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Comment by machina_ex_deus | Hacker News Reader

I'm not a data hoarder, but from the moment Stable Diffusion was released I had a gut feeling that I should download everything available while it's there.

Somewhat similar gut feeling to when popcorn time was released, although it might not be exactly the same.

While I really wish I'm wrong, my gut tells me that broadly trained machine learning models available to the general public won't last and that intellectual property hawks are going to one day cancel and remove these models and code from all convenient access channels.

That somehow international legislation will converge on the strictest possible interpretation of intellectual property, and those models will become illegal by the mere fact they were trained on copyrighted material.

So reminder to everyone: Download! Get it and use it before they try to close the Stable doors after the horses Diffused. Do not be fooled by the illusion that just because it's open source it will be there forever! Popcorn time lost a similar battle.

Get it now when there are trustworthy sources. Once these kinds of things go underground, it gets much harder to get a trustworthy version.

View on HN

From my research the general consensus is that the processing of copyrighted material will be considered fair use. Here is a lengthy legal discussion:

https://texaslawreview.org/fair-learning/

Here is a short quote from an IP lawyer:

“In terms of the ingestion of publicly accessible code, Ochoa said, there may be software license violations but that's probably protected by fair use. While there hasn't been a lot of litigation about that, a number of scholars have taken that position and he said he's inclined to agree.”

https://www.theregister.com/2022/10/19/github_copilot_copyri...

It’s very probably fair use under current copyright laws. The things is that the game is changing very rapidly. Right now it’s suffering from criticism in terms of how it affects the society and allows people to generate unwanted images, and merely copyright laws may not be sufficient to protect them. And it has already caught the regulator’s attention so even the law could be rewritten around these models.

At least in the United States there is the jurisprudence of stare decisis. Case law is the only place where the details of how copyright are applied to software are hashed out. I don’t see this changing in the foreseeable future.

> Case law is the only place where the details of how copyright are applied to software are hashed out

Incorrect. In the absence of new legislation case law is how these things get worked out, but new statutes could be passed and could void prior case law.

Well that's a given in a discussion around common law jurisprudence. I still think it is highly unlikely that this changes. This issue doesn't seem to be making or breaking the political careers of the members of legislative branch anytime soon.

https://www.copyright.gov/title17/title17.pdf

Let me expand on this a bit... if you read through the above text (feel free to search for the below terms) of the current laws around copyright you will notice that there is no discussion of:

https://en.wikipedia.org/wiki/Structure,_sequence_and_organi...

https://en.wikipedia.org/wiki/Abstraction-Filtration-Compari...

https://en.wikipedia.org/wiki/Idea–expression_distinction

These legal doctrines are the result of the details as hashed out in case law.

This is distinct from countries that use civil law jurisprudence. Common law jurisprudence relies heavily on case law.

> Well that's a given

Funny way to admit that your original statement was flat-out wrong.

There is no separate "civil law jurisprudence" and "common law jurisprudence". Common law by itself is by definition not jurisprudence. Civil law combines aspects of statutory and common law (as one form of precedent) into a single system. Some aspects of common law even make their way into criminal law. Instead of just Googling for buzzwords, learn what they mean before you try to bluster your way through an argument with them.

"it has already caught the regulator’s attention so even the law could be rewritten around these models"

The law moves slow. Even were that to happen eventually, the laws will very likely be challenged in courts, and those will take a while to be resolved.

Finally, even if the US outlaws this, there'll be plenty of other countries where it'll be legal. There's plenty of infringement of US copyright in China, for instance. The same is likely to happen in regards to AI that's illegal in the US but legal elsewhere.

By the time the law catches up, model creation may become so easy to create by individuals instead of just by deep-pocketed corporations, it may be practically impossible to stop.

Both of your sources make the point that the output of such models is separate from the ingestion mentioned in your (carefully selected) quote, and that the legal definition of fair use might well change to preclude such "AI washed" (my term) copying. That's almost the opposite of how you portray the state of legal thought on the matter.

I'm sorry that I was not making points that support your feelings on Copilot. I was purely discussing the legality of the models themselves, which is what the commenter I was responding to was worried about.

But sure, let's talk about outputs as well. From the second source we can see this from Tyler Ochoa:

"If there's only one good way to do it, OK, then that's probably not eligible for copyright. But chances are that there's just a lot of code in [the training data] that has used the same open source solution, and that the output is going to look very similar to that. And that's just copying."

I have seen some probable copyright violations from the output of Copilot, such as comments and some certain structural similarities that might be protected, although it is hard to say. But focus on the first part of what Mr. Ochoa is saying here, which is also laid out in this quote:

“In computer programs, concerns for efficiency may limit the possible ways to achieve a particular function, making a particular expression necessary to achieving the idea. In this case, the expression is not protected by copyright."

https://en.wikipedia.org/wiki/Abstraction-Filtration-Compari...

This allows for verbatim copies if they are utilitarian in nature!

As for why we should allow verbatim copies of utilitarian features... First, let's preface this with the substantial similarity of the structure, sequence and organization as established in Whelan v. Jaslow which amongst other things says that you cannot merely change the variable names if the expressive structure of the code remains the same. Now let's imagine 10,000 software developers who all implement Dijkstra's algorithm in C and then run it through clang-format. Aside from variable names, isn't it safe to assume that many of the implementations are going to be exactly the same?

As for why it was carefully selected... more often than not when I bring these things up people who feel upset about Copilot go off to cherry-pick some random quote out of context in order to support their upset feelings. Therefore I'm highlighting the important parts as to help people look beyond their upset feelings.

