  Generative diffusion models showed high success in many fields with a
powerful theoretical background. They convert the data distribution to noise
and remove the noise back to obtain a similar distribution. Many existing
reviews focused on the specific application areas without concentrating on the
research about the algorithm. Unlike them we investigated the theoretical
developments of the generative diffusion models. These approaches mainly divide
into two: training-based and sampling-based. Awakening to this allowed us a
clear and understandable categorization for the researchers who will make new
developments in the future.
