What is a diffusion model?

A diffusion model is a generative model that learns to reverse a gradual noising process. The forward process progressively adds Gaussian noise to data over T timesteps until the signal is pure noise. The reverse process trains a neural network (typically a U-Net) to predict and remove the noise at each step, learning the score function ∇log p(x). At inference, you start from pure noise and iteratively denoise to generate samples. Diffusion models power Stable Diffusion, DALL-E 2, Imagen, and achieve state-of-the-art image generation quality, outperforming GANs in diversity and stability.