Bridging the Manifold Gap: Riemannian Residual Line Search for One-Step Image Editing

• URL: http://arxiv.org/abs/2606.24844v1
• Date: Tue, 23 Jun 2026 17:23:53 GMT
• Title: Bridging the Manifold Gap: Riemannian Residual Line Search for One-Step Image Editing
• Abstract summary: One-step diffusion editors are fast because they avoid inversion and iterative optimization.<n>A single transport update must be aggressive enough to realize the target prompt and conservative enough to preserve the source image.<n>Our proposed method builds a stronger edit by estimating the local time curvature of the prompt-delta field.
• Score: 9.026779521768024
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• Abstract: One-step diffusion editors are fast because they avoid inversion and iterative optimization, but a single transport update must be aggressive enough to realize the target prompt and conservative enough to preserve the source image--and no fixed update strength satisfies both demands across edit types. We treat this tension as a post-hoc candidate-selection problem on top of energy-field transport rather than as a new editing model. Our proposed method, Riemannian Residual Line Search, first builds a stronger edit by estimating the local time curvature of the prompt-delta field and projecting the corrected direction back onto the update norm of the original first-order energy-field transport estimation. It then forms a small residual path from the source image to this strong edit, retains the original first-order output as one candidate, and picks the final image by maximizing target-prompt CLIP alignment. On a 700-sample PIE-Bench++ evaluation across 10 edit type IDs, our method achieves state-of-the-art (SOTA) performance among current one-step update algorithms.

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