The failures were listed in What AI images are good at, and where they fail. This lesson takes them one at a time and says why each happens, starting with exact reproduction of a specific object. A model keeps no copies of the pictures it learned from. It holds a compressed account of how pictures of that kind tend to go, so a request for your client's bottle comes back as something consistent with every bottle it has seen, plausible and wrong in the details. Run it again and you get a different stand-in, with another cap or a reworded label, because every output is a fresh draw from a range of likely answers. The three routes that pin the real thing down are in Holding a product across a set.
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