This is a complicated and nuanced matter. Attempting to channel everything through the lens of "this makes me personally feel bad and must be completely wrong" does not help the discourse. It may make you popular to a certain crowd but it might be unpopular to the public at large and it might also be incoherent from a legal standpoint, akin to bashing your head against a wall at a weekly meetup of the local heads-bashing-against-walls club.

There is plenty of room for discussion on what constitutes not only the legal interpretation of fair use and the idea/expression dichotomy but also the bigger picture. The knife always cuts both ways. Would it be acceptable to the open-source community if Microsoft could stop anyone from publishing Dijkstra's algorithm in C# because they wrote it first?

> I'm sorry that I was not making points that support your feelings on Copilot.

That's a very petulant way to defend cherry picking. I wasn't asking you to support one particular view; in fact that's the problem I was identifying. Your sources presented a balanced view, which you misrepresented by citing only the part that supported your own conclusion.

> focus on the first part

No, because the second part matters too. Here's Lemley and Casey again (emphasis mine):

<<<some purposes—say, ... a translation program that produces a translation of an entire copyrighted work—seem more substitutive than transformative, so that if they run afoul of the ever-broadening definition of similarity in music, fair use is unlikely to save them.>>>

Or the Register:

<<<"I actually think there's a decent chance there is a good copyright claim," said Tyler Ochoa ... the functional nature of the code means that reproducing it in a suggestion may not be seen as particularly transformative, which is one of the criteria for determining fair use>>>

Those are your own sources undermining - if not outright contradicting - your one-sided interpretation.

The limitation to market harms in the four-factor test for fair use should not be considered permanent. Law is, after all, a social construct. There's ample precedent for considering harms to the commons, to communities, and so on in other areas of law. Also, there might indeed be market harms. If a company open-sources some of their code but also hopes to profit by selling it in pre-packaged form or as a service, then AI-washed copying could constitute harm in even the most market-myopic terms. The "transformative" test is also pretty suspect in the context of AI-assisted copying, but this is getting long enough so I'll not go down that rabbit hole just yet.

> <verbosity about "utilitarian" copies which are not the issue here>

Enjoy your red herrings. I don't share your taste for them.

Again, there is a distinction to be made between the outputs of the model and the model itself!

When Tyler Ochoa is saying that there is a decent chance of a copyright claim he is specifically talking about the output of the model.

Here is the full quote:

In the Texas Law Review in March, 2021, Mark Lemley, a Stanford law professor, and Bryan Casey, then a lecturer in law at Stanford, posed a question: "Will copyright law allow robots to learn?" They argue that, at least in the United States, it should.

"[Machine learning] systems should generally be able to use databases for training, whether or not the contents of that database are copyrighted," they wrote, adding that copyright law isn't the right tool to regulate abuses.

But when it comes to the output of these models – the code suggestions automatically made by the likes of Copilot – the potential for the copyright claim proposed by Butterick looks stronger.

"I actually think there's a decent chance there is a good copyright claim," said Tyler Ochoa, a professor in the law department at Santa Clara University in California, in a phone interview with The Register.

The use of the word "but" marks the transition from a discussion around the model itself to the outputs of the model.

Is it not also perfectly clear that Lemley and Casey are also of the opinion that the model itself is fair use?

> Again, there is a distinction to be made between the outputs of the model and the model itself!

Oh, you mean the very first thing I had to explain to you at the beginning of this exchange because you seemed to be ignoring it? Very little of these discussions has been about the models. Most of the discussion is about the outputs, and there the fair-use case is - as Lemley/Casey and Ochoa both concede - much weaker.

But by all means keep going on about feelings. We can all tell it's not others' feelings that are being hurt by mere disagreement.

> From my research the general consensus is that the processing of copyrighted material will be considered fair use. Here is a lengthy legal discussion:

IANAL but I would take any opinions on this right now with a huge grain of salt and treat them more as advocacy than actual predictions of any legal outcomes.

Whether there is a good case for it being considered fair use doesn't matter at all until its actually litigated and historically the result with fair use in relation to new technologies has always been a crapshoot.

The result could easily be affected by the actual cases that get litigated, and one well chosen lawsuit where machine learning software is shown to produce output that's too close to the material it was trained on could result in a completely different outcome.

The big problem is that while under current law it's pretty clear that this stuff should all be fair use, enough people want it not to be that interpretations and/or laws will possibly change. These laws pretty clearly did not anticipate this sort of thing, so it isn't possible to affirmatively say that anything will hold going forward.

What's the point of downloading it when it'd just stagnate? This isn't like regular software where people can easily put in hard work and sweat to improve it.

LLMs have the unfortunate limitation of being both powerful and lending themselves to centralized control choke-points due to how resource intensive they are to train. Under this paradigm, I fear commercial entities will be able to easily navigate the legal landmines and continually improve while open efforts perpetually lag far behind.

There are many vested interests who want this control for various reasons they justify as: protection from x-risk, keeping it out of the hands of abusers and bullies, economic advantage. Their reasons for want of control are either well intended but wrong-headed or profit-motivated and disingenuous.

Rather than challenging the likes of GPT-3 and Copilot enabling freedom, I fear folks will be forced to send all their videos, pictures, text and code to the servers of Microsoft, Amazon and Google or lose access to advantages as LLMs continue to improve at a rapid clip.

