ER-Chord: Spatially Consistent One-Step Image Editing via Equal-Energy Reliability Redistribution

One-step generative models enable fast text-guided image editing, yet they often introduce unintended changes to regions that should remain unchanged. Temporal aggregation stabilizes editing fields across noise levels, but leaves their spatial allo- cation insensitive to local variations in reliability. Consequently, temporal stability alone does not ensure spatially consistent editing. To address this limitation, we propose ER-Chord, a training-free and inversion-free framework for reliability- guided spatial redistribution. Our key insight is that agreement between editing fields at neighboring noise levels provides a local reliability signal for allocating editing strength. We formulate this allocation as an equal-energy optimization that redistributes control energy according to local reliability while preserving the origi- nal editing directions and total control energy. This formulation admits a unique global solution that can be efficiently computed using existing field evaluations, requiring no additional denoiser calls. Experiments on two benchmarks, PIE-Bench and TEdBench, demonstrate improvements over state-of-the-art one-step editing baselines, achieving superior source preservation alongside consistent gains across multiple generative backbones.