Instructions to use Video-Reason/VBVR-Wan2.2 with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Diffusers
How to use Video-Reason/VBVR-Wan2.2 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("Video-Reason/VBVR-Wan2.2", 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
Commit History
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Update metadata and improve model card
Verified signatureHi! I'm Niels from the Hugging Face community science team. This PR improves the model card by: - Adding the `pipeline_tag: image-to-video` to ensure the model is correctly categorized on the Hub. - Setting the `library_name` to `diffusers` as evidenced by the model's configuration files and usage instructions. - Linking the model to the corresponding paper page on Hugging Face. These changes help make the model more discoverable and provide users with the correct automated code snippets for usage.9234e00 -
Fix VBVR-Bench-Leaderboard link: add 'spaces/' prefix to URL
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