For generative models, if the model authors do not publish the architecture of their model; and, the model uses a transformation from text to another kind of media; you can assume that they have delegated some part of their model to a text encoder or similar feature which is trained on data that they do not have an express license to.
Even for rightsholders with tens of millions to hundreds of millions of library items like images or audio snippets, the performance of the encoder or similar feature in text-to-X generative models is too poor on the less than billion tokens of text in the large repositories. This includes Adobe's Firefly.
It is also a misconception that large amounts of similar data, like the kinds that appear in these libraries, is especially useful. Without a powerful text encoder, the net result is that most text-to-X models create things that look or sound very average.
The simplest way to dispel such issues is to publish the architecture of the model.
But anyway, even if it were all true, the only reason we are talking about diffusers, and the only reason we are paying attention to this author's work Fairly Trained, is because of someone training on data that was not expressly licensed.
Even for rightsholders with tens of millions to hundreds of millions of library items like images or audio snippets, the performance of the encoder or similar feature in text-to-X generative models is too poor on the less than billion tokens of text in the large repositories. This includes Adobe's Firefly.
It is also a misconception that large amounts of similar data, like the kinds that appear in these libraries, is especially useful. Without a powerful text encoder, the net result is that most text-to-X models create things that look or sound very average.
The simplest way to dispel such issues is to publish the architecture of the model.
But anyway, even if it were all true, the only reason we are talking about diffusers, and the only reason we are paying attention to this author's work Fairly Trained, is because of someone training on data that was not expressly licensed.