Diffusion models and time reversal
I recently spent some time reading about the algorithms behind Stable Diffusion and similar image generation models. They have been linked with an interesting 40-years-old result on diffusion processes1. In short, this result states that there exists an explicit path from an initial probability distribution $p_0$ to a random noise (a normal distribution), and that this path can be reversed. One application of this concept is sampling : we can draw a sample from a random noise and use the backward diffusion to obtain a sample from $p_0$....