Men wearing only wearing suits

"Not wearing suit" is part of your problem. When you want to include "negative" prompts in the main prompt, consider this: the model does not seem to understand the coupling of two words where one word negates the other. Because of that, you end up with the thing you are trying to eliminate actually getting added.  For example,

• "not wearing suit" ends up with suits
• "no mountain" ends up generating mountains
• "without facial hair" ends up showing beards
• "no men" ends up adding men
• "no stairs" ends up adding a staircase

It is best to try to describe the negative prompt concept in a single word, if possible (even if the word you use is not a real English word):

• "beardless" rather than "without facial hair"
• "mountainless" rather than "no mountains"
• "flat-land" rather than "no mountains"
• "women" rather than "no men"
• "stairless" rather than "no stairs"
• "jacketless shirt-sleeves" rather than "without a suit" (note "suitless" is blocked for some reason)

Hyphenating words seems to connect words more than a space, but not as much as if it was a single word (if that makes sense). If two words is a "zero" connexion and one word is a "ten" connexion, hyphenating seems to be around "three" in my experience (if that makes sense). But hypenating does not seem to solve the negative word coupling issue ("no-mountains" is not better than "no mountains").

I have no inside information on the model. This is what I have pieced together from using it over the past year. I hope this helps some until the negative prompt box returns.

Here is an alternative that follows my guidance above:

High fashion editorial portrait of jacketless colourful shirt-sleeve funky man