Experiment: dtufc_elic-featurecoding_sd35_individual Log file: output-fixed/sd35/lambda0.02/elic-featurecoding-8bit-individual/sd35cond/dtufc_elic-featurecoding_sd35_individual.log DTUFCCodecConfig: arch: elic-featurecoding handler: sd35 checkpoint: codec_weights/elic_hybrid/elic2022-official_lambda0.02_epochs600_lr0.0001_bs60_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/elic_hybrid/elic2022-official_lambda0.02_epochs600_lr0.0001_bs60_patch256-256_checkpoint_best.pth.tar Checkpoint epoch: 520 Loaded elic-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 elic-featurecoding Checkpoint codec_weights/elic_hybrid/elic2022-official_lambda0.02_epochs600_lr0.0001_bs60_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/lambda0.02/elic-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: 0.007s ------------------------------------------------------------ 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: 2,364B, BPFP=24.6250 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: 12,844B, BPFP=1.7376 Using 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: 3,388B, BPFP=21.1750 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: 22,276B, BPFP=1.8081 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: 72,472B, BPFP=1.8383 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: 2,600B, BPFP=27.0833 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: 8,396B, BPFP=1.1358 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: 4,400B, BPFP=27.5000 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: 16,268B, BPFP=1.3205 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: 42,292B, BPFP=1.0727 Using per-key quantization points (vae.encoder_f0: torch.Size([256])) for vae.encoder_f0 vae.encoder_f0: 59,204B, BPFP=0.9034 Using per-key quantization points (vae.encoder_f1: torch.Size([256])) for vae.encoder_f1 vae.encoder_f1: 59,200B, BPFP=0.9033 Using per-key quantization points (vae.decoder: torch.Size([256])) for vae.decoder vae.decoder: 33,328B, BPFP=1.0171 ⌛️ [2/4] FRONTEND: Frontend time: 3.141s (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: 1.657s [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 0.16037265 text_encoder-item0.clip_prompt_embeds 0.00025464 43.41924547 text_encoder_2-item1.clip_pooled_prompt_embeds 0.00020464 0.17734075 text_encoder_2-item1.clip_prompt_embeds 0.00016240 0.05921118 text_encoder_3-item2.t5_prompt_embeds 0.00000839 0.00075367 text_encoder-item3.clip_pooled_prompt_embeds 0.00841799 0.25064182 text_encoder-item3.clip_prompt_embeds 0.00023247 57.42621246 text_encoder_2-item4.clip_pooled_prompt_embeds 0.00016751 0.08740882 text_encoder_2-item4.clip_prompt_embeds 0.00009830 0.08730212 text_encoder_3-item5.t5_prompt_embeds 0.00001263 0.00061542 vae.encoder_f0 0.00635250 0.59309518 vae.encoder_f1 0.00635834 0.59359038 vae.decoder 0.00019940 0.01847607 ------------------------------------------------------------------------------------- TOTAL 0.00300073 2.92178481 (elements=2,260,992) ---------------------- -------------------------------------------------------- SAMPLE-WISE STATISTICS ---------------------- -------------------------------------------------------- Handler sd35 Strategy individual Architecture elic-featurecoding ---------------------- -------------------------------------------------------- Total Elements 2260992 Total Bytes 339032 BPFP 1.1996 bits/point EBPFP 2.3992 equivalent bits/point MSE 2.921785 ---------------------- -------------------------------------------------------- Time: 4.805s Load: 0.007s, Pack+Encode: 3.141s, Decode+Unpack: 1.657s ---------------------- -------------------------------------------------------- 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 2.9218 MSE: from ../datasets/FeatureCoding-StableDiffusion3.5/100samples_features/000000002153.zst to output-fixed/sd35/lambda0.02/elic-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: 0.008s ------------------------------------------------------------ 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: 2,256B, BPFP=23.5000 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: 12,464B, BPFP=1.6861 Using 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: 3,516B, BPFP=21.9750 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: 22,344B, BPFP=1.8136 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: 69,576B, BPFP=1.7648 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: 2,600B, BPFP=27.0833 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: 8,396B, BPFP=1.1358 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: 4,400B, BPFP=27.5000 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: 16,268B, BPFP=1.3205 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: 42,292B, BPFP=1.0727 Using per-key quantization points (vae.encoder_f0: torch.Size([256])) for vae.encoder_f0 vae.encoder_f0: 43,764B, BPFP=0.6678 Using per-key quantization points (vae.encoder_f1: torch.Size([256])) for vae.encoder_f1 vae.encoder_f1: 43,800B, BPFP=0.6683 Using per-key quantization points (vae.decoder: torch.Size([256])) for vae.decoder vae.decoder: 29,720B, BPFP=0.9070 ⌛️ [2/4] FRONTEND: Frontend time: 2.165s (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: 1.599s [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 0.15400416 text_encoder-item0.clip_prompt_embeds 0.00022609 52.99325284 text_encoder_2-item1.clip_pooled_prompt_embeds 0.00019887 0.17610109 text_encoder_2-item1.clip_prompt_embeds 0.00019493 0.05329766 text_encoder_3-item2.t5_prompt_embeds 0.00000845 0.00085085 text_encoder-item3.clip_pooled_prompt_embeds 0.00841799 0.25064182 text_encoder-item3.clip_prompt_embeds 0.00023247 57.42621246 text_encoder_2-item4.clip_pooled_prompt_embeds 0.00016751 0.08740882 text_encoder_2-item4.clip_prompt_embeds 0.00009830 0.08730212 text_encoder_3-item5.t5_prompt_embeds 0.00001263 0.00061542 vae.encoder_f0 0.01130640 0.70730984 vae.encoder_f1 0.01130902 0.70525455 vae.decoder 0.00020860 0.01727775 ------------------------------------------------------------------------------------- TOTAL 0.00529919 3.22418361 (elements=2,260,992) ---------------------- -------------------------------------------------------- SAMPLE-WISE STATISTICS ---------------------- -------------------------------------------------------- Handler sd35 Strategy individual Architecture elic-featurecoding ---------------------- -------------------------------------------------------- Total Elements 2260992 Total Bytes 301396 BPFP 1.0664 bits/point EBPFP 2.1328 equivalent bits/point MSE 3.224184 ---------------------- -------------------------------------------------------- Time: 3.772s Load: 0.008s, Pack+Encode: 2.165s, Decode+Unpack: 1.599s ---------------------- -------------------------------------------------------- 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 3.2242 MSE: from ../datasets/FeatureCoding-StableDiffusion3.5/100samples_features/000000002431.zst to output-fixed/sd35/lambda0.02/elic-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: 2,284B, BPFP=23.7917 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: 11,228B, BPFP=1.5189 Using 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: 3,756B, BPFP=23.4750 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: 19,220B, BPFP=1.5601 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: 60,888B, BPFP=1.5444 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: 2,600B, BPFP=27.0833 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: 8,396B, BPFP=1.1358 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: 4,400B, BPFP=27.5000 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: 16,268B, BPFP=1.3205 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: 42,292B, BPFP=1.0727 Using per-key quantization points (vae.encoder_f0: torch.Size([256])) for vae.encoder_f0 vae.encoder_f0: 23,216B, BPFP=0.3542 Using per-key quantization points (vae.encoder_f1: torch.Size([256])) for vae.encoder_f1 vae.encoder_f1: 23,236B, BPFP=0.3546 Using per-key quantization points (vae.decoder: torch.Size([256])) for vae.decoder vae.decoder: 22,988B, BPFP=0.7015 ⌛️ [2/4] FRONTEND: Frontend time: 2.152s (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: 1.604s [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 0.15975692 text_encoder-item0.clip_prompt_embeds 0.00022402 140.66949067 text_encoder_2-item1.clip_pooled_prompt_embeds 0.00024964 0.17505083 text_encoder_2-item1.clip_prompt_embeds 0.00015987 0.05983251 text_encoder_3-item2.t5_prompt_embeds 0.00000778 0.00069944 text_encoder-item3.clip_pooled_prompt_embeds 0.00841799 0.25064182 text_encoder-item3.clip_prompt_embeds 0.00023247 57.42621246 text_encoder_2-item4.clip_pooled_prompt_embeds 0.00016751 0.08740882 text_encoder_2-item4.clip_prompt_embeds 0.00009830 0.08730212 text_encoder_3-item5.t5_prompt_embeds 0.00001263 0.00061542 vae.encoder_f0 1.19630027 3.07686949 vae.encoder_f1 1.19630098 3.07435679 vae.decoder 0.00023596 0.01691304 ------------------------------------------------------------------------------------- TOTAL 0.55486265 6.61638904 (elements=2,260,992) ---------------------- -------------------------------------------------------- SAMPLE-WISE STATISTICS ---------------------- -------------------------------------------------------- Handler sd35 Strategy individual Architecture elic-featurecoding ---------------------- -------------------------------------------------------- Total Elements 2260992 Total Bytes 240772 BPFP 0.8519 bits/point EBPFP 1.7038 equivalent bits/point MSE 6.616389 ---------------------- -------------------------------------------------------- Time: 3.764s Load: 0.007s, Pack+Encode: 2.152s, Decode+Unpack: 1.604s ---------------------- -------------------------------------------------------- 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 6.6164 MSE: from ../datasets/FeatureCoding-StableDiffusion3.5/100samples_features/000000003661.zst to output-fixed/sd35/lambda0.02/elic-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: 2,288B, BPFP=23.8333 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: 12,040B, BPFP=1.6288 Using 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: 3,360B, BPFP=21.0000 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: 22,036B, BPFP=1.7886 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: 68,112B, BPFP=1.7277 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: 2,600B, BPFP=27.0833 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: 8,396B, BPFP=1.1358 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: 4,400B, BPFP=27.5000 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: 16,268B, BPFP=1.3205 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: 42,292B, BPFP=1.0727 Using per-key quantization points (vae.encoder_f0: torch.Size([256])) for vae.encoder_f0 vae.encoder_f0: 55,032B, BPFP=0.8397 Using per-key quantization points (vae.encoder_f1: torch.Size([256])) for vae.encoder_f1 vae.encoder_f1: 55,036B, BPFP=0.8398 Using per-key quantization points (vae.decoder: torch.Size([256])) for vae.decoder vae.decoder: 47,476B, BPFP=1.4489 ⌛️ [2/4] FRONTEND: Frontend time: 2.155s (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: 1.619s [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 0.15343612 text_encoder-item0.clip_prompt_embeds 0.00030342 42.85457251 text_encoder_2-item1.clip_pooled_prompt_embeds 0.00066702 0.19479941 text_encoder_2-item1.clip_prompt_embeds 0.00020355 0.05823447 text_encoder_3-item2.t5_prompt_embeds 0.00000815 0.00064093 text_encoder-item3.clip_pooled_prompt_embeds 0.00841799 0.25064182 text_encoder-item3.clip_prompt_embeds 0.00023247 57.42621246 text_encoder_2-item4.clip_pooled_prompt_embeds 0.00016751 0.08740882 text_encoder_2-item4.clip_prompt_embeds 0.00009830 0.08730212 text_encoder_3-item5.t5_prompt_embeds 0.00001263 0.00061542 vae.encoder_f0 0.00586287 0.52313739 vae.encoder_f1 0.00587438 0.52311277 vae.decoder 0.00017677 0.02199787 ------------------------------------------------------------------------------------- TOTAL 0.00277565 2.87480867 (elements=2,260,992) ---------------------- -------------------------------------------------------- SAMPLE-WISE STATISTICS ---------------------- -------------------------------------------------------- Handler sd35 Strategy individual Architecture elic-featurecoding ---------------------- -------------------------------------------------------- Total Elements 2260992 Total Bytes 339336 BPFP 1.2007 bits/point EBPFP 2.4013 equivalent bits/point MSE 2.874809 ---------------------- -------------------------------------------------------- Time: 3.782s Load: 0.009s, Pack+Encode: 2.155s, Decode+Unpack: 1.619s ---------------------- -------------------------------------------------------- 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 2.8748 MSE: from ../datasets/FeatureCoding-StableDiffusion3.5/100samples_features/000000011149.zst to output-fixed/sd35/lambda0.02/elic-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: 0.007s ------------------------------------------------------------ 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: 2,240B, BPFP=23.3333 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: 11,048B, BPFP=1.4946 Using 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: 3,464B, BPFP=21.6500 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: 18,940B, BPFP=1.5373 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: 61,432B, BPFP=1.5582 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: 2,600B, BPFP=27.0833 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: 8,396B, BPFP=1.1358 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: 4,400B, BPFP=27.5000 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: 16,268B, BPFP=1.3205 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: 42,292B, BPFP=1.0727 Using per-key quantization points (vae.encoder_f0: torch.Size([256])) for vae.encoder_f0 vae.encoder_f0: 39,280B, BPFP=0.5994 Using per-key quantization points (vae.encoder_f1: torch.Size([256])) for vae.encoder_f1 vae.encoder_f1: 39,284B, BPFP=0.5994 Using per-key quantization points (vae.decoder: torch.Size([256])) for vae.decoder vae.decoder: 26,268B, BPFP=0.8016 ⌛️ [2/4] FRONTEND: Frontend time: 2.153s (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: 1.601s [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 0.15236074 text_encoder-item0.clip_prompt_embeds 0.00024120 286.87635281 text_encoder_2-item1.clip_pooled_prompt_embeds 0.00025189 0.19477203 text_encoder_2-item1.clip_prompt_embeds 0.00017312 0.05964621 text_encoder_3-item2.t5_prompt_embeds 0.00000806 0.00067582 text_encoder-item3.clip_pooled_prompt_embeds 0.00841799 0.25064182 text_encoder-item3.clip_prompt_embeds 0.00023247 57.42621246 text_encoder_2-item4.clip_pooled_prompt_embeds 0.00016751 0.08740882 text_encoder_2-item4.clip_prompt_embeds 0.00009830 0.08730212 text_encoder_3-item5.t5_prompt_embeds 0.00001263 0.00061542 vae.encoder_f0 0.00779453 0.57148510 vae.encoder_f1 0.00779802 0.57272929 vae.decoder 0.00023829 0.01764486 ------------------------------------------------------------------------------------- TOTAL 0.00367359 9.27944948 (elements=2,260,992) ---------------------- -------------------------------------------------------- SAMPLE-WISE STATISTICS ---------------------- -------------------------------------------------------- Handler sd35 Strategy individual Architecture elic-featurecoding ---------------------- -------------------------------------------------------- Total Elements 2260992 Total Bytes 275912 BPFP 0.9763 bits/point EBPFP 1.9525 equivalent bits/point MSE 9.279449 ---------------------- -------------------------------------------------------- Time: 3.762s Load: 0.007s, Pack+Encode: 2.153s, Decode+Unpack: 1.601s ---------------------- -------------------------------------------------------- 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 9.2794 MSE: from ../datasets/FeatureCoding-StableDiffusion3.5/100samples_features/000000023937.zst to output-fixed/sd35/lambda0.02/elic-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: 2,236B, BPFP=23.2917 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: 11,952B, BPFP=1.6169 Using 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: 3,456B, BPFP=21.6000 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: 20,484B, BPFP=1.6627 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: 63,820B, BPFP=1.6188 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: 2,600B, BPFP=27.0833 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: 8,396B, BPFP=1.1358 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: 4,400B, BPFP=27.5000 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: 16,268B, BPFP=1.3205 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: 42,292B, BPFP=1.0727 Using per-key quantization points (vae.encoder_f0: torch.Size([256])) for vae.encoder_f0 vae.encoder_f0: 57,440B, BPFP=0.8765 Using per-key quantization points (vae.encoder_f1: torch.Size([256])) for vae.encoder_f1 vae.encoder_f1: 57,444B, BPFP=0.8765 Using per-key quantization points (vae.decoder: torch.Size([256])) for vae.decoder vae.decoder: 31,316B, BPFP=0.9557 ⌛️ [2/4] FRONTEND: Frontend time: 2.145s (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: 1.610s [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 0.17159563 text_encoder-item0.clip_prompt_embeds 0.00025651 35.10472682 text_encoder_2-item1.clip_pooled_prompt_embeds 0.00023478 0.18491679 text_encoder_2-item1.clip_prompt_embeds 0.00016148 0.05226172 text_encoder_3-item2.t5_prompt_embeds 0.00000844 0.00064153 text_encoder-item3.clip_pooled_prompt_embeds 0.00841799 0.25064182 text_encoder-item3.clip_prompt_embeds 0.00023247 57.42621246 text_encoder_2-item4.clip_pooled_prompt_embeds 0.00016751 0.08740882 text_encoder_2-item4.clip_prompt_embeds 0.00009830 0.08730212 text_encoder_3-item5.t5_prompt_embeds 0.00001263 0.00061542 vae.encoder_f0 0.00655775 0.61628240 vae.encoder_f1 0.00656268 0.61689174 vae.decoder 0.00020283 0.01755184 ------------------------------------------------------------------------------------- TOTAL 0.00309620 2.71468180 (elements=2,260,992) ---------------------- -------------------------------------------------------- SAMPLE-WISE STATISTICS ---------------------- -------------------------------------------------------- Handler sd35 Strategy individual Architecture elic-featurecoding ---------------------- -------------------------------------------------------- Total Elements 2260992 Total Bytes 322104 BPFP 1.1397 bits/point EBPFP 2.2794 equivalent bits/point MSE 2.714682 ---------------------- -------------------------------------------------------- Time: 3.763s Load: 0.008s, Pack+Encode: 2.145s, Decode+Unpack: 1.610s ---------------------- -------------------------------------------------------- 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 2.7147 MSE: from ../datasets/FeatureCoding-StableDiffusion3.5/100samples_features/000000027620.zst to output-fixed/sd35/lambda0.02/elic-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: 2,284B, BPFP=23.7917 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: 10,868B, BPFP=1.4702 Using 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: 3,468B, BPFP=21.6750 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: 18,096B, BPFP=1.4688 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: 57,536B, BPFP=1.4594 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: 2,600B, BPFP=27.0833 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: 8,396B, BPFP=1.1358 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: 4,400B, BPFP=27.5000 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: 16,268B, BPFP=1.3205 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: 42,292B, BPFP=1.0727 Using per-key quantization points (vae.encoder_f0: torch.Size([256])) for vae.encoder_f0 vae.encoder_f0: 57,716B, BPFP=0.8807 Using per-key quantization points (vae.encoder_f1: torch.Size([256])) for vae.encoder_f1 vae.encoder_f1: 57,712B, BPFP=0.8806 Using per-key quantization points (vae.decoder: torch.Size([256])) for vae.decoder vae.decoder: 41,624B, BPFP=1.2703 ⌛️ [2/4] FRONTEND: Frontend time: 2.155s (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: 1.604s [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 0.17383804 text_encoder-item0.clip_prompt_embeds 0.00022242 23.50298887 text_encoder_2-item1.clip_pooled_prompt_embeds 0.00022710 0.19261245 text_encoder_2-item1.clip_prompt_embeds 0.00016311 0.05103497 text_encoder_3-item2.t5_prompt_embeds 0.00000924 0.00062858 text_encoder-item3.clip_pooled_prompt_embeds 0.00841799 0.25064182 text_encoder-item3.clip_prompt_embeds 0.00023247 57.42621246 text_encoder_2-item4.clip_pooled_prompt_embeds 0.00016751 0.08740882 text_encoder_2-item4.clip_prompt_embeds 0.00009830 0.08730212 text_encoder_3-item5.t5_prompt_embeds 0.00001263 0.00061542 vae.encoder_f0 0.00593415 0.55535716 vae.encoder_f1 0.00594307 0.55557418 vae.decoder 0.00018992 0.02106139 ------------------------------------------------------------------------------------- TOTAL 0.00280571 2.38325019 (elements=2,260,992) ---------------------- -------------------------------------------------------- SAMPLE-WISE STATISTICS ---------------------- -------------------------------------------------------- Handler sd35 Strategy individual Architecture elic-featurecoding ---------------------- -------------------------------------------------------- Total Elements 2260992 Total Bytes 323260 BPFP 1.1438 bits/point EBPFP 2.2876 equivalent bits/point MSE 2.383250 ---------------------- -------------------------------------------------------- Time: 3.767s Load: 0.009s, Pack+Encode: 2.155s, Decode+Unpack: 1.604s ---------------------- -------------------------------------------------------- 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 2.3833 MSE: from ../datasets/FeatureCoding-StableDiffusion3.5/100samples_features/000000030504.zst to output-fixed/sd35/lambda0.02/elic-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: 2,232B, BPFP=23.2500 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: 11,964B, BPFP=1.6185 Using 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: 3,324B, BPFP=20.7750 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: 21,548B, BPFP=1.7490 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: 65,112B, BPFP=1.6516 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: 2,600B, BPFP=27.0833 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: 8,396B, BPFP=1.1358 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: 4,400B, BPFP=27.5000 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: 16,268B, BPFP=1.3205 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: 42,292B, BPFP=1.0727 Using per-key quantization points (vae.encoder_f0: torch.Size([256])) for vae.encoder_f0 vae.encoder_f0: 47,896B, BPFP=0.7308 Using per-key quantization points (vae.encoder_f1: torch.Size([256])) for vae.encoder_f1 vae.encoder_f1: 47,864B, BPFP=0.7303 Using per-key quantization points (vae.decoder: torch.Size([256])) for vae.decoder vae.decoder: 27,656B, BPFP=0.8440 ⌛️ [2/4] FRONTEND: Frontend time: 2.164s (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: 1.605s [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 0.16024538 text_encoder-item0.clip_prompt_embeds 0.00022110 75.55318249 text_encoder_2-item1.clip_pooled_prompt_embeds 0.00042957 0.20867701 text_encoder_2-item1.clip_prompt_embeds 0.00091506 0.04644808 text_encoder_3-item2.t5_prompt_embeds 0.00000774 0.00087576 text_encoder-item3.clip_pooled_prompt_embeds 0.00841799 0.25064182 text_encoder-item3.clip_prompt_embeds 0.00023247 57.42621246 text_encoder_2-item4.clip_pooled_prompt_embeds 0.00016751 0.08740882 text_encoder_2-item4.clip_prompt_embeds 0.00009830 0.08730212 text_encoder_3-item5.t5_prompt_embeds 0.00001263 0.00061542 vae.encoder_f0 0.00641770 0.54974091 vae.encoder_f1 0.00642053 0.55022442 vae.decoder 0.00017498 0.01509108 ------------------------------------------------------------------------------------- TOTAL 0.00305947 3.74122130 (elements=2,260,992) ---------------------- -------------------------------------------------------- SAMPLE-WISE STATISTICS ---------------------- -------------------------------------------------------- Handler sd35 Strategy individual Architecture elic-featurecoding ---------------------- -------------------------------------------------------- Total Elements 2260992 Total Bytes 301552 BPFP 1.0670 bits/point EBPFP 2.1339 equivalent bits/point MSE 3.741221 ---------------------- -------------------------------------------------------- Time: 3.777s Load: 0.008s, Pack+Encode: 2.164s, Decode+Unpack: 1.605s ---------------------- -------------------------------------------------------- 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 3.7412 MSE: from ../datasets/FeatureCoding-StableDiffusion3.5/100samples_features/000000031248.zst to output-fixed/sd35/lambda0.02/elic-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: 2,320B, BPFP=24.1667 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: 10,492B, BPFP=1.4194 Using 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: 3,508B, BPFP=21.9250 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: 18,988B, BPFP=1.5412 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: 64,960B, BPFP=1.6477 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: 2,600B, BPFP=27.0833 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: 8,396B, BPFP=1.1358 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: 4,400B, BPFP=27.5000 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: 16,268B, BPFP=1.3205 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: 42,292B, BPFP=1.0727 Using per-key quantization points (vae.encoder_f0: torch.Size([256])) for vae.encoder_f0 vae.encoder_f0: 44,144B, BPFP=0.6736 Using per-key quantization points (vae.encoder_f1: torch.Size([256])) for vae.encoder_f1 vae.encoder_f1: 44,120B, BPFP=0.6732 Using per-key quantization points (vae.decoder: torch.Size([256])) for vae.decoder vae.decoder: 39,068B, BPFP=1.1923 ⌛️ [2/4] FRONTEND: Frontend time: 2.144s (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: 1.609s [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 0.14772820 text_encoder-item0.clip_prompt_embeds 0.00021654 317.93442235 text_encoder_2-item1.clip_pooled_prompt_embeds 0.00022548 0.17563323 text_encoder_2-item1.clip_prompt_embeds 0.00022218 0.05902204 text_encoder_3-item2.t5_prompt_embeds 0.00000780 0.00071474 text_encoder-item3.clip_pooled_prompt_embeds 0.00841799 0.25064182 text_encoder-item3.clip_prompt_embeds 0.00023247 57.42621246 text_encoder_2-item4.clip_pooled_prompt_embeds 0.00016751 0.08740882 text_encoder_2-item4.clip_prompt_embeds 0.00009830 0.08730212 text_encoder_3-item5.t5_prompt_embeds 0.00001263 0.00061542 vae.encoder_f0 0.00577698 0.49973232 vae.encoder_f1 0.00578348 0.50064027 vae.decoder 0.00017559 0.01750978 ------------------------------------------------------------------------------------- TOTAL 0.00273280 10.05836544 (elements=2,260,992) ---------------------- -------------------------------------------------------- SAMPLE-WISE STATISTICS ---------------------- -------------------------------------------------------- Handler sd35 Strategy individual Architecture elic-featurecoding ---------------------- -------------------------------------------------------- Total Elements 2260992 Total Bytes 301556 BPFP 1.0670 bits/point EBPFP 2.1340 equivalent bits/point MSE 10.058365 ---------------------- -------------------------------------------------------- Time: 3.762s Load: 0.008s, Pack+Encode: 2.144s, Decode+Unpack: 1.609s ---------------------- -------------------------------------------------------- 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 10.0584 MSE: from ../datasets/FeatureCoding-StableDiffusion3.5/100samples_features/000000055072.zst to output-fixed/sd35/lambda0.02/elic-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: 2,236B, BPFP=23.2917 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: 11,260B, BPFP=1.5233 Using 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: 3,328B, BPFP=20.8000 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: 20,180B, BPFP=1.6380 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: 68,568B, BPFP=1.7392 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: 2,600B, BPFP=27.0833 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: 8,396B, BPFP=1.1358 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: 4,400B, BPFP=27.5000 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: 16,268B, BPFP=1.3205 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: 42,292B, BPFP=1.0727 Using per-key quantization points (vae.encoder_f0: torch.Size([256])) for vae.encoder_f0 vae.encoder_f0: 49,536B, BPFP=0.7559 Using per-key quantization points (vae.encoder_f1: torch.Size([256])) for vae.encoder_f1 vae.encoder_f1: 49,564B, BPFP=0.7563 Using per-key quantization points (vae.decoder: torch.Size([256])) for vae.decoder vae.decoder: 30,064B, BPFP=0.9175 ⌛️ [2/4] FRONTEND: Frontend time: 2.137s (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: 1.603s [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 0.19917794 text_encoder-item0.clip_prompt_embeds 0.00022160 77.91839996 text_encoder_2-item1.clip_pooled_prompt_embeds 0.00041183 0.17277144 text_encoder_2-item1.clip_prompt_embeds 0.00016827 0.04190491 text_encoder_3-item2.t5_prompt_embeds 0.00000781 0.00067714 text_encoder-item3.clip_pooled_prompt_embeds 0.00841799 0.25064182 text_encoder-item3.clip_prompt_embeds 0.00023247 57.42621246 text_encoder_2-item4.clip_pooled_prompt_embeds 0.00016751 0.08740882 text_encoder_2-item4.clip_prompt_embeds 0.00009830 0.08730212 text_encoder_3-item5.t5_prompt_embeds 0.00001263 0.00061542 vae.encoder_f0 0.00668450 0.54482794 vae.encoder_f1 0.00668875 0.54330224 vae.decoder 0.00023059 0.01879731 ------------------------------------------------------------------------------------- TOTAL 0.00315742 3.80053578 (elements=2,260,992) ---------------------- -------------------------------------------------------- SAMPLE-WISE STATISTICS ---------------------- -------------------------------------------------------- Handler sd35 Strategy individual Architecture elic-featurecoding ---------------------- -------------------------------------------------------- Total Elements 2260992 Total Bytes 308692 BPFP 1.0922 bits/point EBPFP 2.1845 equivalent bits/point MSE 3.800536 ---------------------- -------------------------------------------------------- Time: 3.749s Load: 0.008s, Pack+Encode: 2.137s, Decode+Unpack: 1.603s ---------------------- -------------------------------------------------------- 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 3.8005 MSE: from ../datasets/FeatureCoding-StableDiffusion3.5/100samples_features/000000060932.zst to output-fixed/sd35/lambda0.02/elic-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: 2,376B, BPFP=24.7500 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: 12,328B, BPFP=1.6677 Using 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: 3,336B, BPFP=20.8500 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: 21,076B, BPFP=1.7107 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: 68,320B, BPFP=1.7330 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: 2,600B, BPFP=27.0833 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: 8,396B, BPFP=1.1358 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: 4,400B, BPFP=27.5000 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: 16,268B, BPFP=1.3205 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: 42,292B, BPFP=1.0727 Using per-key quantization points (vae.encoder_f0: torch.Size([256])) for vae.encoder_f0 vae.encoder_f0: 41,436B, BPFP=0.6323 Using per-key quantization points (vae.encoder_f1: torch.Size([256])) for vae.encoder_f1 vae.encoder_f1: 41,428B, BPFP=0.6321 Using per-key quantization points (vae.decoder: torch.Size([256])) for vae.decoder vae.decoder: 20,352B, BPFP=0.6211 ⌛️ [2/4] FRONTEND: Frontend time: 2.139s (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: 1.612s [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 0.14511351 text_encoder-item0.clip_prompt_embeds 0.00023190 89.52374188 text_encoder_2-item1.clip_pooled_prompt_embeds 0.00016235 0.16775749 text_encoder_2-item1.clip_prompt_embeds 0.00020162 0.05306912 text_encoder_3-item2.t5_prompt_embeds 0.00000881 0.00066556 text_encoder-item3.clip_pooled_prompt_embeds 0.00841799 0.25064182 text_encoder-item3.clip_prompt_embeds 0.00023247 57.42621246 text_encoder_2-item4.clip_pooled_prompt_embeds 0.00016751 0.08740882 text_encoder_2-item4.clip_prompt_embeds 0.00009830 0.08730212 text_encoder_3-item5.t5_prompt_embeds 0.00001263 0.00061542 vae.encoder_f0 0.04018118 0.81567943 vae.encoder_f1 0.04018488 0.81805491 vae.decoder 0.00016201 0.01354473 ------------------------------------------------------------------------------------- TOTAL 0.01868571 4.23044400 (elements=2,260,992) ---------------------- -------------------------------------------------------- SAMPLE-WISE STATISTICS ---------------------- -------------------------------------------------------- Handler sd35 Strategy individual Architecture elic-featurecoding ---------------------- -------------------------------------------------------- Total Elements 2260992 Total Bytes 284608 BPFP 1.0070 bits/point EBPFP 2.0140 equivalent bits/point MSE 4.230444 ---------------------- -------------------------------------------------------- Time: 3.760s Load: 0.009s, Pack+Encode: 2.139s, Decode+Unpack: 1.612s ---------------------- -------------------------------------------------------- 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 4.2304 MSE: from ../datasets/FeatureCoding-StableDiffusion3.5/100samples_features/000000062025.zst to output-fixed/sd35/lambda0.02/elic-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: 2,328B, BPFP=24.2500 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: 11,176B, BPFP=1.5119 Using 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: 3,452B, BPFP=21.5750 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: 18,944B, BPFP=1.5377 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: 63,712B, BPFP=1.6161 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: 2,600B, BPFP=27.0833 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: 8,396B, BPFP=1.1358 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: 4,400B, BPFP=27.5000 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: 16,268B, BPFP=1.3205 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: 42,292B, BPFP=1.0727 Using per-key quantization points (vae.encoder_f0: torch.Size([256])) for vae.encoder_f0 vae.encoder_f0: 50,084B, BPFP=0.7642 Using per-key quantization points (vae.encoder_f1: torch.Size([256])) for vae.encoder_f1 vae.encoder_f1: 50,080B, BPFP=0.7642 Using per-key quantization points (vae.decoder: torch.Size([256])) for vae.decoder vae.decoder: 28,460B, BPFP=0.8685 ⌛️ [2/4] FRONTEND: Frontend time: 2.141s (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: 1.606s [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 0.17868036 text_encoder-item0.clip_prompt_embeds 0.00023140 141.70440172 text_encoder_2-item1.clip_pooled_prompt_embeds 0.00025605 0.17372320 text_encoder_2-item1.clip_prompt_embeds 0.00016636 0.04331999 text_encoder_3-item2.t5_prompt_embeds 0.00000797 0.00078179 text_encoder-item3.clip_pooled_prompt_embeds 0.00841799 0.25064182 text_encoder-item3.clip_prompt_embeds 0.00023247 57.42621246 text_encoder_2-item4.clip_pooled_prompt_embeds 0.00016751 0.08740882 text_encoder_2-item4.clip_prompt_embeds 0.00009830 0.08730212 text_encoder_3-item5.t5_prompt_embeds 0.00001263 0.00061542 vae.encoder_f0 0.04874706 0.84256560 vae.encoder_f1 0.04875064 0.83943236 vae.decoder 0.00019641 0.01497553 ------------------------------------------------------------------------------------- TOTAL 0.02266071 5.60618695 (elements=2,260,992) ---------------------- -------------------------------------------------------- SAMPLE-WISE STATISTICS ---------------------- -------------------------------------------------------- Handler sd35 Strategy individual Architecture elic-featurecoding ---------------------- -------------------------------------------------------- Total Elements 2260992 Total Bytes 302192 BPFP 1.0692 bits/point EBPFP 2.1385 equivalent bits/point MSE 5.606187 ---------------------- -------------------------------------------------------- Time: 3.756s Load: 0.008s, Pack+Encode: 2.141s, Decode+Unpack: 1.606s ---------------------- -------------------------------------------------------- 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 5.6062 MSE: from ../datasets/FeatureCoding-StableDiffusion3.5/100samples_features/000000064718.zst to output-fixed/sd35/lambda0.02/elic-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: 0.008s ------------------------------------------------------------ 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: 2,344B, BPFP=24.4167 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: 11,472B, BPFP=1.5519 Using 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: 3,312B, BPFP=20.7000 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: 19,532B, BPFP=1.5854 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: 60,720B, BPFP=1.5402 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: 2,600B, BPFP=27.0833 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: 8,396B, BPFP=1.1358 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: 4,400B, BPFP=27.5000 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: 16,268B, BPFP=1.3205 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: 42,292B, BPFP=1.0727 Using per-key quantization points (vae.encoder_f0: torch.Size([256])) for vae.encoder_f0 vae.encoder_f0: 60,684B, BPFP=0.9260 Using per-key quantization