Image-to-Video
Diffusers
Safetensors
English
Chinese
WanVACEPipeline
video generation
video-to-video editing
reference-to-video
wan2.1
Instructions to use AlekseyCalvin/WanVACE_1.3B_nf4_umT5fp8_Diffusers with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Diffusers
How to use AlekseyCalvin/WanVACE_1.3B_nf4_umT5fp8_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("AlekseyCalvin/WanVACE_1.3B_nf4_umT5fp8_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:
- 672016bb3e6a888cca3754ee37972b7e21817f0dbfc38f1cc6390f99a981d71f
- Size of remote file:
- 254 MB
- SHA256:
- 39ffe6326a57bbc7c8a704d862e482588e3affc858bdbfc123a91b5ac3a3a42a
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