The models presented in the paper are trained on class-conditional ImageNet (where the input is Gaussian noise and one of 1000 classes, e.g., "car") and unconditional FFHQ (where the input is only Gaussian noise).

Not text but an id representing a class/category of images from the dataset. Or they are “unconditional” and the model tries to output something similar to a random image from the dataset each time.