Diffusion models are a class of generative models used in machine learning and artificial intelligence for modeling complex data distributions and generating realistic samples from high-dimensional data spaces. They leverage techniques such as flow-based modeling and invertible neural networks for learning data representations and probability densities from observed data. Readers can explore diffusion models' architectures, training algorithms, and applications for generating diverse and high-quality samples, enhancing generative modeling tasks such as image synthesis, text generation, and data imputation.