Wavelet-Guided Semantic Signal Compensation for Inversion-Free Image Editing

Text-guided image editing aims to modify visual content ac-cording to a target prompt while preserving the background. Recentinversion-free image editing frameworks such as FlowEdit have demon-strated strong editing capability without requiring inversion. Empirically,FlowEdit can achieve substantial semantic changes under appropriatehyperparameter settings. However, we observe that under certain globalattribute shifts, the editing trajectory may not effectively move awayfrom the source distribution in the early timesteps. Our analysis sug-gests that in the high-noise regime, the dominant manifold-seeking flowtoward the data manifold can reduce the influence of the text-conditioneddirection, leading to limited global modification while background struc-tures remain only moderately preserved. Inspired by this observation,we propose an inversion-free, frequency-aware semantic compensationstrategy that strengthens the effective signal in the early stage of gener-ation, while maintaining structural consistency in the background. Theproposed method improves global editing capacity without sacrificingbackground fidelity.