Is it? What about the printing press, photography, the copier, the scanner ...
Sure, if a commercial image is used in a commercial setting, there is a potential legal case that could argue about infringement. This should NOT depend on the production means, but on the merit of the comparisons of the produced images.
Xerox should not be sued because you can use a copier to copy a book (trust me kids, book copying used to be very, very big).
Art by its social nature is always derivative, I can use diffusion models to create uncontestably original imagery. I can also try to get them to generate something close to an image in the training set if the model was large enough compared to the training set or the work just realy formulaic. However. It would be far easier and more efficient to just Google the image in the first place and patch it up with some Photoshop if that was my goal.
Shoulders of giants as a service.
The basic idea is that besides the probabilities, the network also spits out confidence (IIRC based on how out-of-distribution the input is). There's been a ton of work on getting confidence values out of existing neural nets without as much overhead, but I've never seen those approaches replicate in the industry.