The data augmentations we are interested in are in fact 'midstream', as they augment the examples before passing into the D or the classification loss but you m

The data augmentations we are interested in are in fact 'midstream', as they augment the examples before passing into the D or the classification loss but you must backprop from that back through the augmentation into the original model, because you don't want the augmentations to 'leak': the G is not supposed to generate augmented samples, the augmentation is there to regularize the D and reduce its ability to memorize real datapoints. It would probably be better to consider them as a kind of consistency or metric loss along the lines of SimCLR (which has helped inspire these very new GAN data augmentation techniques). It's a bit weird, which is perhaps why despite its simplicity (indicated by no less than 4 simultaneous inventions of it in the past few months), it hasn't been done before. You really should read the linked Github thread if you are interested.