Study
Generative AI · Diffusion models
DDPM from scratch on 2-D data: a fixed forward process that adds Gaussian noise, a small network trained to predict that noise, and a reverse loop that turns pure noise back into data.
- Training is simple regression: add noise at a random step t, then predict the noise that was added.
- Sampling runs the learned denoiser backwards, step by step, from pure noise.