UniEdit-Flow: Unleashing Inversion and Editing in the Era of Flow Models
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arXiv
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| Main Authors: | , , , |
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| Format: | Preprint |
| Published: |
2025
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| _version_ | 1866910237506666496 |
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| author | Jiao, Guanlong Huang, Biqing Wang, Kuan-Chieh Liao, Renjie |
| author_facet | Jiao, Guanlong Huang, Biqing Wang, Kuan-Chieh Liao, Renjie |
| contents | Flow matching models have emerged as a strong alternative to diffusion models, but existing inversion and editing methods designed for diffusion are often ineffective or inapplicable to them. The straight-line, non-crossing trajectories of flow models pose challenges for diffusion-based approaches but also open avenues for novel solutions. In this paper, we introduce a predictor-corrector-based framework for inversion and editing in flow models. First, we propose Uni-Inv, an effective inversion method designed for accurate reconstruction. Building on this, we extend the concept of delayed injection to flow models and introduce Uni-Edit, a region-aware, robust image editing approach. Our methodology is tuning-free, model-agnostic, efficient, and effective, enabling diverse edits while ensuring strong preservation of edit-irrelevant regions. Extensive experiments across various generative models demonstrate the superiority and generalizability of Uni-Inv and Uni-Edit, even under low-cost settings. Project page: https://uniedit-flow.github.io/ |
| format | Preprint |
| id |
arxiv_https___arxiv_org_abs_2504_13109 |
| institution | arXiv |
| publishDate | 2025 |
| record_format | arxiv |
| spellingShingle | UniEdit-Flow: Unleashing Inversion and Editing in the Era of Flow Models Jiao, Guanlong Huang, Biqing Wang, Kuan-Chieh Liao, Renjie Computer Vision and Pattern Recognition Flow matching models have emerged as a strong alternative to diffusion models, but existing inversion and editing methods designed for diffusion are often ineffective or inapplicable to them. The straight-line, non-crossing trajectories of flow models pose challenges for diffusion-based approaches but also open avenues for novel solutions. In this paper, we introduce a predictor-corrector-based framework for inversion and editing in flow models. First, we propose Uni-Inv, an effective inversion method designed for accurate reconstruction. Building on this, we extend the concept of delayed injection to flow models and introduce Uni-Edit, a region-aware, robust image editing approach. Our methodology is tuning-free, model-agnostic, efficient, and effective, enabling diverse edits while ensuring strong preservation of edit-irrelevant regions. Extensive experiments across various generative models demonstrate the superiority and generalizability of Uni-Inv and Uni-Edit, even under low-cost settings. Project page: https://uniedit-flow.github.io/ |
| title | UniEdit-Flow: Unleashing Inversion and Editing in the Era of Flow Models |
| topic | Computer Vision and Pattern Recognition |
| url | https://arxiv.org/abs/2504.13109 |