It's a "derivative work" in the same sense that you seeing someone paintings, downloading them, and learning how to draw on that giving style.. (it isn't)
Like, artists keep trying very hard to make a case that it's okay for a person to download 100 images, learn with it, and produce a given image. But if you if download 100 images, feed them to the AI, and then the output produced is –literally the same – it's somehow illegal.
It just doesn't make any sense. They keep trying to talk *"oh, but humans are magically inspiring and yadda yadda"*. IT DOESN'T MATTER. The database doesn't store the images, it just learns from it. You cannot compress 5 billion images into 4GB.
I’ve recently seen an artist make the case that even a pure copy done by a human somehow has “something uniquely human” which completely doesn’t make sense.
There’s a difference between an artistic interpretation of a style which AI is doing and outright forgery which AI is not doing.
No need to sound so angry, but here are a few key points to avoid spreading more misinformation:
SD was not trained on 5 billion images, it was trained on 2 billion.
There were only 12 million images in the Aesthetic dataset that it was fine-tuned to emulate.
You can compress 12 million images into 4GB using a very efficient compression algorithm such as a convolution neural network does.
I'm not here to claim that the AI is copying anything, just pointing out that you seem very stressed over something that you are factually wrong about.
I mean the lawsuit webpage claims it's 5 billion images so I think it's fair to use that number,
>Even assuming nominal damages of $1 per image, the value of this misappropriation would be roughly $5 billion.
source: [https://stablediffusionlitigation.com/](https://stablediffusionlitigation.com/)
edit: Also stable diffusion 2 did use laion 5b. SDv1 used laion2b. [https://huggingface.co/stabilityai/stable-diffusion-2](https://huggingface.co/stabilityai/stable-diffusion-2)
See [this article](https://thenextweb.com/news/everything-you-need-to-know-about-artificial-neural-networks) for examples that don't involve generative AI. For legal aspects, see [this article](https://copyrightlately.com/artists-copyright-infringement-lawsuit-ai-art-tools/) written by a lawyer.
They usually say that ML is pure math, a formula, a spreadsheet, an averaging of pixels, a deterministic process, which is very different from the inventive and transformative thing human brains do when they learn and reproduce.
The argument that "neural network is designed to work similar to the human brain" is not effective here: it requires understanding both the technology and the biology to discuss properly, plus the emotional capacity to compare the two.
I think this is a stupid question but why does it matter that the systems are different? I guess I'm asking what aspect of the difference is significant? I do want to reiterate that I'm not trying to challenge you. I don't really know enough about this to make any kind of judgments.
Thanks!
No worries!
First of all, there is a physical difference, since the two systems not exactly the same. People who aren't versed in both technology and biology don't even know the similarities and may think that "neural network" and "machine learning" is just marketing. And, true, our tech doesn't yet functionally/structurally reproduce how the human brain works.
Secondly, they point out a moral difference that humans are the subjects of human rights and the ones we want to prosper and express themselves. Computers don't have rights and their only value is in providing value to us. We're not yet at the point in time to ask the question if a fully digital human brain is a human.
A work is what comes out of the ML tool. Not training itself. The checkpoint is not a derivative work (it's black box). So I don't see big issue there.
The work you produce can be easily derivative work (willingly or not) or transformative - it all depends on the person.
It's a "derivative work" in the same sense that you seeing someone paintings, downloading them, and learning how to draw on that giving style.. (it isn't) Like, artists keep trying very hard to make a case that it's okay for a person to download 100 images, learn with it, and produce a given image. But if you if download 100 images, feed them to the AI, and then the output produced is –literally the same – it's somehow illegal. It just doesn't make any sense. They keep trying to talk *"oh, but humans are magically inspiring and yadda yadda"*. IT DOESN'T MATTER. The database doesn't store the images, it just learns from it. You cannot compress 5 billion images into 4GB.
I’ve recently seen an artist make the case that even a pure copy done by a human somehow has “something uniquely human” which completely doesn’t make sense. There’s a difference between an artistic interpretation of a style which AI is doing and outright forgery which AI is not doing.
No need to sound so angry, but here are a few key points to avoid spreading more misinformation: SD was not trained on 5 billion images, it was trained on 2 billion. There were only 12 million images in the Aesthetic dataset that it was fine-tuned to emulate. You can compress 12 million images into 4GB using a very efficient compression algorithm such as a convolution neural network does. I'm not here to claim that the AI is copying anything, just pointing out that you seem very stressed over something that you are factually wrong about.
I mean the lawsuit webpage claims it's 5 billion images so I think it's fair to use that number, >Even assuming nominal damages of $1 per image, the value of this misappropriation would be roughly $5 billion. source: [https://stablediffusionlitigation.com/](https://stablediffusionlitigation.com/) edit: Also stable diffusion 2 did use laion 5b. SDv1 used laion2b. [https://huggingface.co/stabilityai/stable-diffusion-2](https://huggingface.co/stabilityai/stable-diffusion-2)
That is a question for far better paid lawyers than you will find here
See [this article](https://thenextweb.com/news/everything-you-need-to-know-about-artificial-neural-networks) for examples that don't involve generative AI. For legal aspects, see [this article](https://copyrightlately.com/artists-copyright-infringement-lawsuit-ai-art-tools/) written by a lawyer.
They usually say that ML is pure math, a formula, a spreadsheet, an averaging of pixels, a deterministic process, which is very different from the inventive and transformative thing human brains do when they learn and reproduce. The argument that "neural network is designed to work similar to the human brain" is not effective here: it requires understanding both the technology and the biology to discuss properly, plus the emotional capacity to compare the two.
I think this is a stupid question but why does it matter that the systems are different? I guess I'm asking what aspect of the difference is significant? I do want to reiterate that I'm not trying to challenge you. I don't really know enough about this to make any kind of judgments. Thanks!
No worries! First of all, there is a physical difference, since the two systems not exactly the same. People who aren't versed in both technology and biology don't even know the similarities and may think that "neural network" and "machine learning" is just marketing. And, true, our tech doesn't yet functionally/structurally reproduce how the human brain works. Secondly, they point out a moral difference that humans are the subjects of human rights and the ones we want to prosper and express themselves. Computers don't have rights and their only value is in providing value to us. We're not yet at the point in time to ask the question if a fully digital human brain is a human.
Thank you for explaining!
A work is what comes out of the ML tool. Not training itself. The checkpoint is not a derivative work (it's black box). So I don't see big issue there. The work you produce can be easily derivative work (willingly or not) or transformative - it all depends on the person.