_prompt_embeds: torch.Size([77, 768]), torch.float16 text_encoder_2-item1: clip_pooled_prompt_embeds: torch.Size([1280]), torch.float16 clip_prompt_embeds: torch.Size([77, 1280]), torch.float16 text_encoder_3-item2: t5_prompt_embeds: torch.Size([77, 4096]), torch.float16 text_encoder-item3: clip_pooled_prompt_embeds: torch.Size([768]), torch.float16 clip_prompt_embeds: torch.Size([77, 768]), torch.float16 text_encoder_2-item4: clip_pooled_prompt_embeds: torch.Size([1280]), torch.float16 clip_prompt_embeds: torch.Size([77, 1280]), torch.float16 text_encoder_3-item5: t5_prompt_embeds: torch.Size([77, 4096]), torch.float16 vae.decoder latents: torch.Size([16, 128, 128]) vae.encoder features: vae.encoder_f0: torch.Size([32, 128, 128]), torch.float16 vae.encoder_f1: torch.Size([32, 128, 128]), torch.float16 ------------------------------------------------------------ [2/4] FRONTEND: Pack + Encode (strategy: individual)... IndividualPacker: text_encoder-item0.clip_pooled_prompt_embeds: torch.Size([768])->torch.Size([1, 1, 1, 768]) text_encoder-item0.clip_prompt_embeds: torch.Size([77, 768])->torch.Size([1, 1, 77, 768]) text_encoder_2-item1.clip_pooled_prompt_embeds: torch.Size([1280])->torch.Size([1, 1, 1, 1280]) text_encoder_2-item1.clip_prompt_embeds: torch.Size([77, 1280])->torch.Size([1, 1, 77, 1280]) text_encoder_3-item2.t5_prompt_embeds: torch.Size([77, 4096])->torch.Size([1, 1, 77, 4096]) text_encoder-item3.clip_pooled_prompt_embeds: torch.Size([768])->torch.Size([1, 1, 1, 768]) text_encoder-item3.clip_prompt_embeds: torch.Size([77, 768])->torch.Size([1, 1, 77, 768]) text_encoder_2-item4.clip_pooled_prompt_embeds: torch.Size([1280])->torch.Size([1, 1, 1, 1280]) text_encoder_2-item4.clip_prompt_embeds: torch.Size([77, 1280])->torch.Size([1, 1, 77, 1280]) text_encoder_3-item5.t5_prompt_embeds: torch.Size([77, 4096])->torch.Size([1, 1, 77, 4096]) vae.encoder_f0: torch.Size([32, 128, 128])->torch.Size([1, 1, 512, 1024]) vae.encoder_f1: torch.Size([32, 128, 128])->torch.Size([1, 1, 512, 1024]) vae.decoder: torch.Size([16, 128, 128])->torch.Size([1, 1, 512, 512]) Using per-key quantization points (text_encoder-item0.clip_pooled_prompt_embeds: torch.Size([256])) for text_encoder-item0.clip_pooled_prompt_embeds text_encoder-item0.clip_pooled_prompt_embeds: 744B, BPFP=7.7500 Using per-key quantization points (text_encoder-item0.clip_prompt_embeds: torch.Size([256])) for text_encoder-item0.clip_prompt_embeds text_encoder-item0.clip_prompt_embeds: 7,012B, BPFP=0.9486 Using per-key quantization points (text_encoder_2-item1.clip_pooled_prompt_embeds: torch.Size([256])) for text_encoder_2-item1.clip_pooled_prompt_embeds text_encoder_2-item1.clip_pooled_prompt_embeds: 1,212B, BPFP=7.5750 Using per-key quantization points (text_encoder_2-item1.clip_prompt_embeds: torch.Size([256])) for text_encoder_2-item1.clip_prompt_embeds text_encoder_2-item1.clip_prompt_embeds: 11,224B, BPFP=0.9110 Using per-key quantization points (text_encoder_3-item2.t5_prompt_embeds: torch.Size([256])) for text_encoder_3-item2.t5_prompt_embeds text_encoder_3-item2.t5_prompt_embeds: 27,464B, BPFP=0.6966 Using per-key quantization points (text_encoder-item3.clip_pooled_prompt_embeds: torch.Size([256])) for text_encoder-item3.clip_pooled_prompt_embeds text_encoder-item3.clip_pooled_prompt_embeds: 760B, BPFP=7.9167 Using per-key quantization points (text_encoder-item3.clip_prompt_embeds: torch.Size([256])) for text_encoder-item3.clip_prompt_embeds text_encoder-item3.clip_prompt_embeds: 4,024B, BPFP=0.5444 Using per-key quantization points (text_encoder_2-item4.clip_pooled: torch.Size([256])) for