详解扩散与流模型原理，手把手教你构建图像视频生成器。
An Introduction to Flow Matching and Diffusion Models
- 从微分方程出发推导生成模型核心算法
- 提供图像与视频生成的完整训练与架构指南
- 适合想深入理解生成模型原理的研究者
扩散与流模型已成为图像、视频、形状、分子、音乐等多种数据模态生成任务的前沿方法。本教程从基础原理出发，系统介绍生成模型所需的常微分方程与随机微分方程数学背景，推导流匹配与去噪扩散模型的核心算法。随后提供图像与视频生成器的逐步构建指南，涵盖训练方法、引导策略及网络结构设计。本课程适合希望深入掌握生成式AI理论与实践的机器学习研究者。
原文摘要 · Abstract (English)
Diffusion and flow-based models have become the state of the art for generative AI across a wide range of data modalities, including images, videos, shapes, molecules, music, and more. This tutorial provides a self-contained introduction to diffusion and flow-based generative models from first principles. We systematically develop the necessary mathematical background in ordinary and stochastic differential equations and derive the core algorithms of flow matching and denoising diffusion models. We then provide a step-by-step guide to building image and video generators, including training methods, guidance, and architectural design. This course is ideal for machine learning researchers who want to develop a principled understanding of the theory and practice of generative AI.
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