[rayheberer]

When it comes to the short color, I think this an instance of the model "overfitting" to the data. I noticed that images generated during training gradually learned to map changes in shorts colors for each of the ~130 people in the set that had shorts in their photo. When I use overfitting in this context, I mean that the model hasn't learned that certain transformations are things that humans consider invariant in the image-to-image translation problem, and since it has enough parameters to map every such noisy transformation in such a small training set, we get the artifacts you pointed out.

Data augmentation by slightly scaling pixel intensity uniformly across all color channels would be my first idea as to how to counteract this. After that, I would consider regularization by adding a penalty to the cost function that punishes larger differences in average pixel intensity.