Take a look at this url to see how Dilation really works (image is 2/3rds of the way down the page): https://github.com/vdumoulin/conv_arithmetic/blob/master/README.md
Use the "dilation" parameter in your 2D Convolutional filters: https://pytorch.org/docs/stable/generated/torch.nn.Conv2d.html
I've found it works best when only applied to the last layer in my architecture. The intuition is that by increasing the receptive field of your convolutional filter, you enable it to take in more "context" from the surrounding pixels. This has given my images more "cohesion" and increased the resolution of the outputs.
I've only gotten it to work well on the Discriminators, though... YMMV