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

Understand

Recent advances in diffusion-based video restoration (VR) demonstrate significant improvement in visual quality, yet yield a prohibitive computational cost during inference.

• While several distillation-based approaches have exhibited the potential of one-step image restoration, extending existing approaches to VR remains challenging and underexplored, particularly when dealing with high-resolution video in real-world settings.
• In this work, we propose a one-step diffusion-based VR model, termed as SeedVR2, which performs adversarial VR training against real data.
• To handle the challenging high-resolution VR within a single step, we introduce several enhancements to both model architecture and training procedures.