An overview of diffusion models for generative artificial intelligence
This is an incremental overview article for researchers and practitioners in AI, summarizing existing diffusion model methods without introducing new results.
The article provides a mathematically rigorous introduction to denoising diffusion probabilistic models (DDPMs) for generative AI, explaining their framework, training, generation, and reviewing extensions like improved DDPMs and latent diffusion models.
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.