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Stable Diffusion 2.0

stability.ai

421–430 of 519 posts

Re: Stable Diffusion 2.0

#421

Earlier quoted context omitted.

> Specifically, Wikimedia Commons images in the PD-Art-100 category, because the images will be public domain in the US and the labels CC-BY-SA. Doesn't the "BY" part of the license mean you have to provide attribution along with your models' output[0]? I feel you'll have the equivalent of Github Copilot problem: it might be prohibitive to correctly attribute each output, and listing the entire dataset in attribution…

The SA part (ShareAlike) is even more restrictive, as it imposes a license on the derivative work. "— If you remix, transform, or build upon the material, you must distribute your contributions under the same license as the original"

How is that restrictive? Doesn't it just mean that any outputs of the model also fall under the same license so they can be used in public datasets?

Re: Stable Diffusion 2.0

#422
post #388
post #363

In addition to removing NSFW images from the training set, this 2.0 release apparently also removed commercial artist styles and celebrities [1]. While it should be possible to fine tune this model to create them anyway using DreamBooth or a similar approach, they clearly went for the safe route after taking some heat. 1. https://twitter.com/emostaque/status/1595731407095140352?s=4...

Mixing artist names was by far the most effective way to create aesthetically pleasing images, this is a huge change. DreamBooth can only fine-tune on a couple dozen images, and you can't train multiple new concepts in one model, but maybe someone will do a regular fine-tune or train a new model.

[deleted]

Re: Stable Diffusion 2.0

#423

Earlier quoted context omitted.

I fail to see the difference between "underlying structure" and "short-range visual features". Both are just simple statistical relationships between parameters and random variables.

Sure, but why would that not apply to humans? And we don't consider it copyright violation if a human learns painting by looking at art.

Because we made the algorithms and can confirm these theories apply to them.

We can speculate they apply to certain models of slices of human behaviour based on our vague understanding of how we work, but not nearly to the same degree.

Re: Stable Diffusion 2.0

#424

Earlier quoted context omitted.

I predicted back when they started backpedaling that there's a chance that sd1.4 or 1.5 will be the best available model to the general public, for a very long duration, because the backlash will force them to self-castrate themselves. You can see nobody likes this new model in any of the stable diffusion communities. It's a big flop and for a good reason. The reason it was so successful in the first place was becaus…

That's like saying that obtaining the On the Origin of Species or Linux kernel will be harder in future. If anything the SD weights will be increasingly ubiquitous as they start embedding it into consumer electronics.

>If anything the SD weights will be increasingly ubiquitous as they start embedding it into consumer electronics.

I suspect for similar liability issues as SD 2.0, that they will not strt embedding sub-2.0 weights into consumer electronics.

Re: Stable Diffusion 2.0

#426

Earlier quoted context omitted.

The main reason why Stable Diffusion is worried about NSFW is that people will use it to generate disgusting amounts of CSAM. If LAION-5B or OpenAI's CLIP have ever seen CSAM - and given how these datasets are literally just scraped off the Internet, they have - then they're technically distributing it. Imagine the "AI is just copying bits of other people's art" argument, except instead of statutory damages of up to…

Is artificially generated CSAM that doesn't actually involve children in its production not an improvement over the status quo?

No, it's not.

The underlying idea you have is that the artificial CSAM is a viable substitute good - i.e. that pedophiles will use that instead of actually offending and hurting children. This isn't borne out by the scientific evidence; instead of dissuading pedophiles from offending it just trains them to offend more.

This is opposite of what we thought we learned from the debate about violent video games, where we said stuff like "video games don't turn people violent because people can tell fiction from reality". This was the wrong lesson. People confuse the two all the time; it's actually a huge problem in criminal justice. CSI taught juries to expect infallible forensic sci-fi tech, Perry Mason taught juries to expect dramatic confessions, etc. In fact, they literally call it the Perry Mason effect.

