ML//diffusion model//DDPM

DDPM (Denoising Diffusion Probabilistic Models) is a generative image model trained to undo a gradual noising process, introduced by Ho et al. (2020) in the foundational paper that showed diffusion can match GANs on image quality. A forward process adds Gaussian noise gradually, and a reverse process learns to undo it, with a U-Net predicting the noise to subtract at each step.

DDPM (Denoising Diffusion Probabilistic Models) is a generative image model trained to undo a gradual noising process, introduced by Ho et al. (2020) in the foundational paper that showed diffusion can match GANs on image quality. A forward process adds Gaussian noise gradually, and a reverse process learns to undo it, with a U-Net predicting the noise to subtract at each step.