text_encoder_2-item4.clip_pooled_prompt_embeds text_encoder_2-item4.clip_pooled_prompt_embeds: 1,268B, BPFP=7.9250 Using per-key quantization points (text_encoder_2-item4.clip_prompt: torch.Size([256])) for text_encoder_2-item4.clip_prompt_embeds text_encoder_2-item4.clip_prompt_embeds: 6,996B, BPFP=0.5679 Using per-key quantization points (text_encoder_3-item5.t5_prompt_embeds: torch.Size([256])) for text_encoder_3-item5.t5_prompt_embeds text_encoder_3-item5.t5_prompt_embeds: 18,416B, BPFP=0.4671 Using per-key quantization points (vae.encoder_f0: torch.Size([256])) for vae.encoder_f0 vae.encoder_f0: 12,688B, BPFP=0.1936 Using per-key quantization points (vae.encoder_f1: torch.Size([256])) for vae.encoder_f1 vae.encoder_f1: 12,688B, BPFP=0.1936 Using per-key quantization points (vae.decoder: torch.Size([256])) for vae.decoder vae.decoder: 5,576B, BPFP=0.1702 ⌛️ [2/4] FRONTEND: Frontend time: 0.288s (Pack+Encode) [3/4] BACKEND: Decode + Unpack... Using per-key quantization points (text_encoder-item0.clip_pooled_prompt_embeds: torch.Size([256])) for text_encoder-item0.clip_pooled_prompt_embeds Using per-key quantization points (text_encoder-item0.clip_prompt_embeds: torch.Size([256])) for text_encoder-item0.clip_prompt_embeds Using per-key quantization points (text_encoder_2-item1.clip_pooled_prompt_embeds: torch.Size([256])) for text_encoder_2-item1.clip_pooled_prompt_embeds Using per-key quantization points (text_encoder_2-item1.clip_prompt_embeds: torch.Size([256])) for text_encoder_2-item1.clip_prompt_embeds Using per-key quantization points (text_encoder_3-item2.t5_prompt_embeds: torch.Size([256])) for text_encoder_3-item2.t5_prompt_embeds Using per-key quantization points (text_encoder-item3.clip_pooled_prompt_embeds: torch.Size([256])) for text_encoder-item3.clip_pooled_prompt_embeds Using per-key quantization points (text_encoder-item3.clip_prompt_embeds: torch.Size([256])) for text_encoder-item3.clip_prompt_embeds Using per-key quantization points (text_encoder_2-item4.clip_pooled: torch.Size([256])) for text_encoder_2-item4.clip_pooled_prompt_embeds Using per-key quantization points (text_encoder_2-item4.clip_prompt: torch.Size([256])) for text_encoder_2-item4.clip_prompt_embeds Using per-key quantization points (text_encoder_3-item5.t5_prompt_embeds: torch.Size([256])) for text_encoder_3-item5.t5_prompt_embeds Using per-key quantization points (vae.encoder_f0: torch.Size([256])) for vae.encoder_f0 Using per-key quantization points (vae.encoder_f1: torch.Size([256])) for vae.encoder_f1 Using per-key quantization points (vae.decoder: torch.Size([256])) for vae.decoder IndividualUnPacker: text_encoder-item0.clip_pooled_prompt_embeds: torch.Size([768]) text_encoder-item0.clip_prompt_embeds: torch.Size([77, 768]) text_encoder_2-item1.clip_pooled_prompt_embeds: torch.Size([1280]) text_encoder_2-item1.clip_prompt_embeds: torch.Size([77, 1280]) text_encoder_3-item2.t5_prompt_embeds: torch.Size([77, 4096]) text_encoder-item3.clip_pooled_prompt_embeds: torch.Size([768]) text_encoder-item3.clip_prompt_embeds: torch.Size([77, 768]) text_encoder_2-item4.clip_pooled_prompt_embeds: torch.Size([1280]) text_encoder_2-item4.clip_prompt_embeds: torch.Size([77, 1280]) text_encoder_3-item5.t5_prompt_embeds: torch.Size([77, 4096]) vae.decoder: torch.Size([16, 128, 128]) vae.encoder_f0: torch.Size([32, 128, 128]) vae.encoder_f1: torch.Size([32, 128, 128]) ⌛️ [3/4] BACKEND: Backend time: 0.445s [4/4] METRICS: Computing MSE Breakdown... Using per-key quantization points (text_encoder-item0.clip_pooled_prompt_embeds: torch.Size([256])) for text_encoder-item0.clip_pooled_prompt_embeds