And then an elephant appears because “elephant” is a word in the prompt.
It’s interesting because in some ways it’s as if the AI models have a “peekaboo” level of understanding of the world.
If your prompt says something like “a man’s face is hidden behind his hands”, the model goes “aha, I see the word face, I better draw a face”, so you have to get tricky and say “a man’s hands are held up covering up the frontal area of his head”.
Funny thing is it doesn’t happen in LLMs, even if you use couple generations old and in “low effort” settings (which makes mistakes much more often). I don’t know if they have multiple layers of LLMs reconciling the output with the prompt (like how DeepSeek filters certain…ahem…“topics”)
Makes you wonder if it is an architectural thing. It still used a place holder to refer to the elephant in both cases so maybe the AI generating images just use their best version of “that”, don’t know.
And then an elephant appears because “elephant” is a word in the prompt.
It’s interesting because in some ways it’s as if the AI models have a “peekaboo” level of understanding of the world.
If your prompt says something like “a man’s face is hidden behind his hands”, the model goes “aha, I see the word face, I better draw a face”, so you have to get tricky and say “a man’s hands are held up covering up the frontal area of his head”.
Funny thing is it doesn’t happen in LLMs, even if you use couple generations old and in “low effort” settings (which makes mistakes much more often). I don’t know if they have multiple layers of LLMs reconciling the output with the prompt (like how DeepSeek filters certain…ahem…“topics”)
Makes you wonder if it is an architectural thing. It still used a place holder to refer to the elephant in both cases so maybe the AI generating images just use their best version of “that”, don’t know.