> LLMs have the unfortunate limitation of being both powerful and lending themselves to centralized control choke-points

It was hard to accomplish, but you can finetune SD on your computer. They are working on instruction-tuning LLMs as well. In general ML models are not closed boxes inaccessible to us - they can be finetuned, reprompted, you can even average two versions to get a mix of two models. In the last 2 years lots of papers were written on finetuning and prompting, all of them geared towards low resource AI adaptation to new tasks.

But you can't selectively re-train them, can you? As in, don't use elements from this part of the training data anymore, but use elements from this body of work that wasn't part of the training data? If I understand correctly you'd still need a full re-training for that.

What you can do is

- lexical filtering by applying a blacklist of artist names on the original prompt

- perceptual filtering - drop all generated images that look too close to copyrighted images in your training set

- re-captioning based filtering - use a model to generate captions for an image and apply filters on the captions; you can also filter by visual style

- CLIP based filtering where you use embeddings to find nearest neighbours, and if they are copyrighted then you can drop the image

- or train a copyright violation detection model that takes generated images and compares them to images from the original authors

Copyright enforcement struggles are going to be interesting to watch in this decade. But I think it will slowly become irrelevant, because anything can be generate again slightly different until they finally pass the filters.

I was aiming more at the centralized-control angle (though I didn't make that very clear), i.e. are open-source models actually viable long-term? If only orgs with absurd amounts of compute can do updates because those imply a full re-training, wouldn't that effectively centralize control over any such model? Is there the option to to an incremental, limited re-training?

Much of modern deep learning is actually premised on the discovery that training on a large, noisy dataset _first_, and then fine tuning (starting training on new data with the same weights) is generally quicker to converge, and also more accurate.

This is part of the motivation for “foundation models”.

There’s another paradigm called student/teacher models where a randomly initialized model updates it’s weights according to another pretrained model. This could (maybe?) be used to achieve the desired effect of a model that learned in a “clean room”.

you can retrain on completely separate data - I am currently doing this.

From what I've seen, it's possible to take a version of Stable Diffusion and add your training set on top.

That's fine until the next brand new model based on a better architecture where the above hacks won't suffice. My concerns here are long term, like 1 or 2 years out in AI-years.

> What's the point of downloading it when it'd just stagnate?

Because it's already good enough to have made it's way into many of my workflows.

I do feel that many companies will, ironically, use "ethical" as a pretext to not be open.

> I do feel that many companies will, ironically, use "ethical" as a pretext to not be open.

I mean this isn't even speculative anymore after what happened with - hilariously named - OpenAI

I think the reason for closed openai is profit, not ethics.

Oh, absolutely. But the reason given as an excuse was still ethics.

What about future models with fewer artifacts that are much easier to communicate with and better at generation? Opportunity costs might favor just sending your data to and paying corps with compliance guarantees than spend time fiddling with 2022 era diffusion models. And don't forget this affects 3D, video snippets and music going forward.

> that many companies will, ironically, use "ethical" as a pretext to not be open.

Yes, weaponized ethics as sleight of hand for control is a common historical pattern.

"Opportunity costs might favor just sending your data to and paying corps with compliance guarantees than spend time fiddling with 2022 era diffusion models."

This is exactly why I pay $30 per month for MidJourney. The output is just phenomenally better than most of the images coming out of SD, and the UI is much better as well. It's just not worth my time fiddling with SD if the results are so bad in comparison.

If/when SD catches up, I'd jump ship to using it in a heartbeat.

Midjourney is using Stable Diffusion in their pipeline.

> when it'd just stagnate?

While it'd be difficult to improve upon the model, it might be easy enough to finetune it if needed, and it's certainly worth it to USE it as is.

There is a limited number of models costing 6 digits in dollars in train time and are freely available. There is certainly value in preserving them, in a world of artificial scarcity.

> I fear commercial entities will be able to easily navigate the legal landmines and continually improve while open efforts perpetually lag far behind

Is it possible to crowdsource AI training with something that looks similar to folding@home?

It's not just processing power that smaller open projects lack in comparison to large corporations, but data.

AI thrives and depends on large amounts of clean, well labeled data.

Large corporations understand this and have hoarded data for a long time now. Some of them have also managed to label this data by millions of people through things like Recaptcha, or just by hiring lots of people to do it.

Open datasets tend to be much smaller and dirtier than small, open projects have access to.

I suppose it would be possible to, over time, collect lots of data and crowd-source some project to clean it up and label it well enough to be useful, then crowd-source the AI model training itself, but it would probably take a long time and by then corporate-owned AI models will already dominate (as they do now with MidJourney, for example, being way better in my experience than Stable Diffusion, but with time the difference will only get starker).

I'd also be concerned with such ostensibly open projects eventually going closed and commercial as IMDB did after getting lots of work by volunteers freely giving their time to writing reviews.

Data can be crowd sourced, too. Wikipedia demonstrated that crowdsourced data can be pretty competitive.

More recently the open LAION data sets have become widely used by both tech giants and independent researchers.

> Wikipedia demonstrated that crowdsourced data can be pretty competitive.

The problem is DL is really sensitive to dirty data, disproportionately so.

At $DAYJOB once we cleaned the dataset, removed a few mislabeled identity/face pairs (very few, about 1 in 1e4) and the metrics goes up a lot.

