title: SeedVR2: One-Step Video Restoration via Diffusion Adversarial
    Post-Training
summary: 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. Specifically,
  an adaptive window attention mechanism is proposed, where the window size is
  dynamically adjusted to fit the output resolutions, avoiding window
  inconsistency observed under high-resolution VR using window attention with a
  predefined window size. To stabilize and improve the adversarial post-training
  towards VR, we further verify the effectiveness of a series of losses,
  including a proposed feature matching loss without significantly sacrificing
  training efficiency. Extensive experiments show that SeedVR2 can achieve
  comparable or even better performance compared with existing VR approaches in a
  single step.
paper_authors._id: 68428f675738dda052f724d3
paper_authors.name: Jianyi Wang
paper_authors.status: claimed_verified
paper_authors.statusLastChangedAt: 2025-06-07T05:48:45.463Z
paper_authors._id: 63043db17373aacccd89f49d
paper_authors.avatarUrl: https://cdn-avatars.huggingface.co/v1/production/uploads/63043db17373aacccd89f49d/jzP_fPCFXeYJvAD8uA_N7.jpeg
paper_authors.fullname: JIANYI WANG
paper_authors.type: user
paper_authors.user: Iceclear
paper_authors._id: 68428f675738dda052f724d4
paper_authors.name: Shanchuan Lin
paper_authors._id: 68428f675738dda052f724d5
paper_authors.name: Zhijie Lin
paper_authors._id: 68428f675738dda052f724d6
paper_authors.name: Yuxi Ren
paper_authors._id: 68428f675738dda052f724d7
paper_authors.name: Meng Wei
paper_authors._id: 68428f675738dda052f724d8
paper_authors.name: Zongsheng Yue
paper_authors._id: 68428f675738dda052f724d9
paper_authors.name: Shangchen Zhou
paper_authors._id: 68428f675738dda052f724da
paper_authors.name: Hao Chen
paper_authors._id: 68428f675738dda052f724db
paper_authors.name: Yang Zhao
paper_authors._id: 68428f675738dda052f724dc
paper_authors.name: Ceyuan Yang
paper_authors._id: 68428f675738dda052f724dd
paper_authors.name: Xuefeng Xiao
paper_authors._id: 68428f675738dda052f724de
paper_authors.name: Chen Change Loy
paper_authors._id: 68428f675738dda052f724df
paper_authors.name: Lu Jiang
paper_title: SeedVR2: One-Step Video Restoration via Diffusion Adversarial
    Post-Training
paper_summary: 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. Specifically,
  an adaptive window attention mechanism is proposed, where the window size is
  dynamically adjusted to fit the output resolutions, avoiding window
  inconsistency observed under high-resolution VR using window attention with a
  predefined window size. To stabilize and improve the adversarial post-training
  towards VR, we further verify the effectiveness of a series of losses,
  including a proposed feature matching loss without significantly sacrificing
  training efficiency. Extensive experiments show that SeedVR2 can achieve
  comparable or even better performance compared with existing VR approaches in a
  single step.
paper_ai_summary: SeedVR2 is a one-step diffusion-based video restoration model that uses adaptive window attention and feature matching loss to achieve high visual quality with reduced computational cost compared to existing methods.
paper_ai_keywords: diffusion-based video restoration VR adversarial VR training adaptive window attention feature matching loss