Training-free image inversion for one-step diffusion models

• URL: http://arxiv.org/abs/2606.01380v1
• Date: Sun, 31 May 2026 18:10:23 GMT
• Title: Training-free image inversion for one-step diffusion models
• Abstract summary: We introduce a novel training-free inversion framework for one-step diffusion models, addressing key challenges in real image inversion and editing.<n>We first identify two critical factors hamperingreal-image inversion and editing: Initial Latent Editability and Caption Gap.<n>We propose two novel techniques: iterative noise alignment (iterNA), whichminimizes the distribution gap to align with the normal Gaussian distribution, and suffix learning (suffL), which enhances text-to-image caption alignment.
• Score: 31.38241916022748
• License: http://creativecommons.org/licenses/by-nc-nd/4.0/
• Abstract: In this work, we introduce a novel training-free inversion (TFinv) framework for one-step diffusion models,addressing key challenges in real image inversion and editing. We first identify two critical factors hamperingreal-image inversion and editing: (1) Initial Latent Editability, which is related to the distance between theinitial noise and the ideal Gaussian distribution, and (2) Caption Gap, which means the alignment betweentext captions and image representations. Both factors influence inversion efficiency and the editability ofone-step diffusion models. Then, we propose two novel techniques: iterative noise alignment (iterNA), whichminimizes the distribution gap to align with the normal Gaussian distribution, and suffix learning (suffL),which enhances text-to-image caption alignment by introducing learned suffix prompt tokens. These techniquesenable precise inversion of input images into their initial noise representations and facilitate image editing.Furthermore, we propose a mask-based editing technique for localized edits while preserving backgroundintegrity. Comprehensive experiments on the PIE-Bench dataset validate that our method TFinv not onlyachieves state-of-the-art performance in one-step diffusion editing, but also significantly outperforms existingmultistep approaches in efficiency. The code is available at https://github.com/tttao-uwu/TFinv.git.

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