Review: Recent advances for the diffusion model

Abstract

As the generative model technology becomes more and more popular, more and more people have invested in the research of the current State-of-the-art (SOTA) generative model-diffusion model. This paper reviews all SOTA generation models using the diffusion model for text-to-image generation since the emergence of the diffusion model, including the denoising diffusion probabilistic model (DDPM), DALL·E model, imagen model, stable diffusion model, and diffusion transformer architecture (DiT) model. In the theoretical section, the basic principles behind the diffusion model are reviewed in detail in the way of mathematical calculation, including the training process of the model and the mathematical principles behind the sampling process. Moreover, this paper focuses on the technical characteristics of these models and various improvements made after model iteration, such as model structure optimization, more efficient and accurate training methods, and the application of other optimization techniques widely used in the field of deep learning to diffusion models. In the end, the technical route of the development of the diffusion model is summarized, and some predictions are made.