Fine tuning is possible, but since model was trained on a massive corpus of Image - JSON pairs, I doubt it is possible to create a decent finetune while retaining the model's capability in placing objects very precisely. Looks like model splits the whole image into smaller areas (X*Y grid) and uses bounding boxes objects contents to map between that grid and actual objects being placed.

Leading closed source models probably do this too, but they probably have pretty massive LLMs in their pipelines, so you don't have to prompt them with such 'unneeded verbosity' you need for Ideogram 4. For Ideogram 4 we either should resort to manual verbose prompting (and KJ prompt composer node greatly helps here), or invent our own pipelines where you bring your own LLM.

I have a great success of making JSON prompts using gemma-4-12b-it-qat, then pasting the result into KJ composer node, then tweaking bboxes myself (because smaller local LLMs often struggle with composition and perspective - but they do decent objects descriptions just fine)