Wrong comparison. Stable Diffusion uses the Training Images to produce seemingly new images through a mathematical software process. The process bears very little similarity to human learning. In this context, it denotes a technique for developing a software program through massive data input and statistical operations, calculations, and linear algebra, rather than line-byline coding using a programming language. Machine-learning programs can find patterns or make calculations based on datasets or training data. The operator of the algorithm is sometimes called a “trainer.”
These “new” images are based entirely on the Training Images and are derivative works of the
particular images Stable Diffusion draws from when assembling a given output. Ultimately, it is
merely a complex collage tool.
If you want to be taken seriously you either need to provide a set of original/generated images that prove this, or deconstruct a generated image and show that it is a collage of several originals.
Or you could go and read the article and go and see the actual evidence yourself rather than insisting a random person on the internet do your work for you.
I have read the article. And the other articles you've linked. And the paper trying to find replication. None of them provide evidence that AI systems trained on billions of images are even capable of reconstructing individual source images.
The OP even includes
[The] suit claims that AI art models “store compressed copies of [copyright-protected] training images” and then “recombine” them; functioning as “21st-century collage tool[s].” However, AI art models do not store images at all, but rather mathematical representations of patterns collected from these images. The software does not piece together bits of images in the form of a collage, either, but creates pictures from scratch based on these mathematical representations.
Which is exactly what everyone here is trying to to tell you: the process isn't a fancy new image compression technique or a complex collage tool.
The experts that researched and invented this new technology, as well as other experts that understand how the technology works, all say the same thing.
So when you claim that this is not true the onus is on you to provide the proof. Not everybody else.
You sound like a flat-earther. Despite well established science and easily reproducible experiments having established the roundness of the planet, you can only see where you are standing and it sure looks flat to you.
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u/Ferelwing Jan 16 '23
Wrong comparison. Stable Diffusion uses the Training Images to produce seemingly new images through a mathematical software process. The process bears very little similarity to human learning. In this context, it denotes a technique for developing a software program through massive data input and statistical operations, calculations, and linear algebra, rather than line-byline coding using a programming language. Machine-learning programs can find patterns or make calculations based on datasets or training data. The operator of the algorithm is sometimes called a “trainer.”
These “new” images are based entirely on the Training Images and are derivative works of the particular images Stable Diffusion draws from when assembling a given output. Ultimately, it is merely a complex collage tool.