[D-Machine]

> What does out of distribution even mean?

There are in fact ways to directly quantify this, if you are training e.g. a self-supervised anomaly-detection model.

Even with modern models not trained in that manner, looking at e.g. cosine distances of embeddings of "novel" outputs could conceivably provide objective evidence for "out-of-distribution" results. Generally, the embeddings of out-of-distribution outputs will have a large cosine (or even Euclidean) distance from the typical embedding(s). Just, most "out-of-distribution" outputs will be nonsense / junk, so, searching for weird outputs isn't really helpful, in general, if your goal is useful creativity.