SeedVR2 – A video restoration model developed by ByteDance

What is SeedVR2?

SeedVR2 is a new single-step video restoration (VR) model developed by ByteDance. It combines diffusion models with Adversarial Post-Training (APT) techniques. Leveraging innovative components such as adaptive window attention and feature matching loss, SeedVR2 enables high-resolution video restoration in a single step, significantly reducing the computational cost associated with traditional multi-step diffusion models. SeedVR2 outperforms existing methods across multiple datasets, demonstrating outstanding detail recovery and visual quality. It offers a new solution for real-time video restoration and high-resolution video processing.

Key Features of SeedVR2

• Single-Step Video Restoration: Performs high-quality video restoration in a single sampling step, significantly reducing the computation time and cost of traditional multi-step diffusion models.

• High-Resolution Video Processing: Supports the restoration of high-resolution videos (e.g., 1080p) using an adaptive window attention mechanism that dynamically adjusts window sizes to avoid boundary artifacts in high-resolution inputs.

• Detail Recovery and Enhancement: Generates realistic details through adversarial training, improving visual quality while maintaining content consistency and realism.

• Efficient Training and Inference: Employs progressive distillation and adversarial post-training to enhance training efficiency and stability, resulting in strong inference performance.

• Multi-Scenario Compatibility: Supports both synthetic and real-world video restoration tasks, including deblurring, super-resolution, and denoising.

Technical Principles of SeedVR2

• Diffusion Model: A generative model that reconstructs data by gradually removing noise. SeedVR2 uses a diffusion model as its core architecture to generate high-quality video content.

• Adversarial Post-Training (APT): Finetunes the pretrained diffusion model using adversarial training to better adapt to real-world data, significantly improving the model’s generation ability and efficiency.

• Adaptive Window Attention Mechanism: To address boundary inconsistencies in high-resolution video restoration, SeedVR2 introduces an adaptive window attention mechanism. It dynamically adjusts the attention window size based on input resolution, enhancing adaptability and robustness to various resolutions.

• Feature Matching Loss: To improve training efficiency and stability, SeedVR2 introduces a feature matching loss function. It calculates feature distances using features extracted directly from the discriminator, replacing traditional LPIPS loss and avoiding the high computational costs in high-resolution video training.

• Progressive Distillation: During the transition from a multi-step to a single-step diffusion model, SeedVR2 adopts a progressive distillation strategy. It gradually reduces the sampling steps while optimizing the model to preserve restoration quality and significantly accelerate inference.

Project Links for SeedVR2

• Official Website: https://iceclear.github.io/projects/seedvr2/

• GitHub Repository: https://github.com/IceClear/SeedVR2

• arXiv Paper: https://arxiv.org/pdf/2506.05301

Application Scenarios for SeedVR2

• Video Super-Resolution: Upscales low-resolution videos to high resolution. Ideal for online video platforms, video conferencing, and significantly improving user experience.

• Video Deblurring: Restores low-quality videos caused by motion blur or camera shake. Useful for surveillance and sports footage to enhance clarity.

• Video Denoising: Removes noise from videos to improve visual quality. Applicable in low-light video capture and restoration of old videos.

• Video Enhancement: Improves overall video aesthetics, including contrast adjustment, color correction, and detail enhancement. Suitable for video editing and social media content.

• Old Video Restoration: Repairs and enhances old or historical videos, restoring them to original quality. Ideal for archival restoration and home video enhancement.