SeedVR2: One Step Video Restoration via Diffusion Adversarial Post Training Jianyi Wang, Shanchuan Lin, Zhijie Lin, Yuxi Ren, Meng Wei, Zongsheng Yue, Shangchen Zhou, Hao Chen, Yang Zhao, Ceyuan Yang, Xuefeng Xiao, Chen Change Loy, Lu Jiang 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, due to the limited generation ability and poor temporal consistency, 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 inconsist…
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