You need to be very careful about making sweeping generalizations based on a single personal anecdote. The really large data sets typically have very high error rates and sample biases. For instance, Google’s JTF300M is far noisier than ImageNet, which itself is hardly free of errors and biases. Any data set with hundreds of millions to billions of images will generally contain a large proportion of images and labels scraped from the web, w/ automatic filtering or pseudolabeling, perhaps w/ some degree of sampled verification by human labelers.

In fact, generally DL is quite tolerant to label noise, especially using modern training methods such as SSL pretraining.

https://arxiv.org/pdf/1705.10694.pdf https://proceedings.neurips.cc/paper/2018/file/a19744e268754... https://proceedings.mlr.press/v97/hendrycks19a.html

Similar to this approach, at qblocks.cloud we bring under-utilized GPU servers from crypto miners and data centers to use for AI training and deployments at 50-80% low cost than traditional clouds. On-demand and at scale.

It is possible but not practical scaling-factor-wise when synchronization demands, communication bottlenecks on heterogeneous hardware and connection speeds are accounted for. The larger the transformer model, the less practical this quickly becomes.

A fair compromise is any marketplace for clusters with good interconnect but a lot cheaper than the cloud. Tuning distributed training and network transport layer for settings not as homogeneous as the cloud will also help on top of generally good interconnect. Security is a concern.

Building on points raised by pmoriarty, being able to scrape data makes up for lacking labeled data in the era of self-supervised training. IP-hawks are now putting a damper on that option, which is why I worry this might backfire from a freedom perspective.

This is the first time I've heard this idea, but even with all the initial objections, I think this is the future. Something like this is going to happen some day, and I think it'll probably be in the next 5-20 years.

I even think there will be multiple initiatives like this, and there will be at least 1 big repository that accepts inputs and retrains periodically for anyone who wants the model.

What's the point of downloading it when it'd just stagnate?

The quality of the output you can get with the models right now have perpetual utility IMO. If you use it to create patterns, backgrounds, or even just for inspiration creations right now, it might be a shame if it didn't progress (depending on your position) but it's fine as-is if you put in the work to compose and refine the raw output.

The only way to get rid of centralized choke points is to actually go decentralized. At Q Blocks, we're working on making this solution a reality for a lot of the ML devs constrained by the computing costs on cloud.

>LLMs have the unfortunate limitation of being both powerful and lending themselves to centralized control choke-points due to how resource intensive they are to train.

I wonder if that will continue.

My understanding is that's partially because it currently relies on GPUs, which until relatively recently there was a limited demand for, and the market is basically controlled by a single company.

Will we see cheaper special purpose AI accelerators? Like happened with crypto mining ASICs.

Companies have a similar problem now with AI than what the music labels had with Napster and MP3s in the 90's. Music labels tried very hard to legislate the problem away but it failed. I remember Metallica's Lars Ulrich working hard to fight it. They finally embraced the change. If it can't be done in the U.S., it will be done in some other country. That country will have the competitive advantage.

We'll go thru the same with AI but ultimately it won't be stopped. As long as there's no world wide coordination limiting its impact, AI will continue its course.

How did he embrace the change? I just remember years of lawsuits and whatnot.

He sued his fans and no he has no fans?

People are now happily listening to Metallica on streaming services

They did legislate the problem away. Sure, Spotify and YouTube play a part in the reduced music piracy today. But it also helps that all of the music piracy sites have been killed, and the only ones left are shady enough that you fear malware if you go there.

It's streaming that killed piracy. And even piracy was only partially to avoid paying money: It was a huge UX win over CDs.

The iPod would not have had the impact it had without piracy.

There's no one single aspect that reduced piracy. There's multiple aspects that played their part

Worth remembering that both Spotify and Youtube got started with pirated music on their services and then worked it into fully legal platforms.

There's still tons of pirated content on YouTube.

Is it legal? I don't know. I guess they have the fig-leaf of taking down copyrighted content when asked.

Fig-leaf not withstanding, if Google (YouTube's owner) didn't have such deep pockets I'd be amazed they didn't get sued in to oblivion like Napster.

There is a load of pirated content on there, but YouTube have successfully cowed every creator with a decent-sized audience into fearing more than a second or two of copyrighted music appearing in their videos.

The point is that YouTube has managed to survive and even thrive despite hosting tons of content which infringes on copyright.

YouTube didn't get sued early on because it didn't have any money to sue. After it was acquired, it was sued, and the current system is the agreement that Google and the rights holders came to (which has been incredibly profitable for both sides).

They won't legislate AI away, just training them on copyrighted works without attribution and license. As it should be.

Countries that don't do that will be just as successful in the world marketplace as are countries that don't respect copyright.

Wow, thank you. That's a very interesting take.

You mention Popcorn time. I wonder if torrents in general could be a great example of how something like this plays out? Torrenting took the world by storm and had an amazing "product-market fit" for the early internet days. Of course, downloading copyrighted material was always illegal but that didn't stop many.

Over time, legal but paid alternatives rose up: Spotify, iTunes, Netflix. These players found their place in the market by balancing the interest of copyright holders and the needs of users looking for cheap and easy access to entertainment.

Just as Netflix acquired large content libraries, same here. With enough money, large training datasets could be acquired in a legally solid manner.

It's interesting to think where this analogy might fail as well, and how the paths of these technologies could differ. For one, torrenting was mostly for entertainment, and thus impacted B2C first. On the other hand, language models are more so for media _creation_ and the B2B sphere.

They can and do fight dirty. They don't only use legal tactics, they use legal options to get the information off from trustworthy sources.

Like torrents, you first have to resort to random websites who get randomly taken down as they acquire reputation. If a person takes the face and responsibility for something, he gets litigated into oblivion.

