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A generative model that learns to create data by reversing a gradual noising process — the technology behind most modern AI image generation.
Diffusion models, framed by Sohl-Dickstein et al. (2015) and made practical for high-quality image synthesis by Ho et al. (2020), work in two phases: a forward process that progressively adds noise to data, and a learned reverse process that denoises random noise back into a coherent sample.
They underpin today's leading text-to-image systems and are increasingly used for audio and video generation.
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