Going painting > raw photo (derivative work), raw photo > jpg (derivative work), jpg > model (derivative work), model > image (derivative work). At best you can make a fair use argument at that last step, but that falls apart if the resulting images harm the market for the original work.
But currently, first, there is a reasonable argument that the model weights may be not copyrightable at all - it doesn't really fit the criteria of what copyright law protects, no creativity was used in making them, etc, in which case it can't be a derivative work and is effectively outside the scope of copyright law. Second, there is a reasonable argument that the model is a collection of facts about copyrighted works, equivalent to early (pre-computer) statistical ngram language models of copyrighted books used in e.g. lexicography - for which we have solid old legal precedent that creating such models are not derivative works (again, as a collection of facts isn't copyrightable) and thus can be done against the wishes of the authors.
Fair use criteria comes into play as conditions when it is permissible to violate the exclusive rights of the authors. However, if the model is not legally considered a derivative work according to copyright law criteria, then fair use conditions don't matter because in that case copyright law does not assert that making them is somehow restricted.
Note that in this case the resulting image might still be considered derivative work of an original image, even if the "tool-in-the-middle" is not derivative work.
Also, a jpg seemingly fits your definition as “no creativity was used in making them, etc” but clearly they embody the original works creativity. Similarly, a model can’t be trained on random data it needs to extract information from it’s training data to be useful.
The specific choice of algorithm used to extract information doesn’t change if something is derivative.