So you get to the point where trustworthy and untrustworthy sources are indistinguishable

. Now what they do is create untrustworthy sources. Like time for popcorn. Sow discord.

Fork several times, create intentionally malwared versions of both the program and the website. Keep kicking off the trustworthy sources of search engines, while magically skipping takedown requests for the less trustworthy websites.

Find ways to break old versions if possible, just to force them to keep moving. (they can make gradio randomly change APIs just to break the old trustworthy versions)

All of this can happen.

Fight dirty, you say? Capital idea. Let's see, the principals of any company or trade group that try litigating model providers "into oblivion" can have some more legal fun when synthetic images of them diddling their kids find their way to law enforcement or local vigilante organisations. And for anyone too squeamish for that, there should be a way to use the tech to do a really good SWATing for suspected murder -- bit of fine-tuning on screenshots from ISIS and Mexican drug gang videos, etcetera.

Before this gets flagged to oblivion, this is obvious. You just have to recognise that the "regulators" and industry insiders Emad is trying to "shield" you from are enemies and ask yourself, how do I hurt them?

>All of this can happen.

And reasonably technical people have zero issues, as it should be.

Media piracy has been abundant my entire life. It's never slowed down or become inaccessable.

"Over time, legal but paid alternatives rose up: Spotify, iTunes, Netflix. These players found their place in the market by balancing the interest of copyright holders and the needs of users looking for cheap and easy access to entertainment."

You didn't mention one of the largest (perhaps even the largest) distributor of copyright content (which happens to also be free, for now): YouTube.

You can watch/listen to endless amounts of copyrighted content (and other types of content) on there completely for free, and to say it's tremendously popular would be an understatement.

Google has made it work through ads. Perhaps something like that will happen with image-generating AI.

Notably Youtube contained rampant copyright infringement in the early years, but to be able to hold their market position gradually pivoted to a system that treats existing large copyright holders preferentially and clamps down on everyone else.

It still holds rampant copyright infringement.

Models that are trained on data under open source licenses (such as Creative Commons) would likely be much safer from copyright claims. I like to use the Debian Deep Learning Team's Machine Learning Policy to evaluate the openness of ML work.

https://salsa.debian.org/deeplearning-team/ml-policy

Unless they carry with them a library of attributions to every source image, that safety comes mostly from anticipating that authors of CC-licensed works won't be too upset about people using them.

I forked deepfake a few years ago because it seemed interesting. I didn’t have a spidy sense just thought it would be something interesting to look into. But I forked in GitHub rather than doing a proper clone so now it’s gone.

It reminds me to follow the datahoarder maxim that if you don’t admin then servers, you don’t have the data. So now I clone stuff to a local drive.

This lines up with my observation of the sudden and complete absence of celebrity deep fakes (the adult rated or otherwise) from the internet.

There is a legal machinery that works behind the scenes which we aren't always aware of.

I think you're just not looking. It might've disappeared from Twitter and Facebook, but it's still on Reddit, 4Chan and many other sites.

It is, but 4chan is hardly the mainstream internet, and there's a lot worse than celebrity deepfakes on it. On Reddit, it has been relegated to a few pariah subreddits. Earlier, you would have spotted some on the homepage.

Not sure what you mean by "mainstream internet", it's a normal page anyone can access by typing its address into the browser. Well known, too. And if you're looking for this kind of thing it's the first suggestion you're going to find.

Sure, it's not on the most visited homepages of the world - but it hardly went away. Even on the most visited homepage it's just few clicks away.

I think the savvy media companies realize that we're at the cusp of ai generated media - movies and music included. If we have free/open models trained on the past 100 years of media, they may become obsolete and they will fight this to the death.

Irony is the "NSFW" moral concerns, when the media companies put out such negative and filthy content as it is.

It’s interesting how we already are at a point where home users can in theory make a Star Wars fan film with Luke’s actual face and synthesized voice.

As a Star Wars fan, having Luke's actual face and synthesized voice would be the least interesting thing about a fan film.

What I would value much more is the writing, directing, editing, and acting... and you can't yet get very good quality of any of that through AI yet.

Maybe someday, but not today.

The way Disney is churning out rehashed content for its IP they're obsoleting themselves. When your human content is more predictable and stale than something generated by an AI you should hang your head in shame.

Any particular repos/artifacts you suggest downloading?

YALM 100B, and that giant codegen model from Salesforce https://huggingface.co/Salesforce

I am more hopeful. Unlike popcon/napster these models aren't directly impacting existing bottom line of any company/organization. Most of the models are trained on opensource / public datasets, so you won't find any company to sponsor the fight against these models. The cost of these models is an issue right now, but Mr. Moore has always handeled that well.

Ah., but SD does compete with OpenAI's Dalle2 no? I am however not sure if that will cause too much trouble though.

Unfortunately, you’re right. These models are beneficial to large corporations, and they do the most harm to the small individual artists who created the content that made them possible in the first place, so it’s unlikely there will be any serious legal challenges.

I don't think that Moore's law is what's driving down ML compute costs in particular, where there seems to be a lot of innovation going on in terms of hardware architecture and compilers (much of which is proprietary). Even just thinking about memory bandwidth, which historically has scaled much slower than compute: the $/second required to push 10+ TB of training data into some piece of hardware that can do useful work on it isn't going to fall by 100x in a decade.

