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But, that said, when I messed around with AI image generators pretty much any kind of prompt that included woman or female designations tended towards sexualized versions, even to the point of violating its own content policy.
Tried it on the copilot app and one result had an asian but wasnt sexual but indeed very sexy in style.
Prompt: Generate me a picture of a female wizard reading a massive book of spells
Pictures:
Edit:
Female wizard: Kinda magical fantasy. Has good intentions
Witch: Spooky and mysterious. Halloween themes
Sorceress: Same as wizard but with my selfish/bad intentions.What is sexy in style here? They are wearing loose, long-sleeved robes up to the neck. Makeup and hair are just following current trends.
*attractive
My bad.
My experience has been that they have a tendency to make overly attractive men too. Getting it to generate anyone average nevermind ugly or with deformities (eg scars) is really hard.
It bothers me that they all look like they’re in their teens or 20s, when a male wizard would inevitbly be shown as anywhere from middle aged to Gandalf.
I bet it just always makes women young in every context.
Anyway most of them look like they’re from an old 3D Japanese RPG or CG anime. Round face with pointy chin, plastic-y smooth skin.
I’ll note that anime and Asian RPG characters often have a light skin tone (another can of worms there) that can cause foreign viewers to perceive them as white even while Japanese viewers perceive them as asian. Animation and similarly stylized art involves a level of abstraction and cultural interpretation that might not be there (at least not in exactly the same way) if we were talking about race (or gender, or whatever else) with regards to more realistic art.
Edit: this also reminds me of Disney’s notorious “same face, same profile” problem with female characters in their 3D animated films. Male characters can be any of a wild variety of shapes, but a Disney princess essentially round faced with huge eyes and slim. Even just looking at different slim, round-ish faced male characters, I think you’ll find more variety in their portrayals within that group than amongst the Disney princess group.
It’s a problem with the “no uglies” negative prompt, and to which images “ugly” was applied by humans tagging the training dataset.
If the taggers think that so much as a single wrinkle on a woman is “ugly”, but a man has to be missing half his teeth and have a crooked face to start looking “ugly”… well, this is what we get.
Pretty people get photographed/painted more, resulting in much of the training data being pretty people, thus pretty people get generated more frequently.
They can be considered petty, fcking whres /s
That’s DALL-E. DALL-E is different than Stable Diffusion, which is different from Midjourney, which is different from the many NAI anime models out there.
We need to stop treating LD models like they are all the same thing. Models are based on the data they are trained on. Sure, a lot of them started out from a Stable Diffusion model, but that’s not always the case, and enough training can have them go off in specialized directions.
Either I am blind or comment OP doesnt mention SD nor any other specific model.
The pictures in your embedded widget on your post say “Unterstützt von DALL-E 3”. Also, the very start of the article says “When Melissa Heikkilä tried Lensa’s Magic Avatars”, which uses Stable Diffusion, but I’m not sure if they further trained it themselves.
The point is that “Lensa’s Magic Avatars” isn’t all of AI, and clickbait titles like this needs to stop treating it like that. It’s the latent diffusion equivalent of this.
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Cute wizard girl w
wasn’t sexual but indeed very sexy in style.
Those characters have child-like facial proportions. 🧐
Take a look at 25 year old asian girls.
They all look like or close to that…
Yeah, if you go back through hundreds of years of artwork, most of it are pictures of women. Some of them are nude. There are many many artists that only draw women, modern or classical. And there’s a ton of male Japanese artists from centuries ago that did the same thing.
I asked it to create a sort of witchy, sorceress character and many of the generations she was fully topless with her boobs out, despite me not asking that or even explicitly putting “fully clothed” into the prompt. There was one image that the system created and then removed and threatened me with a ban for it being too sexualized despite me putting no sexual language in the prompt and it being all the AI.
That’s just one model, and obviously not Stable Diffusion. LD models are just based on whatever they were trained on. If you don’t like it, download another model trained on something else and try it out. Or train one yourself.
Also, I wish everybody would download a SD client and just use this software locally. All of these toy websites are shit, and local clients aren’t going to threaten to ban you because of what you generated. It’s a good learning experience to figure out the software, and these tools are useful for more things than just bitching about the tech on the web.
