Conditional Geometric Feedback Control for Training-Free Image Editing

Text-guided image editing requires realizing target changes while preserving non-target content in the source image. Existing work uses latent-space geometry to characterize generative representations and editing directions, but how to translate this geometry into preservation feedback adapted to current editing conditions remains less explored. We propose Conditional Geometric Feedback Control (CGFC), a training-free and inversion-free image editing method. We construct local Fisher geometry from the source–target condition-label posterior to characterize the sensitivity of condition discrimination to latent corrections. Under Gaussian linear interpolation and population-optimal conditional velocities, its nonzero sensitive axis aligns with the same-state conditional velocity difference, motivating the model-predicted difference as a practical proxy for the control axis. Using this shared axis, one CGFC update controls both the direction of the current prediction residual at fixed norm within dynamic spatial support and the accumulated source displacement orthogonal to the axis. On PIE-Bench with SD3, CGFC improves content preservation and round-trip consistency. These results support using condition-dependent latent geometry to design feedback control for stepwise image editing.