SeedVR2: One-Step Video Restoration via Diffusion Adversarial
  Post-Training
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.
68428f675738dda052f724d3
Jianyi Wang
claimed_verified
2025-06-07T05:48:45.463Z
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JIANYI WANG
user
Iceclear
68428f675738dda052f724d4
Shanchuan Lin
claimed_verified
2025-06-11T16:02:27.691Z
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Peter Lin
user
PeterL1n
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Zhijie Lin
68428f675738dda052f724d6
Yuxi Ren
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Meng Wei
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Zongsheng Yue
claimed_verified
2025-06-11T16:02:29.685Z
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Zongsheng Yue
user
OAOA
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Shangchen Zhou
claimed_verified
2025-06-11T16:02:22.939Z
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Shangchen Zhou
user
sczhou
68428f675738dda052f724da
Hao Chen
claimed_verified
2025-07-11T08:09:04.143Z
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/avatars/4c4c8d0c511d4350463341b124aedb98.svg
hao chen
user
wanhu
68428f675738dda052f724db
Yang Zhao
68428f675738dda052f724dc
Ceyuan Yang
68428f675738dda052f724dd
Xuefeng Xiao
68428f675738dda052f724de
Chen Change Loy
68428f675738dda052f724df
Lu Jiang
SeedVR2: One-Step Video Restoration via Diffusion Adversarial
  Post-Training
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.
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.
diffusion-based video restoration VR adversarial VR training adaptive window attention feature matching loss