V-HOLD: Stabilizing Flow Trajectories to Rethink the Edit–Preservation Trade-off

Recent diffusion- and flow-based image editing methods en-able high-quality text-guided image editing. However, improving editstrength often degrades identity and background preservation, a phe-nomenon known as the edit—preservation trade-off. This trade-off iswidely observed and commonly regarded as an inherent limitation ofgenerative editing. In this work, we revisit this assumption and iden-tify trajectory instability as a previously overlooked factor behind theedit–preservation trade-off. Through empirical analysis across multipleediting methods and backbones, we find that trajectory instability isclosely associated with degraded semantic preservation while showinglittle association with edit alignment. We characterize instability by us-ing directional consistency and update variability of latent update ve-locities during integration, revealing that unstable integration dynamicscontribute substantially to the edit–preservation trade-off. Motivated bythis insight, we propose V-HOLD, a simple training-free method thatstabilizes editing trajectories by maintaining a target-oriented updatedirection across multiple integration steps instead of recomputing veloc-ities at every step. Experiments on PIE-Bench and the FlowEdit-Datashow that our method consistently improves semantic preservation whilemaintaining competitive edit alignment across multiple backbones. Inaddition, V-HOLD reduces the number of velocity-field evaluations dur-ing editing, improving computational efficiency. These results highlighttrajectory stability as a key factor for improving semantic preservationwithout sacrificing edit alignment.