  GANs can generate photo-realistic images from the domain of their training
data. However, those wanting to use them for creative purposes often want to
generate imagery from a truly novel domain, a task which GANs are inherently
unable to do. It is also desirable to have a level of control so that there is
a degree of artistic direction rather than purely curation of random results.
Here we present a method for interpolating between generative models of the
StyleGAN architecture in a resolution dependent manner. This allows us to
generate images from an entirely novel domain and do this with a degree of
control over the nature of the output.
