Reversible Inversion for Training-Free Exemplar-guided Image Editing

• URL: http://arxiv.org/abs/2512.01382v1
• Date: Mon, 01 Dec 2025 07:56:06 GMT
• Title: Reversible Inversion for Training-Free Exemplar-guided Image Editing
• Authors: Yuke Li, Lianli Gao, Ji Zhang, Pengpeng Zeng, Lichuan Xiang, Hongkai Wen, Heng Tao Shen, Jingkuan Song,
• Abstract summary: Existing approaches often require large-scale pre-training to learn relationships between the source and reference images.<n>Standard inversion is sub-optimal for EIE, leading to poor quality and inefficiency.<n>We introduce textbfReversible Inversion (ReInversion) for effective and efficient EIE.
• Score: 127.97756928865032
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• Abstract: 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.

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