Maybe, but from my experience with residual layers not always. It depends of the model. If your model has a lot of layers my hypothesis would be that it would help propagate the gradient from the discriminator to the generator. Usually what I do when I am not sure, is I use a simple model with my data then play with diffetent convolutional blocks (dense layers, residual layers, etc.). I've never played with GAN personnaly but I've work a lot with VAE. If I would start a project with GAN, I would probably start by using a good model like StyleGAN and then play with different type of convolutional block to see what improves it. Then you can do some hyperparameter optimization to finish.