无需反演的图像编辑，让修改更稳定、可控且可逆。
FlowAlign: Trajectory-Regularized, Inversion-Free Flow-based Image Editing
- 用最优控制优化编辑轨迹，提升稳定性与一致性。
- 终端点正则化平衡语义对齐与源图结构保持。
- 支持反向编辑，适合需要精确控制的生成任务。
近期无反演的基于流的图像编辑方法（如 FlowEdit）利用预训练的噪声到图像流模型（如 Stable Diffusion 3），通过求解常微分方程（ODE）实现文本驱动的图像修改。尽管无需精确潜变量反演是其核心优势，但常导致编辑轨迹不稳定、源图一致性差。为此，我们提出 { exttt{FlowAlign}}，一种新型无反演的流式图像编辑框架，采用最优控制策略进行轨迹正则化。具体地，我们在终点引入源图相似性作为正则项，促进编辑过程中的平滑与一致轨迹。值得注意的是，该终点正则化能显式平衡编辑提示的语义对齐与源图结构一致性。此外，FlowAlign 可自然支持反向编辑，仅需反转 ODE 轨迹，凸显变换的可逆性与一致性。大量实验表明，该方法在源图保留和编辑可控性方面均优于现有方法。
原文摘要 · Abstract (English)
Recent inversion-free, flow-based image editing methods such as FlowEdit leverages a pre-trained noise-to-image flow model such as Stable Diffusion 3, enabling text-driven manipulation by solving an ordinary differential equation (ODE). While the lack of exact latent inversion is a core advantage of these methods, it often results in unstable editing trajectories and poor source consistency. To address this limitation, we propose {\em FlowAlign}, a novel inversion-free flow-based framework for consistent image editing with optimal control-based trajectory control. Specifically, FlowAlign introduces source similarity at the terminal point as a regularization term to promote smoother and more consistent trajectories during the editing process. Notably, our terminal point regularization is shown to explicitly balance semantic alignment with the edit prompt and structural consistency with the source image along the trajectory. Furthermore, FlowAlign naturally supports reverse editing by simply reversing the ODE trajectory, highliting the reversible and consistent nature of the transformation. Extensive experiments demonstrate that FlowAlign outperforms existing methods in both source preservation and editing controllability.
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