Copying pictures is not efficient within the scope of this problem. The whole point of these algorithms is to extract (or ideally understand) essential features of some class of objects and to be able to represent an object of such class with radically smaller amounts of data that would be required for the full description.
That is the only definition of efficiency that matters here.
Human level AI is the goal (at least mine), so every time we see something unexpected or a "failure" in ML it's worth thinking about the "failure" mode when compared with how a human could hack the system.
In general in generative models, you have some "true data distribution" P and an estimator distribution Q.
The goal is to make P and Q the same, generally by minimizing some divergence between them.
The actual objective is defined as being between the actual distributions P and Q, but because we only have so many data points, we define an empirical loss that just uses the real observations from P. So if the model makes Q just memorize the samples from P, then it actually hasn't made P and Q similar, it's only minimized the empirical loss.
One practical way to get around this with GANs is to train a conditional GAN instead of an unconditional GAN, and then run the conditioned generation task on held-out samples from the validation set. Another good and perhaps more general solution is to train an inference network and to generate reconstructions on held out data points. If they look totally different, then the model is probably not very "representative".
Said another way: If we gave a human the task of: [make a painting of a bridge] using a handful of examples of bridges as inspiration, and they did a 1-1 copy of one of them, it would be the most efficient result. However there is generally a culturally implied task of [the new painting should not be a direct replication of one of the examples].
So this "problem" with GAN's is a novelty requirement which is not explicitly built in to the generation chain.