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Denoising Diffusion Models: A Generative Learning Big Bang

Jiaming Song · Chenlin Meng · Arash Vahdat

West 202 - 204


Diffusion models have been widely adopted in various computer vision applications and are becoming a dominating class of generative models. In the year 2022 alone, diffusion models have been applied to many large-scale text-to-image foundation models, such as DALL-E 2, Imagen, Stable Diffusion and eDiff-I. These developments have also driven novel computer vision applications, such as solving inverse problems, semantic image editing, few-shot textual inversion, prompt-to-prompt editing, and lifting 2d models for 3d generation. This popularity is also reflected in the diffusion models tutorial in CVPR 2022, which has accumulated nearly 60,000 views on YouTube over 8 months. The primary goal of the CVPR 2023 tutorial on diffusion models is to make diffusion models more accessible to a wider computer vision audience and introduce recent developments in diffusion models. We will present successful practices on training and sampling from diffusion models and discuss novel applications that are enabled by diffusion models in the computer vision domain. These discussions will also heavily lean on recent research developments that are released in 2022 and 2023. We hope that this year’s tutorial on diffusion models will attract more computer vision practitioners interested in this topic to make further progress in this exciting area.

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