Fig. 2. Typical examples of generative models include generative adversarial networks, variational auto-encoder, flow-based models, and diffusion models with different theories—such as adversarial training, maximization of the variational lower bound, invertible transformation of distribution, and mimicking the Markov chain of the diffusion steps to slowly add random noise to data—and then the models learn to reverse the diffusion process to construct desired data samples from the noise.