SeedVR2: One-Step Video Restoration via Diffusion Adversarial Post-Training

Jianyi Wang, Shanchuan Lin, Zhijie Lin, Yuxi Ren, Meng Wei, Zongsheng Yue, Shangchen Zhou, Hao Chen, Yang Zhao, Ceyuan Yang, Xuefeng Xiao, Chen Change Loy, Lu Jiang

📮 Notice

Limitations: These are the prototype models and the performance may not be perfectly align with the paper. Our methods are sometimes not robust to heavy degradations and very large motions, and shares some failure cases with existing methods, e.g., fail to fully remove the degradation or simply generate unpleasing details. Moreover, due to the strong generation ability, Our methods tend to overly generate details on inputs with very light degradations, e.g., 720p AIGC videos, leading to oversharpened results occasionally.

✍️ Citation

@article{wang2025seedvr2,
      title={SeedVR2: One-Step Video Restoration via Diffusion Adversarial Post-Training},
      author={Wang, Jianyi and Lin, Shanchuan and Lin, Zhijie and Ren, Yuxi and Wei, Meng and Yue, Zongsheng and Zhou, Shangchen and Chen, Hao and Zhao, Yang and Yang, Ceyuan and Xiao, Xuefeng and Loy, Chen Change and Jiang, Lu},
      booktitle={arXiv preprint arXiv:2506.05301},
      year={2025}
   }
@inproceedings{wang2025seedvr,
      title={SeedVR: Seeding Infinity in Diffusion Transformer Towards Generic Video Restoration},
      author={Wang, Jianyi and Lin, Zhijie and Wei, Meng and Zhao, Yang and Yang, Ceyuan and Loy, Chen Change and Jiang, Lu},
      booktitle={CVPR},
      year={2025}
   }

📜 License

SeedVR and SeedVR2 are licensed under the Apache 2.0.