Ask HN: DALL-E was trained on watermarked stock images?
I just got a Dall-E render with a very intact "gettyimages" watermark on it. I'm no legal expert on whether you have to own the license to something to use it as training input to your AI model, but surely you can't just... use stock photos without paying for the license? Maybe I'm just old fashioned.
Prompt: "king of belgium giving a speech to an audience, but the audience members are cucumbers"
All 4 results (all no good as far as the prompt is concerned): https://ibb.co/gz5RDkB
Fullsize of the one with the watermark https://ibb.co/DzGR063
232 comments
[ 4.6 ms ] story [ 274 ms ] thread[0] https://cdn.ca9.uscourts.gov/datastore/opinions/2022/04/18/1...
The ruling you are linking to is about whether scraping violates the Computer Fraud and Abuse Act.
This isn't really applicable here. First of all, that's a separate issue from copyright. Just because scraping publicly accessible data doesn't violate the CFAA doesn't mean that suddenly all images posted on the internet are public domain or that can use copyrighted images from websites for whatever you want, for example.
Furthermore, how copyright applies to training neural networks on copyrighted works is an open question right now.
Until somebody tries to float a trial balloon (case) in court.
Some would argue that technically these people _discover_[0] the law, but it amounts to the same thing
[0] https://www.jstor.org/stable/3143421
Say you were an artist who went to every art show and museum and studied all the art there.
If you produced a work of art solely from memory that contained large portions of other people's copyrighted art, would that still fall under copyright/require licensing?
My understanding of neural networks is that there are no remnants of the original inside it. The training data is used to back propagate a bunch of weights.
Your brain works like those neural network neurons; they learn when to fire, but they don’t know the intricate detail like a photo. Hence why many claim eyewitness testimony is bogus.
Can you prove that? I can prove the opposite.
https://www.moma.org/learn/moma_learning/andy-warhol-campbel...
(Yes I get it's not technically a watermark, but it certainly qualifies as a trade mark in a similar fashion)
a) Dall-e images are without copyright.
b) Original authors of work used by Dall-e can claim copyright over images generated.
There is a comics creator named Kieth Giffen. He's done a lot of solid work over the years for DC and Marvel, there's a playful love of the medium and its history that flows through a lot of his work. At first his style was pretty middling; nothing terrible, nothing to really stand out from the pack. Then one day his work changed dramatically - he got a lot more daring in spotting his blacks, inking with a heavier brush, and doing a lot of panels that were a closeup of a backlit head with rim lighting, and eyes and teeth standing out in white. It was grounded in observation but had a lot of fresh ways to abstract a scene in the service of story. It was like nothing else on the racks and really striking.
It was also completely swiped from the work of an Argentinian artist named José Muñoz. Pick up one of Muñoz's shadow-drenched crime stories, put it next to one of Giffen's superhero tales, and you could clearly see the influence. And not just the influence, influence is okay - Giffen had started entirely cloning Muñoz's style, completely dropping all his other influences in the process. Muñoz was not happy when he heard about this, and neither were other artists in the field of comics. Influence is one thing, everyone's influenced by other artists, and if you're familiar with an artist's influences you can tell. But dropping all your other influences to start drawing almost exactly like a new one? That's just not done.
Giffen got a lot of shit for this. Giffen quit comics for a couple of years after this, and when he came back he had a new look. He still does the Shadowy Muñoz Face now and then but it's more along the lines of one of the many things he's borrowed from his multiple influences rather than one of the ways he was wholesale ripping off Muñoz.
"Style theft" is completely legal in the eyes of the court. There was nothing legally actionable going on here. But in the court of his fellow artists, Giffen was judged, and found guilty.
There's a range here. Nobody's going to care if you pick up a collection of Winsdor McCay's pioneering 19xx comic strip "Little Nemo" and do a dream-themed story that borrows his distinctive panel composition, lettering, and inking choices. Nobody's going to care if you do one drawing that precisely lifts Mike Mignola's heavy use of black and thin, clear lines. If you do superheros long enough then you're pretty much obligated to do at least one story that emulates Jack Kirby as closely as you can. If you worked as someone's assistant for a half a decade then you are very much allowed to bust out a perfect rendition of their style at any point in your entire life. But there is definitely a line you can cross where every artist (and a lot of non-artists) who sees a side-by-side view of what you're doing and what you're swiping from will say "dude, not cool, stop swiping their style".
