关于基于流的扩散模型的精确编辑
On Exact Editing of Flow-Based Diffusion Models
AI总结：
CVC通过双视角速度转换机制改进基于流的扩散编辑，实现更稳定的潜在动态和更准确的图像转换。
摘要：
近期基于流的扩散编辑方法实现了源图像分布与目标图像分布之间的直接转换，而无需显式反向。然而，这些方法中的潜在轨迹往往表现出累积速度误差，导致语义不一致和结构保真度的丧失。我们提出了条件速度校正（CVC），一种原理性的框架，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速度在跨分布转换中的作用。该机制明确地将潜在演变分解为两个组成部分：一个保持结构一致的分支，与源轨迹保持一致，以及一个语义引导的分支，驱动向目标分布的受控偏离。条件速度场相对于真实潜在分布轨迹表现出绝对速度误差，这本质上引入了潜在空间中的潜在不稳定性。为了解决这一可量化的偏差并保持对真实流的保真度，我们对所得到的条件速度场应用后验一致更新。这种更新源自经验贝叶斯推断和 Tweedie 校正，确保了在时间上的数学基础误差补偿。我们的方法产生了稳定且可解释的潜在动态，实现了忠实的重建以及平滑的局部语义转换。全面的实验表明，CVC在各种任务中 consistently 达到了更高的保真度、更好的语义对齐和更可靠的编辑行为。
英文摘要：
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.
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