Classifier-free guidance is a key component for enhancing the performance of conditional generative models across diverse tasks. While it has previously demonstrated remarkable improvements for the sample quality, it has only been exclusively employed for diffusion models. In this paper, we integrate classifier-free guidance into Flow Matching (FM) models, an alternative simulation-free approach that trains Continuous Normalizing Flows (CNFs) based on regressing vector fields. We explore the usage of \emph{Guided Flows} for a variety of downstream applications. We show that Guided Flows significantly improves the sample quality in conditional image generation and zero-shot text-to-speech synthesis, boasting state-of-the-art performance. Notably, we are the first to apply flow models for plan generation in the offline reinforcement learning setting, showcasing a 10x speedup in computation compared to diffusion models while maintaining comparable performance.

翻译：分类器无指引是提升条件生成模型在不同任务中性能的关键组件。尽管此前已在样本质量方面展现出显著改进，但其仅被专门用于扩散模型。本文我们将分类器无指引整合至流程匹配（Flow Matching, FM）模型中——这是一种基于向量场回归训练连续标准化流程（Continuous Normalizing Flows, CNFs）的非模拟替代方法。我们探索了“导向流程”（Guided Flows）在多种下游应用场景中的使用。实验表明，导向流程在条件图像生成和零样本文本转语音合成中显著提升了样本质量，达到了当前最优性能。值得注意的是，我们首次将流程模型应用于离线强化学习场景下的规划生成，在保持与扩散模型相当性能的同时，实现了10倍的计算加速。