Moore's law is not driving anything, it's just an observation. The effet might have multiple causes. So Moore's law also applies here

Moore's law is obsolete, we're thermodynamics boys now. How much power can you turn into CPU cycles rather than heat. It's the next frontier.

>Popcorn time lost a similar battle.

i was actively following torrentfreak at the time and there was genuine excitement with something incredible but that only lasted a week :-(

why do you say they lost the battle? the original team threw in the towel within the week but there are people who have taken the fight

https://github.com/popcorn-official/popcorn-desktop/releases... here, the latest release was on 04 Sep 2022 so it is very much in active development with a lot of people contributing https://github.com/popcorn-official/popcorn-desktop/graphs/c...

so while the original team might not be working on it, like a true free software, the code lives.

Model can be retrained (with some money). But data is harder. I cannot backup LAION 5B unfortunately. If you can, please do! (About 200T)

A distributed backup might be possible.

Get 200 interested people backing up 1 TB each and you have your 200 TB backup.

With redundancy and error correction data added to the mix, you should be able to lose a certain percentage of participants and still have access to the full, error-free backup.

Yeah, need to write the program and distributed in r/datahoarder

This should be next on my list since my current project depends on SD model and having data backed up gives me confidence that I can get rid of all their stuff if needed.

Hmm, you can just create a torrent out of it. Either as a single file (impractical, but you can just avoid downloading it fully), or chunk it into multiple files.

You don't even need to store it all at once on your computer: stream it and generate checksums on the fly. Then distribute the torrent, and seed sections at a time. It can also be distributed on IPFS.

I've seen a lot of torrents being used for distributing neural network (mostly stable diffusion forks).

Good point. Let me think through this. I have about 15T free space so this can be seeded separately in ~12 batches.

You can also distribute different torrents, it's easier.

Or multiple IPFS CIDs. I think you can have a "directory" (CID) that contains multiple CIDs, and only need the content hashes to build it.

You can also publish multiple CIDs and ask people to seed random ones; that's how Libgen does it (and is similar to the multiple torrents concept).

The same file can be used to seed both torrents and IPFS.

Why would you, though? It's just a list of 5B URLs. Some might go down, some new might go up. But it's not like any government body can suddenly take down all photos on the whole internet...

> That somehow international legislation will converge on the strictest possible interpretation of intellectual property, and those models will become illegal by the mere fact they were trained on copyrighted material.

That's the only possible interpretation, really. AI models algorithmically remix input intellectual property en masse, without any significant amount of human creativity, the only thing copyright law protects. As such, the models themselves are wholly derived works, essentially a compressed and compact representation of the artistic features of the original works.

Legally, a AI model is equivalent to a huge tar.gz of copyrighted thumbnails: very limited fair use applies, only in some countries, and only in certain use contexts that generally don't harm the original author or out-compete them in the market place - the polar opposite of what AI models are.

>That somehow international legislation will converge on the strictest possible interpretation of intellectual property, and those models will become illegal by the mere fact they were trained on copyrighted material.

Just feels absurd to me because how is this different from any Human artist who you could equally say was "trained" on copyrighted material.

>Get it now when there are trustworthy sources. Once these kinds of things go underground, it gets much harder to get a trustworthy version.

People have already reverse engineered most text2image models and given enough hardware can train their own. There is no need for this hysterical take. As long as the internet exists you will be able to train these models.

Here's a (not-recommended but amusing) nuclear option:

Tit-for-tat. Regulators and artists don't want this? Okay, include in all open source software licenses that regulators and artists are now barred from using them without payment.

A lot of open source software authors don't want this either because it can circumvent copyleft and attribution requirements.

Also, discriminating like you suggest would make those licenses closed source by definition.

I'm glad you're responding with real issues, though let me reiterate that my comment was more to amuse and vent.

Long live clopen source!

> Also, discriminating like you suggest would make those licenses closed source by definition.

Definitions are not what matters in the end. Why doesn't the viral and restrictive element of GNU's GPL license make the license "non-open"?

Definitions are what matter, they're the reason you can use words and I can understand what you mean by them.

"Open source" has a definition (https://opensource.org/osd) and the GPL meets it, because it doesn't prevent derived worries from being distributed under the same license.

Interestingly it's the same with attribution requirements for art, but since it's not written words, nobody can claim: "this part is exactly my GPL code". But with art it's "this is exactly how I do texture on metal", "this is exactly how I paint steampunk greeble", "this is how I do clouds" etc.

That would be neither an open source license (according to the OSI) nor a free license (according to the FSF).

https://www.gnu.org/licenses/license-list.html

> [...] is a nonfree license because it extends the four freedoms only to some kinds of organizations, not to all. Such a restriction in a software license, in the name of any cause whatsoever, imposes too much power over users. Please don't use this license, and we urge you to avoid any software that has been released under it.

https://opensource.org/osd-annotated

> 1. Free Redistribution

> The license shall not restrict any party from selling or giving away the software as a component of an aggregate software distribution containing programs from several different sources. The license shall not require a royalty or other fee for such sale.

> Rationale: By constraining the license to require free redistribution, we eliminate the temptation for licensors to throw away many long-term gains to make short-term gains. If we didn't do this, there would be lots of pressure for cooperators to defect.

Slight tangent, but you seem to know about licences... Do you happen to know of a licence that has anything like a "can only be used for the benefit of humanity" clause?

I've favoured the MIT licence for what little OSS I've published thus far. But, I'm becoming increasingly concerned that ruthless profit-above-all-else driven companies can include my (benign) work in systems that causes real harm.

