  Diffusion-based generative models are a design framework that allows
generating new images from processes analogous to those found in
non-equilibrium thermodynamics. These models model the reversal of a physical
diffusion process in which two miscible liquids of different colors
progressively mix until they form a homogeneous mixture. Diffusion models can
be applied to signals of a different nature, such as audio and image signals.
In the image case, a progressive pixel corruption process is carried out by
applying random noise, and a neural network is trained to revert each one of
the corruption steps. For the reconstruction process to be reversible, it is
necessary to carry out the corruption very progressively. If the training of
the neural network is successful, it will be possible to generate an image from
random noise by chaining a number of steps similar to those used for image
deconstruction at training time. In this article we present the theoretical
foundations on which this method is based as well as some of its applications.
This article is in Spanish to facilitate the arrival of this scientific
knowledge to the Spanish-speaking community.
