Instructions to use lightx2v/Wan2.2-Distill-Models with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Diffusers
How to use lightx2v/Wan2.2-Distill-Models with Diffusers:
pip install -U diffusers transformers accelerate
import torch from diffusers import DiffusionPipeline # switch to "mps" for apple devices pipe = DiffusionPipeline.from_pretrained("lightx2v/Wan2.2-Distill-Models", dtype=torch.bfloat16, device_map="cuda") prompt = "Astronaut in a jungle, cold color palette, muted colors, detailed, 8k" image = pipe(prompt).images[0] - Diffusion Single File
How to use lightx2v/Wan2.2-Distill-Models with Diffusion Single File:
# No code snippets available yet for this library. # To use this model, check the repository files and the library's documentation. # Want to help? PRs adding snippets are welcome at: # https://github.com/huggingface/huggingface.js
- Notebooks
- Google Colab
- Kaggle
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Add SGMD paper info and improve model card metadata
Verified signatureThis PR improves the model card by: - Linking the repository to the associated paper: [SGMD: Score Gradient Matching Distillation for Few-Step Video Diffusion Distillation](https://huggingface.co/papers/2605.30116). - Updating the metadata to use the standard `pipeline_tag` field. - Fixing a typo in the tags (`video genration` -> `video generation`). - Adding a sample usage section using the `LightX2VPipeline` framework as found in the [official GitHub repository](https://github.com/ModelTC/LightX2V).e9035a1
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