The reason why video games don't turn people violent is because video game violence maps poorly onto the real thing. When I break someone's spine in Mortal Kombat, I input a button combination and get a dramatic, slow-motion X-ray view of every god damned bone in my opponent's back breaking. When I shoot someone in Call of Duty, I pull my controller's trigger and get a satisfyingly bassy gun sound and a well-choreographed death animation out of my opponent. In real life, you can't do any of that by just pressing a few buttons, and violence isn't nearly that sexy.

You know what is that sexy in real life? Sex. Specifically, the whole point of porn is to, well, simulate sex. You absolutely do feel the same feelings consuming porn as you do actually engaging in sex. This is why therapists who work with actual pedophiles tell them to avoid fantasizing about offending, rather than to find CSAM as a substitute.

Re: Stable Diffusion 2.0

#427
post #81

Earlier quoted context omitted.

To put things in perspective, the dataset it's trained on is ~240TB and Stability has over ~4000 Nvidia A100 (which is much faster than a 1080ti). Without those ingredients, you're highly unlikely to get a model that's worth using (it'll produce mostly useless outputs). That argument also makes little sense when you consider that the model is a couple gigabytes itself, it can't memorize 240TB of data, so it "learned"…

Quite right, but… > That argument also makes little sense when you consider that the model is a couple gigabytes itself, it can't memorize 240TB of data, so it "learned". The matter is really very nuanced and trivialising it that way is unhelpful. If I recompress 240TB as super low quality jpgs and manage to zip them up as single file that is significantly smaller than 240TB (because you can), does the fact they are…

> Can’t you just engineer the prompting better so that it generates “by Greg Rutkowski“ images without being trained on actual images by Greg?

Can you please rewrite this in the writing style of Socrates?

Re: Stable Diffusion 2.0

#428

Earlier quoted context omitted.

If you can't process/digest copyrighted content with algorithms/machine learning then Google Search (the whole thing, not just Image Search) is dead. So no, it's not at all clear where the legal lines are drawn. There have been no court cases yet, regarding the training of ML models. People are trying to draw analogies from other types of cases, but this has not been tried in court yet. And then the answer will likel…

> If you can't process/digest copyrighted content with algorithms/machine learning then Google Search (the whole thing, not just Image Search) is dead. Not if Google honors the robots.txt like they say they do. Hosting content with a robots.txt saying "index me please" is essentially an implicit contract with Google for full access to your content in return for showing up in their search results. Hosting an image/cod…

Google also indexes sites without robots.txt. Also, it's not mere classic indexing but ML processing too.

Re: Stable Diffusion 2.0

#429
post #420

Earlier quoted context omitted.

I predicted back when they started backpedaling that there's a chance that sd1.4 or 1.5 will be the best available model to the general public, for a very long duration, because the backlash will force them to self-castrate themselves. You can see nobody likes this new model in any of the stable diffusion communities. It's a big flop and for a good reason. The reason it was so successful in the first place was becaus…

As someone completely unfamiliar with SD but interested in playing around with it in the future, what exactly should I download, to have a fully local instance of 1.4 or 1.5?

For Macs there's Diffusionbee with a no-brainer setup.

Re: Stable Diffusion 2.0

#430
post #420

Earlier quoted context omitted.

I predicted back when they started backpedaling that there's a chance that sd1.4 or 1.5 will be the best available model to the general public, for a very long duration, because the backlash will force them to self-castrate themselves. You can see nobody likes this new model in any of the stable diffusion communities. It's a big flop and for a good reason. The reason it was so successful in the first place was becaus…

As someone completely unfamiliar with SD but interested in playing around with it in the future, what exactly should I download, to have a fully local instance of 1.4 or 1.5?

AUTOMATIC1111's web UI, with the models among dependencies.

https://github.com/AUTOMATIC1111/stable-diffusion-webui

https://github.com/AUTOMATIC1111/stable-diffusion-webui/wiki...

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