用噪声感知策略让视频修复模型少几步就见效，还能保持细节清晰。
SATB-VR: Training Few-Step Video Restoration Diffusion Model using SNR-Aware Trajectory Blending
- 通过辅助预测器跳过早期低信噪比步骤，加速去噪过程。
- 在5步内完成修复，合成、真实和AI生成数据上均超越现有方法。
- 适合需要快速高质量视频修复的场景，如实时编辑或流媒体处理。
尽管扩散模型在视频修复中表现优异，但其依赖大量迭代步骤限制了效率。而激进的单步蒸馏常导致细节丢失。为此，我们提出SATB-VR，一种少步修复范式，通过辅助预测器跳过早期低信噪比（SNR）步骤，直接启动去噪过程。然而，预测器与去噪器联合训练会引发训练-推理不一致问题。为此，我们提出信噪比感知轨迹融合（SATB）策略：前向过程中，根据当前信噪比动态融合预测器输出与真实轨迹生成噪声输入，迫使去噪器鲁棒补偿初始误差并平滑收敛至干净数据流形。此外，引入去噪器驱动一致性（DDC）损失，利用同步更新的去噪器作为动态评估器，显式对齐内部特征以提升预测精度。大量实验表明，在灵活的少步推理设置下（如≤5步），SATB-VR在合成、真实世界及AIGC基准上均优于现有方法。
原文摘要 · Abstract (English)
While diffusion models excel in video restoration, their reliance on extensive iterative steps limits efficiency. Conversely, aggressive single-step distillation often compromises fine texture recovery. To achieve an optimal balance, we present SATB-VR, a few-step paradigm that jump-starts the denoising process via an auxiliary predictor, explicitly bypassing early low signal-to-noise ratio (SNR) steps. However, naive joint training of the predictor and the denoiser inherently introduces a severe train-inference discrepancy. To resolve this, we propose the SNR-Aware Trajectory Blending (SATB) strategy. During the forward process, SATB constructs the noisy input by dynamically blending the predictor's output with the ground-truth trajectory based on the SNRs. This forces the denoiser to robustly compensate for initial prediction errors while smoothly converging to the clean data manifold. Furthermore, we introduce a Denoiser-Driven Consistency (DDC) loss, leveraging the concurrently updated denoiser as a dynamic evaluator to explicitly align internal features and boost predictor accuracy. Extensive experiments demonstrate that, under flexible few-step inference regimes (\eg, $\le 5$ steps), SATB-VR performs favorably against existing approaches on synthetic, real-world, and AIGC benchmarks.
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