Image-to-Video
Diffusers
Safetensors
Wan2.2
English
Chinese
WanPipeline
video-generation
visual-reasoning
reinforcement-learning
rlvr
vbvr-pro
Instructions to use Video-Reason/VBVR-Pro-Wan2.2-TI2V-5B-RLVR with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Diffusers
How to use Video-Reason/VBVR-Pro-Wan2.2-TI2V-5B-RLVR 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-Pro-Wan2.2-TI2V-5B-RLVR", 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") - Wan2.2
How to use Video-Reason/VBVR-Pro-Wan2.2-TI2V-5B-RLVR with Wan2.2:
# No code snippets available yet for this library. # To use this model, check the repository files and the library's documentation. # Want to help? PRs adding snippets are welcome at: # https://github.com/huggingface/huggingface.js
- Notebooks
- Google Colab
- Kaggle
- Xet hash:
- 09c2a2b09f657cacbe7391ea619e41fc60b9e8be0f6e5f4ec116e14c2430801f
- Size of remote file:
- 16.8 MB
- SHA256:
- e87c960c36d5fbf4e7e76c2469b7eab877be7f8c5992efbf97e44d3123cc6521
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