Recent methods in flow-based diffusion editing have enabled direct transformations between source and target image distribution without explicit inversion. However, the latent trajectories in these methods often exhibit accumulated velocity errors, leading to semantic inconsistency and loss of structural fidelity. We propose Conditioned Velocity Correction (CVC), a principled framework that reformulates flow-based editing as a distribution transformation problem driven by a known source prior. CVC rethinks the role of velocity in inter-distribution transformation by introducing a dual-perspective velocity conversion mechanism. This mechanism explicitly decomposes the latent evolution into two components: a structure-preserving branch that remains consistent with the source trajectory, and a semantically-guided branch that drives a controlled deviation toward the target distribution. The conditional velocity field exhibits an absolute velocity error relative to the true underlying distribution trajectory, which inherently introduces potential instability and trajectory drift in the latent space. To address this quantifiable deviation and maintain fidelity to the true flow, we apply a posterior-consistent update to the resulting conditional velocity field. This update is derived from Empirical Bayes Inference and Tweedie correction, which ensures a mathematically grounded error compensation over time. Our method yields stable and interpretable latent dynamics, achieving faithful reconstruction alongside smooth local semantic conversion. Comprehensive experiments demonstrate that CVC consistently achieves superior fidelity, better semantic alignment, and more reliable editing behavior across diverse tasks.

翻译：当前基于流的扩散编辑方法能够实现源图像与目标图像分布之间的直接转换，而无需显式逆运算。然而，这些方法中的潜在轨迹常表现出累积的速度误差，导致语义不一致与结构保真度损失。本文提出条件化速度校正（CVC）框架，该框架将基于流的编辑重新表述为由已知源先验驱动的分布转换问题。CVC通过引入双视角速度转换机制，重新思考了速度在分布间转换中的作用。该机制将潜在演化显式分解为两个分支：保持与源轨迹一致性的结构保留分支，以及驱动向目标分布受控偏移的语义引导分支。条件速度场相对于真实底层分布轨迹存在绝对速度误差，这本质上在潜在空间中引入了潜在的不稳定性与轨迹漂移。为应对这种可量化的偏差并保持对真实流的保真度，我们对所得条件速度场应用后验一致性更新。该更新源于经验贝叶斯推断与Tweedie校正，确保了随时间推移在数学基础上实现误差补偿。我们的方法产生了稳定且可解释的潜在动态，在实现平滑局部语义转换的同时保持了忠实重建。综合实验表明，CVC在不同任务中始终能实现更优的保真度、更好的语义对齐以及更可靠的编辑行为。