Instructions to use lightx2v/Wan2.1-Distill-Models with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Diffusers
How to use lightx2v/Wan2.1-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("Wan-AI/Wan2.1-T2V-14B,Wan-AI/Wan2.1-I2V-14B-480P,Wan-AI/Wan2.1-I2V-14B-720P", dtype=torch.bfloat16, device_map="cuda") pipe.load_lora_weights("lightx2v/Wan2.1-Distill-Models") prompt = "Astronaut in a jungle, cold color palette, muted colors, detailed, 8k" image = pipe(prompt).images[0] - Diffusion Single File
How to use lightx2v/Wan2.1-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
- Local Apps
- Draw Things
Add paper link, pipeline tag and sample usage
#4
by nielsr HF Staff - opened
This PR improves the model card by:
- Adding the
text-to-videopipeline tag for better discoverability. - Linking the model weights to the research paper: SGMD: Score Gradient Matching Distillation for Few-Step Video Diffusion Distillation.
- Including a Python sample usage snippet using the recommended LightX2V framework.
- Fixing a typo in the tags (
video genration->video generation).