What are Diffusion Models?

Lilian Weng · 5 years ago

Diffusion models are generative models that learn to reverse a process of gradually adding noise to data, starting from random noise and reconstructing samples through a Markov chain of denoising steps. Key models include denoising diffusion probabilistic models (DDPM) introduced around 2020, which use a fixed training procedure with high-dimensional latent variables matching the original data distribution. The approach enables stable training without the limitations of GANs, VAEs, or flow-based models, with applications expanded to include classifier-free guidance, latent diffusion, and consistency models through 2024.