That’s far too subjective to be of any legal value. If you want that, you’ll need to (a) spell out what you want to allow, (b) spell out what you want to disallow, or (c) just write the subjective thing out plain and simple and don’t even bother with complying with license norms (e.g. just write “you can do whatever you want with this provided it is for the benefit of humanity”).

That's a fair criticism. My idea of good is not defined, or static - it adapts over time to the norms and values of society.

Perhaps something like the OpenAI approach to their GPT-3 deal with Microsoft is better. That is, if the work Microsoft do with GPT-3 goes in a direction OpenAI doesn't like, OpenAI reserves the right to veto the work [1].

[1]: https://www.ted.com/talks/the_ted_interview_the_race_to_buil...

Is creating weapons for nations military good, or evil? I’m sure those fighting to protect themselves have an opinion…

I think there are some instances where it's definitely bad. E.g. weapons that, if used, can by themselves extinguish humanity. Most instances are not that clear unfortunately - lots of sides to the story, extenuating circumstances, etc. etc.

It's not an easy question. However, as the creator of the software I guess I feel that my opinion should count in how it's used. As a simplistic example, if in some dystopian timeline my OSS were used to facilitate a holocaust I'd like to be able to do something to halt that. It doesn't matter that the perpetrators feel that what they're doing is right.

Of course a person has to have some sort of opinion of it under such conditions, but is it going to come down mostly condemning the weapons enabling the aggressor or thankful for the weapons that enable some measure of violent defense?

The Slaughterbots campaign argued, rightly, I think, that advanced autonomous lethal weapons should be suppressed because they enable unethical uses and unscrupulous actors far more than legitimate defense.

It can't really be seen in isolation from the environment (social, economical, etc) it's going to come into I suppose, but in the real, concrete world we have creating them is not a neutral act, and some of the consequences are reasonably predictable.

The HESSLA is the closest I know of to that kind of thing, although it's not widely used or well regarded.

Thanks for the pointer - the criticisms [1] are interesting to read.

[1]: https://www.gnu.org/licenses/hessla.en.html

http://www.json.org/license.html

>The Software shall be used for Good, not Evil.

AIUI, it was put it mostly as a joke.

https://www.cnet.com/culture/dont-be-evil-google-spurns-no-e...

If only it were that easy to do, and to enforce. Good on them for trying though - at the least it kicks off an interesting debate.

>Do you happen to know of a licence that has anything like a "can only be used for the benefit of humanity" clause?

A terrible idea for a number of reasons (in terms of legal enforceability, unintended side effects, and more). The following two articles do a good job of explaining why such a license really isn't practical:

https://perens.com/2019/09/23/sorry-ms-ehmke-the-hippocratic...

https://www.gnu.org/philosophy/programs-must-not-limit-freed...

Yes, the unintended side effects of HESSLA (sibling comment) were a surprise for me to read about. Thank you for the links - I'd not heard of the Hippocratic License but the criticisms are interesting.

There is a strange psychology at play here.

Your first assumption is that your inventions are important enough to be of use to “bad people”.

The other is your assumption that you have the objective ability to determine good from bad uses of a benign invention.

I’m increasingly looking for the psychological reasons why these ML models and their outputs cause such an emotional reaction in certain individuals.

For example, the language of opponents of Copilot speaks in absolutes. And when presented with the history of copyright when applied to software the opponents seem to not register that copyright (logically) does not extend to the non-expressive parts of a work.

https://en.wikipedia.org/wiki/Abstraction-Filtration-Compari...

This allows for verbatim copies if they are utilitarian in nature!

Now, this doesn’t mean that GitHub is not in violation of other copyright claims, such as clearly expressive parts like comments and more!

What if I don't care much about what the OSI or FSF think and don't buy their rationale? Is there a good, practical argument against such licenses?

then you and the 100 other people who feel that way will be the ones to save humanity. no pressure

Very cool but I don't care about saving humanity either, parent of parent asked a valid question and "FSF says so" with some hand wavy rationale is just not a very satisfying answer.

They call it a "nuclear option" which generally implies some level of effectiveness. The fact that nobody would agree to go along with this sort of scheme renders it ineffective. This isn't a nuclear option, it is a wet fart option.

Thank you, that makes some sense.

I'm not trying to be contrarian here, I was curious why not and why this isn't a thing already. I'm just more of a programmer guy and less of a lawyer guy

Then don't use an open source or free software license. Write your own custom license (perhaps consult with legal counsel in the process) and use it for the software you create.

I don't argue that such licenses are bad (though the FSF might), just that they are neither open source nor free.

Oh, yeah well that's not a real open source license. Apologies I read the "open source" more as in "it's on github" and was a little confused what all the organizations and definitions have to do with the actual idea

Dual license then. Payment required if you are in a category; FLOSS for everyone else.

What a wonderful excuse for a government to pay another 1 billion dollars on crapware to their cronies, who will outsource it to some incompetent software sweatshop on the other side of the world.

We can barely get governments to use open source even today, without restrictions. Hell, we can barely make them manage source code for commercial products they commission and pay for. I've walked into govt shops that were 100% binary dependent to the original software author, which never delivered source code and charged them trough the nose for the basic servicing.

Like it or not, the government and regulators represent us, we need individual accountability but harming the govt. directly harms ourselves firstly. The bureaucrats and the corrupt hardly care.

The UK and the EU have already made to law that text and data mining is excluded from copyright for non-commercial uses, and the UK has even done so for commercial use cases.