These image generators actively encourage adding the names of prominent, living artists to your prompts to get the results you want. Is this crossing the same line Kieth Giffen did?
BTW you can add 'royalty free' to the prompt to get rid of those most of the time (lol?).
Wouldn’t that remove the king of Belgium? Or add a “down with the king” placard?
They aren't hosting the infringing content. Training on the data is probably covered under fair use. Generations are of _learned_ representations of the dataset, not the dataset itself. This makes it closer to outputting original works (probably owned by the person who used the model).
The players involved here are known for being litigious, however. I wouldn't be surprised if OpenAI did in fact pay some hefty fee upfront to get full permission to use these images.
The non watermarked versions are likely internal only and have far less diverse descriptions.
I’m guessing they assumed fair use and there will be lawsuits.
"Probably" is doing a lot of heavy lifting in that sentence.
As for "_learned_", that's pretty debatable considering it's reproducing recognizable trademark infringement.
> The players involved here are known for being litigious, however. I wouldn't be surprised if OpenAI did in fact pay some hefty fee upfront to get full permission to use these images.
I have no idea why anyone would assume the "move fast and break things" disruption mindset that pervades tech companies these days, especially in spaces like ML/"AI", would mean they considered the legality, ethics, or good business sense of their training dataset.
As with Copilot, I suspect the DALL-E terms of use puts the onus on the user to avoid using infringing items.
Copying ideas and styles has always been a fundamental part of art history, so an artwork right holder might have a hard time successfuly sueing a user for the user's generated image looking similar to the right holder's artwork.
The art world's copyright suits are all over the place in terms of what's sufficient to meet the threshold of "fair use" or "not a copy".
It's hard for me as a layperson to see works by Richard Prince[1] as substantially transformative (clearly one work is derived from the other) and even the different courts couldn't agree on this as it was initially found in favor of the plaintiffs but then Prince won his appeal.
My approach to this kind of thing is simply this: Does this technology inherently open me up to lawsuits in undecided or highly unreliable legal territory? If yes, steer well clear of using it in any capacity.
[1]: https://www.artnews.com/art-in-america/features/richard-prin...
Indeed, that's why I used it. It wasn't long ago that DALLE-2 outputs were the ownership of OpenAI (they changed it so the owner is the user recently). Definitely plenty of room for debate on who the owner should be.
> As for "_learned_", that's pretty debatable considering it's reproducing recognizable trademark infringement.
I guess. I meant this strictly in the machine learning sense, where "learned" is typically used to describe models trained via stochastic gradient descent.
> I have no idea why anyone would assume the "move fast and break things" disruption mindset that pervades tech companies these days, especially in spaces like ML/"AI", would mean they considered the legality, ethics, or good business sense of their training dataset.
I agree mostly, except that companies like Alamy have their hooks in everywhere so they can seek rent. I just figured they might be cautious about this if e.g. Microsoft (OpenAI's business partner) had an existing agreement in place for Bing or something.
(IANAL)
In the United States, there are two bits of case law that are widely cited and relevant: In Kelly v. Arriba Soft Corp (9th), found that making thumbnails of images for use in a search engine was sufficiently "transformative" that it was ok. Another case, Perfect 10 (9th), found that thumbnails for image search and cached pages were also transformative.
OTOH, cases like Infinity Broad. Corp. v. Kirkwood found that that retransmission of radio broadcast over telephone lines is not transformative.
If I understand correctly, there are four parts to the US courts' test for transformativness within fair use (1) character of use (2) creative nature of the work (3) amount or substantiality of copying (4) market harm.
I'd think that training a neural network on artwork--including copyrighted stock photos--is almost certainly transformative. However, as you show, a neural network might be overtrained on a specific image and reproduce it too perfectly--that image probably wouldn't fall under fair use.
There are also questions of if they violated the CFAA or some agreement crawling the images (but Hiq v Linkedin makes it seem like it's very possible to do legally) and whether they reproduced Getty's logo in a way that violates trademarks (are they trying to use it in trade in a way there could be confusion though?)
These AI tools on the other hand seem to do the exact opposite. They can (or could, if they got good enough) absolutely compete with a work, and therefore seem like they create substantial market harm. The character of use also seems vastly different; AI tools are creating images explicitly to be consumed, vs a search engine is basically just an index, and only shows the image in so far as it needs to make it discoverable.
