Instructions to use shuolin/HyperMotion with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Diffusers
How to use shuolin/HyperMotion 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("shuolin/HyperMotion", 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
Update model card with pipeline tag, license, and resources
#1
by nielsr HF Staff - opened
Hi! I'm Niels from the Hugging Face community team.
I've updated the model card to improve its metadata and discoverability. This PR:
- Adds the
image-to-videopipeline tag. - Adds
diffusersas the library name (based on theconfig.jsonevidence). - Updates the license to
cc-by-nc-sa-4.0to match the project's official documentation. - Adds a link to the official GitHub repository.
- Includes a sample usage section based on the inference scripts.
Feel free to merge if this looks good!
shuolin changed pull request status to merged
Thank you so much for your help! That’s brilliant.