OpenAI actually has really good adherence, but occasionally tends to introduce its own almost equivalent of "tone mapping", making hyper-localized edits frustrating.

I don’t know how much work it is for you, but one thing a lot of people do, myself included, is take the original image, make a change to it using something like NB, then paste that as the topmost layer in something like Krita/Pixelmator. After that, we’ll mask and feather in only the parts we actually want to change. It doesn’t always work if it changes the overall color balance or filters out certain hues, it can be a real pain but it does the job in some cases.

The Flux models (like Kontext) are actually surprisingly good at making very minimal changes to the rest of the image, but unfortunately their understanding of complex prompts is much weaker than the closed, proprietary models.

I will say that I’ve found Gemini 3.0 (NB Pro) does a relatively decent job of avoiding unnecessary changes - sometimes exceeding the more recent NB2, and it scored quite well on comparative image-editing benchmarks.

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

That's a mistake~ None of the models successfully passed the Flat Earth composition test. I've updated the passing criteria to be more explicit as well. Thanks for catching that!

I’ll have to give it another try. Its predecessor, Hunyuan Image 2.0, scored pretty poorly when I tested it last year: 2 out of 15, so it'll be interesting to see how much it has improved.

Here's ZiT, Gpt-Image-2, and Hunyuan Image 2 for reference:

https://genai-showdown.specr.net/?models=hy2,g2,zt

Note: It won't show up in some of the newer image comparisons (Angelic Forge, Flat Earth, etc) because it's been deprecated for a while but in the tests where it was used (Yarrctic Circle, Not the Bees, etc.) it's pretty rough.

So the prompts are tuned and adjusted on a per-model basis. If you look at the number of attempts, each receives a specific prompt variation depending on the model. This honestly isn't as much of an issue these days because SOTA models natural language parsing (particularly the multimodal ones) has eliminated a lot of the byzantine syntax requirements of the SD/SDXL days.

The template prompt seen in each comparison gets adjusted through a guided LLM which has fine-tuned system prompts to rewrite prompts. The goal is to foster greater diversity while preserving intent, so the image model has a better chance of getting the image right.

Getting to your suggestion for posting all the raw prompts, that's actually a great idea. Too bad I didn't think about it until you suggested it. And if you multiply it out - there's 15 distinct test cases against 22 models at this point, each with an average of about 8 attempts so we’re talking about thousands of prompts many of which are scattered across my hard drive. I might try to do this as a future follow-up.