So three of the four tests for fair use seem clearly against AI image generation, at least to me. The only test that possibly goes in favor of AI is the amount or substantiality of copying, but AIs can easily reproduce images, or if not entire images, other substantial subsets of a composition.
I just don't get how these could possibly be fair use.
(1) The use is highly transformative;
(2) the images used were offered to the anonymous browsing public (with watermarks);
(3) the end effect of training will only retain a tiny spectral distilled essence of any individual photo, or even a giant source corpus;
(4) there's a potential risk of market competition from the ultimate model output, for some uses – but that's also the most 'transformative' aspect.
Getty et al could potentially just ask creators of such models not to include their images – perhaps by blocking their crawling 'User-Agent' – and it might not make any real difference in the models.
The "character of use" is not in favor of dall-e, it is a commercial use.
Copyright law does not require getty to block a user agents or ask them not to include their images.
Another issue here is that removing copyright management info like a watermark is a violation of the DMCA, separate from fair use or copyright infringement. These cases have statutory damages and attorneys fees awarded.
But also, none of these images are direct, or even necessarily subtantial, "copies" of other images. The generator learned from other images – the same as any human artist might.
No watermark has been removed; the bigger issue may be that the spectral watermark violates a trademark. (But, I doubt consumers are likely to be confused.)
Anyway it will be interesting to watch this space.
A lot of people seem to make this comparison, but I don't think it's fair. It's wrong. A computer is capable of ingesting/processing and "learning" from images at a rate no human can possibly come close to matching. To elaborate, it is not actually learning in the way we normally think of it, as its "brain" is completely different from a human's brain. It is doing something entirely different that should have its own word. Human artists learn from other human artists' work. An AI does something else.
It's also worth noting that the art the AI was trained on was posted online when the technology didn't exist (or if it did in some form it was not in the state it is in now). So an artist having posted their art online for public consumption can't be equated with somehow consenting to its consumption by a web scraper / AI.
But it's also great that AI artists can learn from more examples in a few minutes than a human artist might see in lifetime.
To say that's "not actually learning in the way we normally think of it" is superficially true, but it doesn't mean it's "not actually learning", or necessarily any worse than typical learning. It's so new, & we barely understand fully how it works or what its limits are. It might be better in many relevant & valuable aspects!
That said, my main objection to this technology is that:
- The AI's work is based on human artists' work
- Companies are then profiting off of the AI's work
- The companies are indirectly?/directly? profiting off of artists' work
- The companies do not get artists consent or compensate them in any way
- The companies are essentially stealing from artists
Companies should be forced to obtain the creator's consent when using art to train their models.
The representation that feeds the generation is statistical, even to the point of being plausibly factual: these things/people/places/concepts can be abstractly represented as the balanced weights inside the model. And under US law, facts aren't copyrightable.
I could see a case being factored as: (1) the scraping/training/ephemeralization itself involves the usual copying of downloading/locally-processing images, like indexing, but all those 'copying' steps are fair-use protected, as science/transformative/de-minimus/whatever; (2) any subsequent new-image generation no longer involves any 'copying', only new creation from distilled patterns of the entire training corpus, in which Getty retains no 'trace tincture' of copyright-control. So there's no specific acts of illegal copying to penalize.
Also, a human artist would be allowed to review related Getty/etc preview images, free on the web, to familiarize themself with a person or setting, before drawing it themself, with their own flair – as long as they don't copy it substantially. Why wouldn't an AI artist?
People are really underplaying how damaging this is going to be for the industry. It's going to completely decimate it. You can already see people using names of artists in the DALL-E prompt to get "their" work for few dollars avoiding any copyright or social issues.
Artists will suddenly be competing with AI on price and time - why we should pay you living wage when we instantly generate something close enough.
Why would anyone try to create some new aesthetic or push anything further if their effort will be replicated next week when the model gets updated with new source data. Everything is gonna get stuck to aesthetic of 2025 and before.
It's completely inhuman.
And AI "builds exclusively on past experience and work of humans" just like any young new human artist equally does. In many cases, you can even tell the different models' outputs apart, not by raw quality or glitches, but by hard-to-describe aesthetic tendencies.
I share your concern on the effect on human artists – both the market for their work, and even their morale, when learning, knowing that decades of practice will still be outproduced by seconds of computation.
