zlacker

>>zacwes+(OP)
“Sta­ble Dif­fu­sion con­tains unau­tho­rized copies of mil­lions—and pos­si­bly bil­lions—of copy­righted images.”

That’s going to be hard to argue. Where are the copies?

“Hav­ing copied the five bil­lion images—with­out the con­sent of the orig­i­nal artists—Sta­ble Dif­fu­sion relies on a math­e­mat­i­cal process called dif­fu­sion to store com­pressed copies of these train­ing images, which in turn are recom­bined to derive other images. It is, in short, a 21st-cen­tury col­lage tool.“

“Diffu­sion is a way for an AI pro­gram to fig­ure out how to recon­struct a copy of the train­ing data through denois­ing. Because this is so, in copy­right terms it’s no dif­fer­ent from an MP3 or JPEG—a way of stor­ing a com­pressed copy of cer­tain dig­i­tal data.”

The examples of training diffusion (eg, reconstructing a picture out of noise) will be core to their argument in court. Certainly during training the goal is to reconstruct original images out of noise. But, do they exist in SD as copies? Idk

>>dr_dsh+12
You could make the same argument that as long as you are using lossy compression you are unable to infringe on copyright.

>>synu+H4
That's a huge understatement. 5 billion images to a model of 5GB. 1 byte per image. Let's see if one byte per image would constitute a copyright violation in other fields than neural networks.

>>visarg+h6
Another thing worth referencing in this context might be hashing. If a few bytes per image are copyright infringement, then likely so is publishing checksums.

>>cf141q+Kd
What is a 1080p MP4 video of a film if not simply a highly detailed, irreversible but guaranteed unique checksum of that original content?

>>synu+Gh
I think this is overstretching it. That would be a checksum that can be parsed by humans and contains artistic value that serves as the basis for claims to copyright. An actual checksum no longer has artistic value in itself and cant reproduce the original work.

Which is why this is framed as compression, it implies that fundamentally SD makes copies instead of (re)creating art. Leaving out the issue of recreating forgeries of existing works, using the training data for the creation of new pieces should be well covered inside the bounds of appropriation. Demanding anything more then filtering the output of SD for 1:1 reproductions of the training data is really pushing it.

edit: Checksums arent necessarily unique btw. See "Hash collisions".

>>cf141q+5A
Overfitting seems like a fuzzy area here. I could train a model on one image that could consistently produce an output no human could tell apart from the original. And of course, shades of gray from there.

Regarding your edit, what are the chances of a "hash collision" where the hash is two MP4 files for two different movies? Seems wildly astronomical.. impossible even? That's why this hash method is so special, plus the built in preview feature you can use to validate your hash against the source material, even without access to the original.