  Large-scale text-to-image diffusion models achieve unprecedented success in
image generation and editing. However, how to extend such success to video
editing is unclear. Recent initial attempts at video editing require
significant text-to-video data and computation resources for training, which is
often not accessible. In this work, we propose vid2vid-zero, a simple yet
effective method for zero-shot video editing. Our vid2vid-zero leverages
off-the-shelf image diffusion models, and doesn't require training on any
video. At the core of our method is a null-text inversion module for
text-to-video alignment, a cross-frame modeling module for temporal
consistency, and a spatial regularization module for fidelity to the original
video. Without any training, we leverage the dynamic nature of the attention
mechanism to enable bi-directional temporal modeling at test time. Experiments
and analyses show promising results in editing attributes, subjects, places,
etc., in real-world videos. Code is made available at
\url{https://github.com/baaivision/vid2vid-zero}.
