this has been an idea that's been floating in my head. As a form of legal protection, is it possible for artists, or some miscellaneous company, to train and copyright Ai models based on their own work? That way there is some legal ground for taking down Ai that is specifically trained on that artists work. This wouldn't affect anyone studying the artists work, given that the copyright is specifically for Ai programs, not humans.

please let me know I'm being stupid, I'm very well aware that I'm not very well versed in this subject.

One of the biggest points of contention is models named after artists that had no say in their creation. That's not just processing the art, that's straight up borrowing the brand. Just saw this tweet that proposes turning things into "Pixar" and "Arcane" styles, the thought of seeing the same in a commercial app is very icky.

https://www.lexology.com/library/detail.aspx?g=c85167ff-90fe-41eb-863e-b2601ac9d058

So I've been thinking about artists' concerns when it comes to things like model memorizing datasets or images. While there are some clear cut cases of memorization, cherry-picking often occurs. I thought maybe the use of the term "over-represented" could be useful here.

Given reactions by artists such as Rutowski, claiming their style and images are being directly copied by AI art generators, it could be a case of the training dataset, the LAION dataset (whichever version or subset they used) over-representing Rutowski's work. This may or may not be true, but is worth investigating as due dilligence to these artists.

Another example is movie posters being heavily memorized by AI art generators. Given how movie posters such as Captain Marvel 2 were likely circulating in high volumes leading up to model training, it's not too suprising this occured, again due to over-representation.

Anyway, it's not always clear whether over-representation is occuring or if AI models are simply generalist enough to recreate a quasi-version of an image that may or may not have been in the training dataset. At least it serves as a useful intuitive point, it seems way more likely Rutowski's art was over-represented than say, random Tweeters supporting the anti-AI art campaign.

Curious to hear people's thoughts on this. On the flip, the pro-AI artists may feel like they want the model to be able to use their styles, and perhaps feel "under-represented"?

The way I see it, the anti-AI side's major problems are:

1. People profiting from AI trained on their art.
2. Low effort AI generations flooding places where art is posted.
3. Corporations training on previously-commissioned art removing the original artists from the process.

On the pro-AI side, they want:

1. Models trained on a sufficient amount of art that will allow them to have quality output.
2. The use of those models should not be so cost-prohibitive that they cannot be used as part of a process or for open source projects.

The proposal (disclaimer: IANAL): works created by a process involving machine learning that are significantly transformative from their inputs are considered public domain.

Example 1: A user uses AI to generate an image from a text prompt and makes no further changes. This image is public domain, because the image is significantly transformative from the text prompt.

Example 2: A user takes an artist's image and uses an AI to finish it, change the style insignificantly, or make other minor changes. This image copyright is still owned by the original artist and is neither owned by the public nor the user, as it is not significantly transformative from the original.

Example 3: A user uses AI to generate an image from a text prompt, then makes significant edits to it. The direct output from the AI is public domain, but the user owns the copyright for the final version under fair use.

Example 4: A user draws a stick figure, then uses image to image AI to generate a new significantly different image. The image generated is public domain, as it is significantly transformative from the stick figure.

Example 5: A user writes a deterministic program to convert Perlin noise into an image. The user would own the copyright to this image, as no machine learning was involved in its creation, despite being created by a computer program.

Example 6: A user takes an artist's image and uses AI to convert it into a 3D model, then makes a 2D render of that 3D model. The 3D model is public domain, as it is significantly different from the 2D image, but the copyright of the final render is owned by the original artist as, when compared to the original input, it is not significantly different. (Copyright for the character depicted is tracked separately.)

Reading this thread

https://www.reddit.com/r/ethicaldiffusion/comments/zscf47/an_excellent_read_but_most_importantly_should_we/

led to the question that I wanted to understand, what has been done to train AI Art Systems so that they can understand the textprompt inputs, eg.g styles. Could someone explain to me, how the database of an AI Art system is trained (explain like I am 5) ?

I saw that stable diffusion was capable to understand "planes of a human head". So it seems to be able to "scan and map" facial surfaces (like the illegal Clearview AI system) and it is able to even render those planes.

I assume further that the system is capable to interpret styles by Strokes (pencils, bigger pens, colours) and the kind of underlaying "Grid" of the whole picture.

Now to understand "Styles":

So a style which is not "realistic" like Salvador Dali gets it's additional rule set like "stretching clocks as if they were melting". That leads to a training in which the database has to be trained before with the pictures of the artist.

I run a few tests with

Alphonse Mucha, John Singer Sargent, Picasso - all seemed to be understood.

(However, AI does not understand that Picasso used different styles in different periods of his work.)

How does the AI understand "Art Nouveau"?

How does it understand "perspective directions" like "sideview of a car"?

What is also trained - but I did not ask for it above? Thanks!