can you try this:
where you are saving a model, put in a code that you reload it back into new variables. so if you saved net_G, load it back to net_G1. then pass the same noise through both the models to see if you get the exact same output. if you dont then it is confirmed there is something fishy, more than the qualitative observation of good and not good images.
off the top of my head, this may be due to some stochastic part of the code , or it may be due to .train and .eval behaviour which causes normalization layers to behave differently.