But I don't think the genie will be put back in the bottle, by either expansive interpretation of existing copyright law, or even new laws.
I said it in comment above - yes people build on work of others but they also bring lots of their originality and intelect. Part of what people do is truly uniquely theirs and piece by piece we progress as a whole.
The crutial detail is that AI learns only from visual patterns from past and cant think at all. And humans learn from everything around them and think about it deeply.
The thinking is still done by the human prompter.
I am not sure finding new aesthetics is even the playingfield nowdays. Its probably not because we’ve been stuck for decades. Its more about cyclic trends of things forgotten. So who cares. But this will just solidify that even more. But yeah it has already happened and since the tech will be firmly in private hands everybody will be just exploited and pushed by it instead of it helping anyone.
That humans are capable of developing their own style could still be argued that it's just a intermixing of previous work that they've seen, but they've combined it in a different way, which effectively is exactly what these generative systems do.
To think that artists only mash up what was before them is quite obviously wrong.
But its exactly only thing the tech does.
For example:
Andy Warhol died in 1987, 35 years ago. One of his 'Prince' collages dating to the early 80s used another photographer's photo, without permission. In 2019, one federal judge ruled that was not infringement. An appeals judge then said it was.
The Supreme Court has decided to take the case.
The US Copyright Office & Department of Justice agree with the photographer in briefs filed with the court... but the mere fact the Supreme Court took the case indicates they think there might be issues with the appeals court ruling. They might agree with the original judge!
Oral arguments come this October. See:
https://www.reuters.com/legal/litigation/us-backs-photograph...
So, when all the (possible) disputes over AI-training-on-copyrighted-images resolve – maybe in the 2030s or 2040s? – what will the laws say, & courts decide? It'll depend a lot on other specifics, & reasoning, that may not be evident now.
I find legal disputes in fine art interesting, however—IANAL, of course—I understand that fine artists (Richard Prince comes to mind) are subject to very different copyright restrictions than graphic artists under commercial use.
It’s, as you said, up to courts to decide. But AI generated imagery is frequently commercial in nature (KFC, already). AI services are trained on unlicensed commercial stock images, and are able to reproduce enormous quantities of derivative images, and do so at a profit. I think that’s categorically different from a fine artist appropriating imagery in a single artwork or even series of artworks in an entirely different context.
AMP, snippets, Knowledge Base and in-app browsers would like to have a word with you
AMP is completely unrelated so I'm not sure why you mention it. Website owners have to create a specific version of their own site for AMP to even work.
They can also OCR the output to make sure there are no blacklisted words and use an index to skip all images that look too similar to the training data. Then the argument of copyright defenders is going to be weakened.
The fact that a prompt and curation are necessary also goes against the "AI works can't be copyrighted" narrative - it's generated by a human-AI team, so human work is part of the process.
The core of the issue I see is that human and AI both learn from the published media but an AI can both "see" and "draw" more than a human, so there is an important distinction there.
I'm mostly curious about the legal aspects of having a black-box system that can - under some unknown circumstances - attach openly copyrighted or trademarked elements (such as a company logo) to a piece of work.
I tried it with a distinctive banana image:
https://imgur.com/a/0OrIr6e
Fun times ahead
> Different runs can generate different size, orientation and placement of the bananas, as well as different shades of pink.
At that point it's definitely the curation causing any possible derivation. The image generator is innocently doing what you ask in an unbiased way.
You can't copyright an idea.
Another example, picking a random image from the Getty Images site. "A young parkour flips through the city,guangzhou,china, - stock photo":
https://imgur.com/a/pPruwzA
The images are obviously different, but it appears that DALL-E maps the getty images description to similar tone, similar perspective, similar background, and similar weather conditions. I'm sure there are thousands of possible backdrops in Guangzhou, and many ways to show a parkour flip. Even in the Google image search results there's more variance than in the output of DALL-E.
So you can't copyright an idea, but you can certainly scrape a copyrighted DB with image metadata, and use it to create your own product. My point is that DALL-E itself might be a derivative work of Getty Images and thousands of other online catalogs.
