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LOD is something I have a difficult time comprehending the rationale for as well unless wholesale blocks are being regurgitated. So if you just ask for a pictu

LOD is something I have a difficult time comprehending the rationale for as well unless wholesale blocks are being regurgitated. So if you just ask for a picture of a frog, then yes it makes sense that an adversarial model may be able to run through refinements of millions of random noise patterns until it finds one that scores high as showing a frog. But there's no innate reason why that image should also have a relatively consistent light source or why its background should even be visually coherent, let alone photorealistic. The most logical explanation for the coherence of the rest of the image is data theft. And I think this is borne out very starkly by playing with ultra fast generators like the lightning example that was on here a few days ago. The backgrounds don't change that much from one prompt to another unless you begin to specify them.

tavavex 3y ago
I mean, object recognition was never an intended way of solving the problem. When you ask for a "frog" image, the model isn't just trying to randomly find a frog object, it slowly narrows down data to find an image that's classified as "froggy". Sure, the actual frog will be remembered as an extremely important part of a frog image, but everything else in the image is contributing too. The background could be something that frogs are photographed in, and the lighting approximates what a photograph may look like. I don't see why there's a need to explained it with this two-tiered approach (and a "data theft" claim) if the explanation of how it works seems applicable to the entire system. Images in the training dataset would almost always reflect the way lighting and objects work in real life, so the model is incentivized to approximate it as closely as possible.