Is there a reason these pelicans always have roughly the same composition (side-view, 2d, biking right, flat ground beneath, etc)? I don't see any of that detailed in the prompt, yet they all seem to generate roughly the same image of differing quality.
Not when rendered via POV-Ray:
https://blog.nawaz.org/posts/2025/Oct/pelican-on-a-bike-rayt...
I plan to update it with more pelicans from all the models released since.
(Spoiler alert: They haven't improved much since then).
It's really interesting, isn't it? They almost always cycle from left to right - but I have had a few which cycle in the other direction.
The 2D / flat ground feels reasonable for a SVG, which implies a vector illustration.
The canonical view of a bicycle is facing right. Usually, people want to draw/photograph/depict the side of the bicycle with the running gear, which is on the right side of the frame for historical reasons.
Search Google Images for "bicycle". Almost all bicycle product shots are staged the same way: side view, going left-to-right. It makes sense to me that given that skew in the training data, the model grounds itself in the bicycle.
Sun is missing a few rays and not wearing sunglasses.
Yes, I do a thing where I ask the machine to generate responses in the form of a lizard talking to a cat. The lizard is always a green gecko and the cat is always orange, which I never specify.
Is there a reason these pelicans always have roughly the same composition
Because they're computers. They don't have an imagination and the ability to create things from whole cloth the way humans do.
Much like a mother pelican, they regurgitate what they've been fed.
The more generic your prompt, the more generic the response. It's a regression to the "mean" of the training data aka GIGO for AI.
It's like when you ask your average person off the street to draw a house - it'll almost always be square with a triangle roof, one door, and two windows.
In the pelican/bike example, it's probably a bit of a self-perpetuating snowball too. If the earliest examples were bike left-to-right, flat ground, etc. then they are also being scraped up in future LLMs.