Reversible Inversion for Training-Free Exemplar-guided Image Editing
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| Main Authors: | , , , , , , , |
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| Format: | Preprint |
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2025
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| _version_ | 1866911712153698304 |
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| author | Li, Yuke Gao, Lianli Zhang, Ji Zeng, Pengpeng Xiang, Lichuan Wen, Hongkai Shen, Heng Tao Song, Jingkuan |
| author_facet | Li, Yuke Gao, Lianli Zhang, Ji Zeng, Pengpeng Xiang, Lichuan Wen, Hongkai Shen, Heng Tao Song, Jingkuan |
| contents | 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. |
| format | Preprint |
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arxiv_https___arxiv_org_abs_2512_01382 |
| institution | arXiv |
| publishDate | 2025 |
| record_format | arxiv |
| spellingShingle | Reversible Inversion for Training-Free Exemplar-guided Image Editing Li, Yuke Gao, Lianli Zhang, Ji Zeng, Pengpeng Xiang, Lichuan Wen, Hongkai Shen, Heng Tao Song, Jingkuan Computer Vision and Pattern Recognition 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. |
| title | Reversible Inversion for Training-Free Exemplar-guided Image Editing |
| topic | Computer Vision and Pattern Recognition |
| url | https://arxiv.org/abs/2512.01382 |