Denoising Diffusion Probabilistic Models

Why this one

A lot of image generation talk sounds mystical because people describe the final samples, not the training problem. The useful idea here is simpler: learn to undo small steps of noise, then chain that denoiser backward until random noise becomes data. What people often get wrong is treating diffusion as just another GAN replacement. The paper is really about turning generation into a supervised prediction problem with a carefully chosen corruption process, which makes training stable and inspectable. If you are building with modern image, video or audio models, this gives you the mental model behind schedulers, sampling steps, guidance and why inference cost grows with every denoising step. It is worth your evening because many later tricks are just changes to this loop.