Recently, however, with the success of OpenAI's DallE-2 and Google's Imagen , Diffusion Models have quickly risen to achieve state-ofthe-art performance for many types of generative modeling. Diffusion Models are inspired by non-equilibrium thermodynamics and operate by gradually destroying input training data by adding Gaussian noise (forward process) only to learn the transformations necessary to reconstruct each sample (backward process). By continually repeating this process, and incrementally adding more noise in each iteration, fully trained Diffusion Models are able to completely reconstruct a data sample from nothing more than noise. Diffusion Models take longer to train than GANs but do not suffer from many of the same deficiencies that GANs do such as mode collapse and extreme instability.