Image-to-Video
Diffusers
Safetensors
English
Chinese
t2v
video generation
video-to-video editing
refernce-to-video
Instructions to use ali-vilab/VACE-Wan2.1-1.3B-Preview with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Diffusers
How to use ali-vilab/VACE-Wan2.1-1.3B-Preview 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("ali-vilab/VACE-Wan2.1-1.3B-Preview", 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 diffusion_pytorch_model.safetensors from ali-vilab/VACE-Wan2.1-1.3B-Preview: direct link, hf CLI and curl.
- Browser
- Download file 7.15 GB
-
https://huggingface.co/ali-vilab/VACE-Wan2.1-1.3B-Preview/resolve/main/diffusion_pytorch_model.safetensors
- Command line
-
hf download hf://ali-vilab/VACE-Wan2.1-1.3B-Preview/diffusion_pytorch_model.safetensors
-
curl -L -o diffusion_pytorch_model.safetensors https://huggingface.co/ali-vilab/VACE-Wan2.1-1.3B-Preview/resolve/main/diffusion_pytorch_model.safetensors
7.15 GB
- Xet hash:
- fd653298086c88e1aa1e44f5fb4dfc83844daf3490c731ac00d20314a17abbec
- Size of remote file:
- 7.15 GB
- SHA256:
- fcd12e2a8fab331ba0ab28dca371d90036a868287767f35677ff3f8e74db0969
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