Target-aware Image Editing via Cycle-consistent Constraints

Recent pre-trained text-to-image flow models have enabledremarkable progress in text-based image editing. Mainstream approachesadopt a corruption-then-restoration paradigm, where the source im-age is first corrupted into an editable “intermediate state” and then re-stored to the target image under the prompt guidance. However, currentmethods construct this intermediate state in a target-agnostic manner,i.e., they mainly focus on realizing source image reconstruction whileneglecting the semantic gaps towards the specific editing target. Thisdesign inherently results in limited editability or inconsistency when thedesired modifications substantially deviate from the source. In this pa-per, we argue that the intermediate state should be target-aware, i.e.,selectively corrupting editing-relevant contents while preserving editing-irrelevant ones. Thus, we propose FlowCycle, an inversion-free and flow-based editing framework that parameterizes corruption with learnablenoises and optimizes them through a cycle-consistent process. By itera-tively editing the source to the target and recovering back to the sourcewith dual consistency constraints, FlowCycle learns to produce a target-aware intermediate state, enabling faithful modifications while preservingsource consistency. For efficiency, we further accelerate the optimizationby dynamically adjusting the sampling steps. Extensive ablations demon-strated that FlowCycle achieves superior editing performance.