Using per-key quantization points (text_encoder-item0.clip_prompt_embeds: torch.Size([256])) for text_encoder-item0.clip_prompt_embeds Using per-key quantization points (text_encoder_2-item1.clip_pooled_prompt_embeds: torch.Size([256])) for text_encoder_2-item1.clip_pooled_prompt_embeds Using per-key quantization points (text_encoder_2-item1.clip_prompt_embeds: torch.Size([256])) for text_encoder_2-item1.clip_prompt_embeds Using per-key quantization points (text_encoder_3-item2.t5_prompt_embeds: torch.Size([256])) for text_encoder_3-item2.t5_prompt_embeds Using per-key quantization points (text_encoder-item3.clip_pooled_prompt_embeds: torch.Size([256])) for text_encoder-item3.clip_pooled_prompt_embeds Using per-key quantization points (text_encoder-item3.clip_prompt_embeds: torch.Size([256])) for text_encoder-item3.clip_prompt_embeds Using per-key quantization points (text_encoder_2-item4.clip_pooled: torch.Size([256])) for text_encoder_2-item4.clip_pooled_prompt_embeds Using per-key quantization points (text_encoder_2-item4.clip_prompt: torch.Size([256])) for text_encoder_2-item4.clip_prompt_embeds Using per-key quantization points (text_encoder_3-item5.t5_prompt_embeds: torch.Size([256])) for text_encoder_3-item5.t5_prompt_embeds Using per-key quantization points (vae.encoder_f0: torch.Size([256])) for vae.encoder_f0 Using per-key quantization points (vae.encoder_f1: torch.Size([256])) for vae.encoder_f1 Using per-key quantization points (vae.decoder: torch.Size([256])) for vae.decoder IndividualUnPacker: text_encoder-item0.clip_pooled_prompt_embeds: torch.Size([768]) text_encoder-item0.clip_prompt_embeds: torch.Size([77, 768]) text_encoder_2-item1.clip_pooled_prompt_embeds: torch.Size([1280]) text_encoder_2-item1.clip_prompt_embeds: torch.Size([77, 1280]) text_encoder_3-item2.t5_prompt_embeds: torch.Size([77, 4096]) text_encoder-item3.clip_pooled_prompt_embeds: torch.Size([768]) text_encoder-item3.clip_prompt_embeds: torch.Size([77, 768]) text_encoder_2-item4.clip_pooled_prompt_embeds: torch.Size([1280]) text_encoder_2-item4.clip_prompt_embeds: torch.Size([77, 1280]) text_encoder_3-item5.t5_prompt_embeds: torch.Size([77, 4096]) vae.decoder: torch.Size([16, 128, 128]) vae.encoder_f0: torch.Size([32, 128, 128]) vae.encoder_f1: torch.Size([32, 128, 128]) Per-key MSE Breakdown (Quant=Truncation+Quantization, Codec=Compression): Key Quant-MSE Total-MSE ------------------------------------------------------------------------------------- text_encoder-item0.clip_pooled_prompt_embeds 0.00017755 0.90647372 text_encoder-item0.clip_prompt_embeds 0.00022923 23.92745536 text_encoder_2-item1.clip_pooled_prompt_embeds 0.00021530 0.91088858 text_encoder_2-item1.clip_prompt_embeds 0.00015521 0.12287991 text_encoder_3-item2.t5_prompt_embeds 0.00000740 0.00343066 text_encoder-item3.clip_pooled_prompt_embeds 0.00841799 1.01420029 text_encoder-item3.clip_prompt_embeds 0.00023247 23.90570887 text_encoder_2-item4.clip_pooled_prompt_embeds 0.00016751 0.67051430 text_encoder_2-item4.clip_prompt_embeds 0.00009830 0.47049568 text_encoder_3-item5.t5_prompt_embeds 0.00001263 0.00227117 vae.encoder_f0 0.02989292 3.70821238 vae.encoder_f1 0.02989391 3.70740366 vae.decoder 0.00034944 0.04384273 ------------------------------------------------------------------------------------- TOTAL 0.01393319 3.00392672 (elements=2,260,992) ---------------------- -------------------------------------------------------- SAMPLE-WISE STATISTICS ---------------------- -------------------------------------------------------- Handler sd35 Strategy individual Architecture