points (vae.encoder_f1: torch.Size([256])) for vae.encoder_f1 vae.encoder_f1: 60,640B, BPFP=0.9253 Using per-key quantization points (vae.decoder: torch.Size([256])) for vae.decoder vae.decoder: 23,144B, BPFP=0.7063 ⌛️ [2/4] FRONTEND: Frontend time: 2.162s (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: 1.600s [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 0.13324536 text_encoder-item0.clip_prompt_embeds 0.00030893 53.85283922 text_encoder_2-item1.clip_pooled_prompt_embeds 0.00035783 0.18627477 text_encoder_2-item1.clip_prompt_embeds 0.00024047 0.05246485 text_encoder_3-item2.t5_prompt_embeds 0.00000770 0.00064552 text_encoder-item3.clip_pooled_prompt_embeds 0.00841799 0.25064182 text_encoder-item3.clip_prompt_embeds 0.00023247 57.42621246 text_encoder_2-item4.clip_pooled_prompt_embeds 0.00016751 0.08740882 text_encoder_2-item4.clip_prompt_embeds 0.00009830 0.08730212 text_encoder_3-item5.t5_prompt_embeds 0.00001263 0.00061542 vae.encoder_f0 0.01360236 0.79972041 vae.encoder_f1 0.01360807 0.80173808 vae.decoder 0.00023006 0.01714638 ------------------------------------------------------------------------------------- TOTAL 0.00637132 3.29038606 (elements=2,260,992) ---------------------- -------------------------------------------------------- SAMPLE-WISE STATISTICS ---------------------- -------------------------------------------------------- Handler sd35 Strategy individual Architecture elic-featurecoding ---------------------- -------------------------------------------------------- Total Elements 2260992 Total Bytes 315804 BPFP 1.1174 bits/point EBPFP 2.2348 equivalent bits/point MSE 3.290386 ---------------------- -------------------------------------------------------- Time: 3.770s Load: 0.008s, Pack+Encode: 2.162s, Decode+Unpack: 1.600s ---------------------- -------------------------------------------------------- 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 3.2904 MSE: from ../datasets/FeatureCoding-StableDiffusion3.5/100samples_features/000000070739.zst to output-fixed/sd35/lambda0.02/elic-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: 2,248B, BPFP=23.4167 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: 12,396B, BPFP=1.6769 Using 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: 3,376B, BPFP=21.1000 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: 21,388B, BPFP=1.7360 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: 70,516B, BPFP=1.7887 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: 2,600B, BPFP=27.0833 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: 8,396B, BPFP=1.1358 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: 4,400B, BPFP=27.5000 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: 16,268B, BPFP=1.3205 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: 42,292B, BPFP=1.0727 Using per-key quantization points (vae.encoder_f0: torch.Size([256])) for vae.encoder_f0 vae.encoder_f0: 21,428B, BPFP=0.3270 Using per-key quantization points (vae.encoder_f1: torch.Size([256])) for vae.encoder_f1 vae.encoder_f1: 21,428B, BPFP=0.3270 Using per-key quantization points (vae.decoder: torch.Size([256])) for vae.decoder vae.decoder: 11,072B, BPFP=0.3379 ⌛️ [2/4] FRONTEND: Frontend time: 2.131s (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: 1.592s [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 0.17260293 text_encoder-item0.clip_prompt_embeds 0.00024198 63.81886668 text_encoder_2-item1.clip_pooled_prompt_embeds 0.00023989 0.19646239 text_encoder_2-item1.clip_prompt_embeds 0.00015983 0.07884572 text_encoder_3-item2.t5_prompt_embeds 0.00000786 0.00068150 text_encoder-item3.clip_pooled_prompt_embeds 0.00841799 0.25064182 text_encoder-item3.clip_prompt_embeds 0.00023247 57.42621246 text_encoder_2-item4.clip_pooled_prompt_embeds 0.00016751 0.08740882 text_encoder_2-item4.clip_prompt_embeds 0.00009830 0.08730212 text_encoder_3-item5.t5_prompt_embeds 0.00001263 0.00061542 vae.encoder_f0 1.67190456 3.40906739 vae.encoder_f1 1.67190480 3.41801357 vae.decoder 0.00017417 0.00832015 ------------------------------------------------------------------------------------- TOTAL 0.77542609 4.76293577 (elements=2,260,992) ---------------------- -------------------------------------------------------- SAMPLE-WISE STATISTICS ---------------------- -------------------------------------------------------- Handler sd35 Strategy individual Architecture elic-featurecoding ---------------------- -------------------------------------------------------- Total Elements 2260992 Total Bytes 237808 BPFP 0.8414 bits/point EBPFP 1.6829 equivalent bits/point MSE 4.762936 ---------------------- -------------------------------------------------------- Time: 3.731s Load: 0.007s, Pack+Encode: 2.131s, Decode+Unpack: 1.592s ---------------------- -------------------------------------------------------- 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 4.7629 MSE: from ../datasets/FeatureCoding-StableDiffusion3.5/100samples_features/000000074646.zst to output-fixed/sd35/lambda0.02/elic-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: 2,252B, BPFP=23.4583 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: 11,380B, BPFP=1.5395 Using 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: 3,284B, BPFP=20.5250 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: 19,868B, BPFP=1.6127 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: 70,648B, BPFP=1.7920 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: 2,600B, BPFP=27.0833 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: 8,396B, BPFP=1.1358 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: 4,400B, BPFP=27.5000 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: 16,268B, BPFP=1.3205 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: 42,292B, BPFP=1.0727 Using per-key quantization points (vae.encoder_f0: torch.Size([256])) for vae.encoder_f0 vae.encoder_f0: 63,296B, BPFP=0.9658 Using per-key quantization points (vae.encoder_f1: torch.Size([256])) for vae.encoder_f1 vae.encoder_f1: 63,300B, BPFP=0.9659 Using per-key quantization points (vae.decoder: torch.Size([256])) for vae.decoder vae.decoder: 35,572B, BPFP=1.0856 ⌛️ [2/4] FRONTEND: Frontend time: 2.141s (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: 1.601s [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 0.13460732 text_encoder-item0.clip_prompt_embeds 0.00025129 53.17272896 text_encoder_2-item1.clip_pooled_prompt_embeds 0.00023862 0.21362059 text_encoder_2-item1.clip_prompt_embeds 0.00021627 0.04223134 text_encoder_3-item2.t5_prompt_embeds 0.00000880 0.00083364 text_encoder-item3.clip_pooled_prompt_embeds 0.00841799 0.25064182 text_encoder-item3.clip_prompt_embeds 0.00023247 57.42621246 text_encoder_2-item4.clip_pooled_prompt_embeds 0.00016751 0.08740882 text_encoder_2-item4.clip_prompt_embeds 0.00009830 0.08730212 text_encoder_3-item5.t5_prompt_embeds 0.00001263 0.00061542 vae.encoder_f0 0.00621760 0.60191727 vae.encoder_f1 0.00622505 0.60018814 vae.decoder 0.00025114 0.02049554 ------------------------------------------------------------------------------------- TOTAL 0.00294689 3.17997863 (elements=2,260,992) ---------------------- -------------------------------------------------------- SAMPLE-WISE STATISTICS ---------------------- -------------------------------------------------------- Handler sd35 Strategy individual Architecture elic-featurecoding ---------------------- -------------------------------------------------------- Total Elements 2260992 Total Bytes 343556 BPFP 1.2156 bits/point EBPFP 2.4312 equivalent bits/point MSE 3.179979 ---------------------- -------------------------------------------------------- Time: 3.749s Load: 0.008s, Pack+Encode: 2.141s, Decode+Unpack: 1.601s ---------------------- -------------------------------------------------------- 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 3.1800 MSE: from ../datasets/FeatureCoding-StableDiffusion3.5/100samples_features/000000085157.zst to output-fixed/sd35/lambda0.02/elic-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: 2,204B, BPFP=22.9583 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: 10,688B, BPFP=1.4459 Using 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: 3,468B, BPFP=21.6750 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: 19,168B, BPFP=1.5558 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: 60,904B, BPFP=1.5448 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: 2,600B, BPFP=27.0833 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: 8,396B, BPFP=1.1358 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: 4,400B, BPFP=27.5000 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: 16,268B, BPFP=1.3205 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: 42,292B, BPFP=1.0727 Using per-key quantization points (vae.encoder_f0: torch.Size([256])) for vae.encoder_f0 vae.encoder_f0: 65,572B, BPFP=1.0005 Using per-key quantization points (vae.encoder_f1: torch.Size([256])) for vae.encoder_f1 vae.encoder_f1: 65,580B, BPFP=1.0007 Using per-key quantization points (vae.decoder: torch.Size([256])) for vae.decoder vae.decoder: 36,180B, BPFP=1.1041 ⌛️ [2/4] FRONTEND: Frontend time: 2.148s (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: 1.603s [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 0.21120721 text_encoder-item0.clip_prompt_embeds 0.00020838 43.98228659 text_encoder_2-item1.clip_pooled_prompt_embeds 0.00021520 0.18491266 text_encoder_2-item1.clip_prompt_embeds 0.00018543 0.05603871 text_encoder_3-item2.t5_prompt_embeds 0.00000844 0.00067333 text_encoder-item3.clip_pooled_prompt_embeds 0.00841799 0.25064182 text_encoder-item3.clip_prompt_embeds 0.00023247 57.42621246 text_encoder_2-item4.clip_pooled_prompt_embeds 0.00016751 0.08740882 text_encoder_2-item4.clip_prompt_embeds 0.00009830 0.08730212 text_encoder_3-item5.t5_prompt_embeds 0.00001263 0.00061542 vae.encoder_f0 0.00675961 0.68917894 vae.encoder_f1 0.00676652 0.68955696 vae.decoder 0.00021373 0.02084894 ------------------------------------------------------------------------------------- TOTAL 0.00319201 2.98119167 (elements=2,260,992) ---------------------- -------------------------------------------------------- SAMPLE-WISE STATISTICS ---------------------- -------------------------------------------------------- Handler sd35 Strategy individual Architecture elic-featurecoding ---------------------- -------------------------------------------------------- Total Elements 2260992 Total Bytes 337720 BPFP 1.1949 bits/point EBPFP 2.3899 equivalent bits/point MSE 2.981192 ---------------------- -------------------------------------------------------- Time: 3.759s Load: 0.009s, Pack+Encode: 2.148s, Decode+Unpack: 1.603s ---------------------- -------------------------------------------------------- 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 2.9812 MSE: from ../datasets/FeatureCoding-StableDiffusion3.5/100samples_features/000000089648.zst to output-fixed/sd35/lambda0.02/elic-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: 2,232B, BPFP=23.2500 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: 11,268B, BPFP=1.5244 Using 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: 3,300B, BPFP=20.6250 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: 20,400B, BPFP=1.6558 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: 71,704B, BPFP=1.8188 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: 2,600B, BPFP=27.0833 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: 8,396B, BPFP=1.1358 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: 4,400B, BPFP=27.5000 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: 16,268B, BPFP=1.3205 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: 42,292B, BPFP=1.0727 Using per-key quantization points (vae.encoder_f0: torch.Size([256])) for vae.encoder_f0 vae.encoder_f0: 39,036B, BPFP=0.5956 Using per-key quantization points (vae.encoder_f1: torch.Size([256])) for vae.encoder_f1 vae.encoder_f1: 39,036B, BPFP=0.5956 Using per-key quantization points (vae.decoder: torch.Size([256])) for vae.decoder vae.decoder: 54,544B, BPFP=1.6646 ⌛️ [2/4] FRONTEND: Frontend time: 2.142s (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: 1.603s [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 0.14650366 text_encoder-item0.clip_prompt_embeds 0.00021387 90.42366748 text_encoder_2-item1.clip_pooled_prompt_embeds 0.00028145 0.17987248 text_encoder_2-item1.clip_prompt_embeds 0.00018115 0.05365184 text_encoder_3-item2.t5_prompt_embeds 0.00000768 0.00067569 text_encoder-item3.clip_pooled_prompt_embeds 0.00841799 0.25064182 text_encoder-item3.clip_prompt_embeds 0.00023247 57.42621246 text_encoder_2-item4.clip_pooled_prompt_embeds 0.00016751 0.08740882 text_encoder_2-item4.clip_prompt_embeds 0.00009830 0.08730212 text_encoder_3-item5.t5_prompt_embeds 0.00001263 0.00061542 vae.encoder_f0 0.00596338 0.43080303 vae.encoder_f1 0.00596322 0.43075785 vae.decoder 0.00018207 0.02345705 ------------------------------------------------------------------------------------- TOTAL 0.00281657 4.07611014 (elements=2,260,992) ---------------------- -------------------------------------------------------- SAMPLE-WISE STATISTICS ---------------------- -------------------------------------------------------- Handler sd35 Strategy individual Architecture elic-featurecoding ---------------------- -------------------------------------------------------- Total Elements 2260992 Total Bytes 315476 BPFP 1.1162 bits/point EBPFP 2.2325 equivalent bits/point MSE 4.076110 ---------------------- -------------------------------------------------------- Time: 3.754s Load: 0.008s, Pack+Encode: 2.142s, Decode+Unpack: 1.603s ---------------------- -------------------------------------------------------- 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 4.0761 MSE: from ../datasets/FeatureCoding-StableDiffusion3.5/100samples_features/000000093965.zst to output-fixed/sd35/lambda0.02/elic-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: 0.008s ------------------------------------------------------------ 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: 2,248B, BPFP=23.4167 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: 11,424B, BPFP=1.5455 Using 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: 3,400B, BPFP=21.2500 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: 19,448B, BPFP=1.5786 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: 60,860B, BPFP=1.5437 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: 2,600B, BPFP=27.0833 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: 8,396B, BPFP=1.1358 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: 4,400B, BPFP=27.5000 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: 16,268B, BPFP=1.3205 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: 42,292B, BPFP=1.0727 Using per-key quantization points (vae.encoder_f0: torch.Size([256])) for vae.encoder_f0 vae.encoder_f0: 28,572B, BPFP=0.4360 Using per-key quantization points (vae.encoder_f1: torch.Size([256])) for vae.encoder_f1 vae.encoder_f1: 28,572B, BPFP=0.4360 Using per-key quantization points (vae.decoder: torch.Size([256])) for vae.decoder vae.decoder: 44,296B, BPFP=1.3518 ⌛️ [2/4] FRONTEND: Frontend time: 2.150s (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: 1.597s [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 0.12811538 text_encoder-item0.clip_prompt_embeds 0.00022138 203.29822781 text_encoder_2-item1.clip_pooled_prompt_embeds 0.00034234 0.17463790 text_encoder_2-item1.clip_prompt_embeds 0.00019942 0.05530069 text_encoder_3-item2.t5_prompt_embeds 0.00000807 0.00067538 text_encoder-item3.clip_pooled_prompt_embeds 0.00841799 0.25064182 text_encoder-item3.clip_prompt_embeds 0.00023247 57.42621246 text_encoder_2-item4.clip_pooled_prompt_embeds 0.00016751 0.08740882 text_encoder_2-item4.clip_prompt_embeds 0.00009830 0.08730212 text_encoder_3-item5.t5_prompt_embeds 0.00001263 0.00061542 vae.encoder_f0 0.00552804 0.35862291 vae.encoder_f1 0.00552758 0.35856766 vae.decoder 0.00018040 0.02047906 ------------------------------------------------------------------------------------- TOTAL 0.00261550 6.99457217 (elements=2,260,992) ---------------------- -------------------------------------------------------- SAMPLE-WISE STATISTICS ---------------------- -------------------------------------------------------- Handler sd35 Strategy individual Architecture elic-featurecoding ---------------------- -------------------------------------------------------- Total Elements 2260992 Total Bytes 272776 BPFP 0.9652 bits/point EBPFP 1.9303 equivalent bits/point MSE 6.994572 ---------------------- -------------------------------------------------------- Time: 3.755s Load: 0.008s, Pack+Encode: 2.150s, Decode+Unpack: 1.597s ---------------------- -------------------------------------------------------- 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 6.9946 MSE: from ../datasets/FeatureCoding-StableDiffusion3.5/100samples_features/000000094852.zst to output-fixed/sd35/lambda0.02/elic-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: 2,364B, BPFP=24.6250 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: 11,368B, BPFP=1.5379 Using 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: 3,528B, BPFP=22.0500 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: 19,204B, BPFP=1.5588 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: 61,536B, BPFP=1.5609 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: 2,600B, BPFP=27.0833 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: 8,396B, BPFP=1.1358 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: 4,400B, BPFP=27.5000 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: 16,268B, BPFP=1.3205 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: 42,292B, BPFP=1.0727 Using per-key quantization points (vae.encoder_f0: torch.Size([256])) for vae.encoder_f0 vae.encoder_f0: 32,212B, BPFP=0.4915 Using per-key quantization points (vae.encoder_f1: torch.Size([256])) for vae.encoder_f1 vae.encoder_f1: 32,180B, BPFP=0.4910 Using per-key quantization points (vae.decoder: torch.Size([256])) for vae.decoder vae.decoder: 25,224B, BPFP=0.7698 ⌛️ [2/4] FRONTEND: Frontend time: 2.135s (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: 1.602s [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 0.15973969 text_encoder-item0.clip_prompt_embeds 0.00024507 65.62379092 text_encoder_2-item1.clip_pooled_prompt_embeds 0.00020802 0.17414649 text_encoder_2-item1.clip_prompt_embeds 0.00034897 0.06205029 text_encoder_3-item2.t5_prompt_embeds 0.00000820 0.00065593 text_encoder-item3.clip_pooled_prompt_embeds 0.00841799 0.25064182 text_encoder-item3.clip_prompt_embeds 0.00023247 57.42621246 text_encoder_2-item4.clip_pooled_prompt_embeds 0.00016751 0.08740882 text_encoder_2-item4.clip_prompt_embeds 0.00009830 0.08730212 text_encoder_3-item5.t5_prompt_embeds 0.00001263 0.00061542 vae.encoder_f0 0.00721525 0.50486088 vae.encoder_f1 0.00721777 0.50413579 vae.decoder 0.00018707 0.01531457 ------------------------------------------------------------------------------------- TOTAL 0.00340651 3.46108061 (elements=2,260,992) ---------------------- -------------------------------------------------------- SAMPLE-WISE STATISTICS ---------------------- -------------------------------------------------------- Handler sd35 Strategy individual Architecture elic-featurecoding ---------------------- -------------------------------------------------------- Total Elements 2260992 Total Bytes 261572 BPFP 0.9255 bits/point EBPFP 1.8510 equivalent bits/point MSE 3.461081 ---------------------- -------------------------------------------------------- Time: 3.744s Load: 0.008s, Pack+Encode: 2.135s, Decode+Unpack: 1.602s ---------------------- -------------------------------------------------------- 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 3.4611 MSE: from ../datasets/FeatureCoding-StableDiffusion3.5/100samples_features/000000117914.zst to output-fixed/sd35/lambda0.02/elic-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: 2,304B, BPFP=24.0000 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: 10,936B, BPFP=1.4794 Using 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: 3,588B, BPFP=22.4250 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: 18,152B, BPFP=1.4734 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: 57,284B, BPFP=1.4530 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: 2,600B, BPFP=27.0833 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: 8,396B, BPFP=1.1358 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: 4,400B, BPFP=27.5000 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: 16,268B, BPFP=1.3205 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: 42,292B, BPFP=1.0727 Using per-key quantization points (vae.encoder_f0: torch.Size([256])) for vae.encoder_f0 vae.encoder_f0: 47,796B, BPFP=0.7293 Using per-key quantization points (vae.encoder_f1: torch.Size([256])) for vae.encoder_f1 vae.encoder_f1: 47,872B, BPFP=0.7305 Using per-key quantization points (vae.decoder: torch.Size([256])) for vae.decoder vae.decoder: 30,652B, BPFP=0.9354 ⌛️ [2/4] FRONTEND: Frontend time: 2.145s (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: 1.601s [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 0.14543401 text_encoder-item0.clip_prompt_embeds 0.00046272 526.81561147 text_encoder_2-item1.clip_pooled_prompt_embeds 0.00022428 0.16482172 text_encoder_2-item1.clip_prompt_embeds 0.00014574 0.05286449 text_encoder_3-item2.t5_prompt_embeds 0.00000853 0.00063601 text_encoder-item3.clip_pooled_prompt_embeds 0.00841799 0.25064182 text_encoder-item3.clip_prompt_embeds 0.00023247 57.42621246 text_encoder_2-item4.clip_pooled_prompt_embeds 0.00016751 0.08740882 text_encoder_2-item4.clip_prompt_embeds 0.00009830 0.08730212 text_encoder_3-item5.t5_prompt_embeds 0.00001263 0.00061542 vae.encoder_f0 0.01999603 0.74573690 vae.encoder_f1 0.01999529 0.74725121 vae.decoder 0.00024882 0.01906798 ------------------------------------------------------------------------------------- TOTAL 0.00933711 15.63575429 (elements=2,260,992) ---------------------- -------------------------------------------------------- SAMPLE-WISE STATISTICS ---------------------- -------------------------------------------------------- Handler sd35 Strategy individual Architecture elic-featurecoding ---------------------- -------------------------------------------------------- Total Elements 2260992 Total Bytes 292540 BPFP 1.0351 bits/point EBPFP 2.0702 equivalent bits/point MSE 15.635754 ---------------------- -------------------------------------------------------- Time: 3.755s Load: 0.008s, Pack+Encode: 2.145s, Decode+Unpack: 1.601s ---------------------- -------------------------------------------------------- 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 15.6358 MSE: from ../datasets/FeatureCoding-StableDiffusion3.5/100samples_features/000000123321.zst to output-fixed/sd35/lambda0.02/elic-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: 0.008s ------------------------------------------------------------ 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: 2,200B, BPFP=22.9167 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: 11,056B, BPFP=1.4957 Using 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: 3,444B, BPFP=21.5250 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: 18,944B, BPFP=1.5377 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: 58,336B, BPFP=1.4797 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: 2,600B, BPFP=27.0833 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: 8,396B, BPFP=1.1358 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: 4,400B, BPFP=27.5000 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: 16,268B, BPFP=1.3205 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: 42,292B, BPFP=1.0727 Using per-key quantization points (vae.encoder_f0: torch.Size([256])) for vae.encoder_f0 vae.encoder_f0: 49,812B, BPFP=0.7601 Using per-key quantization points (vae.encoder_f1: torch.Size([256])) for vae.encoder_f1 vae.encoder_f1: 49,788B, BPFP=0.7597 Using per-key quantization points (vae.decoder: torch.Size([256])) for vae.decoder vae.decoder: 19,208B, BPFP=0.5862 ⌛️ [2/4] FRONTEND: Frontend time: 2.145s (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: 1.610s [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 0.18092106 text_encoder-item0.clip_prompt_embeds 0.00020334 75.96278916 text_encoder_2-item1.clip_pooled_prompt_embeds 0.00017433 0.15722663 text_encoder_2-item1.clip_prompt_embeds 0.00020202 0.05110690 text_encoder_3-item2.t5_prompt_embeds 0.00000787 0.00063831 text_encoder-item3.clip_pooled_prompt_embeds 0.00841799 0.25064182 text_encoder-item3.clip_prompt_embeds 0.00023247 57.42621246 text_encoder_2-item4.clip_pooled_prompt_embeds 0.00016751 0.08740882 text_encoder_2-item4.clip_prompt_embeds 0.00009830 0.08730212 text_encoder_3-item5.t5_prompt_embeds 0.00001263 0.00061542 vae.encoder_f0 0.01341345 0.70068753 vae.encoder_f1 0.01341645 0.69802886 vae.decoder 0.00018350 0.01297745 ------------------------------------------------------------------------------------- TOTAL 0.00627332 3.82111293 (elements=2,260,992) ---------------------- -------------------------------------------------------- SAMPLE-WISE STATISTICS ---------------------- -------------------------------------------------------- Handler sd35 Strategy individual Architecture elic-featurecoding ---------------------- -------------------------------------------------------- Total Elements 2260992 Total Bytes 286744 BPFP 1.0146 bits/point EBPFP 2.0292 equivalent bits/point MSE 3.821113 ---------------------- -------------------------------------------------------- Time: 3.763s Load: 0.008s, Pack+Encode: 2.145s, Decode+Unpack: 1.610s ---------------------- -------------------------------------------------------- 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 3.8211 MSE: from ../datasets/FeatureCoding-StableDiffusion3.5/100samples_features/000000127182.zst to output-fixed/sd35/lambda0.02/elic-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: 0.009s ------------------------------------------------------------ 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: 2,168B, BPFP=22.5833 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: 10,816B, BPFP=1.4632 Using 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: 3,380B, BPFP=21.1250 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: 19,884B, BPFP=1.6140 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: 66,604B, BPFP=1.6894 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: 2,600B, BPFP=27.0833 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: 8,396B, BPFP=1.1358 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: 4,400B, BPFP=27.5000 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: 16,268B, BPFP=1.3205 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: 42,292B, BPFP=1.0727 Using per-key quantization points (vae.encoder_f0: torch.Size([256])) for vae.encoder_f0 vae.encoder_f0: 56,412B, BPFP=0.8608 Using per-key quantization points (vae.encoder_f1: torch.Size([256])) for vae.encoder_f1 vae.encoder_f1: 56,440B, BPFP=0.8612 Using per-key quantization points (vae.decoder: torch.Size([256])) for vae.decoder vae.decoder: 42,480B, BPFP=1.2964 ⌛️ [2/4] FRONTEND: Frontend time: 2.140s (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: 1.601s [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 0.17500885 text_encoder-item0.clip_prompt_embeds 0.00022316 138.71661086 text_encoder_2-item1.clip_pooled_prompt_embeds 0.00045791 0.19323925 text_encoder_2-item1.clip_prompt_embeds 0.00022852 0.05293774 text_encoder_3-item2.t5_prompt_embeds 0.00000822 0.00072218 text_encoder-item3.clip_pooled_prompt_embeds 0.00841799 0.25064182 text_encoder-item3.clip_prompt_embeds 0.00023247 57.42621246 text_encoder_2-item4.clip_pooled_prompt_embeds 0.00016751 0.08740882 text_encoder_2-item4.clip_prompt_embeds 0.00009830 0.08730212 text_encoder_3-item5.t5_prompt_embeds 0.00001263 0.00061542 vae.encoder_f0 0.00606298 0.54114926 vae.encoder_f1 0.00607096 0.54039824 vae.decoder 0.00023408 0.02190416 ------------------------------------------------------------------------------------- TOTAL 0.00287331 5.39003077 (elements=2,260,992) ---------------------- -------------------------------------------------------- SAMPLE-WISE STATISTICS ---------------------- -------------------------------------------------------- Handler sd35 Strategy individual Architecture elic-featurecoding ---------------------- -------------------------------------------------------- Total Elements 2260992 Total Bytes 332140 BPFP 1.1752 bits/point EBPFP 2.3504 equivalent bits/point MSE 5.390031 ---------------------- -------------------------------------------------------- Time: 3.750s Load: 0.009s, Pack+Encode: 2.140s, Decode+Unpack: 1.601s ---------------------- -------------------------------------------------------- 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 5.3900 MSE: from ../datasets/FeatureCoding-StableDiffusion3.5/100samples_features/000000127394.zst to output-fixed/sd35/lambda0.02/elic-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: 2,208B, BPFP=23.0000 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: 10,996B, BPFP=1.4876 Using 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: 3,512B, BPFP=21.9500 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: 18,388B, BPFP=1.4925 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: 64,244B, BPFP=1.6296 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: 2,600B, BPFP=27.0833 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: 8,396B, BPFP=1.1358 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: 4,400B, BPFP=27.5000 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: 16,268B, BPFP=1.3205 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: 42,292B, BPFP=1.0727 Using per-key quantization points (vae.encoder_f0: torch.Size([256])) for vae.encoder_f0 vae.encoder_f0: 52,144B, BPFP=0.7957 Using per-key quantization points (vae.encoder_f1: torch.Size([256])) for vae.encoder_f1 vae.encoder_f1: 52,124B, BPFP=0.7953 Using per-key quantization points (vae.decoder: torch.Size([256])) for vae.decoder vae.decoder: 37,196B, BPFP=1.1351 ⌛️ [2/4] FRONTEND: Frontend time: 2.146s (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: 1.599s [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 0.17327617 text_encoder-item0.clip_prompt_embeds 0.00023597 65.35782349 text_encoder_2-item1.clip_pooled_prompt_embeds 0.00026316 0.17242184 text_encoder_2-item1.clip_prompt_embeds 0.00018757 0.04477924 text_encoder_3-item2.t5_prompt_embeds 0.00000828 0.00066292 text_encoder-item3.clip_pooled_prompt_embeds 0.00841799 0.25064182 text_encoder-item3.clip_prompt_embeds 0.00023247 57.42621246 text_encoder_2-item4.clip_pooled_prompt_embeds 0.00016751 0.08740882 text_encoder_2-item4.clip_prompt_embeds 0.00009830 0.08730212 text_encoder_3-item5.t5_prompt_embeds 0.00001263 0.00061542 vae.encoder_f0 0.00653100 0.56126887 vae.encoder_f1 0.00653745 0.56055206 vae.decoder 0.00020026 0.01968447 ------------------------------------------------------------------------------------- TOTAL 0.00308450 3.48004479 (elements=2,260,992) ---------------------- -------------------------------------------------------- SAMPLE-WISE STATISTICS ---------------------- -------------------------------------------------------- Handler sd35 Strategy individual Architecture elic-featurecoding ---------------------- -------------------------------------------------------- Total Elements 2260992 Total Bytes 314768 BPFP 1.1137 bits/point EBPFP 2.2275 equivalent bits/point MSE 3.480045 ---------------------- -------------------------------------------------------- Time: 3.754s Load: 0.009s, Pack+Encode: 2.146s, Decode+Unpack: 1.599s ---------------------- -------------------------------------------------------- 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 3.4800 MSE: from ../datasets/FeatureCoding-StableDiffusion3.5/100samples_features/000000133969.zst to output-fixed/sd35/lambda0.02/elic-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: 2,240B, BPFP=23.3333 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: 11,592B, BPFP=1.5682 Using 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: 3,460B, BPFP=21.6250 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: 19,764B, BPFP=1.6042 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: 60,328B, BPFP=1.5302 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: 2,600B, BPFP=27.0833 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: 8,396B, BPFP=1.1358 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: 4,400B, BPFP=27.5000 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: 16,268B, BPFP=1.3205 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: 42,292B, BPFP=1.0727 Using per-key quantization points (vae.encoder_f0: torch.Size([256])) for vae.encoder_f0 vae.encoder_f0: 52,208B, BPFP=0.7966 Using per-key quantization points (vae.encoder_f1: torch.Size([256])) for vae.encoder_f1 vae.encoder_f1: 52,188B, BPFP=0.7963 Using per-key quantization points (vae.decoder: torch.Size([256])) for vae.decoder vae.decoder: 25,656B, BPFP=0.7830 ⌛️ [2/4] FRONTEND: Frontend time: 2.146s (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: 1.602s [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 0.15267690 text_encoder-item0.clip_prompt_embeds 0.00022433 46.47950487 text_encoder_2-item1.clip_pooled_prompt_embeds 0.00107168 0.18215313 text_encoder_2-item1.clip_prompt_embeds 0.00016492 0.04840512 text_encoder_3-item2.t5_prompt_embeds 0.00000806 0.00064631 text_encoder-item3.clip_pooled_prompt_embeds 0.00841799 0.25064182 text_encoder-item3.clip_prompt_embeds 0.00023247 57.42621246 text_encoder_2-item4.clip_pooled_prompt_embeds 0.00016751 0.08740882 text_encoder_2-item4.clip_prompt_embeds 0.00009830 0.08730212 text_encoder_3-item5.t5_prompt_embeds 0.00001263 0.00061542 vae.encoder_f0 0.00869686 0.72573489 vae.encoder_f1 0.00870063 0.73106110 vae.decoder 0.00021246 0.01730566 ------------------------------------------------------------------------------------- TOTAL 0.00408877 3.06383825 (elements=2,260,992) ---------------------- -------------------------------------------------------- SAMPLE-WISE STATISTICS ---------------------- -------------------------------------------------------- Handler sd35 Strategy individual Architecture elic-featurecoding ---------------------- -------------------------------------------------------- Total Elements 2260992 Total Bytes 301392 BPFP 1.0664 bits/point EBPFP 2.1328 equivalent bits/point MSE 3.063838 ---------------------- -------------------------------------------------------- Time: 3.755s Load: 0.008s, Pack+Encode: 2.146s, Decode+Unpack: 1.602s ---------------------- -------------------------------------------------------- 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 3.0638 MSE: from ../datasets/FeatureCoding-StableDiffusion3.5/100samples_features/000000140270.zst to output-fixed/sd35/lambda0.02/elic-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: 2,248B, BPFP=23.4167 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: 11,796B, BPFP=1.5958 Using 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: 3,452B, BPFP=21.5750 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: 21,220B, BPFP=1.7224 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: 65,332B, BPFP=1.6572 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: 2,600B, BPFP=27.0833 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: 8,396B, BPFP=1.1358 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: 4,400B, BPFP=27.5000 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: 16,268B, BPFP=1.3205 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: 42,292B, BPFP=1.0727 Using per-key quantization points (vae.encoder_f0: torch.Size([256])) for vae.encoder_f0 vae.encoder_f0: 59,652B, BPFP=0.9102 Using per-key quantization points (vae.encoder_f1: torch.Size([256])) for vae.encoder_f1 vae.encoder_f1: 59,628B, BPFP=0.9099 Using per-key quantization points (vae.decoder: torch.Size([256])) for vae.decoder vae.decoder: 30,804B, BPFP=0.9401 ⌛️ [2/4] FRONTEND: Frontend time: 2.140s (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: 1.605s [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 0.17643106 text_encoder-item0.clip_prompt_embeds 0.00022433 87.19092431 text_encoder_2-item1.clip_pooled_prompt_embeds 0.00020112 0.17736870 text_encoder_2-item1.clip_prompt_embeds 0.00017331 0.04916840 text_encoder_3-item2.t5_prompt_embeds 0.00000752 0.00069601 text_encoder-item3.clip_pooled_prompt_embeds 0.00841799 0.25064182 text_encoder-item3.clip_prompt_embeds 0.00023247 57.42621246 text_encoder_2-item4.clip_pooled_prompt_embeds 0.00016751 0.08740882 text_encoder_2-item4.clip_prompt_embeds 0.00009830 0.08730212 text_encoder_3-item5.t5_prompt_embeds 0.00001263 0.00061542 vae.encoder_f0 0.00626512 0.65308589 vae.encoder_f1 0.00626949 0.65461457 vae.decoder 0.00018936 0.01765409 ------------------------------------------------------------------------------------- TOTAL 0.00295827 4.09415408 (elements=2,260,992) ---------------------- -------------------------------------------------------- SAMPLE-WISE STATISTICS ---------------------- -------------------------------------------------------- Handler sd35 Strategy individual Architecture elic-featurecoding ---------------------- -------------------------------------------------------- Total Elements 2260992 Total Bytes 328088 BPFP 1.1609 bits/point EBPFP 2.3217 equivalent bits/point MSE 4.094154 ---------------------- -------------------------------------------------------- Time: 3.753s Load: 0.009s, Pack+Encode: 2.140s, Decode+Unpack: 1.605s ---------------------- -------------------------------------------------------- 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 4.0942 MSE: from ../datasets/FeatureCoding-StableDiffusion3.5/100samples_features/000000146358.zst to output-fixed/sd35/lambda0.02/elic-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: 2,296B, BPFP=23.9167 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: 12,000B, BPFP=1.6234 Using 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: 3,300B, BPFP=20.6250 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: 21,680B, BPFP=1.7597 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: 68,484B, BPFP=1.7371 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: 2,600B, BPFP=27.0833 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: 8,396B, BPFP=1.1358 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: 4,400B, BPFP=27.5000 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: 16,268B, BPFP=1.3205 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: 42,292B, BPFP=1.0727 Using per-key quantization points (vae.encoder_f0: torch.Size([256])) for vae.encoder_f0 vae.encoder_f0: 42,104B, BPFP=0.6425 Using per-key quantization points (vae.encoder_f1: torch.Size([256])) for vae.encoder_f1 vae.encoder_f1: 42,108B, BPFP=0.6425 Using per-key quantization points (vae.decoder: torch.Size([256])) for vae.decoder vae.decoder: 21,840B, BPFP=0.6665 ⌛️ [2/4] FRONTEND: Frontend time: 2.144s (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: 1.605s [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 0.22018689 text_encoder-item0.clip_prompt_embeds 0.00026137 197.23224432 text_encoder_2-item1.clip_pooled_prompt_embeds 0.00138553 0.19566448 text_encoder_2-item1.clip_prompt_embeds 0.00019680 0.06308743 text_encoder_3-item2.t5_prompt_embeds 0.00000808 0.00071722 text_encoder-item3.clip_pooled_prompt_embeds 0.00841799 0.25064182 text_encoder-item3.clip_prompt_embeds 0.00023247 57.42621246 text_encoder_2-item4.clip_pooled_prompt_embeds 0.00016751 0.08740882 text_encoder_2-item4.clip_prompt_embeds 0.00009830 0.08730212 text_encoder_3-item5.t5_prompt_embeds 0.00001263 0.00061542 vae.encoder_f0 0.35915655 1.47126102 vae.encoder_f1 0.35915723 1.47830558 vae.decoder 0.00024181 0.01628034 ------------------------------------------------------------------------------------- TOTAL 0.16663024 7.35347109 (elements=2,260,992) ---------------------- -------------------------------------------------------- SAMPLE-WISE STATISTICS ---------------------- -------------------------------------------------------- Handler sd35 Strategy individual Architecture elic-featurecoding ---------------------- -------------------------------------------------------- Total Elements 2260992 Total Bytes 287768 BPFP 1.0182 bits/point EBPFP 2.0364 equivalent bits/point MSE 7.353471 ---------------------- -------------------------------------------------------- Time: 3.757s Load: 0.008s, Pack+Encode: 2.144s, Decode+Unpack: 1.605s ---------------------- -------------------------------------------------------- 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 7.3535 MSE: from ../datasets/FeatureCoding-StableDiffusion3.5/100samples_features/000000148662.zst to output-fixed/sd35/lambda0.02/elic-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: 2,276B, BPFP=23.7083 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: 10,552B, BPFP=1.4275 Using 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: 3,464B, BPFP=21.6500 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: 17,940B, BPFP=1.4562 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: 58,180B, BPFP=1.4758 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: 2,600B, BPFP=27.0833 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: 8,396B, BPFP=1.1358 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: 4,400B, BPFP=27.5000 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: 16,268B, BPFP=1.3205 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: 42,292B, BPFP=1.0727 Using per-key quantization points (vae.encoder_f0: torch.Size([256])) for vae.encoder_f0 vae.encoder_f0: 18,624B, BPFP=0.2842 Using per-key quantization points (vae.encoder_f1: torch.Size([256])) for vae.encoder_f1 vae.encoder_f1: 18,624B, BPFP=0.2842 Using per-key quantization points (vae.decoder: torch.Size([256])) for vae.decoder vae.decoder: 29,384B, BPFP=0.8967 ⌛️ [2/4] FRONTEND: Frontend time: 2.142s (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: 1.599s [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 0.16812940 text_encoder-item0.clip_prompt_embeds 0.00021656 105.15539604 text_encoder_2-item1.clip_pooled_prompt_embeds 0.00019988 0.18997478 text_encoder_2-item1.clip_prompt_embeds 0.00016555 0.05421855 text_encoder_3-item2.t5_prompt_embeds 0.00000783 0.00069842 text_encoder-item3.clip_pooled_prompt_embeds 0.00841799 0.25064182 text_encoder-item3.clip_prompt_embeds 0.00023247 57.42621246 text_encoder_2-item4.clip_pooled_prompt_embeds 0.00016751 0.08740882 text_encoder_2-item4.clip_prompt_embeds 0.00009830 0.08730212 text_encoder_3-item5.t5_prompt_embeds 0.00001263 0.00061542 vae.encoder_f0 0.29031765 1.21397209 vae.encoder_f1 0.29031771 1.21299541 vae.decoder 0.00019965 0.01969854 ------------------------------------------------------------------------------------- TOTAL 0.13469251 4.82401491 (elements=2,260,992) ---------------------- -------------------------------------------------------- SAMPLE-WISE STATISTICS ---------------------- -------------------------------------------------------- Handler sd35 Strategy individual Architecture elic-featurecoding ---------------------- -------------------------------------------------------- Total Elements 2260992 Total Bytes 233000 BPFP 0.8244 bits/point EBPFP 1.6488 equivalent bits/point MSE 4.824015 ---------------------- -------------------------------------------------------- Time: 3.748s Load: 0.008s, Pack+Encode: 2.142s, Decode+Unpack: 1.599s ---------------------- -------------------------------------------------------- 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 4.8240 MSE: from ../datasets/FeatureCoding-StableDiffusion3.5/100samples_features/000000151051.zst to output-fixed/sd35/lambda0.02/elic-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: 2,256B, BPFP=23.5000 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: 10,568B, BPFP=1.4297 Using 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: 3,384B, BPFP=21.1500 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: 18,188B, BPFP=1.4763 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: 56,652B, BPFP=1.4370 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: 2,600B, BPFP=27.0833 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: 8,396B, BPFP=1.1358 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: 4,400B, BPFP=27.5000 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: 16,268B, BPFP=1.3205 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: 42,292B, BPFP=1.0727 Using per-key quantization points (vae.encoder_f0: torch.Size([256])) for vae.encoder_f0 vae.encoder_f0: 44,520B, BPFP=0.6793 Using per-key quantization points (vae.encoder_f1: torch.Size([256])) for vae.encoder_f1 vae.encoder_f1: 44,516B, BPFP=0.6793 Using per-key quantization points (vae.decoder: torch.Size([256])) for vae.decoder vae.decoder: 46,012B, BPFP=1.4042 ⌛️ [2/4] FRONTEND: Frontend time: 2.163s (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: 1.599s [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 0.17505507 text_encoder-item0.clip_prompt_embeds 0.00025451 55.85747261 text_encoder_2-item1.clip_pooled_prompt_embeds 0.00023552 0.18437517 text_encoder_2-item1.clip_prompt_embeds 0.00017758 0.05515794 text_encoder_3-item2.t5_prompt_embeds 0.00000816 0.00057791 text_encoder-item3.clip_pooled_prompt_embeds 0.00841799 0.25064182 text_encoder-item3.clip_prompt_embeds 0.00023247 57.42621246 text_encoder_2-item4.clip_pooled_prompt_embeds 0.00016751 0.08740882 text_encoder_2-item4.clip_prompt_embeds 0.00009830 0.08730212 text_encoder_3-item5.t5_prompt_embeds 0.00001263 0.00061542 vae.encoder_f0 0.00595764 0.44371745 vae.encoder_f1 0.00596395 0.44276246 vae.decoder 0.00019845 0.02220349 ------------------------------------------------------------------------------------- TOTAL 0.00281886 3.17773231 (elements=2,260,992) ---------------------- -------------------------------------------------------- SAMPLE-WISE STATISTICS ---------------------- -------------------------------------------------------- Handler sd35 Strategy individual Architecture elic-featurecoding ---------------------- -------------------------------------------------------- Total Elements 2260992 Total Bytes 300052 BPFP 1.0617 bits/point EBPFP 2.1233 equivalent bits/point MSE 3.177732 ---------------------- -------------------------------------------------------- Time: 3.772s Load: 0.009s, Pack+Encode: 2.163s, Decode+Unpack: 1.599s ---------------------- -------------------------------------------------------- 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 3.1777 MSE: from ../datasets/FeatureCoding-StableDiffusion3.5/100samples_features/000000155443.zst to output-fixed/sd35/lambda0.02/elic-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: 2,284B, BPFP=23.7917 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: 11,868B, BPFP=1.6055 Using 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: 3,412B, BPFP=21.3250 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: 21,348B, BPFP=1.7328 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: 69,060B, BPFP=1.7517 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: 2,600B, BPFP=27.0833 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: 8,396B, BPFP=1.1358 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: 4,400B, BPFP=27.5000 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: 16,268B, BPFP=1.3205 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: 42,292B, BPFP=1.0727 Using per-key quantization points (vae.encoder_f0: torch.Size([256])) for vae.encoder_f0 vae.encoder_f0: 32,500B, BPFP=0.4959 Using per-key quantization points (vae.encoder_f1: torch.Size([256])) for vae.encoder_f1 vae.encoder_f1: 32,504B, BPFP=0.4960 Using per-key quantization points (vae.decoder: torch.Size([256])) for vae.decoder vae.decoder: 21,196B, BPFP=0.6469 ⌛️ [2/4] FRONTEND: Frontend time: 2.152s (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: 1.602s [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 0.16139813 text_encoder-item0.clip_prompt_embeds 0.00026157 234.80600649 text_encoder_2-item1.clip_pooled_prompt_embeds 0.00022221 0.18938017 text_encoder_2-item1.clip_prompt_embeds 0.00022582 0.05413896 text_encoder_3-item2.t5_prompt_embeds 0.00000776 0.00067941 text_encoder-item3.clip_pooled_prompt_embeds 0.00841799 0.25064182 text_encoder-item3.clip_prompt_embeds 0.00023247 57.42621246 text_encoder_2-item4.clip_pooled_prompt_embeds 0.00016751 0.08740882 text_encoder_2-item4.clip_prompt_embeds 0.00009830 0.08730212 text_encoder_3-item5.t5_prompt_embeds 0.00001263 0.00061542 vae.encoder_f0 0.40456498 1.71334231 vae.encoder_f1 0.40456539 1.72682488 vae.decoder 0.00020503 0.01581192 ------------------------------------------------------------------------------------- TOTAL 0.18768128 8.44949802 (elements=2,260,992) ---------------------- -------------------------------------------------------- SAMPLE-WISE STATISTICS ---------------------- -------------------------------------------------------- Handler sd35 Strategy individual Architecture elic-featurecoding ---------------------- -------------------------------------------------------- Total Elements 2260992 Total Bytes 268128 BPFP 0.9487 bits/point EBPFP 1.8974 equivalent bits/point MSE 8.449498 ---------------------- -------------------------------------------------------- Time: 3.761s Load: 0.008s, Pack+Encode: 2.152s, Decode+Unpack: 1.602s ---------------------- -------------------------------------------------------- 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 8.4495 MSE: from ../datasets/FeatureCoding-StableDiffusion3.5/100samples_features/000000159458.zst to output-fixed/sd35/lambda0.02/elic-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: 2,192B, BPFP=22.8333 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: 11,996B, BPFP=1.6228 Using 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: 3,464B, BPFP=21.6500 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: 18,484B, BPFP=1.5003 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: 58,936B, BPFP=1.4949 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: 2,600B, BPFP=27.0833 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: 8,396B, BPFP=1.1358 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: 4,400B, BPFP=27.5000 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: 16,268B, BPFP=1.3205 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: 42,292B, BPFP=1.0727 Using per-key quantization points (vae.encoder_f0: torch.Size([256])) for vae.encoder_f0 vae.encoder_f0: 52,784B, BPFP=0.8054 Using per-key quantization points (vae.encoder_f1: torch.Size([256])) for vae.encoder_f1 vae.encoder_f1: 52,788B, BPFP=0.8055 Using per-key quantization points (vae.decoder: torch.Size([256])) for vae.decoder vae.decoder: 41,152B, BPFP=1.2559 ⌛️ [2/4] FRONTEND: Frontend time: 2.165s (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: 1.619s [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 0.16524514 text_encoder-item0.clip_prompt_embeds 0.00027179 34.21484164 text_encoder_2-item1.clip_pooled_prompt_embeds 0.00025795 0.19483825 text_encoder_2-item1.clip_prompt_embeds 0.00015124 0.04771650 text_encoder_3-item2.t5_prompt_embeds 0.00000794 0.00064736 text_encoder-item3.clip_pooled_prompt_embeds 0.00841799 0.25064182 text_encoder-item3.clip_prompt_embeds 0.00023247 57.42621246 text_encoder_2-item4.clip_pooled_prompt_embeds 0.00016751 0.08740882 text_encoder_2-item4.clip_prompt_embeds 0.00009830 0.08730212 text_encoder_3-item5.t5_prompt_embeds 0.00001263 0.00061542 vae.encoder_f0 0.00673531 0.67256379 vae.encoder_f1 0.00673732 0.67427039 vae.decoder 0.00020129 0.02058178 ------------------------------------------------------------------------------------- TOTAL 0.00317768 2.71792034 (elements=2,260,992) ---------------------- -------------------------------------------------------- SAMPLE-WISE STATISTICS ---------------------- -------------------------------------------------------- Handler sd35 Strategy individual Architecture elic-featurecoding ---------------------- -------------------------------------------------------- Total Elements 2260992 Total Bytes 315752 BPFP 1.1172 bits/point EBPFP 2.2344 equivalent bits/point MSE 2.717920 ---------------------- -------------------------------------------------------- Time: 3.792s Load: 0.009s, Pack+Encode: 2.165s, Decode+Unpack: 1.619s ---------------------- -------------------------------------------------------- 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 2.7179 MSE: from ../datasets/FeatureCoding-StableDiffusion3.5/100samples_features/000000161128.zst to output-fixed/sd35/lambda0.02/elic-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: 2,304B, BPFP=24.0000 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: 12,148B, BPFP=1.6434 Using 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: 3,472B, BPFP=21.7000 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: 20,196B, BPFP=1.6393 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: 69,712B, BPFP=1.7683 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: 2,600B, BPFP=27.0833 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: 8,396B, BPFP=1.1358 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: 4,400B, BPFP=27.5000 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: 16,268B, BPFP=1.3205 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: 42,292B, BPFP=1.0727 Using per-key quantization points (vae.encoder_f0: torch.Size([256])) for vae.encoder_f0 vae.encoder_f0: 42,792B, BPFP=0.6530 Using per-key quantization points (vae.encoder_f1: torch.Size([256])) for vae.encoder_f1 vae.encoder_f1: 42,780B, BPFP=0.6528 Using per-key quantization points (vae.decoder: torch.Size([256])) for vae.decoder vae.decoder: 19,020B, BPFP=0.5804 ⌛️ [2/4] FRONTEND: Frontend time: 2.161s (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: 1.609s [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 0.15402593 text_encoder-item0.clip_prompt_embeds 0.00023057 55.63670184 text_encoder_2-item1.clip_pooled_prompt_embeds 0.00023879 0.19731729 text_encoder_2-item1.clip_prompt_embeds 0.00123217 0.04621179 text_encoder_3-item2.t5_prompt_embeds 0.00000768 0.00073551 text_encoder-item3.clip_pooled_prompt_embeds 0.00841799 0.25064182 text_encoder-item3.clip_prompt_embeds 0.00023247 57.42621246 text_encoder_2-item4.clip_pooled_prompt_embeds 0.00016751 0.08740882 text_encoder_2-item4.clip_prompt_embeds 0.00009830 0.08730212 text_encoder_3-item5.t5_prompt_embeds 0.00001263 0.00061542 vae.encoder_f0 0.00881784 0.66788447 vae.encoder_f1 0.00882136 0.66845489 vae.decoder 0.00017598 0.01359229 ------------------------------------------------------------------------------------- TOTAL 0.00418676 3.27490710 (elements=2,260,992) ---------------------- -------------------------------------------------------- SAMPLE-WISE STATISTICS ---------------------- -------------------------------------------------------- Handler sd35 Strategy individual Architecture elic-featurecoding ---------------------- -------------------------------------------------------- Total Elements 2260992 Total Bytes 286380 BPFP 1.0133 bits/point EBPFP 2.0266 equivalent bits/point MSE 3.274907 ---------------------- -------------------------------------------------------- Time: 3.779s Load: 0.008s, Pack+Encode: 2.161s, Decode+Unpack: 1.609s ---------------------- -------------------------------------------------------- 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 3.2749 MSE: from ../datasets/FeatureCoding-StableDiffusion3.5/100samples_features/000000168458.zst to output-fixed/sd35/lambda0.02/elic-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: 2,272B, BPFP=23.6667 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: 11,428B, BPFP=1.5460 Using 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: 3,452B, BPFP=21.5750 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: 19,652B, BPFP=1.5951 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: 63,484B, BPFP=1.6103 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: 2,600B, BPFP=27.0833 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: 8,396B, BPFP=1.1358 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: 4,400B, BPFP=27.5000 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: 16,268B, BPFP=1.3205 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: 42,292B, BPFP=1.0727 Using per-key quantization points (vae.encoder_f0: torch.Size([256])) for vae.encoder_f0 vae.encoder_f0: 48,056B, BPFP=0.7333 Using per-key quantization points (vae.encoder_f1: torch.Size([256])) for vae.encoder_f1 vae.encoder_f1: 48,056B, BPFP=0.7333 Using per-key quantization points (vae.decoder: torch.Size([256])) for vae.decoder vae.decoder: 46,300B, BPFP=1.4130 ⌛️ [2/4] FRONTEND: Frontend time: 2.148s (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: 1.614s [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 0.15367255 text_encoder-item0.clip_prompt_embeds 0.00025208 64.74622903 text_encoder_2-item1.clip_pooled_prompt_embeds 0.00047028 0.18441378 text_encoder_2-item1.clip_prompt_embeds 0.00113921 0.04352784 text_encoder_3-item2.t5_prompt_embeds 0.00000794 0.00073197 text_encoder-item3.clip_pooled_prompt_embeds 0.00841799 0.25064182 text_encoder-item3.clip_prompt_embeds 0.00023247 57.42621246 text_encoder_2-item4.clip_pooled_prompt_embeds 0.00016751 0.08740882 text_encoder_2-item4.clip_prompt_embeds 0.00009830 0.08730212 text_encoder_3-item5.t5_prompt_embeds 0.00001263 0.00061542 vae.encoder_f0 0.00582247 0.47978622 vae.encoder_f1 0.00582996 0.47914782 vae.decoder 0.00016099 0.01978967 ------------------------------------------------------------------------------------- TOTAL 0.00279351 3.42624514 (elements=2,260,992) ---------------------- -------------------------------------------------------- SAMPLE-WISE STATISTICS ---------------------- -------------------------------------------------------- Handler sd35 Strategy individual Architecture elic-featurecoding ---------------------- -------------------------------------------------------- Total Elements 2260992 Total Bytes 316656 BPFP 1.1204 bits/point EBPFP 2.2408 equivalent bits/point MSE 3.426245 ---------------------- -------------------------------------------------------- Time: 3.771s Load: 0.009s, Pack+Encode: 2.148s, Decode+Unpack: 1.614s ---------------------- -------------------------------------------------------- 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 3.4262 MSE: from ../datasets/FeatureCoding-StableDiffusion3.5/100samples_features/000000171788.zst to output-fixed/sd35/lambda0.02/elic-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: 2,268B, BPFP=23.6250 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: 11,040B, BPFP=1.4935 Using 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: 3,396B, BPFP=21.2250 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: 19,532B, BPFP=1.5854 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: 60,504B, BPFP=1.5347 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: 2,600B, BPFP=27.0833 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: 8,396B, BPFP=1.1358 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: 4,400B, BPFP=27.5000 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: 16,268B, BPFP=1.3205 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: 42,292B, BPFP=1.0727 Using per-key quantization points (vae.encoder_f0: torch.Size([256])) for vae.encoder_f0 vae.encoder_f0: 60,000B, BPFP=0.9155 Using per-key quantization points (vae.encoder_f1: torch.Size([256])) for vae.encoder_f1 vae.encoder_f1: 60,012B, BPFP=0.9157 Using per-key quantization points (vae.decoder: torch.Size([256])) for vae.decoder vae.decoder: 34,672B, BPFP=1.0581 ⌛️ [2/4] FRONTEND: Frontend time: 2.158s (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: 1.609s [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 0.15942344 text_encoder-item0.clip_prompt_embeds 0.00020809 23.40837434 text_encoder_2-item1.clip_pooled_prompt_embeds 0.00035925 0.20109148 text_encoder_2-item1.clip_prompt_embeds 0.00112984 0.05985707 text_encoder_3-item2.t5_prompt_embeds 0.00000832 0.00061308 text_encoder-item3.clip_pooled_prompt_embeds 0.00841799 0.25064182 text_encoder-item3.clip_prompt_embeds 0.00023247 57.42621246 text_encoder_2-item4.clip_pooled_prompt_embeds 0.00016751 0.08740882 text_encoder_2-item4.clip_prompt_embeds 0.00009830 0.08730212 text_encoder_3-item5.t5_prompt_embeds 0.00001263 0.00061542 vae.encoder_f0 0.00602745 0.63162947 vae.encoder_f1 0.00603159 0.63141286 vae.decoder 0.00017526 0.01837862 ------------------------------------------------------------------------------------- TOTAL 0.00288782 2.41611894 (elements=2,260,992) ---------------------- -------------------------------------------------------- SAMPLE-WISE STATISTICS ---------------------- -------------------------------------------------------- Handler sd35 Strategy individual Architecture elic-featurecoding ---------------------- -------------------------------------------------------- Total Elements 2260992 Total Bytes 325380 BPFP 1.1513 bits/point EBPFP 2.3026 equivalent bits/point MSE 2.416119 ---------------------- -------------------------------------------------------- Time: 3.775s Load: 0.009s, Pack+Encode: 2.158s, Decode+Unpack: 1.609s ---------------------- -------------------------------------------------------- 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 2.4161 MSE: from ../datasets/FeatureCoding-StableDiffusion3.5/100samples_features/000000179265.zst to output-fixed/sd35/lambda0.02/elic-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: 2,228B, BPFP=23.2083 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: 10,912B, BPFP=1.4762 Using 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: 3,480B, BPFP=21.7500 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: 18,920B, BPFP=1.5357 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: 59,092B, BPFP=1.4989 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: 2,600B, BPFP=27.0833 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: 8,396B, BPFP=1.1358 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: 4,400B, BPFP=27.5000 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: 16,268B, BPFP=1.3205 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: 42,292B, BPFP=1.0727 Using per-key quantization points (vae.encoder_f0: torch.Size([256])) for vae.encoder_f0 vae.encoder_f0: 61,380B, BPFP=0.9366 Using per-key quantization points (vae.encoder_f1: torch.Size([256])) for vae.encoder_f1 vae.encoder_f1: 61,364B, BPFP=0.9363 Using per-key quantization points (vae.decoder: torch.Size([256])) for vae.decoder vae.decoder: 37,036B, BPFP=1.1302 ⌛️ [2/4] FRONTEND: Frontend time: 2.151s (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: 1.607s [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 0.16331942 text_encoder-item0.clip_prompt_embeds 0.00020908 34.15304764 text_encoder_2-item1.clip_pooled_prompt_embeds 0.00048701 0.18280993 text_encoder_2-item1.clip_prompt_embeds 0.00016227 0.04340225 text_encoder_3-item2.t5_prompt_embeds 0.00000845 0.00064143 text_encoder-item3.clip_pooled_prompt_embeds 0.00841799 0.25064182 text_encoder-item3.clip_prompt_embeds 0.00023247 57.42621246 text_encoder_2-item4.clip_pooled_prompt_embeds 0.00016751 0.08740882 text_encoder_2-item4.clip_prompt_embeds 0.00009830 0.08730212 text_encoder_3-item5.t5_prompt_embeds 0.00001263 0.00061542 vae.encoder_f0 0.00634616 0.62349063 vae.encoder_f1 0.00635208 0.62344021 vae.decoder 0.00022721 0.01934971 ------------------------------------------------------------------------------------- TOTAL 0.00300000 2.69279893 (elements=2,260,992) ---------------------- -------------------------------------------------------- SAMPLE-WISE STATISTICS ---------------------- -------------------------------------------------------- Handler sd35 Strategy individual Architecture elic-featurecoding ---------------------- -------------------------------------------------------- Total Elements 2260992 Total Bytes 328368 BPFP 1.1619 bits/point EBPFP 2.3237 equivalent bits/point MSE 2.692799 ---------------------- -------------------------------------------------------- Time: 3.767s Load: 0.009s, Pack+Encode: 2.151s, Decode+Unpack: 1.607s ---------------------- -------------------------------------------------------- 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 2.6928 MSE: from ../datasets/FeatureCoding-StableDiffusion3.5/100samples_features/000000189752.zst to output-fixed/sd35/lambda0.02/elic-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: 2,348B, BPFP=24.4583 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: 10,908B, BPFP=1.4756 Using 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: 3,548B, BPFP=22.1750 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: 18,192B, BPFP=1.4766 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: 57,856B, BPFP=1.4675 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: 2,600B, BPFP=27.0833 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: 8,396B, BPFP=1.1358 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: 4,400B, BPFP=27.5000 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: 16,268B, BPFP=1.3205 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: 42,292B, BPFP=1.0727 Using per-key quantization points (vae.encoder_f0: torch.Size([256])) for vae.encoder_f0 vae.encoder_f0: 27,196B, BPFP=0.4150 Using per-key quantization points (vae.encoder_f1: torch.Size([256])) for vae.encoder_f1 vae.encoder_f1: 27,192B, BPFP=0.4149 Using per-key quantization points (vae.decoder: torch.Size([256])) for vae.decoder vae.decoder: 15,552B, BPFP=0.4746 ⌛️ [2/4] FRONTEND: Frontend time: 2.140s (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: 1.605s [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 0.16804391 text_encoder-item0.clip_prompt_embeds 0.00022947 67.59145444 text_encoder_2-item1.clip_pooled_prompt_embeds 0.00031292 0.19786594 text_encoder_2-item1.clip_prompt_embeds 0.00017460 0.06590510 text_encoder_3-item2.t5_prompt_embeds 0.00000789 0.00059310 text_encoder-item3.clip_pooled_prompt_embeds 0.00841799 0.25064182 text_encoder-item3.clip_prompt_embeds 0.00023247 57.42621246 text_encoder_2-item4.clip_pooled_prompt_embeds 0.00016751 0.08740882 text_encoder_2-item4.clip_prompt_embeds 0.00009830 0.08730212 text_encoder_3-item5.t5_prompt_embeds 0.00001263 0.00061542 vae.encoder_f0 0.05448642 0.73459268 vae.encoder_f1 0.05448771 0.73431456 vae.decoder 0.00017748 0.01334673 ------------------------------------------------------------------------------------- TOTAL 0.02531999 3.61913793 (elements=2,260,992) ---------------------- -------------------------------------------------------- SAMPLE-WISE STATISTICS ---------------------- -------------------------------------------------------- Handler sd35 Strategy individual Architecture elic-featurecoding ---------------------- -------------------------------------------------------- Total Elements 2260992 Total Bytes 236748 BPFP 0.8377 bits/point EBPFP 1.6754 equivalent bits/point MSE 3.619138 ---------------------- -------------------------------------------------------- Time: 3.753s Load: 0.008s, Pack+Encode: 2.140s, Decode+Unpack: 1.605s ---------------------- -------------------------------------------------------- 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 3.6191 MSE: from ../datasets/FeatureCoding-StableDiffusion3.5/100samples_features/000000222118.zst to output-fixed/sd35/lambda0.02/elic-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: 0.009s ------------------------------------------------------------ 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: 2,252B, BPFP=23.4583 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: 10,876B, BPFP=1.4713 Using 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: 3,548B, BPFP=22.1750 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: 19,460B, BPFP=1.5795 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: 62,184B, BPFP=1.5773 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: 2,600B, BPFP=27.0833 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: 8,396B, BPFP=1.1358 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: 4,400B, BPFP=27.5000 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: 16,268B, BPFP=1.3205 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: 42,292B, BPFP=1.0727 Using per-key quantization points (vae.encoder_f0: torch.Size([256])) for vae.encoder_f0 vae.encoder_f0: 39,240B, BPFP=0.5988 Using per-key quantization points (vae.encoder_f1: torch.Size([256])) for vae.encoder_f1 vae.encoder_f1: 39,252B, BPFP=0.5989 Using per-key quantization points (vae.decoder: torch.Size([256])) for vae.decoder vae.decoder: 15,796B, BPFP=0.4821 ⌛️ [2/4] FRONTEND: Frontend time: 2.141s (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: 1.611s [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 0.16238896 text_encoder-item0.clip_prompt_embeds 0.00020169 54.94558729 text_encoder_2-item1.clip_pooled_prompt_embeds 0.00017591 0.16970854 text_encoder_2-item1.clip_prompt_embeds 0.00015739 0.06073993 text_encoder_3-item2.t5_prompt_embeds 0.00000811 0.00066497 text_encoder-item3.clip_pooled_prompt_embeds 0.00841799 0.25064182 text_encoder-item3.clip_prompt_embeds 0.00023247 57.42621246 text_encoder_2-item4.clip_pooled_prompt_embeds 0.00016751 0.08740882 text_encoder_2-item4.clip_prompt_embeds 0.00009830 0.08730212 text_encoder_3-item5.t5_prompt_embeds 0.00001263 0.00061542 vae.encoder_f0 0.06876971 0.87679124 vae.encoder_f1 0.06877109 0.87631971 vae.decoder 0.00023999 0.01240053 ------------------------------------------------------------------------------------- TOTAL 0.03194988 3.35394626 (elements=2,260,992) ---------------------- -------------------------------------------------------- SAMPLE-WISE STATISTICS ---------------------- -------------------------------------------------------- Handler sd35 Strategy individual Architecture elic-featurecoding ---------------------- -------------------------------------------------------- Total Elements 2260992 Total Bytes 266564 BPFP 0.9432 bits/point EBPFP 1.8864 equivalent bits/point MSE 3.353946 ---------------------- -------------------------------------------------------- Time: 3.760s Load: 0.009s, Pack+Encode: 2.141s, Decode+Unpack: 1.611s ---------------------- -------------------------------------------------------- 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 3.3539 MSE: from ../datasets/FeatureCoding-StableDiffusion3.5/100samples_features/000000222825.zst to output-fixed/sd35/lambda0.02/elic-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: 0.008s ------------------------------------------------------------ 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: 2,288B, BPFP=23.8333 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: 11,212B, BPFP=1.5168 Using 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: 3,472B, BPFP=21.7000 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: 19,244B, BPFP=1.5620 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: 59,220B, BPFP=1.5021 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: 2,600B, BPFP=27.0833 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: 8,396B, BPFP=1.1358 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: 4,400B, BPFP=27.5000 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: 16,268B, BPFP=1.3205 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: 42,292B, BPFP=1.0727 Using per-key quantization points (vae.encoder_f0: torch.Size([256])) for vae.encoder_f0 vae.encoder_f0: 44,968B, BPFP=0.6862 Using per-key quantization points (vae.encoder_f1: torch.Size([256])) for vae.encoder_f1 vae.encoder_f1: 44,972B, BPFP=0.6862 Using per-key quantization points (vae.decoder: torch.Size([256])) for vae.decoder vae.decoder: 46,436B, BPFP=1.4171 ⌛️ [2/4] FRONTEND: Frontend time: 2.142s (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: 1.604s [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 0.14490747 text_encoder-item0.clip_prompt_embeds 0.00025253 43.47165432 text_encoder_2-item1.clip_pooled_prompt_embeds 0.00041073 0.19683256 text_encoder_2-item1.clip_prompt_embeds 0.00018825 0.05724801 text_encoder_3-item2.t5_prompt_embeds 0.00000859 0.00062697 text_encoder-item3.clip_pooled_prompt_embeds 0.00841799 0.25064182 text_encoder-item3.clip_prompt_embeds 0.00023247 57.42621246 text_encoder_2-item4.clip_pooled_prompt_embeds 0.00016751 0.08740882 text_encoder_2-item4.clip_prompt_embeds 0.00009830 0.08730212 text_encoder_3-item5.t5_prompt_embeds 0.00001263 0.00061542 vae.encoder_f0 0.00595097 0.45323032 vae.encoder_f1 0.00595882 0.45258334 vae.decoder 0.00020134 0.02336799 ------------------------------------------------------------------------------------- TOTAL 0.00281645 2.85849555 (elements=2,260,992) ---------------------- -------------------------------------------------------- SAMPLE-WISE STATISTICS ---------------------- -------------------------------------------------------- Handler sd35 Strategy individual Architecture elic-featurecoding ---------------------- -------------------------------------------------------- Total Elements 2260992 Total Bytes 305768 BPFP 1.0819 bits/point EBPFP 2.1638 equivalent bits/point MSE 2.858496 ---------------------- -------------------------------------------------------- Time: 3.754s Load: 0.008s, Pack+Encode: 2.142s, Decode+Unpack: 1.604s ---------------------- -------------------------------------------------------- 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 2.8585 MSE: from ../datasets/FeatureCoding-StableDiffusion3.5/100samples_features/000000227478.zst to output-fixed/sd35/lambda0.02/elic-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: 0.007s ------------------------------------------------------------ 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: 2,260B, BPFP=23.5417 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: 11,468B, BPFP=1.5514 Using 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: 3,484B, BPFP=21.7750 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: 20,548B, BPFP=1.6679 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: 62,508B, BPFP=1.5855 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: 2,600B, BPFP=27.0833 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: 8,396B, BPFP=1.1358 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: 4,400B, BPFP=27.5000 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: 16,268B, BPFP=1.3205 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: 42,292B, BPFP=1.0727 Using per-key quantization points (vae.encoder_f0: torch.Size([256])) for vae.encoder_f0 vae.encoder_f0: 34,400B, BPFP=0.5249 Using per-key quantization points (vae.encoder_f1: torch.Size([256])) for vae.encoder_f1 vae.encoder_f1: 34,396B, BPFP=0.5248 Using per-key quantization points (vae.decoder: torch.Size([256])) for vae.decoder vae.decoder: 30,628B, BPFP=0.9347 ⌛️ [2/4] FRONTEND: Frontend time: 2.142s (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: 1.599s [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 0.16726146 text_encoder-item0.clip_prompt_embeds 0.00022201 43.04717093 text_encoder_2-item1.clip_pooled_prompt_embeds 0.00030500 0.18828499 text_encoder_2-item1.clip_prompt_embeds 0.00020541 0.06209900 text_encoder_3-item2.t5_prompt_embeds 