Imagen and Stable-Diffusion both used subsets of this full 5.8B image set.
> While a subset of our training data was filtered to removed noise and undesirable content, such as pornographic imagery and toxic language, we also utilized LAION-400M dataset which is known to contain a wide range of inappropriate content including pornographic imagery, racist slurs, and harmful social stereotypes.
So for instance for photography generation what may be needed is huge amount of clean photos with obsessively detailed labels, maybe just the exact same single-point-lighting (the exact coordinates of the light being a data point, plus strength/lumens), with 8 pictures of each subject in black background: front, back, left, right, top, bottom, 3/4 mostly-front (AKA the corner), 3/4 mostly-back, and then the same 8 ones but with white background, then also include all the info possible: weight, height, width and depth, plus versions of the most common states of each object (ball: inflated or deflated; bird: flying, idle or walking), plus photos adding two subjects together (one dataset of woman with hat, another wearing the same clothes but without the hat, and one of just the hat without the woman), with properly labeled relationships so it's clear they all refer to the exact same hat (or lack of), you get the idea...
Then the most important thing for "image generation AI" may not be computing power but the most boredom-resiliant staff you can hire; of course that's just an example for photography, for things like text you may need an equivalent rigorous effort by a multitude of linguists.
Other mediums don't have enough people interested in curating and labelling 5 million images with very detailed tag lists for free.
The LAION-5B set in particular is filtered by it's CLIP embeddings - basically run a better AI to classify the images, and check that the two descriptions rate highly enough for similarity, which in turn lets the system have some confidence an image isn't completely wrong (also the step you filter out NSFW and other things if you want, generally).
I'm very surprised how primitive it is. I put in a few names of uncommon technical objects and some of its images were close and others so far off as to be unrecognizable or totally unrelated/useless.
What it needs is a quick/instant feedback system that allows humans to rate an image against the query word. A rating scale of say 1 to 5 where 1 is an identical match, 2 a close match, 3 marginal, 4 ambiguous, 5 wrong/totally unrelated. If in place then likely thousands would make an effort to optimize the matching.
They could use Wikimedia Commons to get a smaller collection of better labeled images. Currently it's images from Common Crawl extracted by Laion, not sure if it already includes Wikimedia Commons.
Pretty sure they are trying to figure out a way their art can’t be used to train an AI and won’t be willingly participating in making it easier.
We don't see this as binary in the long term. Maybe artists want to release art from a prior period, for example, but withhold their current series until they move to the next.
Who told you you could use the data in the first place?
It's copyrighted. Reproducing any of it is a crime.
In which case it should be fairly simple to challenge it in court, no?
This is not true of the artist market. While yes, many artists get into it for the fun/satisfaction of producing their own artworks, the reason that they can remain in it long term is that there are other entities in paying for the results of their work. See: game studios, film studios, etc. Before AI, the only way to get the assets for these various projects produced was by paying artists/experts for their work. Now, the production of those building blocks can be automated.
The ESP game[1] paired two random people looking at the same image while a timer ticked down. The players had to enter labels that described the image, and if both players applied the same label, their score increased, and the label was associated more strongly with that image.
Well-known labels for an image were excluded after a while, so you had to guess less and less obvious labels in order to score as time went by. Once in a while the system would also assign test images with known labels to prevent cheating.
Apparently a lot of people played it because it was fun, so there wasn't even the need to pay them for labeling the dataset...
Can't they crowd-source a proper labeling project - I wonder how much better things like Stable Diffusion would be if its training would include correct, complete labels for the images. I'm sure lots of folks would willingly spend a few minutes here and there to aid with the labeling if it means they get to enjoy the model for free.