Ever had that moment where you stare at an AI-generated picture and think:
“How many hands does this anime girl have?”
Because I did — and I swear I was losing my mind. There were clearly three hands in the image… but the model kept insisting there were only two.
I asked it to remove the extra hand five times.
It didn’t budge.
And the funniest part? It didn’t say “oops.” It doubled down.
That’s a perfect example of how AI hallucinations show up in image generation — and why “just ask it again” doesn’t always work.
What “hallucination” looks like in images
With text, hallucinations are fake facts.
With images, hallucinations are fake anatomy and invented details that feel real at first glance.
Common ones:
- extra fingers / extra limbs
- impossible joints
- duplicated objects
- inconsistent accessories (earrings change, logos morph)
- weird shadows that don’t match the light source
The model doesn’t know it’s wrong — it’s predicting pixels that match patterns it learned. If “anime girl + pose + framing” often correlates with certain shapes, it may “complete” the picture in a way that looks plausible but isn’t physically consistent.
Why the model won’t fix it (even when you point it out)
This part is surprising to people: even if you describe the error perfectly, the model might still fail to correct it.
A few reasons:
-
It can’t reliably “count” or verify details the way a human can.
It’s not doing a proper audit pass; it’s generating a new guess each time. -
Your request fights the composition.
Removing one hand might break the pose, the silhouette, or the balance the model “likes,” so it keeps reconstructing the same structure. -
It’s overconfident by design.
Many models respond with high confidence. So instead of “You’re right, there’s a third hand,” you get “There are two hands,” even when your eyes disagree. -
Edits aren’t always true edits.
Depending on the tool, “edit” can behave more like “regenerate a similar image,” which means the same mistake can keep returning.
Practical takeaway for builders
If you’re building a product that uses image generation, you can’t assume:
- one prompt = one correct result
- the model will follow a correction request
- the model will even acknowledge the mistake
In other words: treat image output as probabilistic, not deterministic.
If you want higher success rates, you usually need product-level tactics:
- strong constraints (pose references, consistent character sheets)
- true inpainting / masking workflows
- multiple generations + selection
- automated checks (even basic heuristics for hands/fingers help)
Still… I love these moments
As annoying as it is, it’s also kind of hilarious.
AI can create stunning art — and then casually invent a third hand and insist you’re imagining it.
And that’s exactly the kind of “real-world AI” chaos I like sharing while I build.
If you’re curious, I’m documenting more of these quirks in my AI Anime Chatbot project — the fun parts and the frustrating parts.