SD is a text-to-image deep learning model, based on diffusion models. Introduced by CompViz group at LMU Munich.
Diffusion models are generative model. A generative model learns a probability distribution of the data set such that we can then sample from the distribution to create new instances of data.
Eg: if we have many pictures of cats and we train a generative model on it, we then sample from this distribution to create new images of cats.
Why do we model data as distributions ?
Imagine you are a criminal, and you want to generate thousands of fake identities. Each fake identity, is made up of variables, repr. the characteristics of a person(Age,Height).
We can ask the Stats dept of govt. to give us statistics about the age and the height of the population and then sample from these distributions.
what does it meean to sample ?
throw a coin that has a very very high change to fall in the median area and a very low chance to fall in the extremes.