Those models were not trained on the restricted dataset i'm talking about.

I'm saying you deliberately construct a dataset which, say, does not include cups at various distances, angles, etc. but has as many as you like at a fixed range of these parameters (lens, distance, lighting, angle...).

Now, you will get, from this model, just coffee cup images with these same parameters (eg., distance from the camera).

Real-world generative systems are deliberately not constrained this way, and require many many PBs of images under various conditions to overcome this problem.

Nevertheless you can actually still see this limitation: most generated photos etc. show subjects in "photographic distance/focus/etc. conditions", ie., its hard to get a photo of a person who isnt framed as if they were teh subject of a photo.

Whereas, if you were in a room with a friend, you can take a photo at any angle/distnace.. even, say, from the top of their ears down. You will not get this freedom with a statistical model of pixel patterns

I can't argue with that, so I think unfortunately I may have missed the original point.

The sun revolved around the earth for a long time until our own model was updated to include more data.