Scalable Minimal-Change Learning for Controllable Image Editing

Excerpt

arXiv:2610.06021v1 Announce Type: cross Abstract: Image editing should change only the attributes specified by an instruction while preserving everything else, yet current methods often make unintended changes. We treat this minimal-change principle as an optimization objective for instruction-based editing. Latent L1 regularization is a poor proxy for output locality in modern nonlinear generators and often requires supervision unavailable at scale. We instead optimize edit outcomes with reinfo