This generative model scheme is set apart from MCMC sampling and ancestral sampling because it is based on nonequilibrium thermodynamics. This approach assumes that the probabilistic distribution we will sample from has some structure. This structure is gradually broken down by some diffusion that increases the entropy in the distribution thus making it unstructured.

To create the generative model, we then train a model that reverses the process and gradually returns structure to our distribution. Similarly to MCMC, we apply many iterations to produce our sample; however, in this case our model is iterating in order to transform the entropy-filled distribution to our target distribution.