hyperprior-featurecoding ---------------------- -------------------------------------------------------- Total Elements 2260992 Total Bytes 110072 BPFP 0.3895 bits/point EBPFP 0.7789 equivalent bits/point MSE 3.003927 ---------------------- -------------------------------------------------------- Time: 0.741s Load: 0.008s, Pack+Encode: 0.288s, Decode+Unpack: 0.445s ---------------------- -------------------------------------------------------- Restored Feature Format: [Dict] with 8 keys key['text_encoder-item0']: [Dict] with 2 keys key['clip_pooled_prompt_embeds']: [Tensor] shape=torch.Size([768]), dtype=torch.float16, device=cpu key['clip_prompt_embeds']: [Tensor] shape=torch.Size([77, 768]), dtype=torch.float16, device=cpu key['text_encoder_2-item1']: [Dict] with 2 keys key['clip_pooled_prompt_embeds']: [Tensor] shape=torch.Size([1280]), dtype=torch.float16, device=cpu key['clip_prompt_embeds']: [Tensor] shape=torch.Size([77, 1280]), dtype=torch.float16, device=cpu key['text_encoder_3-item2']: [Dict] with 1 keys key['t5_prompt_embeds']: [Tensor] shape=torch.Size([77, 4096]), dtype=torch.float16, device=cpu key['text_encoder-item3']: [Dict] with 2 keys key['clip_pooled_prompt_embeds']: [Tensor] shape=torch.Size([768]), dtype=torch.float16, device=cpu key['clip_prompt_embeds']: [Tensor] shape=torch.Size([77, 768]), dtype=torch.float16, device=cpu key['text_encoder_2-item4']: [Dict] with 2 keys key['clip_pooled_prompt_embeds']: [Tensor] shape=torch.Size([1280]), dtype=torch.float16, device=cpu key['clip_prompt_embeds']: [Tensor] shape=torch.Size([77, 1280]), dtype=torch.float16, device=cpu key['text_encoder_3-item5']: [Dict] with 1 keys key['t5_prompt_embeds']: [Tensor] shape=torch.Size([77, 4096]), dtype=torch.float16, device=cpu key['vae.decoder']: [Tensor] shape=torch.Size([16, 128, 128]), dtype=torch.float16, device=cpu key['vae.encoder']: [Dict] with 2 keys key['vae.encoder_f0']: [Tensor] shape=torch.Size([32, 128, 128]), dtype=torch.float16, device=cpu key['vae.encoder_f1']: [Tensor] shape=torch.Size([32, 128, 128]), dtype=torch.float16, device=cpu 💾 Converting with 3.0039 MSE: from ../datasets/FeatureCoding-StableDiffusion3.5/100samples_features/000000565469.zst to output-fixed/sd35/lambda0.004/hyperprior-featurecoding-8bit-individual/sd35cond/000000565469.zst 💪 Processing: ../datasets/FeatureCoding-StableDiffusion3.5/100samples_features/000000575243.zst (100/100) [1/4] FRONTEND: Loading features from ../datasets/FeatureCoding-StableDiffusion3.5/100samples_features/000000575243.zst... Original data structure: root: [Dict] with 8 keys key['text_encoder-item0']: [Dict] with 2 keys key['clip_pooled_prompt_embeds']: [Tensor] shape=torch.Size([768]), dtype=torch.float16, device=cpu key['clip_prompt_embeds']: [Tensor] shape=torch.Size([77, 768]), dtype=torch.float16, device=cpu key['text_encoder_2-item1']: [Dict] with 2 keys key['clip_pooled_prompt_embeds']: [Tensor] shape=torch.Size([1280]), dtype=torch.float16, device=cpu key['clip_prompt_embeds']: [Tensor] shape=torch.Size([77, 1280]), dtype=torch.float16, device=cpu key['text_encoder_3-item2']: [Dict] with 1 keys key['t5_prompt_embeds']: [Tensor] shape=torch.Size([77, 4096]), dtype=torch.float16, device=cpu key['text_encoder-item3']: [Dict] with 2 keys key['clip_pooled_prompt_embeds']: [Tensor] shape=torch.Size([768]), dtype=torch.float16, device=cpu key['clip_prompt_embeds']: [Tensor] shape=torch.Size([77, 768]), dtype=torch.float16, device=cpu key['text_encoder_2-item4']: [Dict] with 2 keys key['clip_pooled_prompt_embeds']: [Tensor] shape=torch.Size([1280]), dtype=torch.float16, device=cpu