So during my Nano Banana Pro experiments I wrote a very fun prompt that tests the ability for these image generation models to follow heuristics, but still requires domain knowledge and/or use of the search tool:

    Create a 8x8 contiguous grid of the Pokémon whose National Pokédex numbers correspond to the first 64 prime numbers. Include a black border between the subimages.

    You MUST obey ALL the FOLLOWING rules for these subimages:
    - Add a label anchored to the top left corner of the subimage with the Pokémon's National Pokédex number.
      - NEVER include a `#` in the label
      - This text is left-justified, white color, and Menlo font typeface
      - The label fill color is black
    - If the Pokémon's National Pokédex number is 1 digit, display the Pokémon in a 8-bit style
    - If the Pokémon's National Pokédex number is 2 digits, display the Pokémon in a charcoal drawing style
    - If the Pokémon's National Pokédex number is 3 digits, display the Pokémon in a Ukiyo-e style

Running that same prompt through gpt-2-image high gave an...interesting contrast: https://cdn.bsky.app/img/feed_fullsize/plain/did:plc:oxaerni...

It did more inventive styles for the images that appear to be original, but:

- The style logic is by row, not raw numbers and are therefore wrong

- Several of the Pokemon are flat-out wrong

- Number font is wrong

- Bottom isn't square for some reason

Odd results.

This is an amazing test and it's kinda' funny how terrible gpt-2-image is. I'd take "plagiarized" images (e.g. Google search & copy-paste) any day over how awful the OpenAI result is. Doesn't even seem like they have a sanity checker/post-processing "did I follow the instructions correctly?" step, because the digit-style constraint violation should be easily caught. It's also expensive as shit to just get an image that's essentially unusable.

Why would you consider this a good prompt?