Personally, I think commercial use cases should get license agreements from the authors for their training data, but I think non-commercial exemptions to advance the field of AI makes sense.

Irregardless of what I think though, the UK has set an international precedent, and the EU is apparently discussing about possibly extending it to commercial use cases as well. So there's that.

I agree that it’s a good idea to download everything now and I agree that the legal powers that be will probably soon force it underground - but I’m less certain the driving reason will be copyright / IP. I think it will be reasons similar to what TA hints at. People are (somewhat understandably) upset with certain classes of output the model is capable of generating and a moral panic is likely to ensue that, historically, has won most cases it’s presented itself in.

I figure these tools fall in a similar category to web scraping which is legal. What you can’t do is copy the file. If you can demonstrate that you are modifying the source data then it’s a new work. Style is not protected by copyright as much as famous artists may want.

Where copyright may be applicable is when the models reproduce original art without modification that a reasonable person wouldn’t know the difference.

> those models will become illegal by the mere fact they were trained on copyrighted material

The blog post says they are worried about the ability to use the model to "use it for illegal purposes and hurting people". I think that they are referring to the ability to create all kinds of compromising pictures (porn) with celebrities, kids, etc. Am I misreading that? They don't mention copyright anywhere.

> Am I misreading that? They don't mention copyright anywhere.

The conspiracy theorist would say that if you were doing something you shouldn’t, you wouldn’t mention it. Instead, you’d give a more palatable excuse to buy yourself some time while you figure out how to get away (legally) with the thing you shouldn’t be doing.

Yesterday I was backing up and old failing HD. I looked at the models I downloaded since 2014 and since I was out of time, I decided to just delete them. But I deleted them with the same thought you just shared: those old models probably don't even exist anymore, they're probably gone. I'm just hoping that time you described isn't happening anytime soon.

Where can we get Stable Diffusion downloaded?

I think it'll be EU-style privacy regulations that make it illegal to train on the majority of data. Perhaps the requirement to be able to remove a user's impact from an already computed model if they file a right-to-be-forgotten.

Something that would make any non-trivial model a legal nightmare.

> close the Stable doors after the horses Diffused

Encapsulates it all well I like this statement, total pottery

I've followed this sort of thing rather loosely so far, any recommendations what other pre-trained models would be worth looking at?

Is there a torrent available? This is an effective way to ensure the models and information remain available indefinitely.

can you point the good source to download? thanks.

Hugging face is the original source for most of the latest models, including the latest Stable Diffusion v1.5[1]

[1]: https://huggingface.co/runwayml/stable-diffusion-v1-5

Can't find a working link for 1.5, but for anyone who cares, I did turn up https://rentry.org/voldy, which has a actually-working link for SD 1.4[0] (in addition to the huggingface paywall) as well as to a couple other compatible models including NovelAI.

0: magnet:?xt=urn:btih:3a4a612d75ed088ea542acac52f9f45987488d1c&dn=sd-v1-4.ckpt&tr=udp%3a%2f%2ftracker.openbittorrent.com%3a6969%2fannounce&tr=udp%3a%2f%2ftracker.opentrackr.org%3a1337

Hugging Face has no paywall

You have to create an account and agree to some stuff and be logged in but you're correct that you don't have to pay.

> You have to create an account and agree to some stuff and be logged in

So a paywall. I agree it's a annoyingly misleading term for the concept and would be happy to hear better alternatives[0], but I haven't found one yet.

0: like eg "passphrase" instead of "password" or "assuming the conclusion" instead of "begging the question" for their respective concepts

If that's what paywall means now then sorry for the noise. :)

I would disagree with their assertion that requiring an account is a paywall. I mean, it's in the name... if you don't have to pay, it's not a paywall. A barrier to entry, sure, but a far easier one to overcome than entering payment details.

You're not paying with money, but you're paying with your identity.

At this point in history, it's very unlikely your identity is secret anyway.

That's no reason to make it any easier for them to get it.

These models may contain executable code. Beware of malware. Official sources are recommended for this reason.

> These models may contain executable code. Beware of malware.

Seconded, actually. I do have a bad habit of assuming people already know this.

> Official sources are recommended for this reason.

Very not seconded; see for example comments elsewhere in this thread about untrustworthy sources for popcorn time, and recall that the GP was specifically discussing the risk of Stability AI deciding to kill this.

Okay, bad habit stikes again apparently. To be explicit: If you get it from official sources, you should still beware of malware just as much as if you got it from a skeezy darkweb site (and vice versa).

what if the models are used to generate a new training set ?

About 150m images have been generated with SD so far. That's already a new large scale training set. Generate and curate before retraining to create a virtuous cycle.

This looks more like polluting the training set to me.

Doesn't this ultimately result in local maxima? All the biases get reinforced and all the novelty (things the system hasn't seen/produced yet) goes away.

A tiny example: Dall-E (and SD) both struggled with eye positioning, for example. Wouldn't training a model on their output then reinforce that particular bias of poorly positioning eyes? Now multiply this by every existing quirk in the models.

Stability AI is formed with that vision to keep is open-source and accessible to the masses. It's very rare that we might see it becoming a closed source

But it might eventually be neutered with filtering out "problematic" content from the models. Maybe that's NSFW now, but then could easily have busybodies start pressing for "bias" and other topics to be removed.

Someone here put it very well to watch out how the masses would try to censor AI-produced content into oblivion in their futile pursuit of trying to shoot the messenger.

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