Diffusion Models: An Accessible Introduction to the State of the Art in Data Generation
Abstract
Generative models have received significant attention in the artificial intelligence community and are expanding their reach beyond the technical sphere.Among the most recent and promising approaches are Diffusion Models, which adopt an innovative generative modeling strategy: they progressively add noise of varying intensities to the data-a technique inspired by stochastic dynamics and known as diffusion-and then learn to reverse these perturbations.Starting from a pure noise sample, these models can generate new images, text, videos, and various other types of data with remarkably high quality, outperforming previous state-of-the-art methods in many of these tasks.This minicourse provides a formal and accessible introduction to the foundations of diffusion models, covering their underlying probabilistic principles-such as Kullback-Leibler divergence, Langevin dynamics, and score matching-as well as visual intuitions to aid comprehension.Then, it presents an overview of the current state of the art in the field, including conditioning techniques and latent diffusion models.Finally, the course discusses the research opportunities and applications that this rapidly evolving area has to offer.
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