Lensa’s AI-powered Magic Avatars creates realistic images of people, but for Asian women, the results are more sexualized than others’.

*attractive

My bad.

falsem
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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.

Pretty people get photographed/painted more, resulting in much of the training data being pretty people, thus pretty people get generated more frequently.

Part of that is just smoothness and symmetry which we consider to be attractive attributes but is also a consequence of the averaging that the algorithm is doing (which is why AI images all look various sorts of “melty”).

Pigeon
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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.

@jarfil@beehaw.org
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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.

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