The Annotated Diffusion Model

Hugging Face Blog · 4 years ago

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.