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To get a bit more technical, they build images in passes; each becoming more coherent than the last. This gives thsm a boost to understanding understand how 'things' relate to one another, knowing them by nothing but that relation. Light + light source being an example, and the angle of lighting being another deeper layer of that a - completed on a less noisy pass.
This is how its able to build images from its training parts, it sorta understands how each of them relate to some things, so its able to sorta organizes an image of random noise each pass, eventually creating a 'unique' image inspired by its training data.
It also gives it that perfect image you mention, cause its specifically trained to look like what looks good to us - its essentially a function optimised on nothing but.