A review of state-of-the-art diffusion-based generative frameworks
Abstract
The goal of this work is to provide the reader of an understandable guide to methods underlying the state of the art generative models; In recent years, the quality of synthetic media (Image, Video, Audio, etc...) generation has reached a quality that has the potential to bring about a fundamental change in our society. Modern generative frameworks mostly rely on Diffusion models that represent the cutting-edge methodology to generate new samples from the underlying unknown distribution of some set of observations. Diffusion models applied to high-dimensional data such as images, videos or audio suffers from scaling difficulties; Latent diffusion methods apply diffusion techniques to compressed latent representations of high-dimensional data employing some autoencoder architecture, taming the dimensional scaling problem and enabling diffusion models to focus on perceptualy relevant details. Diffusion models can also be conditioned to generate data depending on some user specified input (such as text) via conditional generation.Moreover, most recent methods to efficiently implement all of those ideas will be presented. This work is aimed at readers with a basic understanding of machine learning and probability theory who seek to have a clearer idea of how modern generative models work and what enables them, hoping to attract new practitioners in further investigation and experimentation in this fascinating field.
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