Negative-prompt Inversion: Fast Image Inversion for Editing with Text-guided Diffusion Models

Daiki Miyake, Akihiro Iohara, Yu Saito, Toshiyuki Tanaka

Primary Area: generative models

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Keywords: generative models, diffusion models, score-based models, image generation, image editing

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TL;DR: We propose a method that enables image reconstruction at tens of times the speed of existing methods using diffusion models.

Abstract: In image editing employing diffusion models, it is crucial to preserve the reconstruction quality of the original image while changing its style. Although existing methods ensure reconstruction quality through optimization, a drawback of these is the significant amount of time required for optimization. In this paper, we propose negative-prompt inversion, a method capable of achieving equivalent reconstruction solely through forward propagation without optimization, thereby enabling much faster editing processes. We experimentally demonstrate that the reconstruction quality of our method is comparable to that of existing methods, allowing for inversion at a resolution of 512 pixels and with 50 sampling steps within approximately 5 seconds, which is more than 30 times faster than null-text inversion. Reduction of the computation time by the proposed method further allows us to use a larger number of sampling steps in diffusion models to improve the reconstruction quality with a moderate increase in computation time.

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Submission Number: 9257