I'd assume there's no real state the network can "remember" between iterations, so shuffling will at best just waste time.
My thoughts had been related to the ordering, but it makes sense that it doesn’t matter. I have read that it is actually better to train the model in separate batches with generated and real images in their own batches before the gradient step.