In this case, let me give a fair use analysis that is going to suggest that this isn't fair. Factor 1 weighs against fair use: it's not transformative because, well, transformative is extremely narrowly interpreted against fair use. Factor 2 weighs against fair use because, well, it's factor 2 and it weighs against fair use unless the underlying copyright was paper-thin in the first place. In factor 3, it's weighing against fair use because it's not copying the minimal amount of the original work to get what it needs (it copied the watermark after all!). And factor 4 of course weighs against fair use because you're essentially creating stock images which is naturally in the exact same market that a stock image provider is in.
If you wanted to write a fair use analysis that finds fair use, you'd argue instead that the work was transformative, and the amount copied also weighs in favor of fair use (thus converting factors 1 and 3 to weigh in favor of fair use). You might try to argue that it's a completely different market, but I'm incredibly skeptical that such an argument could win over both a district court and an appeals court (although Breyer's opinion in Google v Oracle did basically follow this thread of analysis, its repetition is unlikely since everyone wants to pretend that Google v Oracle has 0 impact to anything outside of software). Such an analysis is possible, but unlikely, since the unspoken factor of "could you have paid for this" tends to be the factor that wins out over everything else.
Note that we are going to have a SCOTUS case in the fall that will specifically explore transformative uses in the context of fair use: Warhol v Goldsmith (https://www.scotusblog.com/case-files/cases/andy-warhol-foun...). I'm not going to hold my breath that the use will be found fair, though.
Is AI even capable of having a creative nature. All that I see is re-use of source images.
When that is finally tried in court, if it fails to any meaningful extent at all (including going all the way up to Supreme Courts as it doubtless will), then Copilot is dead, DALL·E is dead, GPT-3 is dead, all of these things will be immediately discontinued in at least the affected jurisdictions, at least until such a time as they get the laws changed or judgements overturned.
Personally, I agree that a strike against AI fair use would kill these current generation of tools. But I don't see why that would be the end of it. What it would do is to create a market for open source data sets with liberal licenses. We'd lose something by not being able to train models on every piece of media that has ever been on the internet anywhere, but it's not obvious to me that was ever really reasonable in the first place. If the only way to make AI that can produce good writing is to train it on every piece of writing ever produced in the history of the human race... aren't we missing something? Surely if AI has a future, it'll have to overcome this at some point.
This is well said. One of the primary advantages of these businesses was evading the regulation and taxation that their competitors were subject to.
Shouldn't we listen to what people want over bureaucrats?
This is exactly right. Open Datasets is the way to go. I would also say that in the spirit of the Open Access movement for journals and publications, it might be useful to set up an Open Access protocol for training data sets, methods (these are just the algorithms; publishing them openly might be the way to go) and computed models.
This will ensure that models are evaluated for risk by a large set of people and any risks/shortcomings could be addressed soon. Quite similar to how cryptographic algorithms are designed / analyzed in public. Obfuscation might look like it helps, but it doesn't in the long run and just creates more headaches.
And if it kills these things, oh well. "Being an artist" is a precarious enough existence in this world as is, I'd be delighted to stop worrying about having to compete with an endless sea of algorithmically-generated barely-good-enough spam.
“Zero people trying to disrupt my job with art generators” would be ideal, but “a lot fewer people trying to disrupt my job with illegal art generators they can get in trouble for using” is still better than “hey we released this great copyright infringement machine for anyone to use for free”.
All companies I worked with really cared about cleanness of the origin of the code.
But even extending that: knocking copyright'd images out isn't going to stop these systems. We know they work now, so if you have to be careful about licensing then that's just going to be done.
The idea that any of these platforms will "die" if copyright fair use doesn't automatically apply is magical thinking. Most art is worthless - companies hoovering up huge corpuses with the correct rights assignment for machine learning is going to be the new business.
A company like Disney will drop every piece of output from their staff into a dataset for Disney, then license it under terms to other companies - the tech works, so "invent me a disney character looking like..." would have value internally, just to Disney, for idea generation and refinement - arguably a lot more then to anyone else because they would still retain the artist resources to capitalize on it.
Right now, a bunch of people who told themselves that despite the pay, they weren't going to be replaced by AI are shrieking that it's turned out not to be the case (it was obvious for a few years something like this was coming though). They're reaching for every legal tool that they hope will kill these things, forgetting that it's never worked out like that. Copyright being a problem when it happens to you as an individual, is different to when it happens to MegaCorp Inc. which is constantly being sued, has limited liability, and puts payouts down as a line-item expense.
