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{{Short description|Deep learning algorithm}}{{About|the technique in generative statistical modeling|3=Diffusion (disambiguation)}}{{pp-pc|small=yes}}
{{Machine learning|Artificial neural network}} In [[machine learning]], '''diffusion models''', also known as '''diffusion-based generative models''' or '''score-based generative models''', are a class of [[latent variable model|latent variable]] [[generative model|generative]] models. A diffusion model consists of two major components: the forward diffusion process, and the reverse sampling process. The goal of diffusion models is to learn a [[diffusion process]] for a given dataset, such that the process can generate new elements that are distributed similarly as the original dataset. A diffusion model models data as generated by a diffusion process, whereby a new datum performs a [[Wiener process|random walk with drift]] through the space of all possible data.<ref name="song"/> A trained diffusion model can be sampled in many ways, with different efficiency and quality.
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