Instructions to use dhlee3000/music2dance_cont with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Diffusers
How to use dhlee3000/music2dance_cont 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("dhlee3000/music2dance_cont", 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 Animation/face_encoder.pth from dhlee3000/music2dance_cont: direct link, hf CLI and curl.
- Browser
- Download file 229 MB
-
https://huggingface.co/dhlee3000/music2dance_cont/resolve/main/Animation/face_encoder.pth
- Command line
-
hf download hf://dhlee3000/music2dance_cont/Animation/face_encoder.pth
-
curl -L -o face_encoder.pth https://huggingface.co/dhlee3000/music2dance_cont/resolve/main/Animation/face_encoder.pth
229 MB
- Xet hash:
- e94736de4f654a8c409530b8ca8e7b721295070aef550dd8e23bf145154bdae8
- Size of remote file:
- 229 MB
- SHA256:
- d6c1a3ac2ca22bbb1e6e23d63d6f9f03080d95fbe63fe014fb03244fcb5b003c
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