Understanding Diffusion Models for Image Generation and Inpainting
This article is a summary of a YouTube video "What are Diffusion Models?" by Ari Seff
TLDR Diffusion models can be used to generate coherent images from noise and improve inpainting by producing higher quality samples.
The basic idea behind diffusion models is to start from a noisy image, gradually remove the noise, and end up with a coherent image, which has had success in image generation and other conditional settings.
Diffusion models can be interpreted as a kind of latent variable generative model, maximizing a lower bound to calculate p of x0.
Classifier-free diffusion guidance sets the conditioning label to a null label with some probability during training, producing higher quality samples under human evaluation.
Diffusion models can calculate a variational lower bound on log likelihood, competitive on density estimation benchmarks dominated by auto aggressive models.