  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.
