Instructions to use zenlm/zen-video-i2v with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Diffusers
How to use zenlm/zen-video-i2v 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("zenlm/zen-video-i2v", 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 Wan2.1_VAE.pth from zenlm/zen-video-i2v: direct link, hf CLI and curl.
- Browser
- Download file 508 MB
-
https://huggingface.co/zenlm/zen-video-i2v/resolve/main/Wan2.1_VAE.pth
- Command line
-
hf download hf://zenlm/zen-video-i2v/Wan2.1_VAE.pth
-
curl -L -o Wan2.1_VAE.pth https://huggingface.co/zenlm/zen-video-i2v/resolve/main/Wan2.1_VAE.pth
508 MB
- Xet hash:
- d987d97565dcbde743cf5c3c9c15882c037284cdb978cadb840a0c71e2336c72
- Size of remote file:
- 508 MB
- SHA256:
- 38071ab59bd94681c686fa51d75a1968f64e470262043be31f7a094e442fd981
·
Xet efficiently stores Large Files inside Git, intelligently splitting files into unique chunks and accelerating uploads and downloads. More info.