TokenFlow: Consistent Diffusion Features for Consistent Video Editing

• URL: http://arxiv.org/abs/2307.10373v3
• Date: Mon, 20 Nov 2023 10:54:09 GMT
• Title: TokenFlow: Consistent Diffusion Features for Consistent Video Editing
• Authors: Michal Geyer and Omer Bar-Tal and Shai Bagon and Tali Dekel
• Abstract summary: We present a framework that harnesses the power of a text-to-image diffusion model for the task of text-driven video editing. 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 framework does not require any training or fine-tuning, and can work in conjunction with any off-the-shelf text-to-image editing method.
• Score: 27.736354114287725
• License: http://creativecommons.org/licenses/by/4.0/
• Abstract: 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/

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