[vunderba]

I run a fairly high-traffic site for generative image models focusing on complex prompt adherence. Flux.2 doesn’t score anywhere near SOTA proprietary models.

Klein 9b is decent for image-to-image, but when used for pure generative purposes brings back SDXL levels of body horror (have some Gattica pianists).

If you can get past the annoying JSON structuring, Ideogram 4 is probably the best option in the open‑weights world right now for text2image purposes, and even scored higher than the original Nano-Banana.

For ref: Flux.2 scored 5, Ideogram4 scored 8, and gpt-image-2 scored 12 out of 15.

Comparison of Flux.2 [dev], Ideogram4, NB Pro, and gpt-image-2.

https://genai-showdown.specr.net/?models=nbp,f2d,g2,id4

[potsandpans]

I suppose "close" could be interpreted subjectively.

I use flux2.dev on my 5090 with a prompt upsampler (that I host on my 3080ti, it's been so long since I looked at it, its either some qwen or Mistral model). I use the upsampler to produce json structure based on this prompting guide: https://docs.bfl.ml/guides/prompting_guide_flux2

For being able to run on my 5090, it produces incredibly consistent and stable results that work for my use case so well, I find myself reaching for it over proprietary models.

I'm always interested to see objective comparisons though. It's clear from yours that flux lags. But the fact that I can use it locally over closed source sota models is very compelling (to me).

You've convinced me to checkout ideogram4 though!

[vunderba]

Ideogram4 is great! Just make sure that you either use some kind of preprocessor to convert your natural language prompt to the strict structured JSON format otherwise the results can suffer.

I personally use Qwen3 8b LLM for this purpose, but I think that the KJ prompt builder node handles this out of the box in ComfyUI.

https://github.com/kijai/ComfyUI-KJNodes