key['clip_prompt_embeds']: [Tensor] shape=torch.Size([77, 1280]), dtype=torch.float16, device=cpu key['text_encoder_3-item5']: [Dict] with 1 keys key['t5_prompt_embeds']: [Tensor] shape=torch.Size([77, 4096]), dtype=torch.float16, device=cpu key['vae.encoder-item6']: [Dict] with 2 keys key['vae.encoder_f0']: [Tensor] shape=torch.Size([32, 128, 128]), dtype=torch.float16, device=cpu key['vae.encoder_f1']: [Tensor] shape=torch.Size([32, 128, 128]), dtype=torch.float16, device=cpu key['vae.decoder']: [Tensor] shape=torch.Size([16, 128, 128]), dtype=torch.float16, device=cpu ⌛️ [1/4] FRONTEND: Load time: 0.009s ------------------------------------------------------------ SD3.5 Features Summary ------------------------------------------------------------ Number of text encoders: 3 Encoder names: ['text_encoder', 'text_encoder_2', 'text_encoder_3'] Has VAE latents: True Has VAE encoder features: True Data type: torch.float16 text_encoder-item0: clip_pooled_prompt_embeds: torch.Size([768]), torch.float16 clip_prompt_embeds: torch.Size([77, 768]), torch.float16 text_encoder_2-item1: clip_pooled_prompt_embeds: torch.Size([1280]), torch.float16 clip_prompt_embeds: torch.Size([77, 1280]), torch.float16 text_encoder_3-item2: t5_prompt_embeds: torch.Size([77, 4096]), torch.float16 text_encoder-item3: clip_pooled_prompt_embeds: torch.Size([768]), torch.float16 clip_prompt_embeds: torch.Size([77, 768]), torch.float16 text_encoder_2-item4: clip_pooled_prompt_embeds: torch.Size([1280]), torch.float16 clip_prompt_embeds: torch.Size([77, 1280]), torch.float16 text_encoder_3-item5: t5_prompt_embeds: torch.Size([77, 4096]), torch.float16 vae.decoder latents: torch.Size([16, 128, 128]) vae.encoder features: vae.encoder_f0: torch.Size([32, 128, 128]), torch.float16 vae.encoder_f1: torch.Size([32, 128, 128]), torch.float16 ------------------------------------------------------------ [2/4] FRONTEND: Pack + Encode (strategy: individual)... IndividualPacker: text_encoder-item0.clip_pooled_prompt_embeds: torch.Size([768])->torch.Size([1, 1, 1, 768]) text_encoder-item0.clip_prompt_embeds: torch.Size([77, 768])->torch.Size([1, 1, 77, 768]) text_encoder_2-item1.clip_pooled_prompt_embeds: torch.Size([1280])->torch.Size([1, 1, 1, 1280]) text_encoder_2-item1.clip_prompt_embeds: torch.Size([77, 1280])->torch.Size([1, 1, 77, 1280]) text_encoder_3-item2.t5_prompt_embeds: torch.Size([77, 4096])->torch.Size([1, 1, 77, 4096]) text_encoder-item3.clip_pooled_prompt_embeds: torch.Size([768])->torch.Size([1, 1, 1, 768]) text_encoder-item3.clip_prompt_embeds: torch.Size([77, 768])->torch.Size([1, 1, 77, 768]) text_encoder_2-item4.clip_pooled_prompt_embeds: torch.Size([1280])->torch.Size([1, 1, 1, 1280]) text_encoder_2-item4.clip_prompt_embeds: torch.Size([77, 1280])->torch.Size([1, 1, 77, 1280]) text_encoder_3-item5.t5_prompt_embeds: torch.Size([77, 4096])->torch.Size([1, 1, 77, 4096]) vae.encoder_f0: torch.Size([32, 128, 128])->torch.Size([1, 1, 512, 1024]) vae.encoder_f1: torch.Size([32, 128, 128])->torch.Size([1, 1, 512, 1024]) vae.decoder: torch.Size([16, 128, 128])->torch.Size([1, 1, 512, 512]) Using per-key quantization points (text_encoder-item0.clip_pooled_prompt_embeds: torch.Size([256])) for text_encoder-item0.clip_pooled_prompt_embeds text_encoder-item0.clip_pooled_prompt_embeds: 728B, BPFP=7.5833 Using per-key quantization points (text_encoder-item0.clip_prompt_embeds: torch.Size([256])) for text_encoder-item0.clip_prompt_embeds text_encoder-item0.clip_prompt_embeds: 6,468B, BPFP=0.8750 Using per-key quantization points (text_encoder_2-item1.clip_pooled_prompt_embeds: torch.Size([256])) for