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.

中文翻译：

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回顾：扩散模型的最新进展

随着生成模型技术越来越流行，越来越多的人投入到当前State-of-the-art（SOTA）生成模型——扩散模型的研究中。本文回顾了自扩散模型出现以来所有使用扩散模型进行文本到图像生成的SOTA生成模型，包括去噪扩散概率模型（DDPM）、DALL·E模型、imagen模型、稳定扩散模型和扩散变压器架构（DiT）模型。理论部分以数学计算的方式详细回顾了扩散模型背后的基本原理，包括模型的训练过程和采样过程背后的数学原理。而且，本文重点介绍了这些模型的技术特点以及模型迭代后所做的各种改进，例如模型结构优化、更高效准确的训练方法以及深度学习领域广泛使用的其他优化技术在扩散中的应用楷模。最后总结了扩散模型发展的技术路线，并做出了一些预测。