Instructions to use PrunaAI/PrunaVAED with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Diffusers
How to use PrunaAI/PrunaVAED with Diffusers:
pip install -U diffusers transformers accelerate
import torch from diffusers import DiffusionPipeline # switch to "mps" for apple devices pipe = DiffusionPipeline.from_pretrained("PrunaAI/PrunaVAED", torch_dtype=torch.bfloat16, device_map="cuda") prompt = "Astronaut in a jungle, cold color palette, muted colors, detailed, 8k" image = pipe(prompt).images[0] - LTX.io
How to use PrunaAI/PrunaVAED with LTX.io:
# Install the LTX-2 pipelines git clone https://github.com/Lightricks/LTX-2.git cd LTX-2 uv sync --frozen
# Download the weights from this repo, plus the Gemma text encoder hf download PrunaAI/PrunaVAED --local-dir models/PrunaVAED hf download google/gemma-3-12b-it-qat-q4_0-unquantized --local-dir models/gemma-3-12b
# Fast pipeline (distilled model, no distilled LoRA needed) uv run python -m ltx_pipelines.distilled \ --distilled-checkpoint-path models/PrunaVAED/<distilled-checkpoint>.safetensors \ --spatial-upsampler-path models/PrunaVAED/<spatial-upsampler>.safetensors \ --gemma-root models/gemma-3-12b \ --prompt "A beautiful sunset over the ocean" \ --output-path output.mp4 # For image-to-video, add: --image path/to/image.jpg 0 0.8# HQ pipeline (two-stage, higher quality) uv run python -m ltx_pipelines.ti2vid_two_stages_hq \ --checkpoint-path models/PrunaVAED/<checkpoint>.safetensors \ --distilled-lora models/PrunaVAED/<distilled-lora>.safetensors 0.8 \ --spatial-upsampler-path models/PrunaVAED/<spatial-upsampler>.safetensors \ --gemma-root models/gemma-3-12b \ --prompt "A beautiful sunset over the ocean" \ --output-path output.mp4 # For image-to-video, add: --image path/to/image.jpg 0 0.8 - Notebooks
- Google Colab
- Kaggle
| { | |
| "_class_name": "AutoencoderKLLTX2Video", | |
| "_diffusers_version": "0.38.0", | |
| "block_out_channels": [ | |
| 256, | |
| 512, | |
| 1024, | |
| 1024 | |
| ], | |
| "decoder_block_out_channels": [ | |
| 128, | |
| 256, | |
| 384, | |
| 1024 | |
| ], | |
| "decoder_causal": false, | |
| "decoder_inject_noise": [ | |
| false, | |
| false, | |
| false, | |
| false, | |
| false | |
| ], | |
| "decoder_layers_per_block": [ | |
| 4, | |
| 6, | |
| 4, | |
| 2, | |
| 2 | |
| ], | |
| "decoder_spatial_padding_mode": "zeros", | |
| "decoder_spatio_temporal_scaling": [ | |
| true, | |
| true, | |
| true, | |
| true | |
| ], | |
| "down_block_types": [ | |
| "LTX2VideoDownBlock3D", | |
| "LTX2VideoDownBlock3D", | |
| "LTX2VideoDownBlock3D", | |
| "LTX2VideoDownBlock3D" | |
| ], | |
| "downsample_type": [ | |
| "spatial", | |
| "temporal", | |
| "spatiotemporal", | |
| "spatiotemporal" | |
| ], | |
| "encoder_causal": true, | |
| "encoder_spatial_padding_mode": "zeros", | |
| "in_channels": 3, | |
| "latent_channels": 128, | |
| "layers_per_block": [ | |
| 4, | |
| 6, | |
| 4, | |
| 2, | |
| 2 | |
| ], | |
| "out_channels": 3, | |
| "patch_size": 4, | |
| "patch_size_t": 1, | |
| "resnet_norm_eps": 1e-06, | |
| "scaling_factor": 1.0, | |
| "spatial_compression_ratio": 32, | |
| "spatio_temporal_scaling": [ | |
| true, | |
| true, | |
| true, | |
| true | |
| ], | |
| "temporal_compression_ratio": 8, | |
| "timestep_conditioning": false, | |
| "upsample_factor": [ | |
| 2, | |
| 2, | |
| 1, | |
| 2 | |
| ], | |
| "upsample_residual": [ | |
| false, | |
| false, | |
| false, | |
| false | |
| ], | |
| "upsample_type": [ | |
| "spatiotemporal", | |
| "spatiotemporal", | |
| "temporal", | |
| "spatial" | |
| ] | |
| } | |