Diffusion Models Made Easy

In the recent past, I have talked about GANs and VAEs as two important Generative Models that have found a lot of success and recognition. GANs work great for multiple applications however, they are difficult to train, and their output lack diversity due to several challenges such as mode collapse and vanishing gradients to name a few. Although VAEs have the most solid theoretical foundation however, the modelling of a good loss function is a challenge in VAEs which makes their output to be suboptimal. There is another set of techniques which originate from probabilistic likelihood estimation methods and take inspiration from physical phenomenon; it is called, Diffusion Models. The central idea behind Diffusion Models comes from the thermodynamics of gas molecules whereby the molecules diffuse from high density to low density areas.