Image-to-Video
Diffusers
Safetensors
English
Chinese
WanVACEPipeline
video generation
video-to-video editing
refernce-to-video
Instructions to use Wan-AI/Wan2.1-VACE-14B-diffusers with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Diffusers
How to use Wan-AI/Wan2.1-VACE-14B-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("Wan-AI/Wan2.1-VACE-14B-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

- Xet hash:
- 9826668c0caa7dcdefb1e9ef117348829af17e15e18d8884760c39d3633a2cd0
- Size of remote file:
- 301 kB
- SHA256:
- 91db579092446be2a834bc67721a8e4346936f38c4edb912f459ca3e10f8f439
·
Xet efficiently stores Large Files inside Git, intelligently splitting files into unique chunks and accelerating uploads and downloads. More info.