One simple low-hanging-fruit approach would be to include a large repository of additional data (e.g. all of ImageNet) and label it all as a "garbage" class. This way the model could at least learn to distinguish the kinds of images in its training data from the universe of images, and this could be used as one proxy of confidence.

Another simple proxy is to look at the probability of the generated sample, since usually the model tends to assign more diffuse probabilities in more uncertain cases. But this is also not a very clean approach for various reasons.

Another, and probably most appealing, approach would be something along the lines of Bayesian Neural Networks, ensembles, or approximations with dropout, where the disagreement between the predictions of all submodels can be used.