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{{Short description|Deep learning algorithm}}{{About|the technique in generative statistical modeling|3=Diffusion (disambiguation)}}{{Machine learning|Artificial neural network}}
In [[machine learning]], '''diffusion models''', also known as '''diffusion probabilistic models''' or '''score-based generative models''', are a class of [[latent variable model|latent variable]] [[generative model|generative]] models. A diffusion model consists of three major components: the forward process, the reverse process, and the sampling procedure.<ref name="chang23design">{{cite arXiv |last1=Chang |first1=Ziyi |last2=Koulieris |first2=George Alex |last3=Shum |first3=Hubert P. H. |title=On the Design Fundamentals of Diffusion Models: A Survey |date=2023 |eprint=2306.04542 |class=cs.LG}}</ref> 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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=== Latent diffusion model (LDM) ===
{{Main|Latent diffusion model}}
Since the diffusion model is a general method for modelling probability distributions, if one wants to model a distribution over images, one can first encode the images into a lower-dimensional space by an encoder, then use a diffusion model to model the distribution over encoded images. Then to generate an image, one can sample from the diffusion model, then use a decoder to decode it into an image.<ref name=":2">{{Cite arXiv|last1=Rombach |first1=Robin |last2=Blattmann |first2=Andreas |last3=Lorenz |first3=Dominik |last4=Esser |first4=Patrick |last5=Ommer |first5=Björn |date=13 April 2022 |title=High-Resolution Image Synthesis With Latent Diffusion Models |class=cs.CV |eprint=2112.10752 }}</ref>
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