text_encoder_2-item1.clip_pooled_prompt_embeds text_encoder_2-item1.clip_pooled_prompt_embeds: 1,196B, BPFP=7.4750 Using per-key quantization points (text_encoder_2-item1.clip_prompt_embeds: torch.Size([256])) for text_encoder_2-item1.clip_prompt_embeds text_encoder_2-item1.clip_prompt_embeds: 11,236B, BPFP=0.9120 Using per-key quantization points (text_encoder_3-item2.t5_prompt_embeds: torch.Size([256])) for text_encoder_3-item2.t5_prompt_embeds text_encoder_3-item2.t5_prompt_embeds: 28,124B, BPFP=0.7134 Using per-key quantization points (text_encoder-item3.clip_pooled_prompt_embeds: torch.Size([256])) for text_encoder-item3.clip_pooled_prompt_embeds text_encoder-item3.clip_pooled_prompt_embeds: 760B, BPFP=7.9167 Using per-key quantization points (text_encoder-item3.clip_prompt_embeds: torch.Size([256])) for text_encoder-item3.clip_prompt_embeds text_encoder-item3.clip_prompt_embeds: 4,024B, BPFP=0.5444 Using per-key quantization points (text_encoder_2-item4.clip_pooled: torch.Size([256])) for text_encoder_2-item4.clip_pooled_prompt_embeds text_encoder_2-item4.clip_pooled_prompt_embeds: 1,268B, BPFP=7.9250 Using per-key quantization points (text_encoder_2-item4.clip_prompt: torch.Size([256])) for text_encoder_2-item4.clip_prompt_embeds text_encoder_2-item4.clip_prompt_embeds: 6,996B, BPFP=0.5679 Using per-key quantization points (text_encoder_3-item5.t5_prompt_embeds: torch.Size([256])) for text_encoder_3-item5.t5_prompt_embeds text_encoder_3-item5.t5_prompt_embeds: 18,416B, BPFP=0.4671 Using per-key quantization points (vae.encoder_f0: torch.Size([256])) for vae.encoder_f0 vae.encoder_f0: 19,608B, BPFP=0.2992 Using per-key quantization points (vae.encoder_f1: torch.Size([256])) for vae.encoder_f1 vae.encoder_f1: 19,608B, BPFP=0.2992 Using per-key quantization points (vae.decoder: torch.Size([256])) for vae.decoder vae.decoder: 6,884B, BPFP=0.2101 ⌛️ [2/4] FRONTEND: Frontend time: 0.291s (Pack+Encode) [3/4] BACKEND: Decode + Unpack... Using per-key quantization points (text_encoder-item0.clip_pooled_prompt_embeds: torch.Size([256])) for text_encoder-item0.clip_pooled_prompt_embeds Using per-key quantization points (text_encoder-item0.clip_prompt_embeds: torch.Size([256])) for text_encoder-item0.clip_prompt_embeds Using per-key quantization points (text_encoder_2-item1.clip_pooled_prompt_embeds: torch.Size([256])) for text_encoder_2-item1.clip_pooled_prompt_embeds Using per-key quantization points (text_encoder_2-item1.clip_prompt_embeds: torch.Size([256])) for text_encoder_2-item1.clip_prompt_embeds Using per-key quantization points (text_encoder_3-item2.t5_prompt_embeds: torch.Size([256])) for text_encoder_3-item2.t5_prompt_embeds Using per-key quantization points (text_encoder-item3.clip_pooled_prompt_embeds: torch.Size([256])) for text_encoder-item3.clip_pooled_prompt_embeds Using per-key quantization points (text_encoder-item3.clip_prompt_embeds: torch.Size([256])) for text_encoder-item3.clip_prompt_embeds Using per-key quantization points (text_encoder_2-item4.clip_pooled: torch.Size([256])) for text_encoder_2-item4.clip_pooled_prompt_embeds Using per-key quantization points (text_encoder_2-item4.clip_prompt: torch.Size([256])) for text_encoder_2-item4.clip_prompt_embeds Using per-key quantization points (text_encoder_3-item5.t5_prompt_embeds: torch.Size([256])) for text_encoder_3-item5.t5_prompt_embeds Using per-key quantization points (vae.encoder_f0: torch.Size([256])) for vae.encoder_f0 Using per-key quantization points (vae.encoder_f1: torch.Size([256])) for vae.encoder_f1 Using per-key quantization points (vae.decoder: torch.Size([256])) for