MatchEdit: Matched-State Prompt Directions for Inversion-Free Flow-Based Image Editing

Inversion-free flow-based image editing typically constructs an editing direction by computing the difference between source and target velocity predictions. However, these predictions are evaluated at different latent states and under different prompts. Consequently, this difference captures not only the desired prompt change, but also the change in latent state. Through a controlled state-prompt decomposition, we show that this state effect strengthens target semantics, but also increases source deviation and unintended changes outside the edit region. To address this issue, we introduce MatchEdit, an inversion-free image editing method based on matched-state prompt comparisons. By comparing the source and target prompts at the same latent state, MatchEdit removes the additive state effect from the base editing direction and yields a more spatially selective edit. We then adaptively enhance target semantics using a strictly bounded correction guided by the spatial response of the edit. MatchEdit achieves state-of-the-art source preservation on PIE-Bench while maintaining competitive semantic alignment, and further generalizes to long-prompt editing on DNA-Bench and sequential multi-turn editing on MSE-Bench.