Image Generation Models


Introduced by Ho et al. in Denoising Diffusion Probabilistic Models

Diffusion models generate samples by gradually removing noise from a signal, and their training objective can be expressed as a reweighted variational lower-bound (

Source: Denoising Diffusion Probabilistic Models


Paper Code Results Date Stars


Task Papers Share
Image Generation 119 15.80%
Denoising 90 11.95%
Video Generation 26 3.45%
Super-Resolution 24 3.19%
Text to 3D 21 2.79%
Text-to-Image Generation 19 2.52%
Language Modelling 16 2.12%
Large Language Model 14 1.86%
Semantic Segmentation 13 1.73%


Component Type
🤖 No Components Found You can add them if they exist; e.g. Mask R-CNN uses RoIAlign