Exemplar-guided Image Editing (EIE) aims to modify a source image according to a visual reference. Existing approaches often require large-scale pre-training to learn relationships between the source and reference images, incurring high computational costs. As a training-free alternative, inversion techniques can be used to map the source image into a latent space for manipulation. However, our empirical study reveals that standard inversion is sub-optimal for EIE, leading to poor quality and inefficiency. To tackle this challenge, we introduce \textbf{Reversible Inversion ({ReInversion})} for effective and efficient EIE. Specifically, ReInversion operates as a two-stage denoising process, which is first conditioned on the source image and subsequently on the reference. Besides, we introduce a Mask-Guided Selective Denoising (MSD) strategy to constrain edits to target regions, preserving the structural consistency of the background. Both qualitative and quantitative comparisons demonstrate that our ReInversion method achieves state-of-the-art EIE performance with the lowest computational overhead.
@article{arxiv.2512.01382,
title = {Reversible Inversion for Training-Free Exemplar-guided Image Editing},
author = {Yuke Li and Lianli Gao and Ji Zhang and Pengpeng Zeng and Lichuan Xiang and Hongkai Wen and Heng Tao Shen and Jingkuan Song},
journal= {arXiv preprint arXiv:2512.01382},
year = {2026}
}