  Video editing is an emerging task, in which most current methods adopt the
pre-trained text-to-image (T2I) diffusion model to edit the source video in a
zero-shot manner. Despite extensive efforts, maintaining the temporal
consistency of edited videos remains challenging due to the lack of temporal
constraints in the regular T2I diffusion model. To address this issue, we
propose COrrespondence-guided Video Editing (COVE), leveraging the inherent
diffusion feature correspondence to achieve high-quality and consistent video
editing. Specifically, we propose an efficient sliding-window-based strategy to
calculate the similarity among tokens in the diffusion features of source
videos, identifying the tokens with high correspondence across frames. During
the inversion and denoising process, we sample the tokens in noisy latent based
on the correspondence and then perform self-attention within them. To save GPU
memory usage and accelerate the editing process, we further introduce the
temporal-dimensional token merging strategy, which can effectively reduce
redundancy. COVE can be seamlessly integrated into the pre-trained T2I
diffusion model without the need for extra training or optimization. Extensive
experiment results demonstrate that COVE achieves the start-of-the-art
performance in various video editing scenarios, outperforming existing methods
both quantitatively and qualitatively. The code will be release at
https://github.com/wangjiangshan0725/COVE
