  Diffusion-based generative models are machine learning models that use
diffusion processes to learn the probability distribution of high-dimensional
data. In recent years, they have become extremely successful in generating
multimedia content. However, it is still unknown if such models can be used to
generate high-quality datasets of physical models. In this work, we use a
Landau-Ginzburg-like diffusion model to infer the distribution of a $2D$
bond-diluted Ising model. Our approach is simple and effective, and we show
that the generated samples reproduce correctly the statistical and critical
properties of the physical model.
