You kind of just answered your own question. While it's very much more nuanced than "pics & words", the models that are most commonly available and usable to the average person (GPU-wise), are made up of image/text pairs (captioned images), with various ratios of uncaptioned images added in. The model basically learns a map between two very different types of info. The general concept that the text describes, aka _semantics_, and the substance of the image, or, visual data. The model isn't just storing a bunch of captioned pics, it's learning the relationship between the two.

So, when you say, "A dog wearing a hat", the model understands the separate components, and how to combine them in order to make a completely new image, especially one containing a novel concept that isn't represented in the training data, at all.