From the paper

> The pipeline (bottom) shows how diverse OpenImages inputs are edited using Nano-Banana and quality-filtered by Gemini-2.5-Pro, with failed attempts automatically retried.

Pretty interesting. I run a fairly comprehensive image-comparison site for SOTA generative AI in text-to-image and editing. Managing it manually got pretty tiring, so a while back I put together a small program that takes a given starting prompt, a list of GenAI models, and a max number of retries which does something similar.

It generates and evaluates images using a separate multimodal AI, and then rewrites failed prompts automatically repeating up to a set limit.

It's not perfect (nine pointed star example in particular) - but often times the "recognition aspect of a multimodal model" is superior to its generative capabilities so you can run it in a sort of REPL until you get the desired outcome.

https://genai-showdown.specr.net/image-editing

Other Post: https://news.ycombinator.com/item?id=45708493

Really cool - looking to Apple to lead the on-device AI space in short order...

I confess that I don't quite get the point here - is it just that they've paid the inference costs for a dataset than can be used for distillation/other research?

Can it be? Has Apple FINALLY joined the party? Very ironic they are using an open dataset from Google... and Gemini for prompts by Google.

I'm happy to see something from Apple but this seems so low-tech that it could be one of my own local ComfyUI workflows.