I think that depends on the dataset. If you train a GAN on faces only, it will give you excellent images of faces. If you train a GAN on ImageNet, it will give you bad faces. It's the same for all kinds of image generation models. At least to my understanding, it's a data issue and not a model issue, but please correct me if I'm wrong.

Edit: I worked with GANs for the last couple of years in my PhD. The faces that sota models produce when trained on ImageNet or CoCo look like crap. They look similarly bad as the faces I get when I try out the stable diffusion web demo.