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January 3, 2025
Video restoration presents a unique challenge. It requires preserving image fidelity while simultaneously restoring temporally consistent details from unknown, real-world degradations. While diffusion-based restoration methods have recently made progress, they often encounter limitations in their generative capabilities and sampling efficiency. This article highlights SeedVR, a new approach that addresses these challenges.
SeedVR is a diffusion transformer designed for practical video restoration, capable of handling arbitrary lengths and resolutions. The core of SeedVR is the so-called "Shifted Window Attention," which enables effective restoration of long video sequences. Unlike conventional window attention mechanisms, SeedVR supports variable-sized windows at the edges of the spatial and temporal dimensions. This overcomes the resolution limitations of traditional methods.
SeedVR utilizes state-of-the-art techniques, including a causal video autoencoder (CVVAE), mixed image and video training, and progressive training. The CVVAE compresses time and space, significantly reducing the computational cost of video restoration, especially for high-resolution videos, while maintaining high reconstruction quality. Training with mixed image and video data of varying resolutions enhances the model's adaptability. Progressive training accelerates convergence on large datasets.
Through this combination of techniques, SeedVR achieves high performance on synthetic and real-world benchmarks, as well as on AI-generated videos. SeedVR is significantly faster than existing diffusion-based VR methods, despite having a considerably higher number of parameters.
SeedVR uses MM-DiT as its base architecture and replaces full self-attention with a window attention mechanism. Swin-Attention was chosen, leading to Swin-MMDiT. Swin-MMDiT uses a significantly larger attention window compared to previous approaches that operate in pixel space. To handle the variable window sizes resulting from the shifted-window mechanism, SeedVR utilizes 3D rotary position embedding within each window. This allows for the modeling of different-sized windows that occur at the edges of the space-time volume.
SeedVR is one of the first large, scalable diffusion transformer models specifically designed for generic video restoration. The model addresses the challenge of processing inputs with arbitrary resolutions by introducing simple yet effective diffusion transformer blocks based on a shifted-window attention mechanism. The developed causal video autoencoder significantly improves training and inference efficiency while achieving high video reconstruction quality. Through large-scale joint training with image and video data and multi-stage progressive training, SeedVR achieves high performance on various benchmarks.
SeedVR has the potential to push the boundaries of advanced video restoration and inspire future research in the development of large vision models for practical video restoration. The ability to process videos of arbitrary length and resolution opens up new possibilities for applications in various fields.
Bibliography Wang, J., Lin, Z., Wei, M., Zhao, Y., Yang, C., Loy, C. C., & Jiang, L. (2025). SeedVR: Seeding Infinity in Diffusion Transformer Towards Generic Video Restoration. arXiv preprint arXiv:2501.01320v1. https://arxiv.org/html/2501.01320v1 https://paperreading.club/page?id=276363 https://chatpaper.com/chatpaper/ja?id=4&date=1735833600&page=1 https://github.com/zhtjtcz/Mine-Arxiv https://vsehwag.github.io/blog/2023/2/all_papers_on_diffusion.html ```