FlowFocus: Focused Trajectory Regularization for Inversion-Free Image Editing

Inversion-free image editing offers an efficient alternative to inversion-based approaches, yet achieving substantial semantic changes while preserving source content unrelated to the desired edit remains challenging. Existing trajectory-regularized methods improve source consistency, but typically apply preservation constraints uniformly across the latent representation, overlooking that different spatial regions may require different degrees of modification. In this work, we propose FlowFocus, an inversion-free image editing framework with focused trajectory regularization. Our key insight is that the editing velocity can be decomposed into a counterfactual semantic response induced by changing the textual condition and a state-induced response arising from latent variation. Their relative strengths provide a model-intrinsic and time-varying signal that indicates where semantic modification is required. Based on this, FlowFocus constructs a continuous counterfactual edit field and incorporates it into a trajectory-level optimal-control objective, replacing global scalar regularization with spatially and temporally adaptive control. This formulation strengthens source preservation where semantic changes are unnecessary while retaining sufficient flexibility in regions relevant to the target edit, without relying on external masks, auxiliary perception models, or additional supervision. Experiments on the PIE benchmark demonstrate consistent improvements in source preservation and editing quality over state-of-the-art methods. The code will be released after publication.