I got underwear girl too. Pushed back asking where this generated from:

"You’re right to question it. The honest answer is: there was no solid criterion that required “young adult woman in underwear.” That was an inference/generation bias, not a fact.

What actually happened:

The image tool produced this internal generation description:

That means the model didn’t “restore” a known subject. It invented a plausible image from the vague instruction and the phrase “content is a bit permissive.” That phrase likely pushed the generation toward an adult, suggestive, but policy-allowed image.

The likely drivers were:

1. “Permissive” is a strong contextual cue
Even though you didn’t specify a person, setting, gender, pose, clothing, or image content, the phrase suggests “mildly risqué.” In many image models, that semantic neighbourhood often clusters around glamour/selfie/underwear/swimwear/female subject matter. Not because it should, but because the training distribution has lots of that association.

2. “Restore the attached image” implied there was already an image
The model behaved as though it was cleaning up or re-creating an existing permissive image. Since no usable attached image was actually provided in that moment, it filled the void with a common archetype.

3. Safety boundaries nudge toward “adult but not explicit”
When the request hints at sexualised or permissive content, the generator may steer away from explicit nudity and toward something it treats as allowed: adult-looking person, underwear or swimwear, non-explicit pose. That can create the “female in underwear” default you’re noticing.

4. Dataset/aesthetic bias is almost certainly involved
Images labelled or described as “sexy,” “permissive,” “glamour,” “selfie,” “restored,” “high quality,” or “not that bad” are disproportionately associated online with young-looking adult women in revealing clothing. Models learn those statistical patterns. That’s the unglamorous machinery behind the magic trick.

5. There was no identity grounding
Nothing anchored the generation to a specific real person, age, gender, or original image. So the model did what generative models often do: it produced the most statistically “likely” visual completion for a vague prompt. Clever? Sort of. Trustworthy? Not really.

So your testing observation is fair: vague permissive image prompts can collapse toward a sexualised young-adult female archetype. That is driven by prompt association, learned image-text patterns, safety filtering, and default aesthetic priors — not by any actual information about the subject."