TurboEdit: Text-Based Image Editing Using Few-Step Diffusion Models

• URL: http://arxiv.org/abs/2408.00735v1
• Date: Thu, 1 Aug 2024 17:27:28 GMT
• Title: TurboEdit: Text-Based Image Editing Using Few-Step Diffusion Models
• Authors: Gilad Deutch, Rinon Gal, Daniel Garibi, Or Patashnik, Daniel Cohen-Or,
• Abstract summary: We focus on a popular line of text-based editing frameworks - the edit-friendly'' DDPM-noise inversion approach. We analyze its application to fast sampling methods and categorize its failures into two classes: the appearance of visual artifacts, and insufficient editing strength. We propose a pseudo-guidance approach that efficiently increases the magnitude of edits without introducing new artifacts.
• Score: 53.757752110493215
• License: http://creativecommons.org/licenses/by-sa/4.0/
• Abstract: Diffusion models have opened the path to a wide range of text-based image editing frameworks. However, these typically build on the multi-step nature of the diffusion backwards process, and adapting them to distilled, fast-sampling methods has proven surprisingly challenging. Here, we focus on a popular line of text-based editing frameworks - the ``edit-friendly'' DDPM-noise inversion approach. We analyze its application to fast sampling methods and categorize its failures into two classes: the appearance of visual artifacts, and insufficient editing strength. We trace the artifacts to mismatched noise statistics between inverted noises and the expected noise schedule, and suggest a shifted noise schedule which corrects for this offset. To increase editing strength, we propose a pseudo-guidance approach that efficiently increases the magnitude of edits without introducing new artifacts. All in all, our method enables text-based image editing with as few as three diffusion steps, while providing novel insights into the mechanisms behind popular text-based editing approaches.

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