Diffusion Models: Learning to Reverse Destruction
A first-principles derivation of how diffusion models work. Why the forward process has a closed form, why the reverse posterior is tractable only when conditioned on the clean image, how a page of variational bound collapses into one squared error, why the noise predictor is secretly a score function, and how that becomes Stable Diffusion.