Instructions to use DarthZhu/VideoRLVR-Wan2.2-Base with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Diffusers
How to use DarthZhu/VideoRLVR-Wan2.2-Base 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("DarthZhu/VideoRLVR-Wan2.2-Base", 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
Improve model card and metadata
#1
by nielsr HF Staff - opened
Hi! I'm Niels from the Hugging Face community science team.
I've opened this pull request to enhance your model card with metadata and links to your research. Adding this information helps make your model more discoverable and provides users with the necessary context regarding its training and architecture.
Specifically, I have:
- Added the
image-to-videopipeline tag. - Added
library_name: diffusersas the configuration files indicate compatibility. - Added the
apache-2.0license. - Provided links to the paper, project page, and GitHub repository.
- Included the citation information.
Feel free to merge this if it looks good to you!
DarthZhu changed pull request status to merged