| I think the case for Stable Diffusion in general is not too bad, however EFF tempers their optimism when it comes to cases where the model may actually memorize the inputs: > To sum up: a diffusion model can, in rare circumstances, generate images that resemble elements of the training data. De-duplication can substantially reduce the risk of this occurring. But the strongest copyright suit against a diffusion-based AI art generator would likely be one brought by the holder of the copyright in an image that subsequently was actually reproduced this way. EFF's position seems to be (to which I personally agree, FWIW) that Stable Diffusion almost certainly does not run afoul of at least the vast majority of copyright holders of data it was trained from. > The statistics generated about the works entered as input, do not resemble the original works. Nor can those statistics on their own reproduce the original work. At most they are brief mathematical summaries of the work. Of course, this needs a lot of qualification. Compression and intelligence are generally considered to be related, and indeed, compression also works on statistical analysis (like entropy coding a la Huffman, or frequency analysis via Fourier transforms). Granted, compression algorithms are designed to reproduce their input verbatim--it's the entire point. But I think ML weights may exist somewhere "in the middle" so to speak; depending on the model architecture and how it's trained, it may be more or less literally like compression. Vastly overfit models are very much like compression, whereas large generalized models like Stable Diffusion are pretty far away and yes, generally can't reproduce inputs verbatim. (However: I suspect many LoRA models are quite overfit and may not be in the same boat.) However, that's just for image generation. I feel like LLMs and text generation are an entirely different ballgame, and given that we can't actually inspect the model weights in the case of GPT4, the best we can really do to surmise what's going on is to see how badly it seems to overfit its training data. I am unconvinced that this matter is settled as a whole, although I do think the EFF article presents a good overview of the case regarding Stable Diffusion and it does coincide pretty closely with what I actually believe. But this article is about large language models, which may legitimately be a completely different ball game. |