> 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

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.

Definitely very useful, but I’m so curious how the original datasets from these image editing models were created. I’m guessing a lot of it is synthetic data to construct scenes programmatically with layers

Don’t get me started in how “agent” is a term of art that means absolutely nothing, encompassing everything from a plain old shell script to a full language model.

For example, you might ask the second model to cover the person’s face with a black square; a VLM model notes that the person is a man with brown hair and round glasses. Then, during training, the resulting image is presented along with the prompt, “Remove the black square from the man’s face. He has brown hair and round glasses.”

The model now learns how to remove black squares and replace them with a man’s face with brown hair and round glasses.

Since the training data is easily synthesized using existing models, you can generate enormous amounts of it - often very cheaply. For specialized editing tasks, this technique is really powerful. Build your training set for your special purpose task, fine tune an existing image editing model such as Qwen Image Edit to produce a new checkpoint or LoRA (often a LoRA is more than good enough) and then you have a special purpose model to perform whatever narrow editing task you need it to perform on your image data.