The Annotated Diffusion Model
Hugging Face Blog
Researchers have published an annotated guide explaining how denoising diffusion probabilistic models (DDPMs) work, breaking down the mathematical framework and PyTorch implementation for image generation. The approach trains a neural network over 1,000 time steps to gradually reverse a fixed noise-addition process, learning to predict added Gaussian noise at each step. This enables the model to generate new images by sampling random noise and iteratively denoising it, a technique now used in systems like DALL-E 2 and Latent Diffusion.
