Diffusion-based zero-shot image restoration and enhancement models have
achieved great success in various image restoration and enhancement tasks
without training. However, directly applying them to video restoration and
enhancement results in severe temporal flickering artifacts. In this paper, we
propose the first framework for zero-shot video restoration and enhancement
based on a pre-trained image diffusion model. By replacing the self-attention
layer with the proposed cross-previous-frame attention layer, the pre-trained
image diffusion model can take advantage of the temporal correlation between
neighboring frames. We further propose temporal consistency guidance,
spatial-temporal noise sharing, and an early stopping sampling strategy for
better temporally consistent sampling. Our method is a plug-and-play module
that can be inserted into any diffusion-based zero-shot image restoration or
enhancement methods to further improve their performance. Experimental results
demonstrate the superiority of our proposed method in producing temporally
consistent videos with better fidelity.

基于预训练图像扩散模型，提出了用于零样本视频修复和增强的第一种框架，通过在图像扩散模型中使用交叉前一帧注意力层来利用相邻帧之间的时间相关性，进一步提出了时间一致性引导，空间 - 时间噪声共享和早停止采样策略，以实现更好的时间一致性采样，实验结果表明，我们提出的方法在生成具有更好保真度的时间一致视频方面具有优越性。