In Conditional GANs, the condition(class label) is prefixed to the latent space random vector, ie. The input vector becomes one-hot-class + rv. You can learn the 'conditions ' such as style by joint training. I believe similar concept can be applied to transformers, judging from the other answers.

This reminds me of one time, I was halfway through reading a very important paper and in the model architecture part, they had used certain references to some data preprocessing steps from earlier. But, the 'subtrees' being referred to in this section were not the same as the 'subtrees' from the preprocessing part. Usually i don't feel satisfied until i understand every step in a paper that's imp to my work. I was staring at the same paragraph for an entire day. That day was an L.

Next day, my senior had one look at it and asked me if the authors were Chinese and they were. He said it's common to have such unclear statements in Chinese papers but most of the times, the maths also checks out, so not to worry.