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
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!
Because AI is the literal worst.
Because white dudes fetishizing asian women wrote the llms and pointed at the training data
I work in tech and asian guys tend to outnumber white guys in it, especially if you combine east asian and south asian.
Published on December 16, 2022
Please ignore this article. It’s completely out of date.
Are the images above supposed to depict “porn”? I’ve never seen porn like that.
In 2024, the brain washing of people is almost complete.
Sensuality is now porn. :)
Because people are telling it to, I’d wager
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.
Because we have been pornifying asian women on the internet for decades. Does that really beg the question posed in the title?
You’re absolutely correct, yet ask someone who’s very pro AI and they might dismiss such claims as “needing better prompts”. Also many people may not be as tech informed as you are, and bringing light to algorithmic bias can help them understand and navigate the world we now live in. Dismissing the article just because you already know the answer doesn’t really encourage people to participate in a discussion.
Dismissing the article just because you already know the answer doesn’t really encourage people to participate in a discussion.
If the author doesn’t know the answer, then it is helpful to provide it. If they know the answer, then why are they phrasing the title as a question?
If you genuinely don’t know: because it’s an attention-grabbing title (which isn’t inherently bad)
It’s really hard getting dark skin sometimes. A lot of the time it’s not even just the model, LoRAs and Textual Inversions make the skin lighter again so you have to try even harder. It’s going to take conscious effort from people to tune models that are inclusive. With the way media is biased right now, I feel like it’s going to take a lot of effort.
“Inclusive models” would need to be larger.
Right now people seem to prefer smaller quantized models, with whatever set of even smaller LoRAs on top, that make them output what they want… and only include more generic elements in the base model.
I wouldn’t mind. I’m here for it.
Are you ready to run a 100B FP64 parameter model? Or even a 10B FP32 one?
Over time, I wouldn’t be surprised if 500B INT8 models became commonplace with neuromorphic RAM, but there’s still some time for that to happen.
You don’t need that many parameters, 4gb checkpoints work just fine.
For more inclusive models, or for current ones? In order to add something, either the size has to grow, or something would need to get pushed out (content, or quality). 4GB models are already at the limit of usefulness, both DALLE3 and SDXL run at about 12B parameters, so to make them “more inclusive” they’d have to grow.
And every single Asian game and anime tends to go for skimpy or virtual softcore with it’s female characters. Rarely you see a female character in full armor.
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.
Because the Internet is for porn. Always has been, always will be.
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.
Because simps.
Saved you a click.
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…
dystopian level of “prettifying” everything in the AI-generation world.
So like all the ad campaigns, TV shows and movies in the real world?
Garbage in, garbage out 🤷
You train on a bunch of reddit crap, you’re going to get neck beard reddit crap out. It’d look different if they only used art history books.
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.
If we’re talking open source models, it’s because a lot of the people fine-tuning them are Asian, and have that bias.
