Instructions to use weizhou03/Wan2.1-Fun-1.3B-InP-Diffusers with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Diffusers
How to use weizhou03/Wan2.1-Fun-1.3B-InP-Diffusers with Diffusers:
pip install -U diffusers transformers accelerate
import torch from diffusers import DiffusionPipeline from diffusers.utils import load_image, export_to_video # switch to "mps" for apple devices pipe = DiffusionPipeline.from_pretrained("weizhou03/Wan2.1-Fun-1.3B-InP-Diffusers", dtype=torch.bfloat16, device_map="cuda") pipe.to("cuda") prompt = "A man with short gray hair plays a red electric guitar." image = load_image( "https://huggingface.co/datasets/huggingface/documentation-images/resolve/main/diffusers/guitar-man.png" ) output = pipe(image=image, prompt=prompt).frames[0] export_to_video(output, "output.mp4") - Notebooks
- Google Colab
- Kaggle
Download assets/vben_vs_sota.png from weizhou03/Wan2.1-Fun-1.3B-InP-Diffusers: direct link, hf CLI and curl.
- Browser
- Download file 1.55 MB
-
https://huggingface.co/weizhou03/Wan2.1-Fun-1.3B-InP-Diffusers/resolve/main/assets/vben_vs_sota.png
- Command line
-
hf download hf://weizhou03/Wan2.1-Fun-1.3B-InP-Diffusers/assets/vben_vs_sota.png
-
curl -L -o vben_vs_sota.png https://huggingface.co/weizhou03/Wan2.1-Fun-1.3B-InP-Diffusers/resolve/main/assets/vben_vs_sota.png
1.55 MB

- Xet hash:
- e30918d0e1e08abefb28eecf6606311e55b06621b4fbe992938545e44aa5ea0f
- Size of remote file:
- 1.55 MB
- SHA256:
- 9a0e86ca85046d2675f97984b88b6e74df07bba8a62a31ab8a1aef50d4eda44e
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