Instructions to use HappyP4nda/PhysRVG with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Diffusers
How to use HappyP4nda/PhysRVG 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("HappyP4nda/PhysRVG", 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
| license: apache-2.0 | |
| library_name: diffusers | |
| pipeline_tag: image-to-video | |
| base_model: Wan-AI/Wan2.2-TI2V-5B-Diffusers | |
| tags: | |
| - video-generation | |
| - reinforcement-learning | |
| - physics | |
| - diffusion | |
| # PhysRVG: Physics-Aware Unified Reinforcement Learning for Video Generative Models | |
| [](https://arxiv.org/abs/2601.11087) | |
| [](https://lucaria-academy.github.io/PhysRVG/) | |
| [](https://github.com/ant-research/PhysRVG) | |
| This repository hosts the **model weights** for PhysRVG (ECCV 2026). PhysRVG leverages a unified reinforcement learning framework with verifiable rewards to improve rigid-body motion generation in video synthesis. | |
| > 📌 Demos, training, and inference code are in the [**GitHub repository**](https://github.com/ant-research/PhysRVG). This page only provides the checkpoints. | |
| ## Contents | |
| ``` | |
| PhysRVG/ | |
| ├── dit # PhysRVG DiT weights (used with --resume_from_checkpoint) | |
| ├── lora # LoRA weights for memory-efficient fine-tuning / inference | |
| ├── sam2.1-hiera-large # SAM 2 model used to compute the verifiable reward | |
| └── Wan2.2-TI2V-5B-Diffusers # base text/image-to-video diffusion model | |
| ``` | |
| ## Usage | |
| Download the weights into the `./models` directory of the [code repository](https://github.com/ant-research/PhysRVG): | |
| ```bash | |
| huggingface-cli download HappyP4nda/PhysRVG --local-dir ./models | |
| ``` | |
| Then run inference (see the GitHub README for setup): | |
| ```bash | |
| python inference.py --video_path data/example_videos/2/video.mp4 | |
| ``` | |
| ## Citation | |
| ```bibtex | |
| @article{PhysRVG2026, | |
| title={PhysRVG: Physics-Aware Unified Reinforcement Learning for Video Generative Models}, | |
| author={Zhang, Qiyuan and Gong, Biao and Tan, Shuai and Zhang, Zheng and Shen, Yujun and Zhu, Xing and Li, Yuyuan and Yao, Kelu and Shen, Chunhua and Zou, Changqing}, | |
| journal={ECCV 2026}, | |
| year={2026} | |
| } | |
| ``` | |