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:this https URL
View on arXiv@article{jiao2025_2504.13109,
title={ UniEdit-Flow: Unleashing Inversion and Editing in the Era of Flow Models },
author={ Guanlong Jiao and Biqing Huang and Kuan-Chieh Wang and Renjie Liao },
journal={arXiv preprint arXiv:2504.13109},
year={ 2025 }
}