Strange patterns from GAN
Training GANs becomes much more difficult with increasing input dimensionality. I encountered that sometimes even small changes to your network can decide about convergence/divergence of your network (like initialization of your variables). 

I recommend following the tipps from https://github.com/soumith/ganhacks. 

Another thing I personally recognized is that you should not use as many Conv2DTranspose layers as you do in your current network. Try instead increasing the latent space.