GRAPE: Rethinking the Velocity Field and Integration for Inversion-Free Flow Editing

Recent inversion-free image editing methods based on pretrained flow models directly modify source images using differences between source- and target-conditional velocities. However, this process often introduces changes beyond the requested attributes. We revisit these editing dynamics in terms of how the editing velocity is constructed and integrated across noise levels. Since the source- and target-conditional velocities are evaluated at distinct noisy states, their difference mixes the response to the condition change with a denoising response to the state difference, which can affect content that should be preserved. Based on this observation, we construct a gradient-aligned field that evaluates both conditions at a shared noisy observation of the current edit. Under population-optimal velocities, this field is proportional to the target-to-source log-density-ratio gradient. We further find that standard noise-schedule integration concentrates editing progress in late, low-noise steps, revealing a mismatch between noise-schedule progression and clean-state progress. This motivates progress-adaptive integration, which measures and compensates for the mismatch between noise-schedule progression and clean-state progress. We introduce GRAPE (Gradient-Aligned, Progress-adaptive Editing), a training-free and inversion-free method that combines gradient-aligned field construction with progress-adaptive integration. Across three flow backbones on PIE-Bench, GRAPE consistently improves the trade-off between target alignment and source preservation and retains this advantage under substantially reduced inference budgets.