0.00000847 0.00063149 text_encoder-item3.clip_pooled_prompt_embeds 0.00841799 0.25064182 text_encoder-item3.clip_prompt_embeds 0.00023247 57.42621246 text_encoder_2-item4.clip_pooled_prompt_embeds 0.00016751 0.08740882 text_encoder_2-item4.clip_prompt_embeds 0.00009830 0.08730212 text_encoder_3-item5.t5_prompt_embeds 0.00001263 0.00061542 vae.encoder_f0 0.00831743 0.59358579 vae.encoder_f1 0.00831926 0.59582019 vae.decoder 0.00028593 0.01477363 ------------------------------------------------------------------------------------- TOTAL 0.00392223 2.91237217 (elements=2,260,992) ---------------------- -------------------------------------------------------- SAMPLE-WISE STATISTICS ---------------------- -------------------------------------------------------- Handler sd35 Strategy individual Architecture elic-featurecoding ---------------------- -------------------------------------------------------- Total Elements 2260992 Total Bytes 273648 BPFP 0.9682 bits/point EBPFP 1.9365 equivalent bits/point MSE 2.912372 ---------------------- -------------------------------------------------------- Time: 3.749s Load: 0.007s, Pack+Encode: 2.142s, Decode+Unpack: 1.599s ---------------------- -------------------------------------------------------- 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 2.9124 MSE: from ../datasets/FeatureCoding-StableDiffusion3.5/100samples_features/000000239843.zst to output-fixed/sd35/lambda0.02/elic-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: 2,260B, BPFP=23.5417 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: 12,068B, BPFP=1.6326 Using 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: 3,396B, BPFP=21.2250 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: 20,448B, BPFP=1.6597 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: 68,016B, BPFP=1.7252 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: 2,600B, BPFP=27.0833 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: 8,396B, BPFP=1.1358 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: 4,400B, BPFP=27.5000 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: 16,268B, BPFP=1.3205 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: 42,292B, BPFP=1.0727 Using per-key quantization points (vae.encoder_f0: torch.Size([256])) for vae.encoder_f0 vae.encoder_f0: 60,364B, BPFP=0.9211 Using per-key quantization points (vae.encoder_f1: torch.Size([256])) for vae.encoder_f1 vae.encoder_f1: 60,336B, BPFP=0.9207 Using per-key quantization points (vae.decoder: torch.Size([256])) for vae.decoder vae.decoder: 32,532B, BPFP=0.9928 ⌛️ [2/4] FRONTEND: Frontend time: 2.146s (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: 1.605s [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 0.16025658 text_encoder-item0.clip_prompt_embeds 0.00026808 63.35090723 text_encoder_2-item1.clip_pooled_prompt_embeds 0.00033998 0.19454091 text_encoder_2-item1.clip_prompt_embeds 0.00021475 0.05618355 text_encoder_3-item2.t5_prompt_embeds 0.00000768 0.00070044 text_encoder-item3.clip_pooled_prompt_embeds 0.00841799 0.25064182 text_encoder-item3.clip_prompt_embeds 0.00023247 57.42621246 text_encoder_2-item4.clip_pooled_prompt_embeds 0.00016751 0.08740882 text_encoder_2-item4.clip_prompt_embeds 0.00009830 0.08730212 text_encoder_3-item5.t5_prompt_embeds 0.00001263 0.00061542 vae.encoder_f0 0.00606586 0.64368868 vae.encoder_f1 0.00607066 0.64397717 vae.decoder 0.00019664 0.01783791 ------------------------------------------------------------------------------------- TOTAL 0.00286987 3.46630728 (elements=2,260,992) ---------------------- -------------------------------------------------------- SAMPLE-WISE STATISTICS ---------------------- -------------------------------------------------------- Handler sd35 Strategy individual Architecture elic-featurecoding ---------------------- -------------------------------------------------------- Total Elements 2260992 Total Bytes 333376 BPFP 1.1796 bits/point EBPFP 2.3591 equivalent bits/point MSE 3.466307 ---------------------- -------------------------------------------------------- Time: 3.760s Load: 0.009s, Pack+Encode: 2.146s, Decode+Unpack: 1.605s ---------------------- -------------------------------------------------------- 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 3.4663 MSE: from ../datasets/FeatureCoding-StableDiffusion3.5/100samples_features/000000240250.zst to output-fixed/sd35/lambda0.02/elic-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: 0.007s ------------------------------------------------------------ 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: 2,212B, BPFP=23.0417 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: 11,916B, BPFP=1.6120 Using 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: 3,400B, BPFP=21.2500 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: 22,044B, BPFP=1.7893 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: 69,256B, BPFP=1.7567 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: 2,600B, BPFP=27.0833 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: 8,396B, BPFP=1.1358 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: 4,400B, BPFP=27.5000 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: 16,268B, BPFP=1.3205 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: 42,292B, BPFP=1.0727 Using per-key quantization points (vae.encoder_f0: torch.Size([256])) for vae.encoder_f0 vae.encoder_f0: 44,336B, BPFP=0.6765 Using per-key quantization points (vae.encoder_f1: torch.Size([256])) for vae.encoder_f1 vae.encoder_f1: 44,308B, BPFP=0.6761 Using per-key quantization points (vae.decoder: torch.Size([256])) for vae.decoder vae.decoder: 31,492B, BPFP=0.9611 ⌛️ [2/4] FRONTEND: Frontend time: 2.188s (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: 1.616s [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 0.18012738 text_encoder-item0.clip_prompt_embeds 0.00023198 60.15342600 text_encoder_2-item1.clip_pooled_prompt_embeds 0.00035192 0.22237303 text_encoder_2-item1.clip_prompt_embeds 0.00017676 0.05185057 text_encoder_3-item2.t5_prompt_embeds 0.00000830 0.00069018 text_encoder-item3.clip_pooled_prompt_embeds 0.00841799 0.25064182 text_encoder-item3.clip_prompt_embeds 0.00023247 57.42621246 text_encoder_2-item4.clip_pooled_prompt_embeds 0.00016751 0.08740882 text_encoder_2-item4.clip_prompt_embeds 0.00009830 0.08730212 text_encoder_3-item5.t5_prompt_embeds 0.00001263 0.00061542 vae.encoder_f0 0.05216765 0.83568740 vae.encoder_f1 0.05216896 0.83063221 vae.decoder 0.00017960 0.01729849 ------------------------------------------------------------------------------------- TOTAL 0.02424513 3.47025093 (elements=2,260,992) ---------------------- -------------------------------------------------------- SAMPLE-WISE STATISTICS ---------------------- -------------------------------------------------------- Handler sd35 Strategy individual Architecture elic-featurecoding ---------------------- -------------------------------------------------------- Total Elements 2260992 Total Bytes 302920 BPFP 1.0718 bits/point EBPFP 2.1436 equivalent bits/point MSE 3.470251 ---------------------- -------------------------------------------------------- Time: 3.811s Load: 0.007s, Pack+Encode: 2.188s, Decode+Unpack: 1.616s ---------------------- -------------------------------------------------------- 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 3.4703 MSE: from ../datasets/FeatureCoding-StableDiffusion3.5/100samples_features/000000258793.zst to output-fixed/sd35/lambda0.02/elic-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: 2,232B, BPFP=23.2500 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: 11,396B, BPFP=1.5417 Using 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: 3,564B, BPFP=22.2750 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: 19,284B, BPFP=1.5653 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: 60,540B, BPFP=1.5356 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: 2,600B, BPFP=27.0833 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: 8,396B, BPFP=1.1358 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: 4,400B, BPFP=27.5000 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: 16,268B, BPFP=1.3205 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: 42,292B, BPFP=1.0727 Using per-key quantization points (vae.encoder_f0: torch.Size([256])) for vae.encoder_f0 vae.encoder_f0: 61,492B, BPFP=0.9383 Using per-key quantization points (vae.encoder_f1: torch.Size([256])) for vae.encoder_f1 vae.encoder_f1: 61,480B, BPFP=0.9381 Using per-key quantization points (vae.decoder: torch.Size([256])) for vae.decoder vae.decoder: 35,116B, BPFP=1.0717 ⌛️ [2/4] FRONTEND: Frontend time: 2.154s (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: 1.620s [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 0.17191972 text_encoder-item0.clip_prompt_embeds 0.00023125 128.45260078 text_encoder_2-item1.clip_pooled_prompt_embeds 0.00024484 0.17190791 text_encoder_2-item1.clip_prompt_embeds 0.00020589 0.04627994 text_encoder_3-item2.t5_prompt_embeds 0.00000799 0.00073300 text_encoder-item3.clip_pooled_prompt_embeds 0.00841799 0.25064182 text_encoder-item3.clip_prompt_embeds 0.00023247 57.42621246 text_encoder_2-item4.clip_pooled_prompt_embeds 0.00016751 0.08740882 text_encoder_2-item4.clip_prompt_embeds 0.00009830 0.08730212 text_encoder_3-item5.t5_prompt_embeds 0.00001263 0.00061542 vae.encoder_f0 0.00620361 0.58816350 vae.encoder_f1 0.00620966 0.58889437 vae.decoder 0.00020748 0.02039239 ------------------------------------------------------------------------------------- TOTAL 0.00293402 5.14324692 (elements=2,260,992) ---------------------- -------------------------------------------------------- SAMPLE-WISE STATISTICS ---------------------- -------------------------------------------------------- Handler sd35 Strategy individual Architecture elic-featurecoding ---------------------- -------------------------------------------------------- Total Elements 2260992 Total Bytes 329060 BPFP 1.1643 bits/point EBPFP 2.3286 equivalent bits/point MSE 5.143247 ---------------------- -------------------------------------------------------- Time: 3.783s Load: 0.009s, Pack+Encode: 2.154s, Decode+Unpack: 1.620s ---------------------- -------------------------------------------------------- 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 5.1432 MSE: from ../datasets/FeatureCoding-StableDiffusion3.5/100samples_features/000000270402.zst to output-fixed/sd35/lambda0.02/elic-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: 2,284B, BPFP=23.7917 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: 11,320B, BPFP=1.5314 Using 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: 3,328B, BPFP=20.8000 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: 20,368B, BPFP=1.6532 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: 63,420B, BPFP=1.6087 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: 2,600B, BPFP=27.0833 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: 8,396B, BPFP=1.1358 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: 4,400B, BPFP=27.5000 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: 16,268B, BPFP=1.3205 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: 42,292B, BPFP=1.0727 Using per-key quantization points (vae.encoder_f0: torch.Size([256])) for vae.encoder_f0 vae.encoder_f0: 50,200B, BPFP=0.7660 Using per-key quantization points (vae.encoder_f1: torch.Size([256])) for vae.encoder_f1 vae.encoder_f1: 50,176B, BPFP=0.7656 Using per-key quantization points (vae.decoder: torch.Size([256])) for vae.decoder vae.decoder: 33,736B, BPFP=1.0295 ⌛️ [2/4] FRONTEND: Frontend time: 2.192s (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: 1.603s [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 0.14655912 text_encoder-item0.clip_prompt_embeds 0.00023066 55.81180668 text_encoder_2-item1.clip_pooled_prompt_embeds 0.00044687 0.20127709 text_encoder_2-item1.clip_prompt_embeds 0.00018171 0.05871335 text_encoder_3-item2.t5_prompt_embeds 0.00000793 0.00066599 text_encoder-item3.clip_pooled_prompt_embeds 0.00841799 0.25064182 text_encoder-item3.clip_prompt_embeds 0.00023247 57.42621246 text_encoder_2-item4.clip_pooled_prompt_embeds 0.00016751 0.08740882 text_encoder_2-item4.clip_prompt_embeds 0.00009830 0.08730212 text_encoder_3-item5.t5_prompt_embeds 0.00001263 0.00061542 vae.encoder_f0 0.03159856 0.65865707 vae.encoder_f1 0.03160188 0.65870076 vae.decoder 0.00018417 0.01790682 ------------------------------------------------------------------------------------- TOTAL 0.01470700 3.27612065 (elements=2,260,992) ---------------------- -------------------------------------------------------- SAMPLE-WISE STATISTICS ---------------------- -------------------------------------------------------- Handler sd35 Strategy individual Architecture elic-featurecoding ---------------------- -------------------------------------------------------- Total Elements 2260992 Total Bytes 308788 BPFP 1.0926 bits/point EBPFP 2.1852 equivalent bits/point MSE 3.276121 ---------------------- -------------------------------------------------------- Time: 3.803s Load: 0.009s, Pack+Encode: 2.192s, Decode+Unpack: 1.603s ---------------------- -------------------------------------------------------- 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 3.2761 MSE: from ../datasets/FeatureCoding-StableDiffusion3.5/100samples_features/000000274272.zst to output-fixed/sd35/lambda0.02/elic-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: 2,292B, BPFP=23.8750 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: 11,644B, BPFP=1.5752 Using 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: 3,376B, BPFP=21.1000 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: 19,760B, BPFP=1.6039 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: 65,264B, BPFP=1.6554 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: 2,600B, BPFP=27.0833 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: 8,396B, BPFP=1.1358 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: 4,400B, BPFP=27.5000 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: 16,268B, BPFP=1.3205 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: 42,292B, BPFP=1.0727 Using per-key quantization points (vae.encoder_f0: torch.Size([256])) for vae.encoder_f0 vae.encoder_f0: 62,956B, BPFP=0.9606 Using per-key quantization points (vae.encoder_f1: torch.Size([256])) for vae.encoder_f1 vae.encoder_f1: 62,960B, BPFP=0.9607 Using per-key quantization points (vae.decoder: torch.Size([256])) for vae.decoder vae.decoder: 33,824B, BPFP=1.0322 ⌛️ [2/4] FRONTEND: Frontend time: 2.136s (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: 1.598s [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 0.14700795 text_encoder-item0.clip_prompt_embeds 0.00024948 43.18783820 text_encoder_2-item1.clip_pooled_prompt_embeds 0.00032352 0.18315802 text_encoder_2-item1.clip_prompt_embeds 0.00019749 0.05394546 text_encoder_3-item2.t5_prompt_embeds 0.00000781 0.00073411 text_encoder-item3.clip_pooled_prompt_embeds 0.00841799 0.25064182 text_encoder-item3.clip_prompt_embeds 0.00023247 57.42621246 text_encoder_2-item4.clip_pooled_prompt_embeds 0.00016751 0.08740882 text_encoder_2-item4.clip_prompt_embeds 0.00009830 0.08730212 text_encoder_3-item5.t5_prompt_embeds 0.00001263 0.00061542 vae.encoder_f0 0.03490865 1.01297677 vae.encoder_f1 0.03491008 1.00842309 vae.decoder 0.00028462 0.02266718 ------------------------------------------------------------------------------------- TOTAL 0.01625440 3.10954173 (elements=2,260,992) ---------------------- -------------------------------------------------------- SAMPLE-WISE STATISTICS ---------------------- -------------------------------------------------------- Handler sd35 Strategy individual Architecture elic-featurecoding ---------------------- -------------------------------------------------------- Total Elements 2260992 Total Bytes 336032 BPFP 1.1890 bits/point EBPFP 2.3779 equivalent bits/point MSE 3.109542 ---------------------- -------------------------------------------------------- Time: 3.743s Load: 0.008s, Pack+Encode: 2.136s, Decode+Unpack: 1.598s ---------------------- -------------------------------------------------------- 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 3.1095 MSE: from ../datasets/FeatureCoding-StableDiffusion3.5/100samples_features/000000280891.zst to output-fixed/sd35/lambda0.02/elic-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: 2,276B, BPFP=23.7083 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: 10,908B, BPFP=1.4756 Using 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: 3,308B, BPFP=20.6750 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: 19,648B, BPFP=1.5948 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: 69,360B, BPFP=1.7593 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: 2,600B, BPFP=27.0833 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: 8,396B, BPFP=1.1358 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: 4,400B, BPFP=27.5000 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: 16,268B, BPFP=1.3205 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: 42,292B, BPFP=1.0727 Using per-key quantization points (vae.encoder_f0: torch.Size([256])) for vae.encoder_f0 vae.encoder_f0: 32,264B, BPFP=0.4923 Using per-key quantization points (vae.encoder_f1: torch.Size([256])) for vae.encoder_f1 vae.encoder_f1: 32,256B, BPFP=0.4922 Using per-key quantization points (vae.decoder: torch.Size([256])) for vae.decoder vae.decoder: 45,620B, BPFP=1.3922 ⌛️ [2/4] FRONTEND: Frontend time: 2.145s (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: 1.604s [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 0.14271660 text_encoder-item0.clip_prompt_embeds 0.00021560 65.23402834 text_encoder_2-item1.clip_pooled_prompt_embeds 0.00023980 0.18255272 text_encoder_2-item1.clip_prompt_embeds 0.00021108 0.04289608 text_encoder_3-item2.t5_prompt_embeds 0.00000804 0.00079303 text_encoder-item3.clip_pooled_prompt_embeds 0.00841799 0.25064182 text_encoder-item3.clip_prompt_embeds 0.00023247 57.42621246 text_encoder_2-item4.clip_pooled_prompt_embeds 0.00016751 0.08740882 text_encoder_2-item4.clip_prompt_embeds 0.00009830 0.08730212 text_encoder_3-item5.t5_prompt_embeds 0.00001263 0.00061542 vae.encoder_f0 0.00544735 0.37008247 vae.encoder_f1 0.00544843 0.37063712 vae.decoder 0.00018632 0.02184167 ------------------------------------------------------------------------------------- TOTAL 0.00257940 3.38861714 (elements=2,260,992) ---------------------- -------------------------------------------------------- SAMPLE-WISE STATISTICS ---------------------- -------------------------------------------------------- Handler sd35 Strategy individual Architecture elic-featurecoding ---------------------- -------------------------------------------------------- Total Elements 2260992 Total Bytes 289596 BPFP 1.0247 bits/point EBPFP 2.0493 equivalent bits/point MSE 3.388617 ---------------------- -------------------------------------------------------- Time: 3.757s Load: 0.008s, Pack+Encode: 2.145s, Decode+Unpack: 1.604s ---------------------- -------------------------------------------------------- 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 3.3886 MSE: from ../datasets/FeatureCoding-StableDiffusion3.5/100samples_features/000000285788.zst to output-fixed/sd35/lambda0.02/elic-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: 2,132B, BPFP=22.2083 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: 10,992B, BPFP=1.4870 Using 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: 3,452B, BPFP=21.5750 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: 19,536B, BPFP=1.5857 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: 61,196B, BPFP=1.5523 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: 2,600B, BPFP=27.0833 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: 8,396B, BPFP=1.1358 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: 4,400B, BPFP=27.5000 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: 16,268B, BPFP=1.3205 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: 42,292B, BPFP=1.0727 Using per-key quantization points (vae.encoder_f0: torch.Size([256])) for vae.encoder_f0 vae.encoder_f0: 53,220B, BPFP=0.8121 Using per-key quantization points (vae.encoder_f1: torch.Size([256])) for vae.encoder_f1 vae.encoder_f1: 53,228B, BPFP=0.8122 Using per-key quantization points (vae.decoder: torch.Size([256])) for vae.decoder vae.decoder: 33,472B, BPFP=1.0215 ⌛️ [2/4] FRONTEND: Frontend time: 2.145s (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: 1.604s [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 0.20483543 text_encoder-item0.clip_prompt_embeds 0.00022698 160.29070279 text_encoder_2-item1.clip_pooled_prompt_embeds 0.00024914 0.18765749 text_encoder_2-item1.clip_prompt_embeds 0.00021102 0.05644213 text_encoder_3-item2.t5_prompt_embeds 0.00000794 0.00064023 text_encoder-item3.clip_pooled_prompt_embeds 0.00841799 0.25064182 text_encoder-item3.clip_prompt_embeds 0.00023247 57.42621246 text_encoder_2-item4.clip_pooled_prompt_embeds 0.00016751 0.08740882 text_encoder_2-item4.clip_prompt_embeds 0.00009830 0.08730212 text_encoder_3-item5.t5_prompt_embeds 0.00001263 0.00061542 vae.encoder_f0 0.00630479 0.53643835 vae.encoder_f1 0.00631430 0.53502142 vae.decoder 0.00018596 0.01781053 ------------------------------------------------------------------------------------- TOTAL 0.00298001 5.95163330 (elements=2,260,992) ---------------------- -------------------------------------------------------- SAMPLE-WISE STATISTICS ---------------------- -------------------------------------------------------- Handler sd35 Strategy individual Architecture elic-featurecoding ---------------------- -------------------------------------------------------- Total Elements 2260992 Total Bytes 311184 BPFP 1.1011 bits/point EBPFP 2.2021 equivalent bits/point MSE 5.951633 ---------------------- -------------------------------------------------------- Time: 3.757s Load: 0.008s, Pack+Encode: 2.145s, Decode+Unpack: 1.604s ---------------------- -------------------------------------------------------- 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 5.9516 MSE: from ../datasets/FeatureCoding-StableDiffusion3.5/100samples_features/000000287291.zst to output-fixed/sd35/lambda0.02/elic-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: 2,320B, BPFP=24.1667 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: 11,272B, BPFP=1.5249 Using 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: 3,564B, BPFP=22.2750 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: 18,900B, BPFP=1.5341 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: 58,088B, BPFP=1.4734 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: 2,600B, BPFP=27.0833 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: 8,396B, BPFP=1.1358 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: 4,400B, BPFP=27.5000 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: 16,268B, BPFP=1.3205 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: 42,292B, BPFP=1.0727 Using per-key quantization points (vae.encoder_f0: torch.Size([256])) for vae.encoder_f0 vae.encoder_f0: 50,904B, BPFP=0.7767 Using per-key quantization points (vae.encoder_f1: torch.Size([256])) for vae.encoder_f1 vae.encoder_f1: 50,928B, BPFP=0.7771 Using per-key quantization points (vae.decoder: torch.Size([256])) for vae.decoder vae.decoder: 38,632B, BPFP=1.1790 ⌛️ [2/4] FRONTEND: Frontend time: 2.153s (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: 1.607s [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 0.15889954 text_encoder-item0.clip_prompt_embeds 0.00024643 34.44861633 text_encoder_2-item1.clip_pooled_prompt_embeds 0.00022451 0.19307135 text_encoder_2-item1.clip_prompt_embeds 0.00018967 0.05871894 text_encoder_3-item2.t5_prompt_embeds 0.00000778 0.00063685 text_encoder-item3.clip_pooled_prompt_embeds 0.00841799 0.25064182 text_encoder-item3.clip_prompt_embeds 0.00023247 57.42621246 text_encoder_2-item4.clip_pooled_prompt_embeds 0.00016751 0.08740882 text_encoder_2-item4.clip_prompt_embeds 0.00009830 0.08730212 text_encoder_3-item5.t5_prompt_embeds 0.00001263 0.00061542 vae.encoder_f0 0.00612578 0.51922607 vae.encoder_f1 0.00613243 0.51807952 vae.decoder 0.00018179 0.01902582 ------------------------------------------------------------------------------------- TOTAL 0.00289482 2.65255454 (elements=2,260,992) ---------------------- -------------------------------------------------------- SAMPLE-WISE STATISTICS ---------------------- -------------------------------------------------------- Handler sd35 Strategy individual Architecture elic-featurecoding ---------------------- -------------------------------------------------------- Total Elements 2260992 Total Bytes 308564 BPFP 1.0918 bits/point EBPFP 2.1836 equivalent bits/point MSE 2.652555 ---------------------- -------------------------------------------------------- Time: 3.769s Load: 0.009s, Pack+Encode: 2.153s, Decode+Unpack: 1.607s ---------------------- -------------------------------------------------------- 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 2.6526 MSE: from ../datasets/FeatureCoding-StableDiffusion3.5/100samples_features/000000289343.zst to output-fixed/sd35/lambda0.02/elic-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: 0.008s ------------------------------------------------------------ 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: 2,232B, BPFP=23.2500 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: 12,732B, BPFP=1.7224 Using 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: 3,448B, BPFP=21.5500 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: 24,240B, BPFP=1.9675 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: 72,348B, BPFP=1.8351 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: 2,600B, BPFP=27.0833 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: 8,396B, BPFP=1.1358 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: 4,400B, BPFP=27.5000 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: 16,268B, BPFP=1.3205 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: 42,292B, BPFP=1.0727 Using per-key quantization points (vae.encoder_f0: torch.Size([256])) for vae.encoder_f0 vae.encoder_f0: 15,480B, BPFP=0.2362 Using per-key quantization points (vae.encoder_f1: torch.Size([256])) for vae.encoder_f1 vae.encoder_f1: 15,476B, BPFP=0.2361 Using per-key quantization points (vae.decoder: torch.Size([256])) for vae.decoder vae.decoder: 44,832B, BPFP=1.3682 ⌛️ [2/4] FRONTEND: Frontend time: 2.142s (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: 1.604s [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 0.17708192 text_encoder-item0.clip_prompt_embeds 0.00024049 66.92073779 text_encoder_2-item1.clip_pooled_prompt_embeds 0.00023104 0.19279066 text_encoder_2-item1.clip_prompt_embeds 0.00016878 0.05861531 text_encoder_3-item2.t5_prompt_embeds 0.00000794 0.00067447 text_encoder-item3.clip_pooled_prompt_embeds 0.00841799 0.25064182 text_encoder-item3.clip_prompt_embeds 0.00023247 57.42621246 text_encoder_2-item4.clip_pooled_prompt_embeds 0.00016751 0.08740882 text_encoder_2-item4.clip_prompt_embeds 0.00009830 0.08730212 text_encoder_3-item5.t5_prompt_embeds 0.00001263 0.00061542 vae.encoder_f0 0.00526071 0.22158346 vae.encoder_f1 0.00526072 0.22152595 vae.decoder 0.00016981 0.01986113 ------------------------------------------------------------------------------------- TOTAL 0.00248947 3.36417832 (elements=2,260,992) ---------------------- -------------------------------------------------------- SAMPLE-WISE STATISTICS ---------------------- -------------------------------------------------------- Handler sd35 Strategy individual Architecture elic-featurecoding ---------------------- -------------------------------------------------------- Total Elements 2260992 Total Bytes 264744 BPFP 0.9367 bits/point EBPFP 1.8735 equivalent bits/point MSE 3.364178 ---------------------- -------------------------------------------------------- Time: 3.754s Load: 0.008s, Pack+Encode: 2.142s, Decode+Unpack: 1.604s ---------------------- -------------------------------------------------------- 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 3.3642 MSE: from ../datasets/FeatureCoding-StableDiffusion3.5/100samples_features/000000304545.zst to output-fixed/sd35/lambda0.02/elic-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: 0.008s ------------------------------------------------------------ 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: 2,116B, BPFP=22.0417 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: 11,052B, BPFP=1.4951 Using 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: 3,356B, BPFP=20.9750 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: 18,744B, BPFP=1.5214 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: 61,560B, BPFP=1.5615 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: 2,600B, BPFP=27.0833 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: 8,396B, BPFP=1.1358 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: 4,400B, BPFP=27.5000 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: 16,268B, BPFP=1.3205 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: 42,292B, BPFP=1.0727 Using per-key quantization points (vae.encoder_f0: torch.Size([256])) for vae.encoder_f0 vae.encoder_f0: 54,960B, BPFP=0.8386 Using per-key quantization points (vae.encoder_f1: torch.Size([256])) for vae.encoder_f1 vae.encoder_f1: 54,968B, BPFP=0.8387 Using per-key quantization points (vae.decoder: torch.Size([256])) for vae.decoder vae.decoder: 37,060B, BPFP=1.1310 ⌛️ [2/4] FRONTEND: Frontend time: 2.154s (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: 1.599s [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 0.17002666 text_encoder-item0.clip_prompt_embeds 0.00022843 23.32018990 text_encoder_2-item1.clip_pooled_prompt_embeds 0.00086038 0.19234788 text_encoder_2-item1.clip_prompt_embeds 0.00016207 0.05649476 text_encoder_3-item2.t5_prompt_embeds 0.00000746 0.00068242 text_encoder-item3.clip_pooled_prompt_embeds 0.00841799 0.25064182 text_encoder-item3.clip_prompt_embeds 0.00023247 57.42621246 text_encoder_2-item4.clip_pooled_prompt_embeds 0.00016751 0.08740882 text_encoder_2-item4.clip_prompt_embeds 0.00009830 0.08730212 text_encoder_3-item5.t5_prompt_embeds 0.00001263 0.00061542 vae.encoder_f0 0.00622977 0.55642414 vae.encoder_f1 0.00623684 0.55706924 vae.decoder 0.00019755 0.01844364 ------------------------------------------------------------------------------------- TOTAL 0.00294358 2.37900376 (elements=2,260,992) ---------------------- -------------------------------------------------------- SAMPLE-WISE STATISTICS ---------------------- -------------------------------------------------------- Handler sd35 Strategy individual Architecture elic-featurecoding ---------------------- -------------------------------------------------------- Total Elements 2260992 Total Bytes 317772 BPFP 1.1244 bits/point EBPFP 2.2487 equivalent bits/point MSE 2.379004 ---------------------- -------------------------------------------------------- Time: 3.761s Load: 0.008s, Pack+Encode: 2.154s, Decode+Unpack: 1.599s ---------------------- -------------------------------------------------------- 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 2.3790 MSE: from ../datasets/FeatureCoding-StableDiffusion3.5/100samples_features/000000310622.zst to output-fixed/sd35/lambda0.02/elic-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: 0.008s ------------------------------------------------------------ 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: 2,388B, BPFP=24.8750 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: 11,752B, BPFP=1.5898 Using 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: 3,620B, BPFP=22.6250 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: 19,392B, BPFP=1.5740 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: 61,004B, BPFP=1.5474 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: 2,600B, BPFP=27.0833 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: 8,396B, BPFP=1.1358 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: 4,400B, BPFP=27.5000 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: 16,268B, BPFP=1.3205 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: 42,292B, BPFP=1.0727 Using per-key quantization points (vae.encoder_f0: torch.Size([256])) for vae.encoder_f0 vae.encoder_f0: 37,540B, BPFP=0.5728 Using per-key quantization points (vae.encoder_f1: torch.Size([256])) for vae.encoder_f1 vae.encoder_f1: 37,564B, BPFP=0.5732 Using per-key quantization points (vae.decoder: torch.Size([256])) for vae.decoder vae.decoder: 26,764B, BPFP=0.8168 ⌛️ [2/4] FRONTEND: Frontend time: 2.140s (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: 1.612s [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 0.15694069 text_encoder-item0.clip_prompt_embeds 0.00026004 56.17268669 text_encoder_2-item1.clip_pooled_prompt_embeds 0.00025016 0.18005960 text_encoder_2-item1.clip_prompt_embeds 0.00015074 0.05662177 text_encoder_3-item2.t5_prompt_embeds 0.00000873 0.00078440 text_encoder-item3.clip_pooled_prompt_embeds 0.00841799 0.25064182 text_encoder-item3.clip_prompt_embeds 0.00023247 57.42621246 text_encoder_2-item4.clip_pooled_prompt_embeds 0.00016751 0.08740882 text_encoder_2-item4.clip_prompt_embeds 0.00009830 0.08730212 text_encoder_3-item5.t5_prompt_embeds 0.00001263 0.00061542 vae.encoder_f0 0.00725303 0.51790035 vae.encoder_f1 0.00725507 0.51715142 vae.decoder 0.00017991 0.01571170 ------------------------------------------------------------------------------------- TOTAL 0.00341494 3.21975950 (elements=2,260,992) ---------------------- -------------------------------------------------------- SAMPLE-WISE STATISTICS ---------------------- -------------------------------------------------------- Handler sd35 Strategy individual Architecture elic-featurecoding ---------------------- -------------------------------------------------------- Total Elements 2260992 Total Bytes 273980 BPFP 0.9694 bits/point EBPFP 1.9388 equivalent bits/point MSE 3.219759 ---------------------- -------------------------------------------------------- Time: 3.760s Load: 0.008s, Pack+Encode: 2.140s, Decode+Unpack: 1.612s ---------------------- -------------------------------------------------------- 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 3.2198 MSE: from ../datasets/FeatureCoding-StableDiffusion3.5/100samples_features/000000311394.zst to output-fixed/sd35/lambda0.02/elic-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: 0.008s ------------------------------------------------------------ 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: 2,296B, BPFP=23.9167 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: 12,124B, BPFP=1.6402 Using 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: 3,516B, BPFP=21.9750 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: 19,800B, BPFP=1.6071 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: 62,340B, BPFP=1.5813 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: 2,600B, BPFP=27.0833 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: 8,396B, BPFP=1.1358 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: 4,400B, BPFP=27.5000 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: 16,268B, BPFP=1.3205 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: 42,292B, BPFP=1.0727 Using per-key quantization points (vae.encoder_f0: torch.Size([256])) for vae.encoder_f0 vae.encoder_f0: 38,832B, BPFP=0.5925 Using per-key quantization points (vae.encoder_f1: torch.Size([256])) for vae.encoder_f1 vae.encoder_f1: 38,824B, BPFP=0.5924 Using per-key quantization points (vae.decoder: torch.Size([256])) for vae.decoder vae.decoder: 21,308B, BPFP=0.6503 ⌛️ [2/4] FRONTEND: Frontend time: 2.143s (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: 1.602s [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 0.17909304 text_encoder-item0.clip_prompt_embeds 0.00031748 64.69982752 text_encoder_2-item1.clip_pooled_prompt_embeds 0.00022063 0.18854072 text_encoder_2-item1.clip_prompt_embeds 0.00019717 0.05936792 text_encoder_3-item2.t5_prompt_embeds 0.00000812 0.00070075 text_encoder-item3.clip_pooled_prompt_embeds 0.00841799 0.25064182 text_encoder-item3.clip_prompt_embeds 0.00023247 57.42621246 text_encoder_2-item4.clip_pooled_prompt_embeds 0.00016751 0.08740882 text_encoder_2-item4.clip_prompt_embeds 0.00009830 0.08730212 text_encoder_3-item5.t5_prompt_embeds 0.00001263 0.00061542 vae.encoder_f0 0.42111695 1.48051131 vae.encoder_f1 0.42111716 1.47197604 vae.decoder 0.00019827 0.01485208 ------------------------------------------------------------------------------------- TOTAL 0.19535708 3.88742938 (elements=2,260,992) ---------------------- -------------------------------------------------------- SAMPLE-WISE STATISTICS ---------------------- -------------------------------------------------------- Handler sd35 Strategy individual Architecture elic-featurecoding ---------------------- -------------------------------------------------------- Total Elements 2260992 Total Bytes 272996 BPFP 0.9659 bits/point EBPFP 1.9319 equivalent bits/point MSE 3.887429 ---------------------- -------------------------------------------------------- Time: 3.753s Load: 0.008s, Pack+Encode: 2.143s, Decode+Unpack: 1.602s ---------------------- -------------------------------------------------------- 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 3.8874 MSE: from ../datasets/FeatureCoding-StableDiffusion3.5/100samples_features/000000316015.zst to output-fixed/sd35/lambda0.02/elic-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: 2,212B, BPFP=23.0417 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: 