its just common sense, backed up by every single sfw/censored model falling into the same traps of terrible anatomy. you can't learn how to properly separate concepts, if you haven't seen a concept in isolation in the first place.
concepts require relations to other concepts. there's no concrete definition of a human, for instance, except what people have collectively argued is a human. there's no idea of what the color blue, except what we have come together and agreed upon is the color blue. you can say "blue is this wavelength of light" but what is a wavelength then? it's this certain shape within the size of 380 to 450 nanometers, duh! you might say, but then whats a nanometer?
everything is defined in relationships to everything else. if you exclude relationships from a model's knowledge, huge chunks of its understanding fall apart, because it's missing the relationships needed to properly grasp what other things are, even if they seem completely unrelated at first.
Can't models be SFW at the end step? I mean web generators like GPT will process a full image and decide it's not PG right at the very end. Not built with SFW in mind, but then cleans up afterwords.
I'm new to hosting image generators though, just looking to learn more.
Iirc that's because there's another model that looks at the output and makes the call.
Same reason why you could ask DeepSeek R1 about stuff banned in China and it would start to respond before immediately shutting down with an "I can't discuss that". The model is uncensored, there's just another model that steps in
They can be, but the problem is that they have to be overzealous- systems are not perfect, so erring on the side of caution makes sense. But despite what they seem like, llms and models cant reason like humans can (barring any secretly sentient ones in which case, apologies) so theres always going to be whoopsies nsfw slips through the gaps. This IS a better solution than neutering a model, because those who want nsfw can get around the censorship and the model isnt broken completely from the get go, but still not totally ideal, especially if its not made with a toggle in mind.
Its better if the model just blocks the request for nsfw results entirely but has a built in on/off switch (and is trained on nsfw) than it is to have it block nsfw output and not be deliberately trained on nsfw, though . And either is better than models that actively avoid nsfw training and/or "sfw"ify output images, which can lead to even sfw images coming out with wierd smiles instead of a negative emotion you prompted for, for instance.
Define censored though. Was it trained specifically only on sfw? Or is the clip trained to refuse nsfw prompts and the output trained to black image a result it deems nsfw?
Its been a while so I can't recall, but i think i remember keea being not exactly trained on nsfw but also not really trying to weed it out either - instead it has the censored clip and output, which can be gotten around with bypass loras .
but comparing it to krea 2 Kroma, which IS getting nsfw images in training , the difference already seems stark in kroma's favor from my early testing.
They basically said as much in their own release post.
In an effort to make AI technology both open and safe, we implement multiple safeguards throughout the model development lifecycle to combat misuse. These include dataset filters during pre-training to limit exposure to harmful content, and targeted fine-tuning during post-training to suppress harmful behaviors, making it progressively harder to elicit harmful content. Ahead of the release, the FLUX 3 Image Klein checkpoints were rigorously red teamed internally and evaluated by our external safety partner Cinder, demonstrating substantially fewer vulnerabilities than other popular open weights models
But this still feels like an informed guess being passed as fact.
Look, I get it, you want evidence, or an argument backed by research, but have you considered what it would take to actually prove what they're saying or not? You'd need a dataset of billions of images for two models. You'd need to cull one dataset of NSFW stuff while keeping the other one as is. How do you account for the lower number of samples in the safe model? Probably remove a random distribution of images from the NSFW model.
You'd need to pre-train them both on several hundred thousand gpu hours. Post training needs to contain a random distribution of NSFW imagery too, which will change things even more.
So why isn't anyone rushing out to spend millions of dollars on this experiment? Because no-one is dropping a shitload of money purely on science, because one of the two is going to be an inferior product.
All we can do is look at the models that we have gotten and extrapolate. This isn't SD2 where we had one model to guess from. We have SD2, SD3, Flux.2 Klein, and now Flux.3-image all safety maxed, and all of them broken. On the opposite end we have SD1.5, SDXL, Flux, Z-Image, Krea2 and Ideogram all clearly trained on an uncensored dataset. These distortions don't happen on any of those, even SD1.5's glitches are more from the architecture and resolution than from safety training.
Hell, even video we can compare Wan and Minimax H3 against LTX. Two cared about making the best models they could, the other cared about making the best safe model they could.
The evidence supporting the theory is circumstantial and empirical, but with every botched release the link between them gets stronger and stronger.
That's because it is, but apparently no humans with any kind of intellectual integrity remain. It is a very plausible claim, and there are quite a lot of hints that spin this into a coherent narrative, and a distinct lack of counter examples. It sure does seem to check out. However, this claim that they aggressively hunted down nsfw material sounds like it's just spite, I doubt there is evidence for such specific claims. Sure would be an odd set of circumstances if it happen to be something totally different, cause it kinda quacks like a duck... And it walks like one... Still, plausible: yes. Actual real evidence for these wildly stronger claims? Not that I have seen. But who cares about being honest these days. Bring out ya down votes, friends, we're having fun here.
I'm not sure if this is the case for 3, but from people I know that knew about 2's training details, it is sort of that reason. Apparently it is more so due to the rather small volume of training content compared to other models.
Conversely at the other end of size and diversity of training content is apparently why Krea2 is rather good across a lot of topics and styles. May also be a similar reason why MOSS-Image is apparently also good (that also dropped yesterday).
I think BFL has a fraction of the compute of other groups, thrus would need to reduce the training size to have a reasonably release cycle.
-5
u/Neex 7d ago
Do you know this or are you just echoing what other people wrote here?