提出无需调参的流模型图像逆向与编辑框架，实现高效精准修改。
UniEdit-Flow: Unleashing Inversion and Editing in the Era of Flow Models
- 基于预测-校正机制设计统一逆向方法Uni-Inv
- 引入延迟注入实现区域感知编辑，保留无关区域不变
- 适用于多种流模型，低资源下仍保持高性能
流匹配模型已成为扩散模型的有力替代，但现有针对扩散模型设计的逆向与编辑方法在流模型上往往无效或不适用。流模型的直线、非交叉轨迹虽对扩散方法构成挑战，但也为新方法提供了可能。本文提出一种基于预测-校正的流模型逆向与编辑框架。首先，提出Uni-Inv，一种高效且准确的逆向重建方法；在此基础上，将延迟注入概念拓展至流模型，提出Uni-Edit，一种区域感知、鲁棒的图像编辑方法。该方法无需微调、模型无关、高效且有效，可在多种编辑中保持无关区域的高度保真。在多个生成模型上的大量实验表明，Uni-Inv与Uni-Edit在各种设置下均表现优异，具备强泛化能力，即使在低成本条件下亦可稳定工作。
原文摘要 · Abstract (English)
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/
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