Diffusion and flow-matching models are key techniques for the current generative AI boom. However, their fundamental limitation is that they require multiple steps to generate samples. MeanFlow is a recent one-step end-to-end generative model that achieves compelling results without pretraining, distillation, or curriculum training. In this video, we will discuss the fundamental concepts of Flow Matching and Mean Flow. 00:00 Introduction 00:24 Flow Matching 01:18 Conditional Flow Matching 02:46 Iterative sampling 04:06 MeanFlow 09:09 Results Reference: Zhengyang Geng, Mingyang Deng, Xingjian Bai, J. Zico Kolter, and Kaiming He Mean Flows for One-step Generative Modeling, arXiv 2025 Video made with Manim: