  Recent advancements in diffusion-based models have demonstrated significant
success in generating images from text. However, video editing models have not
yet reached the same level of visual quality and user control. To address this,
we introduce RAVE, a zero-shot video editing method that leverages pre-trained
text-to-image diffusion models without additional training. RAVE takes an input
video and a text prompt to produce high-quality videos while preserving the
original motion and semantic structure. It employs a novel noise shuffling
strategy, leveraging spatio-temporal interactions between frames, to produce
temporally consistent videos faster than existing methods. It is also efficient
in terms of memory requirements, allowing it to handle longer videos. RAVE is
capable of a wide range of edits, from local attribute modifications to shape
transformations. In order to demonstrate the versatility of RAVE, we create a
comprehensive video evaluation dataset ranging from object-focused scenes to
complex human activities like dancing and typing, and dynamic scenes featuring
swimming fish and boats. Our qualitative and quantitative experiments highlight
the effectiveness of RAVE in diverse video editing scenarios compared to
existing methods. Our code, dataset and videos can be found in
https://rave-video.github.io.
