论文标题
生成建模的流匹配
Flow Matching for Generative Modeling
论文作者
论文摘要
我们引入了一种新的范式，用于建立在连续归一化流（CNF）上的生成建模，从而使我们能够以前所未有的规模训练CNF。具体而言，我们提出了流量匹配（FM）的概念，这是一种基于固定条件概率路径的回归矢量场训练CNF的模拟方法。流量匹配与高斯概率路径的一般家族兼容，用于在噪声和数据样本之间转换，该途径将现有的扩散路径作为特定实例所包含。有趣的是，我们发现使用扩散路径的FM为训练扩散模型提供了更健壮和稳定的替代方案。此外，流匹配为训练CNF和其他非扩散概率路径打开了大门。特别感兴趣的实例是使用最佳传输（OT）位移插值来定义条件概率路径。这些路径比扩散路径更有效，提供更快的训练和采样，并导致更好的概括。在可能性和样品质量方面，使用ImageNet上的流匹配的训练CNF会导致比基于替代扩散的方法的性能始终如一，并且允许使用现成的数值溶液求解器快速可靠地生成样品。
We introduce a new paradigm for generative modeling built on Continuous Normalizing Flows (CNFs), allowing us to train CNFs at unprecedented scale. Specifically, we present the notion of Flow Matching (FM), a simulation-free approach for training CNFs based on regressing vector fields of fixed conditional probability paths. Flow Matching is compatible with a general family of Gaussian probability paths for transforming between noise and data samples -- which subsumes existing diffusion paths as specific instances. Interestingly, we find that employing FM with diffusion paths results in a more robust and stable alternative for training diffusion models. Furthermore, Flow Matching opens the door to training CNFs with other, non-diffusion probability paths. An instance of particular interest is using Optimal Transport (OT) displacement interpolation to define the conditional probability paths. These paths are more efficient than diffusion paths, provide faster training and sampling, and result in better generalization. Training CNFs using Flow Matching on ImageNet leads to consistently better performance than alternative diffusion-based methods in terms of both likelihood and sample quality, and allows fast and reliable sample generation using off-the-shelf numerical ODE solvers.