Knowing Disney, those terms would 100% include "subject to Disney's approval obtained prior to publication", and a chunk of money extracted from you. That company is very controlling of their IP.
Either that, or you're talking about a dataset that generates very specific looking images, that do not remind you of any major classic Disney property (so not "every piece"). Your own Jedi / Avenger avatar creator maybe, custom Mickey Mouse world character absolutely not.
For DALL·E and Copilot, I’m confident that you couldn’t find anywhere near enough material to produce results anywhere near as good as what there is now. I strongly suspect the results would be too poor to be useful in most places where they may be useful now.
“Permissive” is not enough. You need no-strings-attached, and attribution is a string. Hence mostly talking about public domain materials, which make up the vast majority of suitable materials.
I suspect that covered works of the USA federal government would be quite a large fraction of the public domain material (as reckoned by the USA) from the last 70 years. I don’t believe it’d be enough to be particularly useful, certainly not for pop culture knowledge or colloquial idiom.
You could create your own sentences that you control the copyright of containing the word or idiom, as those words and idioms themselves are not copyrightable. For example: "I fracking hate ice cream!"
For the rest, there is a lot of text upto 1926 (depending on when the author died) that is available for use, so you only need to capture words and idioms changed since then, including any pop culture terms.
- trademarks are protected even when the content itself wouldn't be copyrightable; you can't sell AI-generated T-shirts that "happen to" include the word Nike.
- NBC has a trademark on three tones, total length under 2 seconds[2]
[1]: https://fairuse.stanford.edu/2003/09/09/copyright_protection...
[2]: https://en.wikipedia.org/wiki/NBC_chimes
And of course, they could always work more on sample efficiency.
The only types of licenses suitable are ones that require nothing like attribution. This is why you’d mostly be limited to public domain materials (though if it went down this way, you’d find terms of service popping up that included a license grant for model training and selling either your data for model training or trained models without any sort of attribution or remuneration).
They could also do research on ways to get the model to return the top ten influential works for some output, and make a legal argument that this is a best effort given technical challenges with tracing every source.
For the first point, I get that by reading the license at this image:
https://commons.m.wikimedia.org/wiki/File:3H8A7368.jpg
“attribution – You must give appropriate credit, provide a link to the license, and indicate if changes were made. You may do so in any reasonable manner, but not in any way that suggests the licensor endorses you or your use.
share alike – If you remix, transform, or build upon the material, you must distribute your contributions under the same or compatible license as the original.”
So, output has to have the same license and worst case the image is accompanied by a link to a list of every author in the dataset. This is a far cry from the “this research would be useless if copyright was enforced” as some people suggest.
Here’s an open dataset I found which does not require attribution: https://www.pexels.com/creative-commons-images/
And Wikimedia commons, some of which require attribution: https://commons.m.wikimedia.org/wiki/Category:Images
And it is easy to go take a 4k video camera and start collecting tens of thousands of frames of your own images.
My point is that people are throwing up their hands saying oh, respecting the copyright of artists is impossible. But it feels very unfair that these huge companies are walking all over the copyright of small artists, but if we took their code to re use their lawyers would sink us overnight. This upsets a lot of people and it’s a bad look. I don’t actually like copyright but if everyone else has to follow the rules I don’t like giving them a free pass.
Would we get paid? I'd care a lot less about OpenAI profiting from my work if I was getting commission every time my work was hit in the training data.
For simple prompts with little additional guidance, all the diffusion image generators I've seen/used will produce output about like what the author linked most of the time. There are always a few gems, and honing in via prompt engineering helps immensely.
With Dalle-2 I get a satisfying result in >50% of attempts and I'm a beginner.
With Midjourney the result almost always looks great, but often misses some part of what I wanted. I'd say Stable Diffusion is similar. The results are seldom crap, but it's difficult to bend it to produce unusual situations. And in SD it's difficult to keep the entire objects in the frame, but that's a different problem.
