详解生成式AI中的扩散模型原理与关键改进
An overview of diffusion models for generative artificial intelligence
- 从数学框架出发，系统讲解扩散模型的训练与生成机制
- 综述了改进型扩散模型、隐空间扩散等核心进展
- 适合想深入理解生成模型原理的研究者和开发者
本文提供了一个关于去噪扩散概率模型（DDPMs），也称扩散概率模型或扩散模型，在生成式人工智能中的数学严谨介绍。我们构建了DDPMs的基础数学框架，阐明了训练与生成过程的核心思想。在本综述中，还回顾了文献中选定的若干基础框架的扩展与改进，包括改进型DDPM、去噪扩散隐式模型、无分类器扩散引导模型以及潜空间扩散模型。
原文摘要 · Abstract (English)
This article provides a mathematically rigorous introduction to denoising diffusion probabilistic models (DDPMs), sometimes also referred to as diffusion probabilistic models or diffusion models, for generative artificial intelligence. We provide a detailed basic mathematical framework for DDPMs and explain the main ideas behind training and generation procedures. In this overview article we also review selected extensions and improvements of the basic framework from the literature such as improved DDPMs, denoising diffusion implicit models, classifier-free diffusion guidance models, and latent diffusion models.
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