- https://sander.ai/2022/01/31/diffusion.html is about the link between diffusion models and denoising autoencoders, IMO the easiest to understand out of all interpretations; - https://sander.ai/2023/07/20/perspectives.html covers a slew of different perspectives on diffusion models (including the "autoencoder" one).
In a nutshell, diffusion models break up the difficult task of generating natural signals (such as images or sound) into many smaller partial denoising tasks. This is done by defining a corruption process that gradually adds noise to an input until all of the signal is drowned out (this is the "diffusion"), and then learning how to invert that process step-by-step.
This is not dissimilar to how modern language models work: they break up the task of generating text into a series of easier next-word-prediction tasks. In both cases, the model only solves a small part of the problem at a time, and you apply it repeatedly to generate a signal.