Also: my sense is that getting the best results often requires a lot of extra coaching with style/detail words. As we can't see the prompt here, we don't know what sort of style/details were requested. GIGO.
https://ibb.co/gz5RDkB
https://labs.openai.com/s/9YF5WxF1GoZVdzpLBAQYp2Zg
https://labs.openai.com/s/wBfHevs9hIZXvzkJ686mFmn3
https://labs.openai.com/s/M0i029fZnYjQXHFobUpw7eun (best of batch imo)
https://labs.openai.com/s/Hf4z0M9M3KBr6IaaKsjEt9Mx
A few more tries didn't manage to create any photorealistic shots with actual cucumbers-in-seats – perhaps due to the absurd contrasts required – but shifting to a 'cartoon' style with the prompt "editorial cartoon of the King of Belgium giving a speech to many cheering cucumbers, professional illustrator" got a lot closer:
https://labs.openai.com/s/4IonSKYkl0okhNvzJmEAH30K (good)
https://labs.openai.com/s/ZeadCzZ9WqeASYXPlOb13wDV (good)
https://labs.openai.com/s/nERf6bALKEBsQQBvPVsAH7o4 (good)
https://labs.openai.com/s/7dsZu3bwtfGZZxxJYtg9lTVf
If I had more time & credits to burn, I suspect working off those could eventually hit something really apt... but it takes some work & tinkering.
There's one screenshot showing the prompt – but in $CURRENT_YEAR, I view all screenshots with at least a little suspicion, especially when there was a way to highlight the pseuod-watermarked image – OpenAI's native 'Share' – that would've provided stronger proof, direct from OpenAI, of exactly the prompt associated with an image. Hoaxes are everywhere! I've added DALL-E bottom-right color-squares to non-DALL-E images, & seen others do the same, as a subtle joke.
So I generally believe the OP, but don't rule-out the possibility there's been tampering to make some point.
Here's an example: Stressful Shapes
Dall-E: https://i.imgur.com/JBkSh0y.png
Midjourney: https://i.imgur.com/C02Zq3i.png
On the other hand, here's a specific prompt: "nerdy yellow duck reading a magical book full of spells"
Dall-E: https://i.imgur.com/FMKZ8zc.png
Midjourney: https://i.imgur.com/lpsg6af.png
See https://imgur.com/gallery/U5zJMcU
Comparison of two prompts, "poorly futuristic landscape by a 5 year-old" and "poorly drawnn highly detailed futuristic landscape dotted by mahcinery and tall buildings by a 5 year-old"
Also, https://imgur.com/gallery/jvEClos
Comparison of "poorly drawn red sports car in the street of a city by a 5 year-old"
Edit: forgot about crayons :D
https://i.imgur.com/jKcNkat.png
What puzzles me is if the Getty Images logo can sometimes appear. If you only have a Getty account, you get rid of the logo and can legally use them royalty free?
And I don't see the king of Belgium anywhere, two pictures have absolutely nothing to do with the prompt (no king, no speech, no audience, no cucumber), one has the speech and audience but no king or cucumber. Graphically, they are deep into the uncanny valley. Only the third image is kind of right, if you really stretch your imagination.
- - -
Not sure if this is why, but with OpenAI’s Dall-E, you can’t use public figures. You can use proxies, such as “60 year old banker with salt and pepper hair” and then fill in the rest, e.g. “handsome 60 year old banker with salt and pepper hair giving a speech while standing above 12 cucumbers”:
https://i.imgur.com/qYKOWM1.jpg
Telling it oil painting can fudge who the person is, then pick one that’s close and generate variations:
https://i.imgur.com/QRbV7aM.jpg
Or use a reasonable photo and then use edit and in-painting to try to improve the implausible subject. This takes a photo from the first prompt above, erases the lower half of image, and makes a new prompt for the lower half, while keeping just enough of the upper half to orient the collage, e.g. “[photo_edit] + banker giving a speech to cucumbers bin full of cucumbers”:
https://i.imgur.com/OmOK1HF.jpg
- - -
Over on MidJourney, where it’s happy to use public figures so long as you’re not violating terms of service about their use, first a couple prompt experiments with King Philippe of the Belgians.
https://i.imgur.com/KkgIz2w.jpg
https://i.imgur.com/ekf9ypG.jpg
Then one upsized plausible painting from among those, where the actual command was “King Philippe of Belgium talking in a large group of cucumbers --q 2 --uplight” which is pretty basic.
https://i.imgur.com/QWUaNFv.jpg
Very insightful tip on how to harness the "creativity" of Dall-E and the like.
I see how the phrase "king of belgium" was too vague for Dall-E, so it didn't produce anything recognizable - but changing the words into known details, like "banker" and "salt and pepper hair", worked effectively to generate concrete imagery.
