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 examples/i2v_input.JPG from weizhou03/Wan2.1-Fun-1.3B-InP-Diffusers: direct link, hf CLI and curl.
- Browser
- Download file 251 kB
-
https://huggingface.co/weizhou03/Wan2.1-Fun-1.3B-InP-Diffusers/resolve/main/examples/i2v_input.JPG
- Command line
-
hf download hf://weizhou03/Wan2.1-Fun-1.3B-InP-Diffusers/examples/i2v_input.JPG
-
curl -L -o i2v_input.JPG https://huggingface.co/weizhou03/Wan2.1-Fun-1.3B-InP-Diffusers/resolve/main/examples/i2v_input.JPG
251 kB
- Xet hash:
- ff7a9d173c93b4cee0e286a05c95033152a3a7319ce8f8a08c1ac2287f99deab
- Size of remote file:
- 251 kB
- SHA256:
- 077e3d965090c9028c69c00931675f42e1acc815c6eb450ab291b3b72d211a8e
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