This can be done using a variant of a generative model such as a Variational Autoencoder (VAE) or a Generative Adversarial Network (GAN). Train a deep neural network to predict the probability distribution of image patches in the dataset.Divide each image into a grid of small image patches (e.g., 8x8 pixels).This could be a collection of photographs of a particular subject, such as animals or landscapes. Collect a dataset of images that you want to model.Here is a high-level overview of the process: The basic idea is to start with a random noise image and repeatedly apply a set of diffusion steps to it, in order to 'smooth out' the noise and produce a realistic-looking image. Stable Diffusion is a technique used to generate images by sampling from a learned distribution of image patches.
