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Flux: Open-source text-to-image model with 12B parameters

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Re: Flux: Open-source text-to-image model with 12B parameters

#211

Earlier quoted context omitted.

These models are a product of much more creativity than simply a list of phone numbers in a phone book. I don't see how they wouldn't meet the modicum of creativity required for US copyright protection.

The software that creates the model is the product of creativity. The model itself is the product of mechanically applying that software to datasets that are (a) assembled with minimal, if any creativity, and (b) definitely not assembled with any eye to the specific form of the resulting model. The whole point is to get the software to form the model without you having to worry about what the result is going to look…

There's a lot of effort involved in the training runs too, and you might be able to get away with saying the ops engineers made creative choices too (of which checkpoints were good etc.)

Since it costs millions to produce one of these models, it's not just taking the software and running it to compile them.

Re: Flux: Open-source text-to-image model with 12B parameters

#212

Earlier quoted context omitted.

> mechanically applying that software to datasets that are (a) assembled with minimal, if any creativity, and (b) definitely not assembled with any eye to the specific form of the resulting model. Fair enough, but those datasets are also primarily copyrighted material. If the software here merely transforms the input material (which I agree it does), then the output is a derivative work.

But the pitch-shifted song is still recognizably a creative work . It has identifiable, humanly comprehensible forms of all the original creative elements that Swift originally put into it (plus I guess a de minimis amount of extra creativity from the choice to pitch shift it). If I take a string of data from a true hardware RNG, XOR it with a Taylor Swift song, and throw away the original random stream, is the resul…

> If I take a string of data from a true hardware RNG, XOR it with a Taylor Swift song, and throw away the original random stream, is the resulting fundamentally random bit string still a derivative work of the song? As with the ML model, you can't recognize the song in it. And as with at least some training examples in the inputs of most ML models, you can't recover the song from it either.

It's not a copy of it, and when you distribute it you're not distributing the original. So it's not a derivative for copyright purposes.

It can still be a derivative for other legal purposes. Judges don't appreciate it when you do funny math tricks like that and will see through them.

> It feels like the test for whether X is derivative for copyright purposes should include some kind of attention to whether X is a creative work at all. Maybe not, but then what test do you use?

Yes, that's how US copyright law works. (well sort of…)

Being a transformative work of something makes it less of a copy of it, the more transformed it is, since it falls under fair use exemptions or is clearly a different category of thing.

If a model was a derivative of its training data, then Google snippets/thumbnails would be derivatives of its search results and would be illegal too. Unless you wrote a new law to specifically allow them.

In other countries (Germany, Japan) fair use is weaker, but model training has laws specifically making it legal in certain circumstances, and presumably so do Google snippets.

Re: Flux: Open-source text-to-image model with 12B parameters

#213
I enter an elaborate prompt, press "Sign in to Run", sign in with my GH, get taken back to the previous page and my prompt text has reset to some default with no way to get back what I entered before.

Complete and utter UX/first impression fail. I had no desire to actualy try the model after this.

Re: Flux: Open-source text-to-image model with 12B parameters

#214

Tested it using prompts from ideogram (login walled) which has great prompt adherence. Flux generated very very good images. I have been playing with ideogram but i don't want their filters and want to have a similar powerful system running locally. If this runs locally, this is very very close to that in terms of both image quality and prompt adherence. I did fail at writing text clearly when text was a bit complica…

If you have the hardware (24GB VRAM plus 32GB) the easiest way to run locally seems to be SwarmUI

See: https://www.reddit.com/r/StableSwarmUI/comments/1ei86ar/flux... (SwarmUI is cross platform and runs on macs, and linux)

Re: Flux: Open-source text-to-image model with 12B parameters

#215

Earlier quoted context omitted.

What is the difference between schnell and dev? Just the kind of distillation?

>FLUX.1 [dev]: The base model >FLUX.1 [schnell]: A distilled version of the base model that operates up to 10 times faster It should also be noted that "schnell" is the German word for "fast".

Not quite right, per their github repo:

> Models

> We are offering three models:

> FLUX.1 [pro] the base model, available via API

> FLUX.1 [dev] guidance-distilled variant

> FLUX.1 [schnell] guidance and step-distilled variant

Re: Flux: Open-source text-to-image model with 12B parameters

#216

Earlier quoted context omitted.

But the pitch-shifted song is still recognizably a creative work . It has identifiable, humanly comprehensible forms of all the original creative elements that Swift originally put into it (plus I guess a de minimis amount of extra creativity from the choice to pitch shift it). If I take a string of data from a true hardware RNG, XOR it with a Taylor Swift song, and throw away the original random stream, is the resul…

> If I take a string of data from a true hardware RNG, XOR it with a Taylor Swift song, and throw away the original random stream, is the resulting fundamentally random bit string still a derivative work of the song? As with the ML model, you can't recognize the song in it. And as with at least some training examples in the inputs of most ML models, you can't recover the song from it either. It's not a copy of it, an…

> It's not a copy of it, and when you distribute it you're not distributing the original.

