  The generative AI revolution has recently expanded to videos. Nevertheless,
current state-of-the-art video models are still lagging behind image models in
terms of visual quality and user control over the generated content. In this
work, we present a framework that harnesses the power of a text-to-image
diffusion model for the task of text-driven video editing. Specifically, given
a source video and a target text-prompt, our method generates a high-quality
video that adheres to the target text, while preserving the spatial layout and
motion of the input video. Our method is based on a key observation that
consistency in the edited video can be obtained by enforcing consistency in the
diffusion feature space. We achieve this by explicitly propagating diffusion
features based on inter-frame correspondences, readily available in the model.
Thus, our framework does not require any training or fine-tuning, and can work
in conjunction with any off-the-shelf text-to-image editing method. We
demonstrate state-of-the-art editing results on a variety of real-world videos.
Webpage: https://diffusion-tokenflow.github.io/
