If you approach generative models as ""networks that create images,"" the field quickly turns into a collection of disconnected architectures.
A more useful developer mental model starts one level lower:
What probability structure could have produced the data, and how can we represent, learn, and infer that structure without making the computation impossible?
That question connects autoregressive models, VAEs, flow-based models, GANs, and diffusion models. Their architectures look very different, but they all respond to the same underlying tension: high-dimensional data distributions are difficult to represent, learn, normalize, sample from, and reason about.
Generative modeling can therefore be organized around three interacting problems:
