Invert your logic (‘be straight and to the point; concise’, ‘use balanced and dry wording’) instead, it might not be a definite solution, but you want to avoid triggering the neuron instead of negating its activation.
That older generation of image diffusion models (e.g. Stable Diffusion) used text encoders like CLIP [0], which simply don't have the language understanding that even the smaller modern LLMs do.
Later image models moved on to using variants of T5 [1], sometimes in addition to CLIP variants (this is how FLUX.1 works).
The state of the art for open models in this regard (right now, likely out of date before I can finish formatting this comment..) is probably Qwen-Image [2] which uses Qwen2.5-VL [3]. That is a multimodal LLM with native vision capabilities in addition to text. It comes in a few sizes (up to 72 billion parameters), but the one commonly used with Qwen-Image is still 7b parameters.
[0]: https://openai.com/index/clip/
[1]: https://en.wikipedia.org/wiki/T5_(language_model)
What we’re trying to do here is basically reverse jailbreak the model - make it not say what it wants to say. It’s a matter of overpowering the active by default neurons. (Not easy sometimes.)
Phrasing it as do rather than don't is probably still more effective on both humans and LLMs. :)
Hasn't made a single bit of difference.
This notion that LLM's haven't made it over the "trouble with negatives" hurdle as a hard truth that will never be made untrue is absolutely absurd and bears zero evidence to the contrary with respect to models released in the past year.