11,824B, BPFP=1.5996 Using 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: 3,300B, BPFP=20.6250 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: 20,672B, BPFP=1.6779 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: 71,724B, BPFP=1.8193 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: 2,600B, BPFP=27.0833 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: 8,396B, BPFP=1.1358 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: 4,400B, BPFP=27.5000 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: 16,268B, BPFP=1.3205 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: 42,292B, BPFP=1.0727 Using per-key quantization points (vae.encoder_f0: torch.Size([256])) for vae.encoder_f0 vae.encoder_f0: 46,248B, BPFP=0.7057 Using per-key quantization points (vae.encoder_f1: torch.Size([256])) for vae.encoder_f1 vae.encoder_f1: 46,244B, BPFP=0.7056 Using per-key quantization points (vae.decoder: torch.Size([256])) for vae.decoder vae.decoder: 25,452B, BPFP=0.7767 ⌛️ [2/4] FRONTEND: Frontend time: 2.137s (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: 1.605s [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 0.15780667 text_encoder-item0.clip_prompt_embeds 0.00024951 58.27591484 text_encoder_2-item1.clip_pooled_prompt_embeds 0.00020437 0.19130405 text_encoder_2-item1.clip_prompt_embeds 0.00016387 0.05126611 text_encoder_3-item2.t5_prompt_embeds 0.00000772 0.00073628 text_encoder-item3.clip_pooled_prompt_embeds 0.00841799 0.25064182 text_encoder-item3.clip_prompt_embeds 0.00023247 57.42621246 text_encoder_2-item4.clip_pooled_prompt_embeds 0.00016751 0.08740882 text_encoder_2-item4.clip_prompt_embeds 0.00009830 0.08730212 text_encoder_3-item5.t5_prompt_embeds 0.00001263 0.00061542 vae.encoder_f0 0.10376993 1.30147386 vae.encoder_f1 0.10377157 1.30479109 vae.decoder 0.00019787 0.01650715 ------------------------------------------------------------------------------------- TOTAL 0.04817852 3.63896720 (elements=2,260,992) ---------------------- -------------------------------------------------------- SAMPLE-WISE STATISTICS ---------------------- -------------------------------------------------------- Handler sd35 Strategy individual Architecture elic-featurecoding ---------------------- -------------------------------------------------------- Total Elements 2260992 Total Bytes 301632 BPFP 1.0673 bits/point EBPFP 2.1345 equivalent bits/point MSE 3.638967 ---------------------- -------------------------------------------------------- Time: 3.749s Load: 0.008s, Pack+Encode: 2.137s, Decode+Unpack: 1.605s ---------------------- -------------------------------------------------------- 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 3.6390 MSE: from ../datasets/FeatureCoding-StableDiffusion3.5/100samples_features/000000323571.zst to output-fixed/sd35/lambda0.02/elic-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: 0.008s ------------------------------------------------------------ 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: 2,292B, BPFP=23.8750 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: 11,372B, BPFP=1.5384 Using 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: 3,344B, BPFP=20.9000 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: 19,976B, BPFP=1.6214 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: 61,412B, BPFP=1.5577 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: 2,600B, BPFP=27.0833 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: 8,396B, BPFP=1.1358 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: 4,400B, BPFP=27.5000 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: 16,268B, BPFP=1.3205 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: 42,292B, BPFP=1.0727 Using per-key quantization points (vae.encoder_f0: torch.Size([256])) for vae.encoder_f0 vae.encoder_f0: 48,716B, BPFP=0.7433 Using per-key quantization points (vae.encoder_f1: torch.Size([256])) for vae.encoder_f1 vae.encoder_f1: 48,744B, BPFP=0.7438 Using per-key quantization points (vae.decoder: torch.Size([256])) for vae.decoder vae.decoder: 24,020B, BPFP=0.7330 ⌛️ [2/4] FRONTEND: Frontend time: 2.140s (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: 1.605s [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 0.17014019 text_encoder-item0.clip_prompt_embeds 0.00022350 82.56025095 text_encoder_2-item1.clip_pooled_prompt_embeds 0.00046887 0.19217231 text_encoder_2-item1.clip_prompt_embeds 0.00019271 0.04594270 text_encoder_3-item2.t5_prompt_embeds 0.00000799 0.00065894 text_encoder-item3.clip_pooled_prompt_embeds 0.00841799 0.25064182 text_encoder-item3.clip_prompt_embeds 0.00023247 57.42621246 text_encoder_2-item4.clip_pooled_prompt_embeds 0.00016751 0.08740882 text_encoder_2-item4.clip_prompt_embeds 0.00009830 0.08730212 text_encoder_3-item5.t5_prompt_embeds 0.00001263 0.00061542 vae.encoder_f0 0.01346414 0.72492135 vae.encoder_f1 0.01346933 0.72484279 vae.decoder 0.00019243 0.01489186 ------------------------------------------------------------------------------------- TOTAL 0.00629858 4.00552183 (elements=2,260,992) ---------------------- -------------------------------------------------------- SAMPLE-WISE STATISTICS ---------------------- -------------------------------------------------------- Handler sd35 Strategy individual Architecture elic-featurecoding ---------------------- -------------------------------------------------------- Total Elements 2260992 Total Bytes 293832 BPFP 1.0397 bits/point EBPFP 2.0793 equivalent bits/point MSE 4.005522 ---------------------- -------------------------------------------------------- Time: 3.753s Load: 0.008s, Pack+Encode: 2.140s, Decode+Unpack: 1.605s ---------------------- -------------------------------------------------------- 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 4.0055 MSE: from ../datasets/FeatureCoding-StableDiffusion3.5/100samples_features/000000325483.zst to output-fixed/sd35/lambda0.02/elic-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: 2,312B, BPFP=24.0833 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: 11,520B, BPFP=1.5584 Using 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: 3,572B, BPFP=22.3250 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: 18,768B, BPFP=1.5234 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: 67,312B, BPFP=1.7074 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: 2,600B, BPFP=27.0833 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: 8,396B, BPFP=1.1358 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: 4,400B, BPFP=27.5000 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: 16,268B, BPFP=1.3205 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: 42,292B, BPFP=1.0727 Using per-key quantization points (vae.encoder_f0: torch.Size([256])) for vae.encoder_f0 vae.encoder_f0: 41,752B, BPFP=0.6371 Using per-key quantization points (vae.encoder_f1: torch.Size([256])) for vae.encoder_f1 vae.encoder_f1: 41,752B, BPFP=0.6371 Using per-key quantization points (vae.decoder: torch.Size([256])) for vae.decoder vae.decoder: 24,104B, BPFP=0.7356 ⌛️ [2/4] FRONTEND: Frontend time: 2.148s (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: 1.607s [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 0.14565947 text_encoder-item0.clip_prompt_embeds 0.00024958 76.70555161 text_encoder_2-item1.clip_pooled_prompt_embeds 0.00135081 0.20110595 text_encoder_2-item1.clip_prompt_embeds 0.00018030 0.04976418 text_encoder_3-item2.t5_prompt_embeds 0.00000823 0.00070821 text_encoder-item3.clip_pooled_prompt_embeds 0.00841799 0.25064182 text_encoder-item3.clip_prompt_embeds 0.00023247 57.42621246 text_encoder_2-item4.clip_pooled_prompt_embeds 0.00016751 0.08740882 text_encoder_2-item4.clip_prompt_embeds 0.00009830 0.08730212 text_encoder_3-item5.t5_prompt_embeds 0.00001263 0.00061542 vae.encoder_f0 0.11196710 1.17707825 vae.encoder_f1 0.11196851 1.16917825 vae.decoder 0.00023459 0.01836617 ------------------------------------------------------------------------------------- TOTAL 0.05198575 4.06084811 (elements=2,260,992) ---------------------- -------------------------------------------------------- SAMPLE-WISE STATISTICS ---------------------- -------------------------------------------------------- Handler sd35 Strategy individual Architecture elic-featurecoding ---------------------- -------------------------------------------------------- Total Elements 2260992 Total Bytes 285048 BPFP 1.0086 bits/point EBPFP 2.0172 equivalent bits/point MSE 4.060848 ---------------------- -------------------------------------------------------- Time: 3.763s Load: 0.008s, Pack+Encode: 2.148s, Decode+Unpack: 1.607s ---------------------- -------------------------------------------------------- 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 4.0608 MSE: from ../datasets/FeatureCoding-StableDiffusion3.5/100samples_features/000000325991.zst to output-fixed/sd35/lambda0.02/elic-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: 0.008s ------------------------------------------------------------ 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: 2,344B, BPFP=24.4167 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: 12,444B, BPFP=1.6834 Using 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: 3,504B, BPFP=21.9000 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: 20,272B, BPFP=1.6455 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: 59,680B, BPFP=1.5138 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: 2,600B, BPFP=27.0833 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: 8,396B, BPFP=1.1358 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: 4,400B, BPFP=27.5000 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: 16,268B, BPFP=1.3205 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: 42,292B, BPFP=1.0727 Using per-key quantization points (vae.encoder_f0: torch.Size([256])) for vae.encoder_f0 vae.encoder_f0: 59,604B, BPFP=0.9095 Using per-key quantization points (vae.encoder_f1: torch.Size([256])) for vae.encoder_f1 vae.encoder_f1: 59,604B, BPFP=0.9095 Using per-key quantization points (vae.decoder: torch.Size([256])) for vae.decoder vae.decoder: 32,060B, BPFP=0.9784 ⌛️ [2/4] FRONTEND: Frontend time: 2.138s (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: 1.607s [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 0.14764025 text_encoder-item0.clip_prompt_embeds 0.00025929 54.44634825 text_encoder_2-item1.clip_pooled_prompt_embeds 0.00021916 0.18161049 text_encoder_2-item1.clip_prompt_embeds 0.00042246 0.05164745 text_encoder_3-item2.t5_prompt_embeds 0.00000781 0.00065477 text_encoder-item3.clip_pooled_prompt_embeds 0.00841799 0.25064182 text_encoder-item3.clip_prompt_embeds 0.00023247 57.42621246 text_encoder_2-item4.clip_pooled_prompt_embeds 0.00016751 0.08740882 text_encoder_2-item4.clip_prompt_embeds 0.00009830 0.08730212 text_encoder_3-item5.t5_prompt_embeds 0.00001263 0.00061542 vae.encoder_f0 0.00675017 0.65804160 vae.encoder_f1 0.00675421 0.65729773 vae.decoder 0.00023635 0.02057406 ------------------------------------------------------------------------------------- TOTAL 0.00320042 3.23992807 (elements=2,260,992) ---------------------- -------------------------------------------------------- SAMPLE-WISE STATISTICS ---------------------- -------------------------------------------------------- Handler sd35 Strategy individual Architecture elic-featurecoding ---------------------- -------------------------------------------------------- Total Elements 2260992 Total Bytes 323468 BPFP 1.1445 bits/point EBPFP 2.2890 equivalent bits/point MSE 3.239928 ---------------------- -------------------------------------------------------- Time: 3.753s Load: 0.008s, Pack+Encode: 2.138s, Decode+Unpack: 1.607s ---------------------- -------------------------------------------------------- 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 3.2399 MSE: from ../datasets/FeatureCoding-StableDiffusion3.5/100samples_features/000000329319.zst to output-fixed/sd35/lambda0.02/elic-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: 2,232B, BPFP=23.2500 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: 11,388B, BPFP=1.5406 Using 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: 3,440B, BPFP=21.5000 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: 19,060B, BPFP=1.5471 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: 66,796B, BPFP=1.6943 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: 2,600B, BPFP=27.0833 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: 8,396B, BPFP=1.1358 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: 4,400B, BPFP=27.5000 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: 16,268B, BPFP=1.3205 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: 42,292B, BPFP=1.0727 Using per-key quantization points (vae.encoder_f0: torch.Size([256])) for vae.encoder_f0 vae.encoder_f0: 68,600B, BPFP=1.0468 Using per-key quantization points (vae.encoder_f1: torch.Size([256])) for vae.encoder_f1 vae.encoder_f1: 68,584B, BPFP=1.0465 Using per-key quantization points (vae.decoder: torch.Size([256])) for vae.decoder vae.decoder: 31,992B, BPFP=0.9763 ⌛️ [2/4] FRONTEND: Frontend time: 2.310s (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: 1.604s [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 0.14690455 text_encoder-item0.clip_prompt_embeds 0.00064775 143.37209145 text_encoder_2-item1.clip_pooled_prompt_embeds 0.00128483 0.20201049 text_encoder_2-item1.clip_prompt_embeds 0.00019620 0.01727503 text_encoder_3-item2.t5_prompt_embeds 0.00000792 0.00073125 text_encoder-item3.clip_pooled_prompt_embeds 0.00841799 0.25064182 text_encoder-item3.clip_prompt_embeds 0.00023247 57.42621246 text_encoder_2-item4.clip_pooled_prompt_embeds 0.00016751 0.08740882 text_encoder_2-item4.clip_prompt_embeds 0.00009830 0.08730212 text_encoder_3-item5.t5_prompt_embeds 0.00001263 0.00061542 vae.encoder_f0 0.00728993 0.76399535 vae.encoder_f1 0.00729572 0.76287431 vae.decoder 0.00026488 0.02097866 ------------------------------------------------------------------------------------- TOTAL 0.00345536 5.61339226 (elements=2,260,992) ---------------------- -------------------------------------------------------- SAMPLE-WISE STATISTICS ---------------------- -------------------------------------------------------- Handler sd35 Strategy individual Architecture elic-featurecoding ---------------------- -------------------------------------------------------- Total Elements 2260992 Total Bytes 346048 BPFP 1.2244 bits/point EBPFP 2.4488 equivalent bits/point MSE 5.613392 ---------------------- -------------------------------------------------------- Time: 3.923s Load: 0.009s, Pack+Encode: 2.310s, Decode+Unpack: 1.604s ---------------------- -------------------------------------------------------- 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 5.6134 MSE: from ../datasets/FeatureCoding-StableDiffusion3.5/100samples_features/000000335081.zst to output-fixed/sd35/lambda0.02/elic-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: 2,316B, BPFP=24.1250 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: 11,796B, BPFP=1.5958 Using 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: 3,440B, BPFP=21.5000 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: 19,544B, BPFP=1.5864 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: 65,360B, BPFP=1.6579 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: 2,600B, BPFP=27.0833 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: 8,396B, BPFP=1.1358 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: 4,400B, BPFP=27.5000 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: 16,268B, BPFP=1.3205 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: 42,292B, BPFP=1.0727 Using per-key quantization points (vae.encoder_f0: torch.Size([256])) for vae.encoder_f0 vae.encoder_f0: 59,476B, BPFP=0.9075 Using per-key quantization points (vae.encoder_f1: torch.Size([256])) for vae.encoder_f1 vae.encoder_f1: 59,460B, BPFP=0.9073 Using per-key quantization points (vae.decoder: torch.Size([256])) for vae.decoder vae.decoder: 34,028B, BPFP=1.0385 ⌛️ [2/4] FRONTEND: Frontend time: 2.170s (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: 1.610s [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 0.15812045 text_encoder-item0.clip_prompt_embeds 0.00023188 30.73165669 text_encoder_2-item1.clip_pooled_prompt_embeds 0.00022679 0.16469988 text_encoder_2-item1.clip_prompt_embeds 0.00015622 0.05811748 text_encoder_3-item2.t5_prompt_embeds 0.00000782 0.00068156 text_encoder-item3.clip_pooled_prompt_embeds 0.00841799 0.25064182 text_encoder-item3.clip_prompt_embeds 0.00023247 57.42621246 text_encoder_2-item4.clip_pooled_prompt_embeds 0.00016751 0.08740882 text_encoder_2-item4.clip_prompt_embeds 0.00009830 0.08730212 text_encoder_3-item5.t5_prompt_embeds 0.00001263 0.00061542 vae.encoder_f0 0.00613207 0.61146992 vae.encoder_f1 0.00613899 0.61205173 vae.decoder 0.00023812 0.01984278 ------------------------------------------------------------------------------------- TOTAL 0.00290239 2.59857681 (elements=2,260,992) ---------------------- -------------------------------------------------------- SAMPLE-WISE STATISTICS ---------------------- -------------------------------------------------------- Handler sd35 Strategy individual Architecture elic-featurecoding ---------------------- -------------------------------------------------------- Total Elements 2260992 Total Bytes 329376 BPFP 1.1654 bits/point EBPFP 2.3308 equivalent bits/point MSE 2.598577 ---------------------- -------------------------------------------------------- Time: 3.789s Load: 0.009s, Pack+Encode: 2.170s, Decode+Unpack: 1.610s ---------------------- -------------------------------------------------------- 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 2.5986 MSE: from ../datasets/FeatureCoding-StableDiffusion3.5/100samples_features/000000342186.zst to output-fixed/sd35/lambda0.02/elic-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: 2,228B, BPFP=23.2083 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: 11,772B, BPFP=1.5925 Using 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: 3,376B, BPFP=21.1000 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: 20,264B, BPFP=1.6448 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: 63,264B, BPFP=1.6047 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: 2,600B, BPFP=27.0833 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: 8,396B, BPFP=1.1358 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: 4,400B, BPFP=27.5000 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: 16,268B, BPFP=1.3205 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: 42,292B, BPFP=1.0727 Using per-key quantization points (vae.encoder_f0: torch.Size([256])) for vae.encoder_f0 vae.encoder_f0: 48,688B, BPFP=0.7429 Using per-key quantization points (vae.encoder_f1: torch.Size([256])) for vae.encoder_f1 vae.encoder_f1: 48,692B, BPFP=0.7430 Using per-key quantization points (vae.decoder: torch.Size([256])) for vae.decoder vae.decoder: 29,376B, BPFP=0.8965 ⌛️ [2/4] FRONTEND: Frontend time: 2.147s (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: 1.600s [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 0.14377297 text_encoder-item0.clip_prompt_embeds 0.00023678 56.18664604 text_encoder_2-item1.clip_pooled_prompt_embeds 0.00028948 0.21778450 text_encoder_2-item1.clip_prompt_embeds 0.00019061 0.06202599 text_encoder_3-item2.t5_prompt_embeds 0.00000767 0.00063112 text_encoder-item3.clip_pooled_prompt_embeds 0.00841799 0.25064182 text_encoder-item3.clip_prompt_embeds 0.00023247 57.42621246 text_encoder_2-item4.clip_pooled_prompt_embeds 0.00016751 0.08740882 text_encoder_2-item4.clip_prompt_embeds 0.00009830 0.08730212 text_encoder_3-item5.t5_prompt_embeds 0.00001263 0.00061542 vae.encoder_f0 0.00636537 0.61048186 vae.encoder_f1 0.00636991 0.60996342 vae.decoder 0.00025538 0.01893326 ------------------------------------------------------------------------------------- TOTAL 0.00301360 3.26371900 (elements=2,260,992) ---------------------- -------------------------------------------------------- SAMPLE-WISE STATISTICS ---------------------- -------------------------------------------------------- Handler sd35 Strategy individual Architecture elic-featurecoding ---------------------- -------------------------------------------------------- Total Elements 2260992 Total Bytes 301616 BPFP 1.0672 bits/point EBPFP 2.1344 equivalent bits/point MSE 3.263719 ---------------------- -------------------------------------------------------- Time: 3.755s Load: 0.009s, Pack+Encode: 2.147s, Decode+Unpack: 1.600s ---------------------- -------------------------------------------------------- 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 3.2637 MSE: from ../datasets/FeatureCoding-StableDiffusion3.5/100samples_features/000000343976.zst to output-fixed/sd35/lambda0.02/elic-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: 2,368B, BPFP=24.6667 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: 11,384B, BPFP=1.5400 Using 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: 3,404B, BPFP=21.2750 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: 18,488B, BPFP=1.5006 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: 59,692B, BPFP=1.5141 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: 2,600B, BPFP=27.0833 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: 8,396B, BPFP=1.1358 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: 4,400B, BPFP=27.5000 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: 16,268B, BPFP=1.3205 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: 42,292B, BPFP=1.0727 Using per-key quantization points (vae.encoder_f0: torch.Size([256])) for vae.encoder_f0 vae.encoder_f0: 46,112B, BPFP=0.7036 Using per-key quantization points (vae.encoder_f1: torch.Size([256])) for vae.encoder_f1 vae.encoder_f1: 46,116B, BPFP=0.7037 Using per-key quantization points (vae.decoder: torch.Size([256])) for vae.decoder vae.decoder: 23,140B, BPFP=0.7062 ⌛️ [2/4] FRONTEND: Frontend time: 2.144s (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: 1.593s [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 0.15820582 text_encoder-item0.clip_prompt_embeds 0.00023432 32.52944907 text_encoder_2-item1.clip_pooled_prompt_embeds 0.00018703 0.16797398 text_encoder_2-item1.clip_prompt_embeds 0.00017889 0.03956272 text_encoder_3-item2.t5_prompt_embeds 0.00000811 0.00070969 text_encoder-item3.clip_pooled_prompt_embeds 0.00841799 0.25064182 text_encoder-item3.clip_prompt_embeds 0.00023247 57.42621246 text_encoder_2-item4.clip_pooled_prompt_embeds 0.00016751 0.08740882 text_encoder_2-item4.clip_prompt_embeds 0.00009830 0.08730212 text_encoder_3-item5.t5_prompt_embeds 0.00001263 0.00061542 vae.encoder_f0 0.23155926 1.46278691 vae.encoder_f1 0.23156048 1.47171021 vae.decoder 0.00018572 0.01594066 ------------------------------------------------------------------------------------- TOTAL 0.10744199 3.04109036 (elements=2,260,992) ---------------------- -------------------------------------------------------- SAMPLE-WISE STATISTICS ---------------------- -------------------------------------------------------- Handler sd35 Strategy individual Architecture elic-featurecoding ---------------------- -------------------------------------------------------- Total Elements 2260992 Total Bytes 284660 BPFP 1.0072 bits/point EBPFP 2.0144 equivalent bits/point MSE 3.041090 ---------------------- -------------------------------------------------------- Time: 3.746s Load: 0.008s, Pack+Encode: 2.144s, Decode+Unpack: 1.593s ---------------------- -------------------------------------------------------- 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 3.0411 MSE: from ../datasets/FeatureCoding-StableDiffusion3.5/100samples_features/000000351362.zst to output-fixed/sd35/lambda0.02/elic-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: 2,256B, BPFP=23.5000 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: 11,112B, BPFP=1.5032 Using 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: 3,468B, BPFP=21.6750 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: 18,924B, BPFP=1.5360 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: 61,724B, BPFP=1.5656 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: 2,600B, BPFP=27.0833 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: 8,396B, BPFP=1.1358 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: 4,400B, BPFP=27.5000 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: 16,268B, BPFP=1.3205 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: 42,292B, BPFP=1.0727 Using per-key quantization points (vae.encoder_f0: torch.Size([256])) for vae.encoder_f0 vae.encoder_f0: 60,544B, BPFP=0.9238 Using per-key quantization points (vae.encoder_f1: torch.Size([256])) for vae.encoder_f1 vae.encoder_f1: 60,544B, BPFP=0.9238 Using per-key quantization points (vae.decoder: torch.Size([256])) for vae.decoder vae.decoder: 38,156B, BPFP=1.1644 ⌛️ [2/4] FRONTEND: Frontend time: 2.141s (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: 1.603s [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 0.17466384 text_encoder-item0.clip_prompt_embeds 0.00022528 68.60819552 text_encoder_2-item1.clip_pooled_prompt_embeds 0.00022839 0.18699622 text_encoder_2-item1.clip_prompt_embeds 0.00016484 0.04594975 text_encoder_3-item2.t5_prompt_embeds 0.00000786 0.00065757 text_encoder-item3.clip_pooled_prompt_embeds 0.00841799 0.25064182 text_encoder-item3.clip_prompt_embeds 0.00023247 57.42621246 text_encoder_2-item4.clip_pooled_prompt_embeds 0.00016751 0.08740882 text_encoder_2-item4.clip_prompt_embeds 0.00009830 0.08730212 text_encoder_3-item5.t5_prompt_embeds 0.00001263 0.00061542 vae.encoder_f0 0.00729824 0.65644693 vae.encoder_f1 0.00730369 0.65518546 vae.decoder 0.00019938 0.02164071 ------------------------------------------------------------------------------------- TOTAL 0.00343853 3.60935798 (elements=2,260,992) ---------------------- -------------------------------------------------------- SAMPLE-WISE STATISTICS ---------------------- -------------------------------------------------------- Handler sd35 Strategy individual Architecture elic-featurecoding ---------------------- -------------------------------------------------------- Total Elements 2260992 Total Bytes 330684 BPFP 1.1700 bits/point EBPFP 2.3401 equivalent bits/point MSE 3.609358 ---------------------- -------------------------------------------------------- Time: 3.753s Load: 0.008s, Pack+Encode: 2.141s, Decode+Unpack: 1.603s ---------------------- -------------------------------------------------------- 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 3.6094 MSE: from ../datasets/FeatureCoding-StableDiffusion3.5/100samples_features/000000357816.zst to output-fixed/sd35/lambda0.02/elic-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: 2,240B, BPFP=23.3333 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: 10,864B, BPFP=1.4697 Using 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: 3,560B, BPFP=22.2500 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: 18,988B, BPFP=1.5412 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: 57,744B, BPFP=1.4647 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: 2,600B, BPFP=27.0833 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: 8,396B, BPFP=1.1358 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: 4,400B, BPFP=27.5000 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: 16,268B, BPFP=1.3205 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: 42,292B, BPFP=1.0727 Using per-key quantization points (vae.encoder_f0: torch.Size([256])) for vae.encoder_f0 vae.encoder_f0: 40,768B, BPFP=0.6221 Using per-key quantization points (vae.encoder_f1: torch.Size([256])) for vae.encoder_f1 vae.encoder_f1: 40,768B, BPFP=0.6221 Using per-key quantization points (vae.decoder: torch.Size([256])) for vae.decoder vae.decoder: 43,968B, BPFP=1.3418 ⌛️ [2/4] FRONTEND: Frontend time: 2.147s (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: 1.601s [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 0.15112911 text_encoder-item0.clip_prompt_embeds 0.00022149 53.75340740 text_encoder_2-item1.clip_pooled_prompt_embeds 0.00018477 0.16661683 text_encoder_2-item1.clip_prompt_embeds 0.00103146 0.06308012 text_encoder_3-item2.t5_prompt_embeds 0.00000778 0.00075957 text_encoder-item3.clip_pooled_prompt_embeds 0.00841799 0.25064182 text_encoder-item3.clip_prompt_embeds 0.00023247 57.42621246 text_encoder_2-item4.clip_pooled_prompt_embeds 0.00016751 0.08740882 text_encoder_2-item4.clip_prompt_embeds 0.00009830 0.08730212 text_encoder_3-item5.t5_prompt_embeds 0.00001263 0.00061542 vae.encoder_f0 0.00564371 0.47970742 vae.encoder_f1 0.00565042 0.48054156 vae.decoder 0.00019980 0.02193057 ------------------------------------------------------------------------------------- TOTAL 0.00270919 3.14012744 (elements=2,260,992) ---------------------- -------------------------------------------------------- SAMPLE-WISE STATISTICS ---------------------- -------------------------------------------------------- Handler sd35 Strategy individual Architecture elic-featurecoding ---------------------- -------------------------------------------------------- Total Elements 2260992 Total Bytes 292856 BPFP 1.0362 bits/point EBPFP 2.0724 equivalent bits/point MSE 3.140127 ---------------------- -------------------------------------------------------- Time: 3.756s Load: 0.009s, Pack+Encode: 2.147s, Decode+Unpack: 1.601s ---------------------- -------------------------------------------------------- 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 3.1401 MSE: from ../datasets/FeatureCoding-StableDiffusion3.5/100samples_features/000000361180.zst to output-fixed/sd35/lambda0.02/elic-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: 0.008s ------------------------------------------------------------ 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: 2,268B, BPFP=23.6250 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: 11,644B, BPFP=1.5752 Using 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: 3,500B, BPFP=21.8750 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: 19,816B, BPFP=1.6084 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: 59,956B, BPFP=1.5208 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: 2,600B, BPFP=27.0833 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: 8,396B, BPFP=1.1358 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: 4,400B, BPFP=27.5000 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: 16,268B, BPFP=1.3205 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: 42,292B, BPFP=1.0727 Using per-key quantization points (vae.encoder_f0: torch.Size([256])) for vae.encoder_f0 vae.encoder_f0: 43,004B, BPFP=0.6562 Using per-key quantization points (vae.encoder_f1: torch.Size([256])) for vae.encoder_f1 vae.encoder_f1: 43,028B, BPFP=0.6566 Using per-key quantization points (vae.decoder: torch.Size([256])) for vae.decoder vae.decoder: 41,428B, BPFP=1.2643 ⌛️ [2/4] FRONTEND: Frontend time: 2.156s (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: 1.611s [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 0.14613802 text_encoder-item0.clip_prompt_embeds 0.00022173 56.99800037 text_encoder_2-item1.clip_pooled_prompt_embeds 0.00022739 0.17969291 text_encoder_2-item1.clip_prompt_embeds 0.00103962 0.06081925 text_encoder_3-item2.t5_prompt_embeds 0.00000788 0.00066209 text_encoder-item3.clip_pooled_prompt_embeds 0.00841799 0.25064182 text_encoder-item3.clip_prompt_embeds 0.00023247 57.42621246 text_encoder_2-item4.clip_pooled_prompt_embeds 0.00016751 0.08740882 text_encoder_2-item4.clip_prompt_embeds 0.00009830 0.08730212 text_encoder_3-item5.t5_prompt_embeds 0.00001263 0.00061542 vae.encoder_f0 0.00576096 0.45442855 vae.encoder_f1 0.00576981 0.45452225 vae.decoder 0.00019592 0.01975720 ------------------------------------------------------------------------------------- TOTAL 0.00276400 3.21273576 (elements=2,260,992) ---------------------- -------------------------------------------------------- SAMPLE-WISE STATISTICS ---------------------- -------------------------------------------------------- Handler sd35 Strategy individual Architecture elic-featurecoding ---------------------- -------------------------------------------------------- Total Elements 2260992 Total Bytes 298600 BPFP 1.0565 bits/point EBPFP 2.1131 equivalent bits/point MSE 3.212736 ---------------------- -------------------------------------------------------- Time: 3.774s Load: 0.008s, Pack+Encode: 2.156s, Decode+Unpack: 1.611s ---------------------- -------------------------------------------------------- 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 3.2127 MSE: from ../datasets/FeatureCoding-StableDiffusion3.5/100samples_features/000000361268.zst to output-fixed/sd35/lambda0.02/elic-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: 0.008s ------------------------------------------------------------ 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: 2,336B, BPFP=24.3333 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: 12,584B, BPFP=1.7024 Using 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: 3,444B, BPFP=21.5250 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: 21,744B, BPFP=1.7649 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: 70,116B, BPFP=1.7785 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: 2,600B, BPFP=27.0833 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: 8,396B, BPFP=1.1358 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: 4,400B, BPFP=27.5000 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: 16,268B, BPFP=1.3205 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: 42,292B, BPFP=1.0727 Using per-key quantization points (vae.encoder_f0: torch.Size([256])) for vae.encoder_f0 vae.encoder_f0: 36,648B, BPFP=0.5592 Using per-key quantization points (vae.encoder_f1: torch.Size([256])) for vae.encoder_f1 vae.encoder_f1: 36,644B, BPFP=0.5591 Using per-key quantization points (vae.decoder: torch.Size([256])) for vae.decoder vae.decoder: 27,344B, BPFP=0.8345 ⌛️ [2/4] FRONTEND: Frontend time: 2.149s (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: 1.612s [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 0.14590506 text_encoder-item0.clip_prompt_embeds 0.00025917 23.33977400 text_encoder_2-item1.clip_pooled_prompt_embeds 0.00023350 0.19405370 text_encoder_2-item1.clip_prompt_embeds 0.00019057 0.05570461 text_encoder_3-item2.t5_prompt_embeds 0.00000791 0.00068784 text_encoder-item3.clip_pooled_prompt_embeds 0.00841799 0.25064182 text_encoder-item3.clip_prompt_embeds 0.00023247 57.42621246 text_encoder_2-item4.clip_pooled_prompt_embeds 0.00016751 0.08740882 text_encoder_2-item4.clip_prompt_embeds 0.00009830 0.08730212 text_encoder_3-item5.t5_prompt_embeds 0.00001263 0.00061542 vae.encoder_f0 0.00594818 0.46699357 vae.encoder_f1 0.00595328 0.46749210 vae.decoder 0.00023462 0.01722861 ------------------------------------------------------------------------------------- TOTAL 0.00281845 2.33782516 (elements=2,260,992) ---------------------- -------------------------------------------------------- SAMPLE-WISE STATISTICS ---------------------- -------------------------------------------------------- Handler sd35 Strategy individual Architecture elic-featurecoding ---------------------- -------------------------------------------------------- Total Elements 2260992 Total Bytes 284816 BPFP 1.0078 bits/point EBPFP 2.0155 equivalent bits/point MSE 2.337825 ---------------------- -------------------------------------------------------- Time: 3.769s Load: 0.008s, Pack+Encode: 2.149s, Decode+Unpack: 1.612s ---------------------- -------------------------------------------------------- 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 2.3378 MSE: from ../datasets/FeatureCoding-StableDiffusion3.5/100samples_features/000000367228.zst to output-fixed/sd35/lambda0.02/elic-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: 2,240B, BPFP=23.3333 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: 12,828B, BPFP=1.7354 Using 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: 3,552B, BPFP=22.2000 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: 24,372B, BPFP=1.9782 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: 73,152B, BPFP=1.8555 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: 2,600B, BPFP=27.0833 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: 8,396B, BPFP=1.1358 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: 4,400B, BPFP=27.5000 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: 16,268B, BPFP=1.3205 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: 42,292B, BPFP=1.0727 Using per-key quantization points (vae.encoder_f0: torch.Size([256])) for vae.encoder_f0 vae.encoder_f0: 30,584B, BPFP=0.4667 Using per-key quantization points (vae.encoder_f1: torch.Size([256])) for vae.encoder_f1 vae.encoder_f1: 30,584B, BPFP=0.4667 Using per-key quantization points (vae.decoder: torch.Size([256])) for vae.decoder vae.decoder: 12,228B, BPFP=0.3732 ⌛️ [2/4] FRONTEND: Frontend time: 2.151s (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: 1.613s [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 0.15062476 text_encoder-item0.clip_prompt_embeds 0.00022579 75.14831067 text_encoder_2-item1.clip_pooled_prompt_embeds 0.00020263 0.16743939 text_encoder_2-item1.clip_prompt_embeds 0.00017578 0.07721050 text_encoder_3-item2.t5_prompt_embeds 0.00000800 0.00066821 text_encoder-item3.clip_pooled_prompt_embeds 0.00841799 0.25064182 text_encoder-item3.clip_prompt_embeds 0.00023247 57.42621246 text_encoder_2-item4.clip_pooled_prompt_embeds 0.00016751 0.08740882 text_encoder_2-item4.clip_prompt_embeds 