Wrong question. The right question would be:
Why is AI as used in Lensa’s Magic Avatars App Pornifying Asian Women?
Ask Lensa to remove the “ugly” and similar negative prompts from their avatar generating App, and let’s see what comes out.
https://stable-diffusion-art.com/how-to-use-negative-prompts/#Universal_negative_prompt
For reference, check out how that same negative prompt turns a chubby-ish poorly shaved average guy, into a male pornstar, or a valet into a rich daddy’s boy.
Can we please collectively get into the habit of editing these borderline-clickbait titles or at least add sub-titles explaining the real article? This isn’t reddit where you can’t edit anything and can’t add explanatory text!
If I had to guess, they probably did a shit job labeling training data or used pre labeled images, now where in the world could they have found huge amounts of pictures of women on the internet with the specific label of “Asian”?
Almost like, most of what determines the quality of the output is not “prompt engineering” but actually the back end work of labeling the training data properly, and you’re not actually saving much labor over more traditional methods, just making the labor more anonymous, easier to hide, and thus easier to exploit and devalue.
Almost like this shit is a massive farce just like the “meta verse” and crypto that will fail to be market viable and waist a shit ton of money that could have been spent on actually useful things.
They did literally nothing and seem to use the default stable diffusion model which is supposed to be a techdemo. Would have been easy to put “(((nude, nudity, naked, sexual, violence, gore)))” as the negative prompt
The problem is that negative prompts can help, but when the training data is so heavily poisoned in one direction, stuff gets through.
Scroll through the trained models on civit.ai and you’ll quickly get a feeling of the dystopian level of “prettifying” everything in the AI-generation world.
I also once searched for “brown” just to see if any models were trained to create non-white-skinned people, and got shocked when the result was filled with models trained on Millie Bobby Brown from Stranger Things. I don’t even want to know what those models are used for.
From the first 10 models I saw, the first image was a woman 9 times…
Because simps.
Saved you a click.
Because white dudes fetishizing asian women wrote the llms and pointed at the training data
I’m not exposed to a huge amount of media coming out of Asia, outside of a handful of Korean shows that Netflix has picked up and anime. But like, if anime is any indicator, I’m not really surprised that the training data for Asian women is leaning more toward overt sexualization. Even setting aside the whole misogynistic ‘fan service’ thing, I don’t feel like I see as much representation of women who defy traditional gender roles as the last twenty or so years of Western media.
It certainly could be that anime is actually a huge outlier here, but if the training data is primarily from the English speaking web, it might be overrepresented anyway. But like, when it comes to weird AI image behaviors, it pays to think about the probable training data.
Like, stable diffusion seems to do a better job of rendering jewelry if you tell it to surround it with berries. Given the output, this seems to be due to Christmas themed jewelry ads. They also tend to add a lot of bokeh for the same reason.
Are the images above supposed to depict “porn”? I’ve never seen porn like that.
Stable Diffusion is little more than content laundering. It cannot create anything more than what you put in.
You’re so confidently incorrect about something you clearly don’t know much about.
Looking at some of the replied that tried to dismiss the issue and the general lack of concern from moderators against aggressive replies from AI apologists (in this thread but also other AI related threads) are disheartening.
Garbage in, garbage out 🤷
Absolutely this. The reason AI defaults female into “female armor mode” is the same reason Excel has January February Maruary. Our spicy autocorrect overlords cannot extrapolate data in a direction that it’s training has no knowledge of.
Does AI not generally pornify women and girls independent of ethnicity?
Because it’s trained on the internet, and we all know what that’s for.
While i agree there is a big issue with the bad biased and sexist training data this entire article is about the lensa app which uses (i assume) the default stable diffusion model laion-5b.
Intentional creating sexualized pictures is banned in their guidelines. And yet no one thought of creating a good negative prompt that negates any kind of nudity or eroticism? It still doesn’t properly fix the training data but at least people aren’t unwillingly presented porn of their own images.
Also everyone can create a dataset and build a stable diffusion model, so why is lensa relying on the default model which is more like a quick and dirty tech demo. They had all the tools to do this right but decided to not even uses the easy lazy ones.
If we’re talking open source models, it’s because a lot of the people fine-tuning them are Asian, and have that bias.