vae.decoder IndividualUnPacker: text_encoder-item0.clip_pooled_prompt_embeds: torch.Size([768]) text_encoder-item0.clip_prompt_embeds: torch.Size([77, 768]) text_encoder_2-item1.clip_pooled_prompt_embeds: torch.Size([1280]) text_encoder_2-item1.clip_prompt_embeds: torch.Size([77, 1280]) text_encoder_3-item2.t5_prompt_embeds: torch.Size([77, 4096]) text_encoder-item3.clip_pooled_prompt_embeds: torch.Size([768]) text_encoder-item3.clip_prompt_embeds: torch.Size([77, 768]) text_encoder_2-item4.clip_pooled_prompt_embeds: torch.Size([1280]) text_encoder_2-item4.clip_prompt_embeds: torch.Size([77, 1280]) text_encoder_3-item5.t5_prompt_embeds: torch.Size([77, 4096]) vae.decoder: torch.Size([16, 128, 128]) vae.encoder_f0: torch.Size([32, 128, 128]) vae.encoder_f1: torch.Size([32, 128, 128]) ⌛️ [3/4] BACKEND: Backend time: 0.440s [4/4] METRICS: Computing MSE Breakdown... Using per-key quantization points (text_encoder-item0.clip_pooled_prompt_embeds: torch.Size([256])) for text_encoder-item0.clip_pooled_prompt_embeds Using per-key quantization points (text_encoder-item0.clip_prompt_embeds: torch.Size([256])) for text_encoder-item0.clip_prompt_embeds Using per-key quantization points (text_encoder_2-item1.clip_pooled_prompt_embeds: torch.Size([256])) for text_encoder_2-item1.clip_pooled_prompt_embeds Using per-key quantization points (text_encoder_2-item1.clip_prompt_embeds: torch.Size([256])) for text_encoder_2-item1.clip_prompt_embeds Using per-key quantization points (text_encoder_3-item2.t5_prompt_embeds: torch.Size([256])) for text_encoder_3-item2.t5_prompt_embeds Using per-key quantization points (text_encoder-item3.clip_pooled_prompt_embeds: torch.Size([256])) for text_encoder-item3.clip_pooled_prompt_embeds Using per-key quantization points (text_encoder-item3.clip_prompt_embeds: torch.Size([256])) for text_encoder-item3.clip_prompt_embeds Using per-key quantization points (text_encoder_2-item4.clip_pooled: torch.Size([256])) for text_encoder_2-item4.clip_pooled_prompt_embeds Using per-key quantization points (text_encoder_2-item4.clip_prompt: torch.Size([256])) for text_encoder_2-item4.clip_prompt_embeds Using per-key quantization points (text_encoder_3-item5.t5_prompt_embeds: torch.Size([256])) for text_encoder_3-item5.t5_prompt_embeds Using per-key quantization points (vae.encoder_f0: torch.Size([256])) for vae.encoder_f0 Using per-key quantization points (vae.encoder_f1: torch.Size([256])) for vae.encoder_f1 Using per-key quantization points (vae.decoder: torch.Size([256])) for vae.decoder IndividualUnPacker: text_encoder-item0.clip_pooled_prompt_embeds: torch.Size([768]) text_encoder-item0.clip_prompt_embeds: torch.Size([77, 768]) text_encoder_2-item1.clip_pooled_prompt_embeds: torch.Size([1280]) text_encoder_2-item1.clip_prompt_embeds: torch.Size([77, 1280]) text_encoder_3-item2.t5_prompt_embeds: torch.Size([77, 4096]) text_encoder-item3.clip_pooled_prompt_embeds: torch.Size([768]) text_encoder-item3.clip_prompt_embeds: torch.Size([77, 768]) text_encoder_2-item4.clip_pooled_prompt_embeds: torch.Size([1280]) text_encoder_2-item4.clip_prompt_embeds: torch.Size([77, 1280]) text_encoder_3-item5.t5_prompt_embeds: torch.Size([77, 4096]) vae.decoder: torch.Size([16, 128, 128]) vae.encoder_f0: torch.Size([32, 128, 128]) vae.encoder_f1: torch.Size([32, 128, 128]) Per-key MSE Breakdown (Quant=Truncation+Quantization, Codec=Compression): Key Quant-MSE Total-MSE ------------------------------------------------------------------------------------- text_encoder-item0.clip_pooled_prompt_embeds 0.00020076 0.95712709 text_encoder-item0.clip_prompt_embeds 