I have no idea if that is legal or not, but it is an extremely common practice. For example, that is the scenario when someone on Deviant Art creates fan art of any cartoon.

And why should that be a problem? This is a King Canute and the tide scenario. We may as well bow to reality and admit that it is ok.

Yeah well, remember Napster?

Now it’s so easy to share music, it’s meaningless to try to stop people doing it.

Apply 20 years of law cases and punishment for random people and now…

…everyone pays to stream their music.

Right? Wrong? Eh.

I’m just saying, you are kidding yourself if you think that the Powers That Be will just let people decide copyright isnt a thing anymore because of (insert reason here).

Once there is money involved, there will be court cases, and you know, I’ll be shocked if a combination of “needs bigger GPUs to run” and “legal issues” don’t cause these sorts of models to be locked away behind cloud APIs in the future.

It is what it is. Enjoy it while you can; some things are quite predictable, and:

“Law takes a while to catch up with new technology, but it eventually does, and when it does it favours the status quo”

Is one of those things.

Not always; eg. Uber, but… predictably often.

The models are already locked away behind cloud APIs. Stable Diffusion wasn't supposed to happen; OpenAI thought that nobody else could afford to train a U-Net on CLIP at their scale and give it away for free.

I will point out that the usual copyright maximalists have been pretty silent on the issue of AI art. The biggest opposition to AI is coming from the Free Software community - i.e. the people who want to abolish artists' ownership over their work outright.

The Free Software movement originates in academia and has academic value - they don't care about receiving direct compensation, but attribution is critical, and attribution is what image generation models can't provide.

>OpenAI thought that nobody else could afford to train a U-Net on CLIP at their scale and give it away for free.

I'm surprised here; I thought it only cost like $600k to train SDv1.4. Plenty of wall street folks and programmers have that much money to burn.

I think your example doesn't quite work here. People pay for music now because handing Spotify or whoever ten dollars a month is a easier then torrenting, easier then managing directories of mp3 files and moving them to your phone, and has value adds in the form of discovery.

I feel like a rebuttal could almost be https://www.youtube.com/user/kmmusic without comment.

You don't need to pay to listen to music if you don't want to. The corpus of good music on YouTube for free is probably bigger than what you can listen to in a lifetime. If it isn't already it will be in time.

Napster's model won that war. Effortlessly. If you're paying for your music, you are paying on your terms based on the value you think is being provided to you. It isn't a legal framework making you do it.

No, it was ease of use that won out in the end. It's easier to pay YT Premium than to torrent and manage music locally.

The difference between music and AI art is that music is still made by humans. No one would make the music if we didn't pay them. And the music industry currently has a monopoly on producing new music, so of course they have leverage to get paid for that new music.

However, if models to freely generate good music become ubiquitous like SD did, then there'd be no incentive to pay for music, and these arguments would become less valid.

In order for these models to end up being solely controlled by a couple of big corporations as in your scenario (ex: Google, OpenAI) they have to either be prohibitively expensive to train or to run.

The former is more likely, although in the case of SD, it wasn't the case. It will be interesting to see if music generation ends up being more complex (in terms of model size) than image generation.

The latter is much less likely because it's difficult to stop people from just using AWS. I doubt that a cluster of BFGPUs couldn't generate enough music for a month in a couple of hours.

Surely someone is busy training a model on a million pop songs. Why wouldn't "AI music" work very much like "AI art"?

Not yet though. In the future, for sure.

My point is that Napster-to-SD is not a fair comparison. Napster didn't eliminate or replace human artists, while SD certainly did. Therefore, it's not fair to assume the government can regulate themselves out of this one like they did with Napster. Because even though Napster enabled widespread piracy, the human musicians still had leverage in that they were needed to create new music.

So my answer to the question posed by parent comment of "will SD play out the same way that Napster did?" is "no" because there are fundamentally different economics at play.

Obviously, if a good generative model comes out, the music industry will be in a similar boat to the art industry right now. Google was working on it in 2017 (https://magenta.tensorflow.org/performance-rnn) but I don't know if they've made any progress since.

Spotify won because it's better than The Pirate Bay. The Playlist on Netflix explains why Spotify surpassed TBP and how the record labels still had to surrender even though they "beat" the pirates.

There's a million Stary Night variants and even Warhol's Soup. Shepard Fairey ripped from They Live. A good artist copies but a great artist steals.

I'm going to need to see a source on ripping the same color palette as being illegal.

This reminded me of https://www.reuters.com/article/electronicarts-zynga/update-... , but alas they settled outside of court so we didn't get a precedent on digital palettes in this particular case.

I bet Pantone believes they can copyright a color palette, considering Adobe just pulled their colors out of Photoshop.

Stary Night is in the public domain which is why you see so many near copies in corporate works.

The other is amusing in light of this case: https://www.supremecourt.gov/DocketPDF/21/21-869/213328/2022...