Hilarious results. :)
> Dall-E: https://i.imgur.com/FMKZ8zc.png
How well it learned all the common prejudices!
"nerdy" == wears glasses
I'm applauding.
I'm looking already forward to AGI based on the current approaches… It will lead us finally into a better world, for sure. /s
Isn't that great? The world will become a better place with AI everywhere.
We need especially more AI in law enforcement, and such…
AI should make important decisions. Because it bears the same prejudices as humans. So it can replace humans just great. ;-)
Now, if 'criminal' rendered as a black male 90% of the time rather than a crouched white male wearing a cheesy burglar mask and a sack over his shoulder, then I could see your point about perpetuating prejudice rather than stereotypes.
And from my experience getting high-quality output from AIs takes a bit of finesse. Not quite unlike crafting a good Google query
so... yes
On top of that DALL-E2 has generally issues with anything dealing with multiple objects. A single person will render fine, groups of people will generally give artifacts. Attributes will also be spread across all objects in the scenes, not just the ones you specified in your prompt, so doing anything more complex will require manual uncropping und inpainting, not just a single prompt.
Anyway, if you avoid the obvious weak spots and holes in the training set, DALL-E2 output is for most part pretty amazing out of the box. It's really more a top 50% than a top 1%.
The biggest bias when it comes to published DALL-E2 images are the prompts. Most prompts you see online are not the actual prompts, but funny descriptions made by a human after the fact. The actual prompt are often much longer and sometimes completely different.
Perhaps this rewrite may yield better results:
"King of Belgium gives a speech to an audience of cucumbers"
Let's not confuse the AI with "buts", just say that he is giving the speech to cucumbers.
Lastly, specify some style, because this would probably not work out as a photo.
My single try is not bad at all and it could definitely be improved.
https://labs.openai.com/s/3OUmUxKefJCeLhAk4hkeKX4V
On the other hand, it is more difficult to get it to produce absurd results like these.
my prompt: King Philippe I. of Belgium giving a speech surrounded by [[[[large green vertical cucumbers]]]], digital art in the style of Greg Rutkowski
https://files.catbox.moe/1ej1a4.png
Yes, people tend to share the best of the best. However these results seem especially bad, like bottom 10% bad.
[1] https://news.ycombinator.com/item?id=32433821
It's a bit risky to invest too much time because every generator is different and they change the underlying model frequently (see the beta of MidJourney yesterday), but if you do it for passion or curiosity there is no problem.
Now I'm experimenting with a local installation of Stable Diffusion (well, not really "local" because I have an old computer) and the prompt is only one of the things you can tweak. There are num_inference_steps, guidance_scale and other parameters.
Example: https://news.ycombinator.com/item?id=32088718
A twitter user figured out which words they were using by generating a lot of images with the starting prompt "A sign being held that says "
You'll get a public link, at `labs.openai.com` rather than some random image-sharing site, which will show the image & the prompt used to generate it (including a credit to "your-first-name × DALL·E").
https://www.reddit.com/r/KidsAreFuckingStupid/comments/8tgxs...
If something's public domain, anybody can use for anything they want, even if that's just rehosting it with your watermark.
This makes me think back to the controversy over github copilot; if these AIs are going to be trained on other peoples' IP then somebody needs to be held accountable when they commit plagiarism.
Otherwise, im sure Microsoft won't mind my new "gamemaker AI" that i trained on that new halo game last year, or this "OS AI" that I trained on windows 11.
BTW, Copilot also ignored all licenses of the source code it memorized.
Datasets are the new capital. If they could, most employees would probably also object to their company using the result of their work to replace their job. But they can't. It's the same with artists here.
They probably already have specialized filtering models built to filter out censorable terms. They may be imperfect, but they are there. A watermark remover might be an easy addition.
When Stable Diffusion released their model playground, I used the prompt Peter at the pearly gates dressed as a security guard and got three images two of which were censored and one that was an ordinary image. So, the capability is there already. Just a matter of time before they get good at watermark removal.
There are lots of photos with watermark circulating on web, for example in memes and unfinished webpages (when finished, these will be replaced with paid variant without watermark).
That being said, arguments about copyright are just a fig leaf as far as I am concerned. The outcome of whether this is allowed or not will depend on the net impact of using those models on the job market and whether society will be willing to tolerate it.