A compressed (or normally encrypted) version wouldn't be a copy that way, either, but I would still absolutely go down for distributing it. The difference is that the compression can be reversed to recover the original. Even lossy compression would create such a close derivative that nobody would probably even bother to make the distinction.

You're right that "math games" don't work in the law, but that cuts both ways. If you do something that truly makes the original unrecoverable and in fact undetectable, and if nothing salient to the legal issues at hand about the new version derives from the original, then judges are going to "see through" the "math trick" of pretending that it is a derivative.

> then Google snippets/thumbnails would be derivatives of its search results

Thumbnails are legally derivative works, in the US and probably most other places. In the US, they're protected by the fair use defense, and in other places they're protected by whatever carveouts those places have. But that doesn't mean they're not derivative works.

In fact, if I remember the US "taxonomy" correctly, thumbnails are infringing. It's just that certain kinds of ingfringement are accepted because they're fair use.

If thumbnails weren't derivative works at all, then the question of fair use wouldn't arise, because there can be no infringement to begin with if the putatively infringing work isn't either derivative or a direct copy.

Where thumbnails are different from ML models is that they're clearly works of authorship. In a thumbnail, you can directly see many of the elements that the author put into the original image it's derived from.

The questions are (a) whether ML models are works of authorship to begin with (I say they're not), and (b) whether something that's not a work of authorship can still be a derivative work for purposes of copyright infringment (I'm not sure about that).

So far as I know, neither one is the subject of either explicit legislation or definitive precedent in most of the world, including the US.

Re: Flux: Open-source text-to-image model with 12B parameters

#217

Anyone know why text-to-image models have so many fewer parameters than text models? Are there any large image models (>70b, 400b, etc)?

The way someone explained it to me is that text-to-image models are essentially just de-noisers.

They train them by taking an image with a label, ie, "cat", and then adding some noise to it, run a training step, add more noise, run another step, and so on until the image is total (or near total) noise and still being told it's a cat.

Then, when you want to generate "cat", you start with noise, and it finds a cat in the noise and cancels some of the noise repeatedly. If you're able to watch an image get generated, sometimes you'll even see two cats on top of each other, but one ends up fading away.

Turns out, these denoisers don't require that many parameters, and if your resulting image has a few pixels that are just a tiny bit off color, you won't even notice.

Re: Flux: Open-source text-to-image model with 12B parameters

#218

Am I missing something? The beach image they give still fails to follow the prompt in major ways.

You're not. I'm surprised at their selections because neither the cooking one nor the beach one adhere to the prompt in very well, and that first one only does because it prompt largely avoids much detail altogether. Overall, the announcement gives the sense that it can make pretty pictures but not very precise ones.

Sounds to me like it's an issue with their VLM captions creating very "pretty" but not actually useful captions. Like one of the example image prompts includes this absolute garbage:

> Convey compassion and altruism through scene details.

Re: Flux: Open-source text-to-image model with 12B parameters

#219

Earlier quoted context omitted.

> it is not open source It would be nice here if you give some examples of what you call open source model. Please ;) Because the impression is that these things do not exist, it's just a dream which does not deserve such a nice term..

As far as I know, none have been released. And it doesn't even really make sense, because, as I said, the models aren't copyrightable to begin with and therefore aren't licensable either. However, plenty of open source software exists. The fact that open source models don't exist doesn't excuse attempts to falsely claim the prestige of the phrase "open source".

> As far as I know, none have been released.

I can tell you a secret. What you call 'open source' models are impossible. Because massive randomness is a part of training process. They are not reproducible. Having everything you cannot even tell if the given model was trained on the given dataset. Copyright is a different thing.

And a bad news, what's coming is even worst. Those will be the whole things with self awareness and personal experience. They can be copied, but not reproduced. More over, it's hard or almost impossible to detect if something undeclared was planted in their 'minds'.

All together means 'open source' model in strict interpretation is a myth, great idea which happen to be not. Like Turing test.

> However, plenty of open source software exists.

Attempt to switch topic detected.

PS: as for that massive downvote, I even wasn't rude, don't care. This account will be abandoned soon regardless, like all before and after.

Re: Flux: Open-source text-to-image model with 12B parameters

#220

You can try the models here: (available without sign-in) FLUX.1 [schnell] (Apache 2.0, open weights, step distilled): https://fal.ai/models/fal-ai/flux/schnell (requires sign-in) FLUX.1 [dev] (non-commercial, open weights, guidance distilled): https://fal.ai/models/fal-ai/flux/dev FLUX.1 [pro] (closed source [only available thru APIs], SOTA, raw): https://fal.ai/models/fal-ai/flux-pro

What is the difference between schnell and dev? Just the kind of distillation?

Also worth mentioning schnell is a 4-step model, so comparable to SD lightning in that regard
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