Training-free image inversion for one-step diffusion models

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 hampering real-image inversion and editing: (1) Initial Latent Editability, which is related to the distance between the initial noise and the ideal Gaussian distribution, and (2) Caption Gap, which means the alignment between text captions and image representations. Both factors influence inversion efficiency and the editability of one-step diffusion models. Then, we propose two novel techniques: iterative noise alignment (iterNA), which minimizes 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 techniques enable 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 background integrity. Comprehensive experiments on the PIE-Bench dataset validate that our method TFinv not only achieves state-of-the-art performance in one-step diffusion editing, but also significantly outperforms existing multistep approaches in efficiency.