学习直线流：面向高效生成的变分流匹配
Learning Straight Flows: Variational Flow Matching for Efficient Generation
把变分潜变量作为「生成全局概览」注入流匹配，显式约束轨迹笔直，为视觉生成模型提供可复用的一步采样训练范式，可直接替换扩散/流匹配的采样器设计。
原文依据 we propose Straight Variational Flow Matching (S-VFM), which integrates a variational latent code representing the "generation overview" into the Flow Matching framework
摘要译文
由模型翻译 · 术语统一 · 请对照原文核对关键结论流匹配由于依赖学习到的弯曲轨迹，在实现一步生成方面能力有限。已有研究试图通过修改耦合分布以避免插值轨迹相交，或引入一致性与平均速度建模来促进直线轨迹学习，从而缓解这一局限。然而，这些方法往往存在离散近似误差、训练不稳定和收敛困难等问题。为解决上述问题，本文提出直线变分流匹配（S-VFM），将表示「生成全局概览」的变分潜变量整合进流匹配框架。S-VFM 显式地强制轨迹的直线性，理想情况下可产生线性的生成路径。所提方法在三个具有挑战性的基准上取得了有竞争力的性能，并且与现有方法相比，在训练和推理效率上均展现出优势。
英文原文摘要 981 字符
Flow Matching has limited ability in achieving one-step generation due to its reliance on learned curved trajectories. Previous studies have attempted to address this limitation by either modifying the coupling distribution to prevent interpolant intersections or introducing consistency and mean-velocity modeling to promote straight trajectory learning. However, these approaches often suffer from discrete approximation errors, training instability, and convergence difficulties. To tackle these issues, in the present work, we propose Straight Variational Flow Matching (S-VFM), which integrates a variational latent code representing the "generation overview" into the Flow Matching framework. S-VFM explicitly enforces trajectory straightness, ideally producing linear generation paths. The proposed method achieves competitive performance across three challenge benchmarks and demonstrates advantages in both training and inference efficiency compared with existing methods.