0.00024627 23.96090791 text_encoder_2-item1.clip_pooled_prompt_embeds 0.00020424 0.98602676 text_encoder_2-item1.clip_prompt_embeds 0.00017521 0.14285280 text_encoder_3-item2.t5_prompt_embeds 0.00000803 0.00342156 text_encoder-item3.clip_pooled_prompt_embeds 0.00841799 1.01420029 text_encoder-item3.clip_prompt_embeds 0.00023247 23.90570887 text_encoder_2-item4.clip_pooled_prompt_embeds 0.00016751 0.67051430 text_encoder_2-item4.clip_prompt_embeds 0.00009830 0.47049568 text_encoder_3-item5.t5_prompt_embeds 0.00001263 0.00227117 vae.encoder_f0 0.00613025 2.19635606 vae.encoder_f1 0.00613536 2.19571853 vae.decoder 0.00018697 0.05836669 ------------------------------------------------------------------------------------- TOTAL 0.00289634 2.30630367 (elements=2,260,992) ---------------------- -------------------------------------------------------- SAMPLE-WISE STATISTICS ---------------------- -------------------------------------------------------- Handler sd35 Strategy individual Architecture hyperprior-featurecoding ---------------------- -------------------------------------------------------- Total Elements 2260992 Total Bytes 125316 BPFP 0.4434 bits/point EBPFP 0.8868 equivalent bits/point MSE 2.306304 ---------------------- -------------------------------------------------------- Time: 0.740s Load: 0.009s, Pack+Encode: 0.291s, Decode+Unpack: 0.440s ---------------------- -------------------------------------------------------- Restored Feature Format: [Dict] with 8 keys key['text_encoder-item0']: [Dict] with 2 keys key['clip_pooled_prompt_embeds']: [Tensor] shape=torch.Size([768]), dtype=torch.float16, device=cpu key['clip_prompt_embeds']: [Tensor] shape=torch.Size([77, 768]), dtype=torch.float16, device=cpu key['text_encoder_2-item1']: [Dict] with 2 keys key['clip_pooled_prompt_embeds']: [Tensor] shape=torch.Size([1280]), dtype=torch.float16, device=cpu key['clip_prompt_embeds']: [Tensor] shape=torch.Size([77, 1280]), dtype=torch.float16, device=cpu key['text_encoder_3-item2']: [Dict] with 1 keys key['t5_prompt_embeds']: [Tensor] shape=torch.Size([77, 4096]), dtype=torch.float16, device=cpu key['text_encoder-item3']: [Dict] with 2 keys key['clip_pooled_prompt_embeds']: [Tensor] shape=torch.Size([768]), dtype=torch.float16, device=cpu key['clip_prompt_embeds']: [Tensor] shape=torch.Size([77, 768]), dtype=torch.float16, device=cpu key['text_encoder_2-item4']: [Dict] with 2 keys key['clip_pooled_prompt_embeds']: [Tensor] shape=torch.Size([1280]), dtype=torch.float16, device=cpu key['clip_prompt_embeds']: [Tensor] shape=torch.Size([77, 1280]), dtype=torch.float16, device=cpu key['text_encoder_3-item5']: [Dict] with 1 keys key['t5_prompt_embeds']: [Tensor] shape=torch.Size([77, 4096]), dtype=torch.float16, device=cpu key['vae.decoder']: [Tensor] shape=torch.Size([16, 128, 128]), dtype=torch.float16, device=cpu key['vae.encoder']: [Dict] with 2 keys key['vae.encoder_f0']: [Tensor] shape=torch.Size([32, 128, 128]), dtype=torch.float16, device=cpu key['vae.encoder_f1']: [Tensor] shape=torch.Size([32, 128, 128]), dtype=torch.float16, device=cpu 💾 Converting with 2.3063 MSE: from ../datasets/FeatureCoding-StableDiffusion3.5/100samples_features/000000575243.zst to output-fixed/sd35/lambda0.004/hyperprior-featurecoding-8bit-individual/sd35cond/000000575243.zst ------------------------ ---------------------------- TOTAL PROCESSING SUMMARY ------------------------ ---------------------------- Total files 100 Avg BPFP 0.4208 bits/point Avg EBPFP 0.8416 equivalent bits/point Avg MSE 3.199663 Avg Time 0.755s ------------------------ ----------------------------