Straightening the probability flow of the continuous-time generative models, such as diffusion models or flow-based models, is the key to fast sampling through the numerical solvers, existing methods learn a linear path by directly generating the probability path the joint distribution between the noise and data distribution. One key reason for the slow sampling speed of the ODE-based solvers that simulate these generative models is the global truncation error of the ODE solver, caused by the high curvature of the ODE trajectory, which explodes the truncation error of the numerical solvers in the low-NFE regime. To address this challenge, We propose a novel method called SeqRF, a learning technique that straightens the probability flow to reduce the global truncation error and hence enable acceleration of sampling and improve the synthesis quality. In both theoretical and empirical studies, we first observe the straightening property of our SeqRF. Through empirical evaluations via SeqRF over flow-based generative models, We achieve surpassing results on CIFAR-10, CelebA-$64 \times 64$, and LSUN-Church datasets.

SeqRF是一种学习技术，通过改善概率流的直线性来减少全局截断误差，加速采样并提高合成质量。在理论和实证研究中，我们观察到了SeqRF的直线性属性，并通过SeqRF在基于流模型的生成模型上进行的实证评估，在CIFAR-10、CelebA-$64 	imes 64$和LSUN-Church数据集上取得了优异的结果。

生成模型的顺序流匹配