Generation controls

    Negative prompt

    A negative prompt lists what you do not want in the output, steering the generation away from those terms instead of towards them.

    It is a second prompt running in the opposite direction. The model computes what the positive text pulls towards and what the negative text pulls towards, and pushes away from the second. That is why it works where a negation in the main prompt does not: "no text" in a positive prompt still puts the concept of text into the mix, and models frequently render it anyway.

    It is most useful against recurring, describable failures — watermarks and stray captions, extra limbs, a plastic sheen you keep getting. It is close to useless as a quality incantation. A stack of vague terms like "bad, ugly, low quality" mostly narrows the space the model can work in without telling it anything specific.

    Not every model exposes one. Where the field is absent, the model was either trained without that mechanism or the provider chose not to surface it, and the equivalent move is to describe the thing you do want more precisely.

    In practice

    • Name concrete nouns you keep seeing, not moods you dislike.
    • Keep it short. A negative prompt longer than the prompt is usually fighting the prompt.
    • If a term appears in both prompts, the two cancel and you have wasted the slot.

    The mistake to avoid

    Copying a hundred-word negative prompt from a forum. Most of those terms do nothing on your model and a few actively suppress detail you wanted.

    Where you will run into it

    Related terms

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