The Annotated Diffusion Model

This post provides a detailed, step-by-step implementation of Denoising Diffusion Probabilistic Models (DDPMs) in PyTorch. It covers the mathematical formulation, defines the forward diffusion process, implements a PyTorch Dataset and DataLoader, outlines the neural network architecture (including ResNet blocks, attention modules, and a conditional U-Net), and demonstrates both training and sampling (inference) procedures. The post aims to demystify diffusion models by offering a practical, annotated code walkthrough.