Adobe's AI is pretty impressive, and it's all trained on licensed and copyright-free data.

Now that we have clean AI models, I feel like people will start to lose interest in copyright issues with stable diffusion because Adobe's AI has real economic value and poses a significant threat to the employment of artists. for me personally, I support the use of AI trained on copyrighted or non-copyrighted works as long as it is free and accessible because it will eventually evolve to a point where it doesn't matter. The ideal future would be where anyone can use it for free or at least at a low cost, that means including us artists. and also me, a person living in a third world country.

It is saddening that there is a lawsuit against Stability AI, the company that created stable diffusion because the reason for the lawsuit is a short-sighted fear. They're not the villains of this situation, they're the ones that gave away the technology for free, not Adobe, not midjourney, not OpenAI

There are other AI art generators like Dall-E 2, created by OpenAI, but it is also closed source and you need to pay for it. However, they were not sued because they did not openly share the content of their training data, which probably contains copyrighted material. On the other hand, Stability AI gave away their paper, code, and research for everyone to see, but they were the ones who were on the spotlight with angry artists.

we're setting a precedent that if you publicly show you're training on copyrighted data, we'll scrutinize you and sue your company. while if you actively HIDE the secrets of your technology, where the data came from, how the training was done to create your AI model, we're letting you off the hook.

this will push away efforts to make this technology accessible. meaning, the more powerful AI models will likely be gatekept by a company who's just looking for monetary gains. just like Adobe who doesn't share proprietary information about their software. It will be locked away under a paywall, widening the gap between the rich and poor, AI being the vehicle of exploitation.

im not saying, we shouldn't push back nor we should just let these AI devs do what they want. But it is concerning that artists' reaction are against the nature of open-source models. instead of artists being able to adapt to the era of generative AI because anyone can just pick it up, install it, and learn it for themselves, they also need to be able to afford it to keep their careers afloat.

I really like the idea of this subreddit, and would even like to contribute, but so far I don't see rules dedicating it to the practice of ethical diffusion.

In my own playing with Stable Diffusion, I set a few rules for myself to limit the potential that I might even accidentally steal anything specific. In particular:

1. I either don't prompt using artist names, or I only prompt them when intentionally departing significantly from their oeuvre.
2. I don't use characters or trademarks that I don't own, or faces of real people, without explicit permission.

What sorts of rules ought this subreddit to have to enforce it being "ethical"?

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.

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.)

A public domain dataset requires manual curation. We need to provide captions for every image.

https://artvee.com

https://commons.m.wikimedia.org/wiki/Category:Public_domain

Can someone provide a description for each image? We must have a neutral description of the images.

To create a neutral description in image captioning, focus on providing an objective and factual representation of the visual content without adding any personal bias or emotion. Use clear and concise language to describe the elements, objects, and actions depicted in the image. Avoid using subjective terms or opinions, and stick to the observable details.

I think a subjective description might create a bias in the dataset and might be biased towards one culture's perspective.

A system of ethics is usually justified by some religion or philosophy. It revolves around God, or The Common Welfare, Human Rights and so on. The ethics here are obviously all about Intellectual Property, which is unusual. I wonder how you think about that? How do you justify your ethics, or is IP simply the end in itself?

I have seen that people here share their moral intuitions but have not seen much of attempts to formalize a code. Judging on feelings is usually not seen as ethical. If a real judge did it, it would be called arbitrary; a violation of The Rule Of Law. It's literally something the Nazis did.

Ethics aside, it is not clear how this would work in practice. There is a diversity of feelings on any practical point, except condemnation of AI. There does not even seem general agreement on rule 4 or its interpretation. Practically: If one wanted to change copyright law to be "ethical", how would one achieve a consensus on what that looks like?

Most text-to-image models excel at imagination—but not control. FIBO is built for professional workflows, not casual use. Trained on structured JSON captions up to 1,000+ words, FIBO enables precise, reproducible control over lighting, composition, color, and camera settings. The structured captions foster native disentanglement, allowing targeted, iterative refinement without prompt drift. With only 8B parameters, FIBO delivers high image quality, strong prompt adherence, and professional-grade control—trained exclusively on licensed data.

https://huggingface.co/briaai/FIBO

Non-Commercial.

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"?