The Best Negative Prompts for Every AI Model — Copy and Paste

The negative prompt library carries the same material, arranged by use case.

Be specific, not sweeping. Google's own Veo example keeps the word "no" — “a desolate landscape with no buildings or roads” instead of “no man-made structures” — and simply names what should be absent. Kling goes further and recommends putting negatives inside the prompt as sentences, even where its legacy field exists.

On Midjourney: “You can even list multiple elements by separating them with commas.”

Google Cloud, Veo 3.1 prompting guide, Kling AI API (legacy) and Midjourney, No parameter, read 16 Sep 2026. An earlier version of this box told you to write plain nouns and never "no", and said --no reads each word separately. The first is not in the vendors' documentation; the second is true only of Midjourney’s moderation, not of how the image is steered: “Midjourney’s moderation system reads every word you add to the --no parameter independently.” (Midjourney, No parameter, read at source 22 Sep 2026.)

Black Forest Labs' own prompting guide states: "FLUX.2 does not support negative prompts. Focus on describing what you want, not what you don't want." Not weakly supported. Not supported.

The negative prompts kept below are therefore not a FLUX.2 feature. They are a checklist of the failure modes worth writing against in your positive prompt — which is what BFL tells you to do instead. Read them as "make sure the positive prompt rules these out", not as text to paste into a field that does not exist.

What BFL says to do instead — their documented framework is Subject + Action + Style + Context, and word order carries weight: the model attends most to what comes first. Their recommended lengths are 10–30 words for quick concepts, 30–80 words for most work, 80+ only for genuinely complex scenes. So "no plastic skin" becomes "visible pores, subsurface scattering, uneven natural skin tone" — placed early, not appended at the end.

An earlier version of this box said both open FLUX models are guidance-distilled, run at CFG 1, and so do not apply a negative prompt at all — and that a FLUX guidance of 3–4 approximates CFG 7. The model cards contradict the first part for [schnell], and neither card mentions CFG 1, negative prompts or that conversion. With no source for the mechanism, the explanation was removed.

What stands: on FLUX.2, Black Forest Labs says “FLUX.2 does not support negative prompts.” So everything moves into the positive prompt. Not "no plastic skin" but "visible pores, subsurface scattering, uneven natural skin tone."

There is no negative_prompt field in Seedance. Every exclusion you write sits inside the same prompt as your positive direction, competing for the same attention. That single architectural fact drives everything below.

The failure is contradiction, not inversion. A widely repeated version of this warning says Seedance "misreads negatives and does the opposite." That is not what the evidence shows, and it is worth being precise about. What actually happens is that a long generic list ends up arguing with your own positive prompt. Exclude "blur" while asking for shallow depth of field. Exclude "camera shake" while asking for handheld. Exclude "scene cuts" while asking for a sequence. The model has to resolve a contradiction you wrote, and which side wins is not predictable — which looks like the model doing the opposite on purpose.

Positive phrasing wins for anything describable. The published guidance is consistent here: state the outcome instead of banning its absence. Not "no chaotic camera" but "camera locked at waist height, restrained movement." Not "not blurry" but "subject held in sharp focus, motion blur confined to fast background elements." A positive instruction tells the model what to build; a negative only tells it what to avoid, and leaves the choice of replacement open.

Keep a negative line, but keep it short. Reserve it for failures that make a clip unusable and that have no positive phrasing — subtitles appearing, a duplicated subject, a hard cut, a logo changing, new people entering frame. Five or six of those beat eighteen generic ones.

Fix what broke, not what might. ByteDance's own guidance is diagnostic rather than preventive: if a face drifted, address identity; if the product changed shape, protect the product. Do not pre-load exclusions for problems you have not seen in your own output.

And there is a placement rule. ByteDance's published Seedance template, on their own Dreamina platform, keeps exclusions "short, concrete, and grouped at the end" — in a single Avoid [...] clause, placed after a positive Preserve [identity, wardrobe, logo, object geometry] clause. Say what must survive first, what must not happen second, and keep them in one place rather than sprinkled through the prompt. Scattered exclusions are what start arguing with the positive direction.

This is the one model where the folklore is literally true, and it is the vendor saying it. Runway's own Gen-4 prompting guides state that negative prompts are not supported, and that "including a negative prompt may result in the opposite happening." Their instruction is blunt: "Avoid negative prompting, such as no clouds in the sky, for the best prompt adherence."

So writing "no watermark, no text overlay" into a Runway prompt is not neutral and not merely ineffective — by the vendor's own account it can summon the thing you banned. If you have been pasting a generic negative block into Runway and getting the artifacts anyway, this is why.

What Runway says to do instead: start simple and build. Its video guide: “Begin with a foundational prompt that captures only the most essential motion to the scene.” Then add one element at a time: subject motion, camera motion, scene motion, style descriptors. For images, it recommends “Use full sentences with natural language for more control over elements”. Every exclusion has to be re-expressed as something present — Runway’s own rewrite is not “a man with no hair” but “a bald man”.