0.00009830 0.08730212 text_encoder_3-item5.t5_prompt_embeds 0.00001263 0.00061542 vae.encoder_f0 0.85445058 2.30711269 vae.encoder_f1 0.85445166 2.29214311 vae.decoder 0.00025257 0.01067699 ------------------------------------------------------------------------------------- TOTAL 0.39632643 4.54283523 (elements=2,260,992) ---------------------- -------------------------------------------------------- SAMPLE-WISE STATISTICS ---------------------- -------------------------------------------------------- Handler sd35 Strategy individual Architecture elic-featurecoding ---------------------- -------------------------------------------------------- Total Elements 2260992 Total Bytes 263496 BPFP 0.9323 bits/point EBPFP 1.8646 equivalent bits/point MSE 4.542835 ---------------------- -------------------------------------------------------- Time: 3.772s Load: 0.008s, Pack+Encode: 2.151s, Decode+Unpack: 1.613s ---------------------- -------------------------------------------------------- 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 4.5428 MSE: from ../datasets/FeatureCoding-StableDiffusion3.5/100samples_features/000000369503.zst to output-fixed/sd35/lambda0.02/elic-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: 2,312B, BPFP=24.0833 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: 11,092B, BPFP=1.5005 Using 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: 3,408B, BPFP=21.3000 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: 19,396B, BPFP=1.5744 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: 61,788B, BPFP=1.5673 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: 2,600B, BPFP=27.0833 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: 8,396B, BPFP=1.1358 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: 4,400B, BPFP=27.5000 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: 16,268B, BPFP=1.3205 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: 42,292B, BPFP=1.0727 Using per-key quantization points (vae.encoder_f0: torch.Size([256])) for vae.encoder_f0 vae.encoder_f0: 65,076B, BPFP=0.9930 Using per-key quantization points (vae.encoder_f1: torch.Size([256])) for vae.encoder_f1 vae.encoder_f1: 65,060B, BPFP=0.9927 Using per-key quantization points (vae.decoder: torch.Size([256])) for vae.decoder vae.decoder: 41,352B, BPFP=1.2620 ⌛️ [2/4] FRONTEND: Frontend time: 2.162s (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: 1.616s [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 0.14680291 text_encoder-item0.clip_prompt_embeds 0.00025458 53.70972842 text_encoder_2-item1.clip_pooled_prompt_embeds 0.00158787 0.20335569 text_encoder_2-item1.clip_prompt_embeds 0.00016969 0.06220918 text_encoder_3-item2.t5_prompt_embeds 0.00000826 0.00063900 text_encoder-item3.clip_pooled_prompt_embeds 0.00841799 0.25064182 text_encoder-item3.clip_prompt_embeds 0.00023247 57.42621246 text_encoder_2-item4.clip_pooled_prompt_embeds 0.00016751 0.08740882 text_encoder_2-item4.clip_prompt_embeds 0.00009830 0.08730212 text_encoder_3-item5.t5_prompt_embeds 0.00001263 0.00061542 vae.encoder_f0 0.00628510 0.61650687 vae.encoder_f1 0.00629234 0.61684138 vae.decoder 0.00023521 0.02318382 ------------------------------------------------------------------------------------- TOTAL 0.00297516 3.20242224 (elements=2,260,992) ---------------------- -------------------------------------------------------- SAMPLE-WISE STATISTICS ---------------------- -------------------------------------------------------- Handler sd35 Strategy individual Architecture elic-featurecoding ---------------------- -------------------------------------------------------- Total Elements 2260992 Total Bytes 343440 BPFP 1.2152 bits/point EBPFP 2.4304 equivalent bits/point MSE 3.202422 ---------------------- -------------------------------------------------------- Time: 3.787s Load: 0.009s, Pack+Encode: 2.162s, Decode+Unpack: 1.616s ---------------------- -------------------------------------------------------- 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 3.2024 MSE: from ../datasets/FeatureCoding-StableDiffusion3.5/100samples_features/000000370486.zst to output-fixed/sd35/lambda0.02/elic-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: 0.008s ------------------------------------------------------------ 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: 2,296B, BPFP=23.9167 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: 11,392B, BPFP=1.5411 Using 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: 3,584B, BPFP=22.4000 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: 18,888B, BPFP=1.5331 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: 64,300B, BPFP=1.6310 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: 2,600B, BPFP=27.0833 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: 8,396B, BPFP=1.1358 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: 4,400B, BPFP=27.5000 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: 16,268B, BPFP=1.3205 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: 42,292B, BPFP=1.0727 Using per-key quantization points (vae.encoder_f0: torch.Size([256])) for vae.encoder_f0 vae.encoder_f0: 45,332B, BPFP=0.6917 Using per-key quantization points (vae.encoder_f1: torch.Size([256])) for vae.encoder_f1 vae.encoder_f1: 45,328B, BPFP=0.6917 Using per-key quantization points (vae.decoder: torch.Size([256])) for vae.decoder vae.decoder: 36,072B, BPFP=1.1008 ⌛️ [2/4] FRONTEND: Frontend time: 2.150s (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: 1.609s [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 0.18238997 text_encoder-item0.clip_prompt_embeds 0.00022807 47.31118946 text_encoder_2-item1.clip_pooled_prompt_embeds 0.00029471 0.17919170 text_encoder_2-item1.clip_prompt_embeds 0.00018746 0.04175095 text_encoder_3-item2.t5_prompt_embeds 0.00000782 0.00070745 text_encoder-item3.clip_pooled_prompt_embeds 0.00841799 0.25064182 text_encoder-item3.clip_prompt_embeds 0.00023247 57.42621246 text_encoder_2-item4.clip_pooled_prompt_embeds 0.00016751 0.08740882 text_encoder_2-item4.clip_prompt_embeds 0.00009830 0.08730212 text_encoder_3-item5.t5_prompt_embeds 0.00001263 0.00061542 vae.encoder_f0 0.00573429 0.46906576 vae.encoder_f1 0.00574192 0.46878031 vae.decoder 0.00017875 0.01781913 ------------------------------------------------------------------------------------- TOTAL 0.00271248 2.96504106 (elements=2,260,992) ---------------------- -------------------------------------------------------- SAMPLE-WISE STATISTICS ---------------------- -------------------------------------------------------- Handler sd35 Strategy individual Architecture elic-featurecoding ---------------------- -------------------------------------------------------- Total Elements 2260992 Total Bytes 301148 BPFP 1.0655 bits/point EBPFP 2.1311 equivalent bits/point MSE 2.965041 ---------------------- -------------------------------------------------------- Time: 3.767s Load: 0.008s, Pack+Encode: 2.150s, Decode+Unpack: 1.609s ---------------------- -------------------------------------------------------- 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 2.9650 MSE: from ../datasets/FeatureCoding-StableDiffusion3.5/100samples_features/000000377635.zst to output-fixed/sd35/lambda0.02/elic-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: 0.008s ------------------------------------------------------------ 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: 2,344B, BPFP=24.4167 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: 14,260B, BPFP=1.9291 Using 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: 3,456B, BPFP=21.6000 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: 24,940B, BPFP=2.0244 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: 87,832B, BPFP=2.2279 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: 2,600B, BPFP=27.0833 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: 8,396B, BPFP=1.1358 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: 4,400B, BPFP=27.5000 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: 16,268B, BPFP=1.3205 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: 42,292B, BPFP=1.0727 Using per-key quantization points (vae.encoder_f0: torch.Size([256])) for vae.encoder_f0 vae.encoder_f0: 58,616B, BPFP=0.8944 Using per-key quantization points (vae.encoder_f1: torch.Size([256])) for vae.encoder_f1 vae.encoder_f1: 58,608B, BPFP=0.8943 Using per-key quantization points (vae.decoder: torch.Size([256])) for vae.decoder vae.decoder: 37,260B, BPFP=1.1371 ⌛️ [2/4] FRONTEND: Frontend time: 2.152s (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: 1.612s [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 0.14257499 text_encoder-item0.clip_prompt_embeds 0.00027120 43.47874814 text_encoder_2-item1.clip_pooled_prompt_embeds 0.00023509 0.18615652 text_encoder_2-item1.clip_prompt_embeds 0.00019567 0.05469350 text_encoder_3-item2.t5_prompt_embeds 0.00000829 0.00107247 text_encoder-item3.clip_pooled_prompt_embeds 0.00841799 0.25064182 text_encoder-item3.clip_prompt_embeds 0.00023247 57.42621246 text_encoder_2-item4.clip_pooled_prompt_embeds 0.00016751 0.08740882 text_encoder_2-item4.clip_prompt_embeds 0.00009830 0.08730212 text_encoder_3-item5.t5_prompt_embeds 0.00001263 0.00061542 vae.encoder_f0 0.00781570 0.70655751 vae.encoder_f1 0.00781878 0.70689619 vae.decoder 0.00029724 0.02201514 ------------------------------------------------------------------------------------- TOTAL 0.00369190 2.97618182 (elements=2,260,992) ---------------------- -------------------------------------------------------- SAMPLE-WISE STATISTICS ---------------------- -------------------------------------------------------- Handler sd35 Strategy individual Architecture elic-featurecoding ---------------------- -------------------------------------------------------- Total Elements 2260992 Total Bytes 361272 BPFP 1.2783 bits/point EBPFP 2.5566 equivalent bits/point MSE 2.976182 ---------------------- -------------------------------------------------------- Time: 3.772s Load: 0.008s, Pack+Encode: 2.152s, Decode+Unpack: 1.612s ---------------------- -------------------------------------------------------- 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 2.9762 MSE: from ../datasets/FeatureCoding-StableDiffusion3.5/100samples_features/000000377814.zst to output-fixed/sd35/lambda0.02/elic-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: 0.009s ------------------------------------------------------------ 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: 2,356B, BPFP=24.5417 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: 11,608B, BPFP=1.5703 Using 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: 3,320B, BPFP=20.7500 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: 20,516B, BPFP=1.6653 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: 63,024B, BPFP=1.5986 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: 2,600B, BPFP=27.0833 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: 8,396B, BPFP=1.1358 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: 4,400B, BPFP=27.5000 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: 16,268B, BPFP=1.3205 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: 42,292B, BPFP=1.0727 Using per-key quantization points (vae.encoder_f0: torch.Size([256])) for vae.encoder_f0 vae.encoder_f0: 52,208B, BPFP=0.7966 Using per-key quantization points (vae.encoder_f1: torch.Size([256])) for vae.encoder_f1 vae.encoder_f1: 52,208B, BPFP=0.7966 Using per-key quantization points (vae.decoder: torch.Size([256])) for vae.decoder vae.decoder: 44,544B, BPFP=1.3594 ⌛️ [2/4] FRONTEND: Frontend time: 2.151s (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: 1.600s [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 0.15226430 text_encoder-item0.clip_prompt_embeds 0.00022930 34.03493219 text_encoder_2-item1.clip_pooled_prompt_embeds 0.00047978 0.18299549 text_encoder_2-item1.clip_prompt_embeds 0.00018160 0.05869320 text_encoder_3-item2.t5_prompt_embeds 0.00000828 0.00073607 text_encoder-item3.clip_pooled_prompt_embeds 0.00841799 0.25064182 text_encoder-item3.clip_prompt_embeds 0.00023247 57.42621246 text_encoder_2-item4.clip_pooled_prompt_embeds 0.00016751 0.08740882 text_encoder_2-item4.clip_prompt_embeds 0.00009830 0.08730212 text_encoder_3-item5.t5_prompt_embeds 0.00001263 0.00061542 vae.encoder_f0 0.00577752 0.48993260 vae.encoder_f1 0.00578475 0.48937353 vae.decoder 0.00024190 0.02166146 ------------------------------------------------------------------------------------- TOTAL 0.00273964 2.62859587 (elements=2,260,992) ---------------------- -------------------------------------------------------- SAMPLE-WISE STATISTICS ---------------------- -------------------------------------------------------- Handler sd35 Strategy individual Architecture elic-featurecoding ---------------------- -------------------------------------------------------- Total Elements 2260992 Total Bytes 323740 BPFP 1.1455 bits/point EBPFP 2.2910 equivalent bits/point MSE 2.628596 ---------------------- -------------------------------------------------------- Time: 3.759s Load: 0.009s, Pack+Encode: 2.151s, Decode+Unpack: 1.600s ---------------------- -------------------------------------------------------- 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 2.6286 MSE: from ../datasets/FeatureCoding-StableDiffusion3.5/100samples_features/000000379800.zst to output-fixed/sd35/lambda0.02/elic-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: 2,396B, BPFP=24.9583 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: 13,264B, BPFP=1.7944 Using 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: 3,588B, BPFP=22.4250 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: 22,168B, BPFP=1.7994 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: 72,492B, BPFP=1.8388 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: 2,600B, BPFP=27.0833 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: 8,396B, BPFP=1.1358 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: 4,400B, BPFP=27.5000 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: 16,268B, BPFP=1.3205 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: 42,292B, BPFP=1.0727 Using per-key quantization points (vae.encoder_f0: torch.Size([256])) for vae.encoder_f0 vae.encoder_f0: 55,556B, BPFP=0.8477 Using per-key quantization points (vae.encoder_f1: torch.Size([256])) for vae.encoder_f1 vae.encoder_f1: 55,564B, BPFP=0.8478 Using per-key quantization points (vae.decoder: torch.Size([256])) for vae.decoder vae.decoder: 18,600B, BPFP=0.5676 ⌛️ [2/4] FRONTEND: Frontend time: 2.151s (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: 1.615s [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 0.16777096 text_encoder-item0.clip_prompt_embeds 0.00028764 65.67873208 text_encoder_2-item1.clip_pooled_prompt_embeds 0.00021081 0.17476597 text_encoder_2-item1.clip_prompt_embeds 0.00018283 0.04374508 text_encoder_3-item2.t5_prompt_embeds 0.00000777 0.00075682 text_encoder-item3.clip_pooled_prompt_embeds 0.00841799 0.25064182 text_encoder-item3.clip_prompt_embeds 0.00023247 57.42621246 text_encoder_2-item4.clip_pooled_prompt_embeds 0.00016751 0.08740882 text_encoder_2-item4.clip_prompt_embeds 0.00009830 0.08730212 text_encoder_3-item5.t5_prompt_embeds 0.00001263 0.00061542 vae.encoder_f0 0.03343784 0.96358573 vae.encoder_f1 0.03344063 0.96850485 vae.decoder 0.00016139 0.01290431 ------------------------------------------------------------------------------------- TOTAL 0.01555870 3.67550811 (elements=2,260,992) ---------------------- -------------------------------------------------------- SAMPLE-WISE STATISTICS ---------------------- -------------------------------------------------------- Handler sd35 Strategy individual Architecture elic-featurecoding ---------------------- -------------------------------------------------------- Total Elements 2260992 Total Bytes 317584 BPFP 1.1237 bits/point EBPFP 2.2474 equivalent bits/point MSE 3.675508 ---------------------- -------------------------------------------------------- Time: 3.775s Load: 0.009s, Pack+Encode: 2.151s, Decode+Unpack: 1.615s ---------------------- -------------------------------------------------------- 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 3.6755 MSE: from ../datasets/FeatureCoding-StableDiffusion3.5/100samples_features/000000384808.zst to output-fixed/sd35/lambda0.02/elic-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: 2,156B, BPFP=22.4583 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: 10,744B, BPFP=1.4535 Using 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: 3,360B, BPFP=21.0000 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: 18,856B, BPFP=1.5305 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: 62,216B, BPFP=1.5781 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: 2,600B, BPFP=27.0833 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: 8,396B, BPFP=1.1358 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: 4,400B, BPFP=27.5000 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: 16,268B, BPFP=1.3205 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: 42,292B, BPFP=1.0727 Using per-key quantization points (vae.encoder_f0: torch.Size([256])) for vae.encoder_f0 vae.encoder_f0: 64,572B, BPFP=0.9853 Using per-key quantization points (vae.encoder_f1: torch.Size([256])) for vae.encoder_f1 vae.encoder_f1: 64,584B, BPFP=0.9855 Using per-key quantization points (vae.decoder: torch.Size([256])) for vae.decoder vae.decoder: 39,784B, BPFP=1.2141 ⌛️ [2/4] FRONTEND: Frontend time: 2.153s (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: 1.615s [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 0.19762673 text_encoder-item0.clip_prompt_embeds 0.00023094 179.51110998 text_encoder_2-item1.clip_pooled_prompt_embeds 0.00027942 0.19078257 text_encoder_2-item1.clip_prompt_embeds 0.00018965 0.05406885 text_encoder_3-item2.t5_prompt_embeds 0.00000768 0.00065579 text_encoder-item3.clip_pooled_prompt_embeds 0.00841799 0.25064182 text_encoder-item3.clip_prompt_embeds 0.00023247 57.42621246 text_encoder_2-item4.clip_pooled_prompt_embeds 0.00016751 0.08740882 text_encoder_2-item4.clip_prompt_embeds 0.00009830 0.08730212 text_encoder_3-item5.t5_prompt_embeds 0.00001263 0.00061542 vae.encoder_f0 0.00637455 0.62283111 vae.encoder_f1 0.00637988 0.62169218 vae.decoder 0.00020059 0.02027907 ------------------------------------------------------------------------------------- TOTAL 0.00301333 6.49465587 (elements=2,260,992) ---------------------- -------------------------------------------------------- SAMPLE-WISE STATISTICS ---------------------- -------------------------------------------------------- Handler sd35 Strategy individual Architecture elic-featurecoding ---------------------- -------------------------------------------------------- Total Elements 2260992 Total Bytes 340228 BPFP 1.2038 bits/point EBPFP 2.4076 equivalent bits/point MSE 6.494656 ---------------------- -------------------------------------------------------- Time: 3.777s Load: 0.009s, Pack+Encode: 2.153s, Decode+Unpack: 1.615s ---------------------- -------------------------------------------------------- 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 6.4947 MSE: from ../datasets/FeatureCoding-StableDiffusion3.5/100samples_features/000000396338.zst to output-fixed/sd35/lambda0.02/elic-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: 2,264B, BPFP=23.5833 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: 11,592B, BPFP=1.5682 Using 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: 3,580B, BPFP=22.3750 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: 19,944B, BPFP=1.6188 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: 64,668B, BPFP=1.6403 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: 2,600B, BPFP=27.0833 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: 8,396B, BPFP=1.1358 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: 4,400B, BPFP=27.5000 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: 16,268B, BPFP=1.3205 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: 42,292B, BPFP=1.0727 Using per-key quantization points (vae.encoder_f0: torch.Size([256])) for vae.encoder_f0 vae.encoder_f0: 51,336B, BPFP=0.7833 Using per-key quantization points (vae.encoder_f1: torch.Size([256])) for vae.encoder_f1 vae.encoder_f1: 51,356B, BPFP=0.7836 Using per-key quantization points (vae.decoder: torch.Size([256])) for vae.decoder vae.decoder: 38,960B, BPFP=1.1890 ⌛️ [2/4] FRONTEND: Frontend time: 2.152s (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: 1.607s [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 0.19460050 text_encoder-item0.clip_prompt_embeds 0.00025217 42.83069535 text_encoder_2-item1.clip_pooled_prompt_embeds 0.00026091 0.18787322 text_encoder_2-item1.clip_prompt_embeds 0.00018200 0.04970380 text_encoder_3-item2.t5_prompt_embeds 0.00000809 0.00062938 text_encoder-item3.clip_pooled_prompt_embeds 0.00841799 0.25064182 text_encoder-item3.clip_prompt_embeds 0.00023247 57.42621246 text_encoder_2-item4.clip_pooled_prompt_embeds 0.00016751 0.08740882 text_encoder_2-item4.clip_prompt_embeds 0.00009830 0.08730212 text_encoder_3-item5.t5_prompt_embeds 0.00001263 0.00061542 vae.encoder_f0 0.00581597 0.50067455 vae.encoder_f1 0.00582356 0.50037801 vae.decoder 0.00019494 0.01984366 ------------------------------------------------------------------------------------- TOTAL 0.00275264 2.86309038 (elements=2,260,992) ---------------------- -------------------------------------------------------- SAMPLE-WISE STATISTICS ---------------------- -------------------------------------------------------- Handler sd35 Strategy individual Architecture elic-featurecoding ---------------------- -------------------------------------------------------- Total Elements 2260992 Total Bytes 317656 BPFP 1.1240 bits/point EBPFP 2.2479 equivalent bits/point MSE 2.863090 ---------------------- -------------------------------------------------------- Time: 3.767s Load: 0.009s, Pack+Encode: 2.152s, Decode+Unpack: 1.607s ---------------------- -------------------------------------------------------- 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 2.8631 MSE: from ../datasets/FeatureCoding-StableDiffusion3.5/100samples_features/000000397303.zst to output-fixed/sd35/lambda0.02/elic-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: 2,276B, BPFP=23.7083 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: 10,876B, BPFP=1.4713 Using 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: 3,512B, BPFP=21.9500 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: 17,680B, BPFP=1.4351 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: 60,436B, BPFP=1.5330 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: 2,600B, BPFP=27.0833 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: 8,396B, BPFP=1.1358 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: 4,400B, BPFP=27.5000 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: 16,268B, BPFP=1.3205 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: 42,292B, BPFP=1.0727 Using per-key quantization points (vae.encoder_f0: torch.Size([256])) for vae.encoder_f0 vae.encoder_f0: 28,544B, BPFP=0.4355 Using per-key quantization points (vae.encoder_f1: torch.Size([256])) for vae.encoder_f1 vae.encoder_f1: 28,548B, BPFP=0.4356 Using per-key quantization points (vae.decoder: torch.Size([256])) for vae.decoder vae.decoder: 31,784B, BPFP=0.9700 ⌛️ [2/4] FRONTEND: Frontend time: 2.144s (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: 1.599s [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 0.19348019 text_encoder-item0.clip_prompt_embeds 0.00026975 53.81836783 text_encoder_2-item1.clip_pooled_prompt_embeds 0.00022593 0.17603602 text_encoder_2-item1.clip_prompt_embeds 0.00015480 0.05556345 text_encoder_3-item2.t5_prompt_embeds 0.00000862 0.00069523 text_encoder-item3.clip_pooled_prompt_embeds 0.00841799 0.25064182 text_encoder-item3.clip_prompt_embeds 0.00023247 57.42621246 text_encoder_2-item4.clip_pooled_prompt_embeds 0.00016751 0.08740882 text_encoder_2-item4.clip_prompt_embeds 0.00009830 0.08730212 text_encoder_3-item5.t5_prompt_embeds 0.00001263 0.00061542 vae.encoder_f0 1.11695218 2.48088098 vae.encoder_f1 1.11695278 2.47955489 vae.decoder 0.00019720 0.01899914 ------------------------------------------------------------------------------------- TOTAL 0.51806274 4.06874925 (elements=2,260,992) ---------------------- -------------------------------------------------------- SAMPLE-WISE STATISTICS ---------------------- -------------------------------------------------------- Handler sd35 Strategy individual Architecture elic-featurecoding ---------------------- -------------------------------------------------------- Total Elements 2260992 Total Bytes 257612 BPFP 0.9115 bits/point EBPFP 1.8230 equivalent bits/point MSE 4.068749 ---------------------- -------------------------------------------------------- Time: 3.751s Load: 0.008s, Pack+Encode: 2.144s, Decode+Unpack: 1.599s ---------------------- -------------------------------------------------------- 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 4.0687 MSE: from ../datasets/FeatureCoding-StableDiffusion3.5/100samples_features/000000402473.zst to output-fixed/sd35/lambda0.02/elic-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: 0.008s ------------------------------------------------------------ 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: 2,284B, BPFP=23.7917 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: 11,788B, BPFP=1.5947 Using 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: 3,352B, BPFP=20.9500 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: 19,480B, BPFP=1.5812 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: 65,388B, BPFP=1.6586 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: 2,600B, BPFP=27.0833 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: 8,396B, BPFP=1.1358 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: 4,400B, BPFP=27.5000 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: 16,268B, BPFP=1.3205 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: 42,292B, BPFP=1.0727 Using per-key quantization points (vae.encoder_f0: torch.Size([256])) for vae.encoder_f0 vae.encoder_f0: 51,340B, BPFP=0.7834 Using per-key quantization points (vae.encoder_f1: torch.Size([256])) for vae.encoder_f1 vae.encoder_f1: 51,340B, BPFP=0.7834 Using per-key quantization points (vae.decoder: torch.Size([256])) for vae.decoder vae.decoder: 32,912B, BPFP=1.0044 ⌛️ [2/4] FRONTEND: Frontend time: 2.152s (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: 1.605s [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 0.15367620 text_encoder-item0.clip_prompt_embeds 0.00025545 42.92855875 text_encoder_2-item1.clip_pooled_prompt_embeds 0.00018422 0.15926769 text_encoder_2-item1.clip_prompt_embeds 0.00016916 0.05507268 text_encoder_3-item2.t5_prompt_embeds 0.00000823 0.00068132 text_encoder-item3.clip_pooled_prompt_embeds 0.00841799 0.25064182 text_encoder-item3.clip_prompt_embeds 0.00023247 57.42621246 text_encoder_2-item4.clip_pooled_prompt_embeds 0.00016751 0.08740882 text_encoder_2-item4.clip_prompt_embeds 0.00009830 0.08730212 text_encoder_3-item5.t5_prompt_embeds 0.00001263 0.00061542 vae.encoder_f0 0.01535016 0.75488424 vae.encoder_f1 0.01535382 0.75678027 vae.decoder 0.00021460 0.01936697 ------------------------------------------------------------------------------------- TOTAL 0.00717511 2.98420868 (elements=2,260,992) ---------------------- -------------------------------------------------------- SAMPLE-WISE STATISTICS ---------------------- -------------------------------------------------------- Handler sd35 Strategy individual Architecture elic-featurecoding ---------------------- -------------------------------------------------------- Total Elements 2260992 Total Bytes 311840 BPFP 1.1034 bits/point EBPFP 2.2067 equivalent bits/point MSE 2.984209 ---------------------- -------------------------------------------------------- Time: 3.765s Load: 0.008s, Pack+Encode: 2.152s, Decode+Unpack: 1.605s ---------------------- -------------------------------------------------------- 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 2.9842 MSE: from ../datasets/FeatureCoding-StableDiffusion3.5/100samples_features/000000409211.zst to output-fixed/sd35/lambda0.02/elic-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: 0.009s ------------------------------------------------------------ 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: 2,316B, BPFP=24.1250 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: 12,068B, BPFP=1.6326 Using 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: 3,464B, BPFP=21.6500 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: 21,584B, BPFP=1.7519 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: 70,368B, BPFP=1.7849 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: 2,600B, BPFP=27.0833 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: 8,396B, BPFP=1.1358 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: 4,400B, BPFP=27.5000 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: 16,268B, BPFP=1.3205 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: 42,292B, BPFP=1.0727 Using per-key quantization points (vae.encoder_f0: torch.Size([256])) for vae.encoder_f0 vae.encoder_f0: 44,744B, BPFP=0.6827 Using per-key quantization points (vae.encoder_f1: torch.Size([256])) for vae.encoder_f1 vae.encoder_f1: 44,752B, BPFP=0.6829 Using per-key quantization points (vae.decoder: torch.Size([256])) for vae.decoder vae.decoder: 49,256B, BPFP=1.5032 ⌛️ [2/4] FRONTEND: Frontend time: 2.150s (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: 1.610s [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 0.15662664 text_encoder-item0.clip_prompt_embeds 0.00022628 64.49747616 text_encoder_2-item1.clip_pooled_prompt_embeds 0.00027089 0.20211825 text_encoder_2-item1.clip_prompt_embeds 0.00017658 0.05832563 text_encoder_3-item2.t5_prompt_embeds 0.00000761 0.00069903 text_encoder-item3.clip_pooled_prompt_embeds 0.00841799 0.25064182 text_encoder-item3.clip_prompt_embeds 0.00023247 57.42621246 text_encoder_2-item4.clip_pooled_prompt_embeds 0.00016751 0.08740882 text_encoder_2-item4.clip_prompt_embeds 0.00009830 0.08730212 text_encoder_3-item5.t5_prompt_embeds 0.00001263 0.00061542 vae.encoder_f0 0.00589589 0.45638311 vae.encoder_f1 0.00590398 0.45689851 vae.decoder 0.00017838 0.02207431 ------------------------------------------------------------------------------------- TOTAL 0.00278687 3.41006934 (elements=2,260,992) ---------------------- -------------------------------------------------------- SAMPLE-WISE STATISTICS ---------------------- -------------------------------------------------------- Handler sd35 Strategy individual Architecture elic-featurecoding ---------------------- -------------------------------------------------------- Total Elements 2260992 Total Bytes 322508 BPFP 1.1411 bits/point EBPFP 2.2822 equivalent bits/point MSE 3.410069 ---------------------- -------------------------------------------------------- Time: 3.769s Load: 0.009s, Pack+Encode: 2.150s, Decode+Unpack: 1.610s ---------------------- -------------------------------------------------------- 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 3.4101 MSE: from ../datasets/FeatureCoding-StableDiffusion3.5/100samples_features/000000427500.zst to output-fixed/sd35/lambda0.02/elic-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: 2,316B, BPFP=24.1250 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: 11,520B, BPFP=1.5584 Using 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: 3,412B, BPFP=21.3250 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: 19,320B, BPFP=1.5682 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: 58,952B, BPFP=1.4953 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: 2,600B, BPFP=27.0833 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: 8,396B, BPFP=1.1358 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: 4,400B, BPFP=27.5000 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: 16,268B, BPFP=1.3205 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: 42,292B, BPFP=1.0727 Using per-key quantization points (vae.encoder_f0: torch.Size([256])) for vae.encoder_f0 vae.encoder_f0: 63,188B, BPFP=0.9642 Using per-key quantization points (vae.encoder_f1: torch.Size([256])) for vae.encoder_f1 vae.encoder_f1: 63,204B, BPFP=0.9644 Using per-key quantization points (vae.decoder: torch.Size([256])) for vae.decoder vae.decoder: 26,444B, BPFP=0.8070 ⌛️ [2/4] FRONTEND: Frontend time: 2.169s (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: 1.603s [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 0.14034181 text_encoder-item0.clip_prompt_embeds 0.00031548 44.63247430 text_encoder_2-item1.clip_pooled_prompt_embeds 0.00020720 0.19037170 text_encoder_2-item1.clip_prompt_embeds 0.00018318 0.06561415 text_encoder_3-item2.t5_prompt_embeds 0.00000772 0.00072936 text_encoder-item3.clip_pooled_prompt_embeds 0.00841799 0.25064182 text_encoder-item3.clip_prompt_embeds 0.00023247 57.42621246 text_encoder_2-item4.clip_pooled_prompt_embeds 0.00016751 0.08740882 text_encoder_2-item4.clip_prompt_embeds 0.00009830 0.08730212 text_encoder_3-item5.t5_prompt_embeds 0.00001263 0.00061542 vae.encoder_f0 0.00725484 0.70919484 vae.encoder_f1 0.00725992 0.70843613 vae.decoder 0.00019960 0.01708962 ------------------------------------------------------------------------------------- TOTAL 0.00342155 3.00718478 (elements=2,260,992) ---------------------- -------------------------------------------------------- SAMPLE-WISE STATISTICS ---------------------- -------------------------------------------------------- Handler sd35 Strategy individual Architecture elic-featurecoding ---------------------- -------------------------------------------------------- Total Elements 2260992 Total Bytes 322312 BPFP 1.1404 bits/point EBPFP 2.2809 equivalent bits/point MSE 3.007185 ---------------------- -------------------------------------------------------- Time: 3.781s Load: 0.009s, Pack+Encode: 2.169s, Decode+Unpack: 1.603s ---------------------- -------------------------------------------------------- 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 3.0072 MSE: from ../datasets/FeatureCoding-StableDiffusion3.5/100samples_features/000000435208.zst to output-fixed/sd35/lambda0.02/elic-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: 2,276B, BPFP=23.7083 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: 10,884B, BPFP=1.4724 Using 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: 3,772B, BPFP=23.5750 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: 17,900B, BPFP=1.4529 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: 56,424B, BPFP=1.4312 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: 2,600B, BPFP=27.0833 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: 8,396B, BPFP=1.1358 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: 4,400B, BPFP=27.5000 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: 16,268B, BPFP=1.3205 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: 42,292B, BPFP=1.0727 Using per-key quantization points (vae.encoder_f0: torch.Size([256])) for vae.encoder_f0 vae.encoder_f0: 52,412B, BPFP=0.7997 Using per-key quantization points (vae.encoder_f1: torch.Size([256])) for vae.encoder_f1 vae.encoder_f1: 52,400B, BPFP=0.7996 Using per-key quantization points (vae.decoder: torch.Size([256])) for vae.decoder vae.decoder: 23,444B, BPFP=0.7155 ⌛️ [2/4] FRONTEND: Frontend time: 2.149s (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: 1.611s [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 0.15820683 text_encoder-item0.clip_prompt_embeds 0.00021831 179.33796672 text_encoder_2-item1.clip_pooled_prompt_embeds 0.00025602 0.17963928 text_encoder_2-item1.clip_prompt_embeds 0.00016110 0.05110688 text_encoder_3-item2.t5_prompt_embeds 0.00000740 0.00062886 text_encoder-item3.clip_pooled_prompt_embeds 0.00841799 0.25064182 text_encoder-item3.clip_prompt_embeds 0.00023247 57.42621246 text_encoder_2-item4.clip_pooled_prompt_embeds 0.00016751 0.08740882 text_encoder_2-item4.clip_prompt_embeds 0.00009830 0.08730212 text_encoder_3-item5.t5_prompt_embeds 0.00001263 0.00061542 vae.encoder_f0 0.00923516 0.69015270 vae.encoder_f1 0.00923823 0.68631876 vae.decoder 0.00019521 0.01426383 ------------------------------------------------------------------------------------- TOTAL 0.00433552 6.51987402 (elements=2,260,992) ---------------------- -------------------------------------------------------- SAMPLE-WISE STATISTICS ---------------------- -------------------------------------------------------- Handler sd35 Strategy individual Architecture elic-featurecoding ---------------------- -------------------------------------------------------- Total Elements 2260992 Total Bytes 293468 BPFP 1.0384 bits/point EBPFP 2.0767 equivalent bits/point MSE 6.519874 ---------------------- -------------------------------------------------------- Time: 3.770s Load: 0.009s, Pack+Encode: 2.149s, Decode+Unpack: 1.611s ---------------------- -------------------------------------------------------- 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 6.5199 MSE: from ../datasets/FeatureCoding-StableDiffusion3.5/100samples_features/000000435880.zst to output-fixed/sd35/lambda0.02/elic-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: 0.008s ------------------------------------------------------------ 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: 2,252B, BPFP=23.4583 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: 11,188B, BPFP=1.5135 Using 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: 3,432B, BPFP=21.4500 