Midjourney vs Stable Diffusion vs Flux: Prompt Syntax Compared

The three families do not read prompts the same way, and pasting between them is the single most common reason a prompt "stops working".

Midjourney: a weighted bag of concepts plus parameters

Midjourney takes its structural instructions from parameters: aspect ratio, stylize, chaos, version. Move a Midjourney prompt to another model and those parameters become literal words in the description, which is worse than dropping them.

Stable Diffusion: weighted tags and a real negative prompt

Half of what makes an SD image look finished is what you excluded — extra limbs, watermarks, text artefacts, oversaturation. Models that do not take negative prompts get none of that benefit, which is why a straight copy across looks worse rather than merely different.

Flux and the natural-language models: write sentences

Tag soup that works beautifully in SD reads as noise to them; a plain descriptive paragraph outperforms it consistently.

What actually breaks when you paste across

Three failures account for nearly all of it. Parameters become words: --ar 16:9 is read as text and quietly steers the image. Weights become punctuation: (masterpiece:1.4) means nothing to a model that does not parse weights, so it lands as literal brackets and a number. And negatives vanish: everything you were excluding comes back, which is why the same prompt suddenly produces the artefacts you thought you had solved.

None of these announce themselves. The image is merely worse, so people conclude the second model is weaker when they have really just handed it a prompt written in a language it does not read.

Keep one description, three renderings

The practical consequence: hold one description of the image and three renderings of it, rather than one prompt you keep patching. Our AI prompt generator writes all three from a single idea, and image to prompt does the same starting from a finished picture.