Generative models can combine different concepts from the training data. For example, the training data might contain a single image of a new missile launcher at a military parade. The model can then generate an image of that missile launcher hiding in a bush, because it has internalized the general concept of things hiding in bushes, so it can apply it to new objects it has never seen hiding in bushes.