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: 19,900B, BPFP=1.6153 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: 65,060B, BPFP=1.6503 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: 2,600B, BPFP=27.0833 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: 8,396B, BPFP=1.1358 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: 4,400B, BPFP=27.5000 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: 16,268B, BPFP=1.3205 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: 42,292B, BPFP=1.0727 Using per-key quantization points (vae.encoder_f0: torch.Size([256])) for vae.encoder_f0 vae.encoder_f0: 55,256B, BPFP=0.8431 Using per-key quantization points (vae.encoder_f1: torch.Size([256])) for vae.encoder_f1 vae.encoder_f1: 55,252B, BPFP=0.8431 Using per-key quantization points (vae.decoder: torch.Size([256])) for vae.decoder vae.decoder: 28,752B, BPFP=0.8774 ⌛️ [2/4] FRONTEND: Frontend time: 2.148s (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: 1.603s [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 0.17915984 text_encoder-item0.clip_prompt_embeds 0.00062166 422.93310335 text_encoder_2-item1.clip_pooled_prompt_embeds 0.00050487 0.18623586 text_encoder_2-item1.clip_prompt_embeds 0.00018638 0.04606338 text_encoder_3-item2.t5_prompt_embeds 0.00000762 0.00066977 text_encoder-item3.clip_pooled_prompt_embeds 0.00841799 0.25064182 text_encoder-item3.clip_prompt_embeds 0.00023247 57.42621246 text_encoder_2-item4.clip_pooled_prompt_embeds 0.00016751 0.08740882 text_encoder_2-item4.clip_prompt_embeds 0.00009830 0.08730212 text_encoder_3-item5.t5_prompt_embeds 0.00001263 0.00061542 vae.encoder_f0 0.00831779 0.69413573 vae.encoder_f1 0.00832197 0.69305187 vae.decoder 0.00023271 0.01771433 ------------------------------------------------------------------------------------- TOTAL 0.00392639 12.89376000 (elements=2,260,992) ---------------------- -------------------------------------------------------- SAMPLE-WISE STATISTICS ---------------------- -------------------------------------------------------- Handler sd35 Strategy individual Architecture elic-featurecoding ---------------------- -------------------------------------------------------- Total Elements 2260992 Total Bytes 315048 BPFP 1.1147 bits/point EBPFP 2.2294 equivalent bits/point MSE 12.893760 ---------------------- -------------------------------------------------------- Time: 3.759s Load: 0.008s, Pack+Encode: 2.148s, Decode+Unpack: 1.603s ---------------------- -------------------------------------------------------- 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 12.8938 MSE: from ../datasets/FeatureCoding-StableDiffusion3.5/100samples_features/000000439593.zst to output-fixed/sd35/lambda0.02/elic-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: 0.008s ------------------------------------------------------------ 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: 2,316B, BPFP=24.1250 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: 11,552B, BPFP=1.5628 Using 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: 3,284B, BPFP=20.5250 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: 19,036B, BPFP=1.5451 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: 63,100B, BPFP=1.6005 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: 2,600B, BPFP=27.0833 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: 8,396B, BPFP=1.1358 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: 4,400B, BPFP=27.5000 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: 16,268B, BPFP=1.3205 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: 42,292B, BPFP=1.0727 Using per-key quantization points (vae.encoder_f0: torch.Size([256])) for vae.encoder_f0 vae.encoder_f0: 46,988B, BPFP=0.7170 Using per-key quantization points (vae.encoder_f1: torch.Size([256])) for vae.encoder_f1 vae.encoder_f1: 46,968B, BPFP=0.7167 Using per-key quantization points (vae.decoder: torch.Size([256])) for vae.decoder vae.decoder: 32,520B, BPFP=0.9924 ⌛️ [2/4] FRONTEND: Frontend time: 2.149s (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: 1.598s [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 0.15985668 text_encoder-item0.clip_prompt_embeds 0.00022938 56.10103828 text_encoder_2-item1.clip_pooled_prompt_embeds 0.00028331 0.20092483 text_encoder_2-item1.clip_prompt_embeds 0.00016501 0.05025087 text_encoder_3-item2.t5_prompt_embeds 0.00000786 0.00075474 text_encoder-item3.clip_pooled_prompt_embeds 0.00841799 0.25064182 text_encoder-item3.clip_prompt_embeds 0.00023247 57.42621246 text_encoder_2-item4.clip_pooled_prompt_embeds 0.00016751 0.08740882 text_encoder_2-item4.clip_prompt_embeds 0.00009830 0.08730212 text_encoder_3-item5.t5_prompt_embeds 0.00001263 0.00061542 vae.encoder_f0 0.00626977 0.53709018 vae.encoder_f1 0.00627489 0.53582978 vae.decoder 0.00017842 0.01888705 ------------------------------------------------------------------------------------- TOTAL 0.00295919 3.22676568 (elements=2,260,992) ---------------------- -------------------------------------------------------- SAMPLE-WISE STATISTICS ---------------------- -------------------------------------------------------- Handler sd35 Strategy individual Architecture elic-featurecoding ---------------------- -------------------------------------------------------- Total Elements 2260992 Total Bytes 299720 BPFP 1.0605 bits/point EBPFP 2.1210 equivalent bits/point MSE 3.226766 ---------------------- -------------------------------------------------------- Time: 3.755s Load: 0.008s, Pack+Encode: 2.149s, Decode+Unpack: 1.598s ---------------------- -------------------------------------------------------- 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 3.2268 MSE: from ../datasets/FeatureCoding-StableDiffusion3.5/100samples_features/000000441286.zst to output-fixed/sd35/lambda0.02/elic-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: 0.009s ------------------------------------------------------------ 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: 2,256B, BPFP=23.5000 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: 11,444B, BPFP=1.5482 Using 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: 3,256B, BPFP=20.3500 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: 20,560B, BPFP=1.6688 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: 64,812B, BPFP=1.6440 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: 2,600B, BPFP=27.0833 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: 8,396B, BPFP=1.1358 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: 4,400B, BPFP=27.5000 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: 16,268B, BPFP=1.3205 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: 42,292B, BPFP=1.0727 Using per-key quantization points (vae.encoder_f0: torch.Size([256])) for vae.encoder_f0 vae.encoder_f0: 53,272B, BPFP=0.8129 Using per-key quantization points (vae.encoder_f1: torch.Size([256])) for vae.encoder_f1 vae.encoder_f1: 53,252B, BPFP=0.8126 Using per-key quantization points (vae.decoder: torch.Size([256])) for vae.decoder vae.decoder: 46,592B, BPFP=1.4219 ⌛️ [2/4] FRONTEND: Frontend time: 2.150s (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: 1.608s [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 0.13282778 text_encoder-item0.clip_prompt_embeds 0.00022180 119.06301576 text_encoder_2-item1.clip_pooled_prompt_embeds 0.00120074 0.19760857 text_encoder_2-item1.clip_prompt_embeds 0.00017918 0.05408633 text_encoder_3-item2.t5_prompt_embeds 0.00000774 0.00063614 text_encoder-item3.clip_pooled_prompt_embeds 0.00841799 0.25064182 text_encoder-item3.clip_prompt_embeds 0.00023247 57.42621246 text_encoder_2-item4.clip_pooled_prompt_embeds 0.00016751 0.08740882 text_encoder_2-item4.clip_prompt_embeds 0.00009830 0.08730212 text_encoder_3-item5.t5_prompt_embeds 0.00001263 0.00061542 vae.encoder_f0 0.00585720 0.52103591 vae.encoder_f1 0.00586586 0.52073389 vae.decoder 0.00016520 0.02001363 ------------------------------------------------------------------------------------- TOTAL 0.00276807 4.86657634 (elements=2,260,992) ---------------------- -------------------------------------------------------- SAMPLE-WISE STATISTICS ---------------------- -------------------------------------------------------- Handler sd35 Strategy individual Architecture elic-featurecoding ---------------------- -------------------------------------------------------- Total Elements 2260992 Total Bytes 329400 BPFP 1.1655 bits/point EBPFP 2.3310 equivalent bits/point MSE 4.866576 ---------------------- -------------------------------------------------------- Time: 3.768s Load: 0.009s, Pack+Encode: 2.150s, Decode+Unpack: 1.608s ---------------------- -------------------------------------------------------- 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 4.8666 MSE: from ../datasets/FeatureCoding-StableDiffusion3.5/100samples_features/000000445365.zst to output-fixed/sd35/lambda0.02/elic-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: 2,248B, BPFP=23.4167 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: 10,908B, BPFP=1.4756 Using 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: 3,100B, BPFP=19.3750 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: 19,508B, BPFP=1.5834 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: 63,360B, BPFP=1.6071 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: 2,600B, BPFP=27.0833 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: 8,396B, BPFP=1.1358 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: 4,400B, BPFP=27.5000 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: 16,268B, BPFP=1.3205 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: 42,292B, BPFP=1.0727 Using per-key quantization points (vae.encoder_f0: torch.Size([256])) for vae.encoder_f0 vae.encoder_f0: 37,708B, BPFP=0.5754 Using per-key quantization points (vae.encoder_f1: torch.Size([256])) for vae.encoder_f1 vae.encoder_f1: 37,708B, BPFP=0.5754 Using per-key quantization points (vae.decoder: torch.Size([256])) for vae.decoder vae.decoder: 20,808B, BPFP=0.6350 ⌛️ [2/4] FRONTEND: Frontend time: 2.160s (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: 1.609s [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 0.18807779 text_encoder-item0.clip_prompt_embeds 0.00025784 20.58772786 text_encoder_2-item1.clip_pooled_prompt_embeds 0.00017733 0.18339553 text_encoder_2-item1.clip_prompt_embeds 0.00015430 0.04621671 text_encoder_3-item2.t5_prompt_embeds 0.00000823 0.00063706 text_encoder-item3.clip_pooled_prompt_embeds 0.00841799 0.25064182 text_encoder-item3.clip_prompt_embeds 0.00023247 57.42621246 text_encoder_2-item4.clip_pooled_prompt_embeds 0.00016751 0.08740882 text_encoder_2-item4.clip_prompt_embeds 0.00009830 0.08730212 text_encoder_3-item5.t5_prompt_embeds 0.00001263 0.00061542 vae.encoder_f0 0.00734802 0.55913723 vae.encoder_f1 0.00734987 0.55737591 vae.decoder 0.00018093 0.01249202 ------------------------------------------------------------------------------------- TOTAL 0.00345989 2.30709340 (elements=2,260,992) ---------------------- -------------------------------------------------------- SAMPLE-WISE STATISTICS ---------------------- -------------------------------------------------------- Handler sd35 Strategy individual Architecture elic-featurecoding ---------------------- -------------------------------------------------------- Total Elements 2260992 Total Bytes 269304 BPFP 0.9529 bits/point EBPFP 1.9057 equivalent bits/point MSE 2.307093 ---------------------- -------------------------------------------------------- Time: 3.777s Load: 0.008s, Pack+Encode: 2.160s, Decode+Unpack: 1.609s ---------------------- -------------------------------------------------------- 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 2.3071 MSE: from ../datasets/FeatureCoding-StableDiffusion3.5/100samples_features/000000449996.zst to output-fixed/sd35/lambda0.02/elic-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: 0.009s ------------------------------------------------------------ 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: 2,260B, BPFP=23.5417 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: 12,080B, BPFP=1.6342 Using 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: 3,360B, BPFP=21.0000 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: 20,256B, BPFP=1.6442 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: 61,536B, BPFP=1.5609 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: 2,600B, BPFP=27.0833 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: 8,396B, BPFP=1.1358 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: 4,400B, BPFP=27.5000 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: 16,268B, BPFP=1.3205 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: 42,292B, BPFP=1.0727 Using per-key quantization points (vae.encoder_f0: torch.Size([256])) for vae.encoder_f0 vae.encoder_f0: 61,592B, BPFP=0.9398 Using per-key quantization points (vae.encoder_f1: torch.Size([256])) for vae.encoder_f1 vae.encoder_f1: 61,596B, BPFP=0.9399 Using per-key quantization points (vae.decoder: torch.Size([256])) for vae.decoder vae.decoder: 37,032B, BPFP=1.1301 ⌛️ [2/4] FRONTEND: Frontend time: 2.161s (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: 1.608s [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 0.16339815 text_encoder-item0.clip_prompt_embeds 0.00023510 99.28581575 text_encoder_2-item1.clip_pooled_prompt_embeds 0.00023039 0.17870212 text_encoder_2-item1.clip_prompt_embeds 0.00019044 0.05787637 text_encoder_3-item2.t5_prompt_embeds 0.00000826 0.00066755 text_encoder-item3.clip_pooled_prompt_embeds 0.00841799 0.25064182 text_encoder-item3.clip_prompt_embeds 0.00023247 57.42621246 text_encoder_2-item4.clip_pooled_prompt_embeds 0.00016751 0.08740882 text_encoder_2-item4.clip_prompt_embeds 0.00009830 0.08730212 text_encoder_3-item5.t5_prompt_embeds 0.00001263 0.00061542 vae.encoder_f0 0.00637359 0.62979424 vae.encoder_f1 0.00637830 0.62912381 vae.decoder 0.00018566 0.02011509 ------------------------------------------------------------------------------------- TOTAL 0.00300937 4.39984008 (elements=2,260,992) ---------------------- -------------------------------------------------------- SAMPLE-WISE STATISTICS ---------------------- -------------------------------------------------------- Handler sd35 Strategy individual Architecture elic-featurecoding ---------------------- -------------------------------------------------------- Total Elements 2260992 Total Bytes 333668 BPFP 1.1806 bits/point EBPFP 2.3612 equivalent bits/point MSE 4.399840 ---------------------- -------------------------------------------------------- Time: 3.777s Load: 0.009s, Pack+Encode: 2.161s, Decode+Unpack: 1.608s ---------------------- -------------------------------------------------------- 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 4.3998 MSE: from ../datasets/FeatureCoding-StableDiffusion3.5/100samples_features/000000451714.zst to output-fixed/sd35/lambda0.02/elic-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: 2,312B, BPFP=24.0833 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: 11,208B, BPFP=1.5162 Using 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: 3,516B, BPFP=21.9750 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: 20,036B, BPFP=1.6263 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: 64,776B, BPFP=1.6431 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: 2,600B, BPFP=27.0833 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: 8,396B, BPFP=1.1358 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: 4,400B, BPFP=27.5000 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: 16,268B, BPFP=1.3205 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: 42,292B, BPFP=1.0727 Using per-key quantization points (vae.encoder_f0: torch.Size([256])) for vae.encoder_f0 vae.encoder_f0: 43,024B, BPFP=0.6565 Using per-key quantization points (vae.encoder_f1: torch.Size([256])) for vae.encoder_f1 vae.encoder_f1: 43,028B, BPFP=0.6566 Using per-key quantization points (vae.decoder: torch.Size([256])) for vae.decoder vae.decoder: 23,740B, BPFP=0.7245 ⌛️ [2/4] FRONTEND: Frontend time: 2.147s (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: 1.600s [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 0.15664427 text_encoder-item0.clip_prompt_embeds 0.00026418 140.35617898 text_encoder_2-item1.clip_pooled_prompt_embeds 0.00018200 0.16684606 text_encoder_2-item1.clip_prompt_embeds 0.00017999 0.01931407 text_encoder_3-item2.t5_prompt_embeds 0.00000755 0.00068394 text_encoder-item3.clip_pooled_prompt_embeds 0.00841799 0.25064182 text_encoder-item3.clip_prompt_embeds 0.00023247 57.42621246 text_encoder_2-item4.clip_pooled_prompt_embeds 0.00016751 0.08740882 text_encoder_2-item4.clip_prompt_embeds 0.00009830 0.08730212 text_encoder_3-item5.t5_prompt_embeds 0.00001263 0.00061542 vae.encoder_f0 0.01530954 0.71514499 vae.encoder_f1 0.01531230 0.71620607 vae.decoder 0.00017892 0.01463184 ------------------------------------------------------------------------------------- TOTAL 0.00715252 5.51169198 (elements=2,260,992) ---------------------- -------------------------------------------------------- SAMPLE-WISE STATISTICS ---------------------- -------------------------------------------------------- Handler sd35 Strategy individual Architecture elic-featurecoding ---------------------- -------------------------------------------------------- Total Elements 2260992 Total Bytes 285596 BPFP 1.0105 bits/point EBPFP 2.0210 equivalent bits/point MSE 5.511692 ---------------------- -------------------------------------------------------- Time: 3.755s Load: 0.008s, Pack+Encode: 2.147s, Decode+Unpack: 1.600s ---------------------- -------------------------------------------------------- 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 5.5117 MSE: from ../datasets/FeatureCoding-StableDiffusion3.5/100samples_features/000000464358.zst to output-fixed/sd35/lambda0.02/elic-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: 2,236B, BPFP=23.2917 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: 11,792B, BPFP=1.5952 Using 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: 3,380B, BPFP=21.1250 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: 20,680B, BPFP=1.6786 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: 67,584B, BPFP=1.7143 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: 2,600B, BPFP=27.0833 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: 8,396B, BPFP=1.1358 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: 4,400B, BPFP=27.5000 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: 16,268B, BPFP=1.3205 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: 42,292B, BPFP=1.0727 Using per-key quantization points (vae.encoder_f0: torch.Size([256])) for vae.encoder_f0 vae.encoder_f0: 60,104B, BPFP=0.9171 Using per-key quantization points (vae.encoder_f1: torch.Size([256])) for vae.encoder_f1 vae.encoder_f1: 60,080B, BPFP=0.9167 Using per-key quantization points (vae.decoder: torch.Size([256])) for vae.decoder vae.decoder: 35,352B, BPFP=1.0789 ⌛️ [2/4] FRONTEND: Frontend time: 2.146s (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: 1.607s [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 0.14075704 text_encoder-item0.clip_prompt_embeds 0.00021481 119.67242458 text_encoder_2-item1.clip_pooled_prompt_embeds 0.00019636 0.18261530 text_encoder_2-item1.clip_prompt_embeds 0.00020983 0.05563975 text_encoder_3-item2.t5_prompt_embeds 0.00000831 0.00073410 text_encoder-item3.clip_pooled_prompt_embeds 0.00841799 0.25064182 text_encoder-item3.clip_prompt_embeds 0.00023247 57.42621246 text_encoder_2-item4.clip_pooled_prompt_embeds 0.00016751 0.08740882 text_encoder_2-item4.clip_prompt_embeds 0.00009830 0.08730212 text_encoder_3-item5.t5_prompt_embeds 0.00001263 0.00061542 vae.encoder_f0 0.00591154 0.62185925 vae.encoder_f1 0.00591973 0.62146735 vae.decoder 0.00025286 0.01981688 ------------------------------------------------------------------------------------- TOTAL 0.00280398 4.92930594 (elements=2,260,992) ---------------------- -------------------------------------------------------- SAMPLE-WISE STATISTICS ---------------------- -------------------------------------------------------- Handler sd35 Strategy individual Architecture elic-featurecoding ---------------------- -------------------------------------------------------- Total Elements 2260992 Total Bytes 335164 BPFP 1.1859 bits/point EBPFP 2.3718 equivalent bits/point MSE 4.929306 ---------------------- -------------------------------------------------------- Time: 3.762s Load: 0.009s, Pack+Encode: 2.146s, Decode+Unpack: 1.607s ---------------------- -------------------------------------------------------- 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 4.9293 MSE: from ../datasets/FeatureCoding-StableDiffusion3.5/100samples_features/000000466256.zst to output-fixed/sd35/lambda0.02/elic-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: 2,296B, BPFP=23.9167 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: 12,192B, BPFP=1.6494 Using 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: 3,328B, BPFP=20.8000 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: 21,732B, BPFP=1.7640 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: 70,796B, BPFP=1.7958 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: 2,600B, BPFP=27.0833 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: 8,396B, BPFP=1.1358 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: 4,400B, BPFP=27.5000 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: 16,268B, BPFP=1.3205 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: 42,292B, BPFP=1.0727 Using per-key quantization points (vae.encoder_f0: torch.Size([256])) for vae.encoder_f0 vae.encoder_f0: 35,668B, BPFP=0.5443 Using per-key quantization points (vae.encoder_f1: torch.Size([256])) for vae.encoder_f1 vae.encoder_f1: 35,668B, BPFP=0.5443 Using per-key quantization points (vae.decoder: torch.Size([256])) for vae.decoder vae.decoder: 37,868B, BPFP=1.1556 ⌛️ [2/4] FRONTEND: Frontend time: 2.162s (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: 1.605s [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 0.16581404 text_encoder-item0.clip_prompt_embeds 0.00023458 55.70361117 text_encoder_2-item1.clip_pooled_prompt_embeds 0.00219611 0.21788883 text_encoder_2-item1.clip_prompt_embeds 0.00186620 0.06235682 text_encoder_3-item2.t5_prompt_embeds 0.00000775 0.00076691 text_encoder-item3.clip_pooled_prompt_embeds 0.00841799 0.25064182 text_encoder-item3.clip_prompt_embeds 0.00023247 57.42621246 text_encoder_2-item4.clip_pooled_prompt_embeds 0.00016751 0.08740882 text_encoder_2-item4.clip_prompt_embeds 0.00009830 0.08730212 text_encoder_3-item5.t5_prompt_embeds 0.00001263 0.00061542 vae.encoder_f0 0.00588703 0.41212189 vae.encoder_f1 0.00589573 0.41325063 vae.decoder 0.00053402 0.02049440 ------------------------------------------------------------------------------------- TOTAL 0.00289910 3.15969611 (elements=2,260,992) ---------------------- -------------------------------------------------------- SAMPLE-WISE STATISTICS ---------------------- -------------------------------------------------------- Handler sd35 Strategy individual Architecture elic-featurecoding ---------------------- -------------------------------------------------------- Total Elements 2260992 Total Bytes 293504 BPFP 1.0385 bits/point EBPFP 2.0770 equivalent bits/point MSE 3.159696 ---------------------- -------------------------------------------------------- Time: 3.775s Load: 0.009s, Pack+Encode: 2.162s, Decode+Unpack: 1.605s ---------------------- -------------------------------------------------------- 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 3.1597 MSE: from ../datasets/FeatureCoding-StableDiffusion3.5/100samples_features/000000467848.zst to output-fixed/sd35/lambda0.02/elic-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: 2,244B, BPFP=23.3750 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: 11,856B, BPFP=1.6039 Using 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: 3,308B, BPFP=20.6750 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: 20,900B, BPFP=1.6964 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: 68,032B, BPFP=1.7256 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: 2,600B, BPFP=27.0833 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: 8,396B, BPFP=1.1358 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: 4,400B, BPFP=27.5000 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: 16,268B, BPFP=1.3205 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: 42,292B, BPFP=1.0727 Using per-key quantization points (vae.encoder_f0: torch.Size([256])) for vae.encoder_f0 vae.encoder_f0: 52,360B, BPFP=0.7990 Using per-key quantization points (vae.encoder_f1: torch.Size([256])) for vae.encoder_f1 vae.encoder_f1: 52,364B, BPFP=0.7990 Using per-key quantization points (vae.decoder: torch.Size([256])) for vae.decoder vae.decoder: 24,068B, BPFP=0.7345 ⌛️ [2/4] FRONTEND: Frontend time: 2.138s (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: 1.596s [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 0.17310770 text_encoder-item0.clip_prompt_embeds 0.00022882 182.13790246 text_encoder_2-item1.clip_pooled_prompt_embeds 0.00110871 0.21414397 text_encoder_2-item1.clip_prompt_embeds 0.00019473 0.05591573 text_encoder_3-item2.t5_prompt_embeds 0.00000770 0.00065653 text_encoder-item3.clip_pooled_prompt_embeds 0.00841799 0.25064182 text_encoder-item3.clip_prompt_embeds 0.00023247 57.42621246 text_encoder_2-item4.clip_pooled_prompt_embeds 0.00016751 0.08740882 text_encoder_2-item4.clip_prompt_embeds 0.00009830 0.08730212 text_encoder_3-item5.t5_prompt_embeds 0.00001263 0.00061542 vae.encoder_f0 0.00659691 0.60191941 vae.encoder_f1 0.00660300 0.60415864 vae.decoder 0.00023739 0.01636362 ------------------------------------------------------------------------------------- TOTAL 0.00311972 6.55407605 (elements=2,260,992) ---------------------- -------------------------------------------------------- SAMPLE-WISE STATISTICS ---------------------- -------------------------------------------------------- Handler sd35 Strategy individual Architecture elic-featurecoding ---------------------- -------------------------------------------------------- Total Elements 2260992 Total Bytes 309088 BPFP 1.0936 bits/point EBPFP 2.1873 equivalent bits/point MSE 6.554076 ---------------------- -------------------------------------------------------- Time: 3.743s Load: 0.009s, Pack+Encode: 2.138s, Decode+Unpack: 1.596s ---------------------- -------------------------------------------------------- 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 6.5541 MSE: from ../datasets/FeatureCoding-StableDiffusion3.5/100samples_features/000000468501.zst to output-fixed/sd35/lambda0.02/elic-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: 2,264B, BPFP=23.5833 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: 11,588B, BPFP=1.5676 Using 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: 3,480B, BPFP=21.7500 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: 19,600B, BPFP=1.5909 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: 61,404B, BPFP=1.5575 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: 2,600B, BPFP=27.0833 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: 8,396B, BPFP=1.1358 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: 4,400B, BPFP=27.5000 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: 16,268B, BPFP=1.3205 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: 42,292B, BPFP=1.0727 Using per-key quantization points (vae.encoder_f0: torch.Size([256])) for vae.encoder_f0 vae.encoder_f0: 43,020B, BPFP=0.6564 Using per-key quantization points (vae.encoder_f1: torch.Size([256])) for vae.encoder_f1 vae.encoder_f1: 43,016B, BPFP=0.6564 Using per-key quantization points (vae.decoder: torch.Size([256])) for vae.decoder vae.decoder: 46,672B, BPFP=1.4243 ⌛️ [2/4] FRONTEND: Frontend time: 2.140s (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: 1.628s [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 0.15927272 text_encoder-item0.clip_prompt_embeds 0.00023928 53.67016707 text_encoder_2-item1.clip_pooled_prompt_embeds 0.00022734 0.18524423 text_encoder_2-item1.clip_prompt_embeds 0.00018899 0.05394920 text_encoder_3-item2.t5_prompt_embeds 0.00000832 0.00064152 text_encoder-item3.clip_pooled_prompt_embeds 0.00841799 0.25064182 text_encoder-item3.clip_prompt_embeds 0.00023247 57.42621246 text_encoder_2-item4.clip_pooled_prompt_embeds 0.00016751 0.08740882 text_encoder_2-item4.clip_prompt_embeds 0.00009830 0.08730212 text_encoder_3-item5.t5_prompt_embeds 0.00001263 0.00061542 vae.encoder_f0 0.00583864 0.43363869 vae.encoder_f1 0.00583800 0.43370426 vae.decoder 0.00018889 0.02147261 ------------------------------------------------------------------------------------- TOTAL 0.00276073 3.11595259 (elements=2,260,992) ---------------------- -------------------------------------------------------- SAMPLE-WISE STATISTICS ---------------------- -------------------------------------------------------- Handler sd35 Strategy individual Architecture elic-featurecoding ---------------------- -------------------------------------------------------- Total Elements 2260992 Total Bytes 305000 BPFP 1.0792 bits/point EBPFP 2.1583 equivalent bits/point MSE 3.115953 ---------------------- -------------------------------------------------------- Time: 3.776s Load: 0.008s, Pack+Encode: 2.140s, Decode+Unpack: 1.628s ---------------------- -------------------------------------------------------- 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 3.1160 MSE: from ../datasets/FeatureCoding-StableDiffusion3.5/100samples_features/000000468632.zst to output-fixed/sd35/lambda0.02/elic-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: 2,292B, BPFP=23.8750 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: 11,872B, BPFP=1.6061 Using 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: 3,152B, BPFP=19.7000 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: 20,748B, BPFP=1.6841 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: 70,520B, BPFP=1.7888 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: 2,600B, BPFP=27.0833 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: 8,396B, BPFP=1.1358 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: 4,400B, BPFP=27.5000 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: 16,268B, BPFP=1.3205 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: 42,292B, BPFP=1.0727 Using per-key quantization points (vae.encoder_f0: torch.Size([256])) for vae.encoder_f0 vae.encoder_f0: 35,420B, BPFP=0.5405 Using per-key quantization points (vae.encoder_f1: torch.Size([256])) for vae.encoder_f1 vae.encoder_f1: 35,404B, BPFP=0.5402 Using per-key quantization points (vae.decoder: torch.Size([256])) for vae.decoder vae.decoder: 39,408B, BPFP=1.2026 ⌛️ [2/4] FRONTEND: Frontend time: 2.159s (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: 1.612s [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 0.15821056 text_encoder-item0.clip_prompt_embeds 0.00024821 85.31078362 text_encoder_2-item1.clip_pooled_prompt_embeds 0.00060829 0.16694061 text_encoder_2-item1.clip_prompt_embeds 0.00018297 0.05649911 text_encoder_3-item2.t5_prompt_embeds 0.00002546 0.00081404 text_encoder-item3.clip_pooled_prompt_embeds 0.00841799 0.25064182 text_encoder-item3.clip_prompt_embeds 0.00023247 57.42621246 text_encoder_2-item4.clip_pooled_prompt_embeds 0.00016751 0.08740882 text_encoder_2-item4.clip_prompt_embeds 0.00009830 0.08730212 text_encoder_3-item5.t5_prompt_embeds 0.00001263 0.00061542 vae.encoder_f0 0.00570467 0.38725421 vae.encoder_f1 0.00570488 0.38774273 vae.decoder 0.00017302 0.01950349 ------------------------------------------------------------------------------------- TOTAL 0.00269931 3.92199210 (elements=2,260,992) ---------------------- -------------------------------------------------------- SAMPLE-WISE STATISTICS ---------------------- -------------------------------------------------------- Handler sd35 Strategy individual Architecture elic-featurecoding ---------------------- -------------------------------------------------------- Total Elements 2260992 Total Bytes 292772 BPFP 1.0359 bits/point EBPFP 2.0718 equivalent bits/point MSE 3.921992 ---------------------- -------------------------------------------------------- Time: 3.780s Load: 0.009s, Pack+Encode: 2.159s, Decode+Unpack: 1.612s ---------------------- -------------------------------------------------------- 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 3.9220 MSE: from ../datasets/FeatureCoding-StableDiffusion3.5/100samples_features/000000471087.zst to output-fixed/sd35/lambda0.02/elic-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: 0.007s ------------------------------------------------------------ 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: 2,292B, BPFP=23.8750 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: 11,596B, BPFP=1.5687 Using 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: 3,320B, BPFP=20.7500 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: 20,288B, BPFP=1.6468 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: 63,732B, BPFP=1.6166 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: 2,600B, BPFP=27.0833 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: 8,396B, BPFP=1.1358 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: 4,400B, BPFP=27.5000 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: 16,268B, BPFP=1.3205 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: 42,292B, BPFP=1.0727 Using per-key quantization points (vae.encoder_f0: torch.Size([256])) for vae.encoder_f0 vae.encoder_f0: 33,180B, BPFP=0.5063 Using per-key quantization points (vae.encoder_f1: torch.Size([256])) for vae.encoder_f1 vae.encoder_f1: 33,152B, BPFP=0.5059 Using per-key quantization points (vae.decoder: torch.Size([256])) for vae.decoder vae.decoder: 19,700B, BPFP=0.6012 ⌛️ [2/4] FRONTEND: Frontend time: 2.149s (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: 1.609s [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 0.16180082 text_encoder-item0.clip_prompt_embeds 0.00021458 43.41963440 text_encoder_2-item1.clip_pooled_prompt_embeds 0.00020115 0.15277643 text_encoder_2-item1.clip_prompt_embeds 0.00017334 0.05794355 text_encoder_3-item2.t5_prompt_embeds 0.00000867 0.00066016 text_encoder-item3.clip_pooled_prompt_embeds 0.00841799 0.25064182 text_encoder-item3.clip_prompt_embeds 0.00023247 57.42621246 text_encoder_2-item4.clip_pooled_prompt_embeds 0.00016751 0.08740882 text_encoder_2-item4.clip_prompt_embeds 0.00009830 0.08730212 text_encoder_3-item5.t5_prompt_embeds 0.00001263 0.00061542 vae.encoder_f0 0.00914783 0.62959158 vae.encoder_f1 0.00914958 0.62924743 vae.decoder 0.00017527 0.01320085 ------------------------------------------------------------------------------------- TOTAL 0.00429285 2.93783287 (elements=2,260,992) ---------------------- -------------------------------------------------------- SAMPLE-WISE STATISTICS ---------------------- -------------------------------------------------------- Handler sd35 Strategy individual Architecture elic-featurecoding ---------------------- -------------------------------------------------------- Total Elements 2260992 Total Bytes 261216 BPFP 0.9243 bits/point EBPFP 1.8485 equivalent bits/point MSE 2.937833 ---------------------- -------------------------------------------------------- Time: 3.765s Load: 0.007s, Pack+Encode: 2.149s, Decode+Unpack: 1.609s ---------------------- -------------------------------------------------------- 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 2.9378 MSE: from ../datasets/FeatureCoding-StableDiffusion3.5/100samples_features/000000482477.zst to output-fixed/sd35/lambda0.02/elic-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: 0.008s ------------------------------------------------------------ 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: 2,292B, BPFP=23.8750 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: 13,044B, BPFP=1.7646 Using 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: 3,404B, BPFP=21.2750 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: 22,840B, BPFP=1.8539 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: 72,060B, BPFP=1.8278 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: 2,600B, BPFP=27.0833 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: 8,396B, BPFP=1.1358 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: 4,400B, BPFP=27.5000 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: 16,268B, BPFP=1.3205 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: 42,292B, BPFP=1.0727 Using per-key quantization points (vae.encoder_f0: torch.Size([256])) for vae.encoder_f0 vae.encoder_f0: 41,560B, BPFP=0.6342 Using per-key quantization points (vae.encoder_f1: torch.Size([256])) for vae.encoder_f1 vae.encoder_f1: 41,580B, BPFP=0.6345 Using per-key quantization points (vae.decoder: torch.Size([256])) for vae.decoder vae.decoder: 46,588B, BPFP=1.4218 ⌛️ [2/4] FRONTEND: Frontend time: 2.154s (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: 1.616s [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 0.15767978 text_encoder-item0.clip_prompt_embeds 0.00022150 53.65137987 text_encoder_2-item1.clip_pooled_prompt_embeds 0.00048959 0.20873795 text_encoder_2-item1.clip_prompt_embeds 0.00016852 0.05679515 text_encoder_3-item2.t5_prompt_embeds 0.00000767 0.00079742 text_encoder-item3.clip_pooled_prompt_embeds 0.00841799 0.25064182 text_encoder-item3.clip_prompt_embeds 0.00023247 57.42621246 text_encoder_2-item4.clip_pooled_prompt_embeds 0.00016751 0.08740882 text_encoder_2-item4.clip_prompt_embeds 