StableCtrl: One-Step Stable Image Editing via Optimal Control

Recent one-step text-to-image models enable real-time image editing by completing semantic transformations in a single denoising step. However, existing training-free editors often become unstable in this setting. Source-target residuals can act as high-energy control signals, leading to structural distortion and background inconsistency. To address this issue, we introduce StableCtrl, a model-agnostic, training-free, and inversion-free framework for stable one-step image editing. StableCtrl casts one-step editing as a regularized optimal control problem and treats source-target residuals as noisy control cues instead of directly applying them as editing directions. This formulation yields an elliptic control equation with an explicit spectral solution. The solution admits efficient one-step optimization without iterative refinement. It regularizes the control field while retaining the dominant semantic direction. The resulting operator constrains control energy, enforces spatial smoothness, and preserves temporal reference consistency. It therefore balances semantic editing with structural preservation while suppressing unstable high-frequency perturbations through spectral low-pass filtering. StableCtrl consequently produces stable control fields that preserve scene geometry and background consistency without additional model training or latent inversion. Extensive experiments on PIE-Bench show that StableCtrl consistently improves background preservation and editing robustness.