Diffusion model
In a nutshell, diffusion models work by making something more random/noisy, and through the process finding the inverse, which is making noisy things less random, thus generating data.
Diffusion models are frequently used for computer vision tasks such as text-to-image. This page will focus exclusively on that use case.
Principle
More specifically, diffusion models consists of two steps: forward diffusion, which turns an image into noise, and backward diffusion, which turns noise back into an image.
Forward diffusion
Mathematically, forward diffusion is defined as Failed to parse (SVG (MathML can be enabled via browser plugin): Invalid response ("Math extension cannot connect to Restbase.") from server "https://wikimedia.org/api/rest_v1/":): {\displaystyle q(x_t|x_{t-1})=N(x_t, \sqrt{1-\beta_t}, \beta_tI)} , where q is the forward diffusion process,