0.00009830 0.08730212 text_encoder_3-item5.t5_prompt_embeds 0.00001263 0.00061542 vae.encoder_f0 0.00578482 0.43088913 vae.encoder_f1 0.00579739 0.43026626 vae.decoder 0.00017668 0.02062439 ------------------------------------------------------------------------------------- TOTAL 0.00273588 3.11408664 (elements=2,260,992) ---------------------- -------------------------------------------------------- SAMPLE-WISE STATISTICS ---------------------- -------------------------------------------------------- Handler sd35 Strategy individual Architecture elic-featurecoding ---------------------- -------------------------------------------------------- Total Elements 2260992 Total Bytes 317324 BPFP 1.1228 bits/point EBPFP 2.2456 equivalent bits/point MSE 3.114087 ---------------------- -------------------------------------------------------- Time: 3.779s Load: 0.008s, Pack+Encode: 2.154s, Decode+Unpack: 1.616s ---------------------- -------------------------------------------------------- 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 3.1141 MSE: from ../datasets/FeatureCoding-StableDiffusion3.5/100samples_features/000000499768.zst to output-fixed/sd35/lambda0.02/elic-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: 0.008s ------------------------------------------------------------ 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: 2,176B, BPFP=22.6667 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: 11,164B, BPFP=1.5103 Using 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: 3,428B, BPFP=21.4250 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: 19,208B, BPFP=1.5591 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: 60,944B, BPFP=1.5459 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: 2,600B, BPFP=27.0833 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: 8,396B, BPFP=1.1358 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: 4,400B, BPFP=27.5000 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: 16,268B, BPFP=1.3205 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: 42,292B, BPFP=1.0727 Using per-key quantization points (vae.encoder_f0: torch.Size([256])) for vae.encoder_f0 vae.encoder_f0: 39,276B, BPFP=0.5993 Using per-key quantization points (vae.encoder_f1: torch.Size([256])) for vae.encoder_f1 vae.encoder_f1: 39,272B, BPFP=0.5992 Using per-key quantization points (vae.decoder: torch.Size([256])) for vae.decoder vae.decoder: 34,128B, BPFP=1.0415 ⌛️ [2/4] FRONTEND: Frontend time: 2.166s (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: 1.600s [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 0.18954796 text_encoder-item0.clip_prompt_embeds 0.00023894 78.18363603 text_encoder_2-item1.clip_pooled_prompt_embeds 0.00033417 0.19694004 text_encoder_2-item1.clip_prompt_embeds 0.00016768 0.05081510 text_encoder_3-item2.t5_prompt_embeds 0.00000784 0.00064416 text_encoder-item3.clip_pooled_prompt_embeds 0.00841799 0.25064182 text_encoder-item3.clip_prompt_embeds 0.00023247 57.42621246 text_encoder_2-item4.clip_pooled_prompt_embeds 0.00016751 0.08740882 text_encoder_2-item4.clip_prompt_embeds 0.00009830 0.08730212 text_encoder_3-item5.t5_prompt_embeds 0.00001263 0.00061542 vae.encoder_f0 0.00958025 0.59536600 vae.encoder_f1 0.00958229 0.59262460 vae.decoder 0.00019995 0.01933042 ------------------------------------------------------------------------------------- TOTAL 0.00449688 3.83108507 (elements=2,260,992) ---------------------- -------------------------------------------------------- SAMPLE-WISE STATISTICS ---------------------- -------------------------------------------------------- Handler sd35 Strategy individual Architecture elic-featurecoding ---------------------- -------------------------------------------------------- Total Elements 2260992 Total Bytes 283552 BPFP 1.0033 bits/point EBPFP 2.0066 equivalent bits/point MSE 3.831085 ---------------------- -------------------------------------------------------- Time: 3.774s Load: 0.008s, Pack+Encode: 2.166s, Decode+Unpack: 1.600s ---------------------- -------------------------------------------------------- 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 3.8311 MSE: from ../datasets/FeatureCoding-StableDiffusion3.5/100samples_features/000000499775.zst to output-fixed/sd35/lambda0.02/elic-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: 0.008s ------------------------------------------------------------ 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: 2,244B, BPFP=23.3750 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: 12,988B, BPFP=1.7570 Using 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: 3,440B, BPFP=21.5000 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: 24,072B, BPFP=1.9539 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: 71,840B, BPFP=1.8222 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: 2,600B, BPFP=27.0833 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: 8,396B, BPFP=1.1358 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: 4,400B, BPFP=27.5000 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: 16,268B, BPFP=1.3205 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: 42,292B, BPFP=1.0727 Using per-key quantization points (vae.encoder_f0: torch.Size([256])) for vae.encoder_f0 vae.encoder_f0: 34,240B, BPFP=0.5225 Using per-key quantization points (vae.encoder_f1: torch.Size([256])) for vae.encoder_f1 vae.encoder_f1: 34,264B, BPFP=0.5228 Using per-key quantization points (vae.decoder: torch.Size([256])) for vae.decoder vae.decoder: 48,564B, BPFP=1.4821 ⌛️ [2/4] FRONTEND: Frontend time: 2.156s (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: 1.607s [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 0.15380625 text_encoder-item0.clip_prompt_embeds 0.00023387 136.73048566 text_encoder_2-item1.clip_pooled_prompt_embeds 0.00060859 0.18595668 text_encoder_2-item1.clip_prompt_embeds 0.00021718 0.06135110 text_encoder_3-item2.t5_prompt_embeds 0.00000840 0.00078498 text_encoder-item3.clip_pooled_prompt_embeds 0.00841799 0.25064182 text_encoder-item3.clip_prompt_embeds 0.00023247 57.42621246 text_encoder_2-item4.clip_pooled_prompt_embeds 0.00016751 0.08740882 text_encoder_2-item4.clip_prompt_embeds 0.00009830 0.08730212 text_encoder_3-item5.t5_prompt_embeds 0.00001263 0.00061542 vae.encoder_f0 0.00567713 0.39475328 vae.encoder_f1 0.00567905 0.39397302 vae.decoder 0.00019376 0.02362059 ------------------------------------------------------------------------------------- TOTAL 0.00268802 5.27074651 (elements=2,260,992) ---------------------- -------------------------------------------------------- SAMPLE-WISE STATISTICS ---------------------- -------------------------------------------------------- Handler sd35 Strategy individual Architecture elic-featurecoding ---------------------- -------------------------------------------------------- Total Elements 2260992 Total Bytes 305608 BPFP 1.0813 bits/point EBPFP 2.1626 equivalent bits/point MSE 5.270747 ---------------------- -------------------------------------------------------- Time: 3.770s Load: 0.008s, Pack+Encode: 2.156s, Decode+Unpack: 1.607s ---------------------- -------------------------------------------------------- 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 5.2707 MSE: from ../datasets/FeatureCoding-StableDiffusion3.5/100samples_features/000000506454.zst to output-fixed/sd35/lambda0.02/elic-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: 0.008s ------------------------------------------------------------ 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: 2,288B, BPFP=23.8333 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: 11,652B, BPFP=1.5763 Using 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: 3,412B, BPFP=21.3250 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: 19,816B, BPFP=1.6084 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: 69,664B, BPFP=1.7670 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: 2,600B, BPFP=27.0833 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: 8,396B, BPFP=1.1358 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: 4,400B, BPFP=27.5000 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: 16,268B, BPFP=1.3205 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: 42,292B, BPFP=1.0727 Using per-key quantization points (vae.encoder_f0: torch.Size([256])) for vae.encoder_f0 vae.encoder_f0: 39,616B, BPFP=0.6045 Using per-key quantization points (vae.encoder_f1: torch.Size([256])) for vae.encoder_f1 vae.encoder_f1: 39,608B, BPFP=0.6044 Using per-key quantization points (vae.decoder: torch.Size([256])) for vae.decoder vae.decoder: 27,640B, BPFP=0.8435 ⌛️ [2/4] FRONTEND: Frontend time: 2.147s (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: 1.601s [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 0.14564219 text_encoder-item0.clip_prompt_embeds 0.00024281 61.95745400 text_encoder_2-item1.clip_pooled_prompt_embeds 0.00020758 0.17362514 text_encoder_2-item1.clip_prompt_embeds 0.00017819 0.05332747 text_encoder_3-item2.t5_prompt_embeds 0.00000960 0.00066898 text_encoder-item3.clip_pooled_prompt_embeds 0.00841799 0.25064182 text_encoder-item3.clip_prompt_embeds 0.00023247 57.42621246 text_encoder_2-item4.clip_pooled_prompt_embeds 0.00016751 0.08740882 text_encoder_2-item4.clip_prompt_embeds 0.00009830 0.08730212 text_encoder_3-item5.t5_prompt_embeds 0.00001263 0.00061542 vae.encoder_f0 0.02387581 0.64126581 vae.encoder_f1 0.02387858 0.64355707 vae.decoder 0.00018648 0.01516625 ------------------------------------------------------------------------------------- TOTAL 0.01112583 3.42874697 (elements=2,260,992) ---------------------- -------------------------------------------------------- SAMPLE-WISE STATISTICS ---------------------- -------------------------------------------------------- Handler sd35 Strategy individual Architecture elic-featurecoding ---------------------- -------------------------------------------------------- Total Elements 2260992 Total Bytes 287652 BPFP 1.0178 bits/point EBPFP 2.0356 equivalent bits/point MSE 3.428747 ---------------------- -------------------------------------------------------- Time: 3.756s Load: 0.008s, Pack+Encode: 2.147s, Decode+Unpack: 1.601s ---------------------- -------------------------------------------------------- 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 3.4287 MSE: from ../datasets/FeatureCoding-StableDiffusion3.5/100samples_features/000000515828.zst to output-fixed/sd35/lambda0.02/elic-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: 2,368B, BPFP=24.6667 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: 11,544B, BPFP=1.5617 Using 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: 3,328B, BPFP=20.8000 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: 19,660B, BPFP=1.5958 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: 63,292B, BPFP=1.6054 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: 2,600B, BPFP=27.0833 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: 8,396B, BPFP=1.1358 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: 4,400B, BPFP=27.5000 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: 16,268B, BPFP=1.3205 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: 42,292B, BPFP=1.0727 Using per-key quantization points (vae.encoder_f0: torch.Size([256])) for vae.encoder_f0 vae.encoder_f0: 61,916B, BPFP=0.9448 Using per-key quantization points (vae.encoder_f1: torch.Size([256])) for vae.encoder_f1 vae.encoder_f1: 61,900B, BPFP=0.9445 Using per-key quantization points (vae.decoder: torch.Size([256])) for vae.decoder vae.decoder: 21,820B, BPFP=0.6659 ⌛️ [2/4] FRONTEND: Frontend time: 2.158s (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: 1.609s [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 0.14379510 text_encoder-item0.clip_prompt_embeds 0.00022399 11.37072278 text_encoder_2-item1.clip_pooled_prompt_embeds 0.00031391 0.18890476 text_encoder_2-item1.clip_prompt_embeds 0.00020480 0.04526055 text_encoder_3-item2.t5_prompt_embeds 0.00000727 0.00065532 text_encoder-item3.clip_pooled_prompt_embeds 0.00841799 0.25064182 text_encoder-item3.clip_prompt_embeds 0.00023247 57.42621246 text_encoder_2-item4.clip_pooled_prompt_embeds 0.00016751 0.08740882 text_encoder_2-item4.clip_prompt_embeds 0.00009830 0.08730212 text_encoder_3-item5.t5_prompt_embeds 0.00001263 0.00061542 vae.encoder_f0 0.01169517 0.81736290 vae.encoder_f1 0.01169969 0.81258380 vae.decoder 0.00021186 0.01532731 ------------------------------------------------------------------------------------- TOTAL 0.00548058 2.18535837 (elements=2,260,992) ---------------------- -------------------------------------------------------- SAMPLE-WISE STATISTICS ---------------------- -------------------------------------------------------- Handler sd35 Strategy individual Architecture elic-featurecoding ---------------------- -------------------------------------------------------- Total Elements 2260992 Total Bytes 319784 BPFP 1.1315 bits/point EBPFP 2.2630 equivalent bits/point MSE 2.185358 ---------------------- -------------------------------------------------------- Time: 3.775s Load: 0.009s, Pack+Encode: 2.158s, Decode+Unpack: 1.609s ---------------------- -------------------------------------------------------- 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 2.1854 MSE: from ../datasets/FeatureCoding-StableDiffusion3.5/100samples_features/000000517056.zst to output-fixed/sd35/lambda0.02/elic-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: 0.008s ------------------------------------------------------------ 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: 2,264B, BPFP=23.5833 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: 11,420B, BPFP=1.5449 Using 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: 3,368B, BPFP=21.0500 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: 19,848B, BPFP=1.6110 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: 71,392B, BPFP=1.8109 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: 2,600B, BPFP=27.0833 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: 8,396B, BPFP=1.1358 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: 4,400B, BPFP=27.5000 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: 16,268B, BPFP=1.3205 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: 42,292B, BPFP=1.0727 Using per-key quantization points (vae.encoder_f0: torch.Size([256])) for vae.encoder_f0 vae.encoder_f0: 48,500B, BPFP=0.7401 Using per-key quantization points (vae.encoder_f1: torch.Size([256])) for vae.encoder_f1 vae.encoder_f1: 48,500B, BPFP=0.7401 Using per-key quantization points (vae.decoder: torch.Size([256])) for vae.decoder vae.decoder: 34,400B, BPFP=1.0498 ⌛️ [2/4] FRONTEND: Frontend time: 2.150s (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: 1.601s [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 0.14856638 text_encoder-item0.clip_prompt_embeds 0.00022123 407.71530032 text_encoder_2-item1.clip_pooled_prompt_embeds 0.00020346 0.18398854 text_encoder_2-item1.clip_prompt_embeds 0.00016509 0.05587563 text_encoder_3-item2.t5_prompt_embeds 0.00000793 0.00068811 text_encoder-item3.clip_pooled_prompt_embeds 0.00841799 0.25064182 text_encoder-item3.clip_prompt_embeds 0.00023247 57.42621246 text_encoder_2-item4.clip_pooled_prompt_embeds 0.00016751 0.08740882 text_encoder_2-item4.clip_prompt_embeds 0.00009830 0.08730212 text_encoder_3-item5.t5_prompt_embeds 0.00001263 0.00061542 vae.encoder_f0 0.32749966 1.93270302 vae.encoder_f1 0.32750070 1.92354023 vae.decoder 0.00039956 0.02111486 ------------------------------------------------------------------------------------- TOTAL 0.15195981 13.06908755 (elements=2,260,992) ---------------------- -------------------------------------------------------- SAMPLE-WISE STATISTICS ---------------------- -------------------------------------------------------- Handler sd35 Strategy individual Architecture elic-featurecoding ---------------------- -------------------------------------------------------- Total Elements 2260992 Total Bytes 313648 BPFP 1.1098 bits/point EBPFP 2.2195 equivalent bits/point MSE 13.069088 ---------------------- -------------------------------------------------------- Time: 3.760s Load: 0.008s, Pack+Encode: 2.150s, Decode+Unpack: 1.601s ---------------------- -------------------------------------------------------- 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 13.0691 MSE: from ../datasets/FeatureCoding-StableDiffusion3.5/100samples_features/000000523100.zst to output-fixed/sd35/lambda0.02/elic-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: 0.008s ------------------------------------------------------------ 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: 2,176B, BPFP=22.6667 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: 11,504B, BPFP=1.5563 Using 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: 3,336B, BPFP=20.8500 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: 20,684B, BPFP=1.6789 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: 60,480B, BPFP=1.5341 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: 2,600B, BPFP=27.0833 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: 8,396B, BPFP=1.1358 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: 4,400B, BPFP=27.5000 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: 16,268B, BPFP=1.3205 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: 42,292B, BPFP=1.0727 Using per-key quantization points (vae.encoder_f0: torch.Size([256])) for vae.encoder_f0 vae.encoder_f0: 41,824B, BPFP=0.6382 Using per-key quantization points (vae.encoder_f1: torch.Size([256])) for vae.encoder_f1 vae.encoder_f1: 41,800B, BPFP=0.6378 Using per-key quantization points (vae.decoder: torch.Size([256])) for vae.decoder vae.decoder: 35,600B, BPFP=1.0864 ⌛️ [2/4] FRONTEND: Frontend time: 2.141s (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: 1.597s [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 0.16041439 text_encoder-item0.clip_prompt_embeds 0.00024675 67.10553216 text_encoder_2-item1.clip_pooled_prompt_embeds 0.00084628 0.18374945 text_encoder_2-item1.clip_prompt_embeds 0.00016730 0.04894915 text_encoder_3-item2.t5_prompt_embeds 0.00000841 0.00065151 text_encoder-item3.clip_pooled_prompt_embeds 0.00841799 0.25064182 text_encoder-item3.clip_prompt_embeds 0.00023247 57.42621246 text_encoder_2-item4.clip_pooled_prompt_embeds 0.00016751 0.08740882 text_encoder_2-item4.clip_prompt_embeds 0.00009830 0.08730212 text_encoder_3-item5.t5_prompt_embeds 0.00001263 0.00061542 vae.encoder_f0 0.00566967 0.43980837 vae.encoder_f1 0.00567867 0.43960518 vae.decoder 0.00017839 0.01868597 ------------------------------------------------------------------------------------- TOTAL 0.00268303 3.46961197 (elements=2,260,992) ---------------------- -------------------------------------------------------- SAMPLE-WISE STATISTICS ---------------------- -------------------------------------------------------- Handler sd35 Strategy individual Architecture elic-featurecoding ---------------------- -------------------------------------------------------- Total Elements 2260992 Total Bytes 291360 BPFP 1.0309 bits/point EBPFP 2.0618 equivalent bits/point MSE 3.469612 ---------------------- -------------------------------------------------------- Time: 3.746s Load: 0.008s, Pack+Encode: 2.141s, Decode+Unpack: 1.597s ---------------------- -------------------------------------------------------- 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 3.4696 MSE: from ../datasets/FeatureCoding-StableDiffusion3.5/100samples_features/000000526751.zst to output-fixed/sd35/lambda0.02/elic-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: 2,268B, BPFP=23.6250 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: 10,680B, BPFP=1.4448 Using 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: 3,584B, BPFP=22.4000 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: 18,428B, BPFP=1.4958 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: 62,908B, BPFP=1.5957 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: 2,600B, BPFP=27.0833 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: 8,396B, BPFP=1.1358 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: 4,400B, BPFP=27.5000 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: 16,268B, BPFP=1.3205 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: 42,292B, BPFP=1.0727 Using per-key quantization points (vae.encoder_f0: torch.Size([256])) for vae.encoder_f0 vae.encoder_f0: 41,300B, BPFP=0.6302 Using per-key quantization points (vae.encoder_f1: torch.Size([256])) for vae.encoder_f1 vae.encoder_f1: 41,312B, BPFP=0.6304 Using per-key quantization points (vae.decoder: torch.Size([256])) for vae.decoder vae.decoder: 51,280B, BPFP=1.5649 ⌛️ [2/4] FRONTEND: Frontend time: 2.160s (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: 1.609s [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 0.13417966 text_encoder-item0.clip_prompt_embeds 0.00022364 96.43557224 text_encoder_2-item1.clip_pooled_prompt_embeds 0.00036756 0.19375907 text_encoder_2-item1.clip_prompt_embeds 0.00015289 0.05494312 text_encoder_3-item2.t5_prompt_embeds 0.00000784 0.00064217 text_encoder-item3.clip_pooled_prompt_embeds 0.00841799 0.25064182 text_encoder-item3.clip_prompt_embeds 0.00023247 57.42621246 text_encoder_2-item4.clip_pooled_prompt_embeds 0.00016751 0.08740882 text_encoder_2-item4.clip_prompt_embeds 0.00009830 0.08730212 text_encoder_3-item5.t5_prompt_embeds 0.00001263 0.00061542 vae.encoder_f0 0.00580750 0.42359689 vae.encoder_f1 0.00580664 0.42391086 vae.decoder 0.00018044 0.02222184 ------------------------------------------------------------------------------------- TOTAL 0.00274301 4.23000423 (elements=2,260,992) ---------------------- -------------------------------------------------------- SAMPLE-WISE STATISTICS ---------------------- -------------------------------------------------------- Handler sd35 Strategy individual Architecture elic-featurecoding ---------------------- -------------------------------------------------------- Total Elements 2260992 Total Bytes 305716 BPFP 1.0817 bits/point EBPFP 2.1634 equivalent bits/point MSE 4.230004 ---------------------- -------------------------------------------------------- Time: 3.778s Load: 0.009s, Pack+Encode: 2.160s, Decode+Unpack: 1.609s ---------------------- -------------------------------------------------------- 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 4.2300 MSE: from ../datasets/FeatureCoding-StableDiffusion3.5/100samples_features/000000535578.zst to output-fixed/sd35/lambda0.02/elic-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: 2,232B, BPFP=23.2500 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: 10,992B, BPFP=1.4870 Using 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: 3,352B, BPFP=20.9500 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: 19,164B, BPFP=1.5555 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: 60,824B, BPFP=1.5428 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: 2,600B, BPFP=27.0833 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: 8,396B, BPFP=1.1358 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: 4,400B, BPFP=27.5000 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: 16,268B, BPFP=1.3205 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: 42,292B, BPFP=1.0727 Using per-key quantization points (vae.encoder_f0: torch.Size([256])) for vae.encoder_f0 vae.encoder_f0: 43,580B, BPFP=0.6650 Using per-key quantization points (vae.encoder_f1: torch.Size([256])) for vae.encoder_f1 vae.encoder_f1: 43,560B, BPFP=0.6647 Using per-key quantization points (vae.decoder: torch.Size([256])) for vae.decoder vae.decoder: 28,764B, BPFP=0.8778 ⌛️ [2/4] FRONTEND: Frontend time: 3.383s (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: 1.610s [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 0.17671879 text_encoder-item0.clip_prompt_embeds 0.00030118 488.85650027 text_encoder_2-item1.clip_pooled_prompt_embeds 0.00020381 0.17056533 text_encoder_2-item1.clip_prompt_embeds 0.00019649 0.04982888 text_encoder_3-item2.t5_prompt_embeds 0.00000770 0.00067525 text_encoder-item3.clip_pooled_prompt_embeds 0.00841799 0.25064182 text_encoder-item3.clip_prompt_embeds 0.00023247 57.42621246 text_encoder_2-item4.clip_pooled_prompt_embeds 0.00016751 0.08740882 text_encoder_2-item4.clip_prompt_embeds 0.00009830 0.08730212 text_encoder_3-item5.t5_prompt_embeds 0.00001263 0.00061542 vae.encoder_f0 0.03869025 0.81964707 vae.encoder_f1 0.03869358 0.81821758 vae.decoder 0.00021614 0.01830192 ------------------------------------------------------------------------------------- TOTAL 0.01800198 14.67633063 (elements=2,260,992) ---------------------- -------------------------------------------------------- SAMPLE-WISE STATISTICS ---------------------- -------------------------------------------------------- Handler sd35 Strategy individual Architecture elic-featurecoding ---------------------- -------------------------------------------------------- Total Elements 2260992 Total Bytes 286424 BPFP 1.0134 bits/point EBPFP 2.0269 equivalent bits/point MSE 14.676331 ---------------------- -------------------------------------------------------- Time: 5.002s Load: 0.009s, Pack+Encode: 3.383s, Decode+Unpack: 1.610s ---------------------- -------------------------------------------------------- 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 14.6763 MSE: from ../datasets/FeatureCoding-StableDiffusion3.5/100samples_features/000000546325.zst to output-fixed/sd35/lambda0.02/elic-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: 2,232B, BPFP=23.2500 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: 11,152B, BPFP=1.5087 Using 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: 3,452B, BPFP=21.5750 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: 18,844B, BPFP=1.5295 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: 61,244B, BPFP=1.5535 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: 2,600B, BPFP=27.0833 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: 8,396B, BPFP=1.1358 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: 4,400B, BPFP=27.5000 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: 16,268B, BPFP=1.3205 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: 42,292B, BPFP=1.0727 Using per-key quantization points (vae.encoder_f0: torch.Size([256])) for vae.encoder_f0 vae.encoder_f0: 60,888B, BPFP=0.9291 Using per-key quantization points (vae.encoder_f1: torch.Size([256])) for vae.encoder_f1 vae.encoder_f1: 60,856B, BPFP=0.9286 Using per-key quantization points (vae.decoder: torch.Size([256])) for vae.decoder vae.decoder: 27,672B, BPFP=0.8445 ⌛️ [2/4] FRONTEND: Frontend time: 2.151s (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: 1.612s [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 0.18397707 text_encoder-item0.clip_prompt_embeds 0.00023260 43.35973857 text_encoder_2-item1.clip_pooled_prompt_embeds 0.00026409 0.19491327 text_encoder_2-item1.clip_prompt_embeds 0.00016683 0.05126536 text_encoder_3-item2.t5_prompt_embeds 0.00000828 0.00073655 text_encoder-item3.clip_pooled_prompt_embeds 0.00841799 0.25064182 text_encoder-item3.clip_prompt_embeds 0.00023247 57.42621246 text_encoder_2-item4.clip_pooled_prompt_embeds 0.00016751 0.08740882 text_encoder_2-item4.clip_prompt_embeds 0.00009830 0.08730212 text_encoder_3-item5.t5_prompt_embeds 0.00001263 0.00061542 vae.encoder_f0 0.00839879 0.76181096 vae.encoder_f1 0.00840224 0.76436156 vae.decoder 0.00019463 0.01746907 ------------------------------------------------------------------------------------- TOTAL 0.00394849 2.99850248 (elements=2,260,992) ---------------------- -------------------------------------------------------- SAMPLE-WISE STATISTICS ---------------------- -------------------------------------------------------- Handler sd35 Strategy individual Architecture elic-featurecoding ---------------------- -------------------------------------------------------- Total Elements 2260992 Total Bytes 320296 BPFP 1.1333 bits/point EBPFP 2.2666 equivalent bits/point MSE 2.998502 ---------------------- -------------------------------------------------------- Time: 3.772s Load: 0.009s, Pack+Encode: 2.151s, Decode+Unpack: 1.612s ---------------------- -------------------------------------------------------- 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 2.9985 MSE: from ../datasets/FeatureCoding-StableDiffusion3.5/100samples_features/000000551780.zst to output-fixed/sd35/lambda0.02/elic-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: 2,264B, BPFP=23.5833 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: 12,660B, BPFP=1.7127 Using 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: 3,484B, BPFP=21.7750 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: 22,792B, BPFP=1.8500 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: 69,236B, BPFP=1.7562 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: 2,600B, BPFP=27.0833 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: 8,396B, BPFP=1.1358 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: 4,400B, BPFP=27.5000 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: 16,268B, BPFP=1.3205 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: 42,292B, BPFP=1.0727 Using per-key quantization points (vae.encoder_f0: torch.Size([256])) for vae.encoder_f0 vae.encoder_f0: 55,320B, BPFP=0.8441 Using per-key quantization points (vae.encoder_f1: torch.Size([256])) for vae.encoder_f1 vae.encoder_f1: 55,300B, BPFP=0.8438 Using per-key quantization points (vae.decoder: torch.Size([256])) for vae.decoder vae.decoder: 24,308B, BPFP=0.7418 ⌛️ [2/4] FRONTEND: Frontend time: 2.144s (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: 1.610s [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 0.15710566 text_encoder-item0.clip_prompt_embeds 0.00023544 75.33776380 text_encoder_2-item1.clip_pooled_prompt_embeds 0.00022156 0.18074419 text_encoder_2-item1.clip_prompt_embeds 0.00018986 0.04546070 text_encoder_3-item2.t5_prompt_embeds 0.00000832 0.00086737 text_encoder-item3.clip_pooled_prompt_embeds 0.00841799 0.25064182 text_encoder-item3.clip_prompt_embeds 0.00023247 57.42621246 text_encoder_2-item4.clip_pooled_prompt_embeds 0.00016751 0.08740882 text_encoder_2-item4.clip_prompt_embeds 0.00009830 0.08730212 text_encoder_3-item5.t5_prompt_embeds 0.00001263 0.00061542 vae.encoder_f0 0.01160815 0.71895570 vae.encoder_f1 0.01161249 0.71370322 vae.decoder 0.00021720 0.01642360 ------------------------------------------------------------------------------------- TOTAL 0.00544054 3.81282679 (elements=2,260,992) ---------------------- -------------------------------------------------------- SAMPLE-WISE STATISTICS ---------------------- -------------------------------------------------------- Handler sd35 Strategy individual Architecture elic-featurecoding ---------------------- -------------------------------------------------------- Total Elements 2260992 Total Bytes 319320 BPFP 1.1298 bits/point EBPFP 2.2597 equivalent bits/point MSE 3.812827 ---------------------- -------------------------------------------------------- Time: 3.764s Load: 0.009s, Pack+Encode: 2.144s, Decode+Unpack: 1.610s ---------------------- -------------------------------------------------------- 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 3.8128 MSE: from ../datasets/FeatureCoding-StableDiffusion3.5/100samples_features/000000555009.zst to output-fixed/sd35/lambda0.02/elic-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: 2,304B, BPFP=24.0000 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: 11,708B, BPFP=1.5839 Using 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: 3,400B, BPFP=21.2500 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: 20,084B, BPFP=1.6302 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: 63,696B, BPFP=1.6157 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: 2,600B, BPFP=27.0833 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: 8,396B, BPFP=1.1358 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: 4,400B, BPFP=27.5000 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: 16,268B, BPFP=1.3205 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: 42,292B, BPFP=1.0727 Using per-key quantization points (vae.encoder_f0: torch.Size([256])) for vae.encoder_f0 vae.encoder_f0: 36,448B, BPFP=0.5562 Using per-key quantization points (vae.encoder_f1: torch.Size([256])) for vae.encoder_f1 vae.encoder_f1: 36,440B, BPFP=0.5560 Using per-key quantization points (vae.decoder: torch.Size([256])) for vae.decoder vae.decoder: 20,820B, BPFP=0.6354 ⌛️ [2/4] FRONTEND: Frontend time: 2.144s (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: 1.612s [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 0.14456732 text_encoder-item0.clip_prompt_embeds 0.00022923 66.36008945 text_encoder_2-item1.clip_pooled_prompt_embeds 0.00021530 0.18278028 text_encoder_2-item1.clip_prompt_embeds 0.00015521 0.05339306 text_encoder_3-item2.t5_prompt_embeds 0.00000740 0.00065419 text_encoder-item3.clip_pooled_prompt_embeds 0.00841799 0.25064182 text_encoder-item3.clip_prompt_embeds 0.00023247 57.42621246 text_encoder_2-item4.clip_pooled_prompt_embeds 0.00016751 0.08740882 text_encoder_2-item4.clip_prompt_embeds 0.00009830 0.08730212 text_encoder_3-item5.t5_prompt_embeds 0.00001263 0.00061542 vae.encoder_f0 0.02989292 0.83714265 vae.encoder_f1 0.02989391 0.83703035 vae.decoder 0.00034944 0.01517834 ------------------------------------------------------------------------------------- TOTAL 0.01393319 3.63418852 (elements=2,260,992) ---------------------- -------------------------------------------------------- SAMPLE-WISE STATISTICS ---------------------- -------------------------------------------------------- Handler sd35 Strategy individual Architecture elic-featurecoding ---------------------- -------------------------------------------------------- Total Elements 2260992 Total Bytes 268856 BPFP 0.9513 bits/point EBPFP 1.9026 equivalent bits/point MSE 3.634189 ---------------------- -------------------------------------------------------- Time: 3.764s Load: 0.008s, Pack+Encode: 2.144s, Decode+Unpack: 1.612s ---------------------- -------------------------------------------------------- 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 3.6342 MSE: from ../datasets/FeatureCoding-StableDiffusion3.5/100samples_features/000000565469.zst to output-fixed/sd35/lambda0.02/elic-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: 2,360B, BPFP=24.5833 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: 11,984B, BPFP=1.6212 Using 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: 3,580B, BPFP=22.3750 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: 20,180B, BPFP=1.6380 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: 64,548B, BPFP=1.6373 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: 2,600B, BPFP=27.0833 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: 8,396B, BPFP=1.1358 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: 4,400B, BPFP=27.5000 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: 16,268B, BPFP=1.3205 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: 42,292B, BPFP=1.0727 Using per-key quantization points (vae.encoder_f0: torch.Size([256])) for vae.encoder_f0 vae.encoder_f0: 57,180B, BPFP=0.8725 Using per-key quantization points (vae.encoder_f1: torch.Size([256])) for vae.encoder_f1 vae.encoder_f1: 57,220B, BPFP=0.8731 Using per-key quantization points (vae.decoder: torch.Size([256])) for vae.decoder vae.decoder: 35,304B, BPFP=1.0774 ⌛️ [2/4] FRONTEND: Frontend time: 2.161s (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: 1.609s [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 0.16567891 text_encoder-item0.clip_prompt_embeds 0.00024627 211.64456507 text_encoder_2-item1.clip_pooled_prompt_embeds 0.00020424 0.17155350 text_encoder_2-item1.clip_prompt_embeds 0.00017521 0.04590938 text_encoder_3-item2.t5_prompt_embeds 0.00000803 0.00069433 text_encoder-item3.clip_pooled_prompt_embeds 0.00841799 0.25064182 text_encoder-item3.clip_prompt_embeds 0.00023247 57.42621246 text_encoder_2-item4.clip_pooled_prompt_embeds 0.00016751 0.08740882 text_encoder_2-item4.clip_prompt_embeds 0.00009830 0.08730212 text_encoder_3-item5.t5_prompt_embeds 0.00001263 0.00061542 vae.encoder_f0 0.00613025 0.55162370 vae.encoder_f1 0.00613536 0.55013245 vae.decoder 0.00018697 0.01967573 ------------------------------------------------------------------------------------- TOTAL 0.00289634 7.30155548 (elements=2,260,992) ---------------------- -------------------------------------------------------- SAMPLE-WISE STATISTICS ---------------------- -------------------------------------------------------- Handler sd35 Strategy individual Architecture elic-featurecoding ---------------------- -------------------------------------------------------- Total Elements 2260992 Total Bytes 326312 BPFP 1.1546 bits/point EBPFP 2.3092 equivalent bits/point MSE 7.301555 ---------------------- -------------------------------------------------------- Time: 3.779s Load: 0.009s, Pack+Encode: 2.161s, Decode+Unpack: 1.609s ---------------------- -------------------------------------------------------- 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 7.3016 MSE: from ../datasets/FeatureCoding-StableDiffusion3.5/100samples_features/000000575243.zst to output-fixed/sd35/lambda0.02/elic-featurecoding-8bit-individual/sd35cond/000000575243.zst ------------------------ ---------------------------- TOTAL PROCESSING SUMMARY ------------------------ ---------------------------- Total files 100 Avg BPFP 1.0720 bits/point Avg EBPFP 2.1441 equivalent bits/point Avg MSE 4.360150 Avg Time 3.789s ------------------------ ----------------------------