What is a negative prompt?
A negative prompt is a piece of text that tells an AI image or video model what you do not want in the output. Where the main (positive) prompt describes what to create, the negative prompt lists things to avoid, such as "blurry, extra fingers, watermark, text, distorted face."
Negative prompts are most associated with diffusion models, the family behind Stable Diffusion and many AI video generators. Depending on the tool, the feature may appear as a separate "negative prompt" box, an "avoid" field, or a parameter such as Midjourney's --no.
How negative prompts work
Diffusion models create an image by starting from random noise and removing it step by step, guided by your text. Most of them use a technique called classifier-free guidance: at each step the model makes two predictions, one guided by your prompt and one without it (the "unconditional" prediction), then pushes the result toward the prompted version.
A negative prompt replaces that unconditional prediction with one based on the text you want to avoid. The model is then pushed toward your positive prompt and away from your negative prompt at the same time. That is why negative prompts work best for visual concepts the model already understands, like "blurry" or "watermark," and less well for abstract instructions.
A few practical consequences:
- The guidance scale matters. Higher guidance (often called CFG scale) strengthens both prompts, which can make the negative prompt more effective but also more likely to cause oversaturated or stiff results.
- Short and specific beats long lists. Very long negative prompts can dilute each term or push the model away from things you actually want.
- Negation in the positive prompt often fails. Writing "a street with no cars" in the main prompt can add cars, because the model sees the word "cars." Putting "cars" in the negative prompt is usually more reliable, on models that support it.
Prompt vs negative prompt examples
| Goal | Positive prompt | Negative prompt |
|---|---|---|
| Clean product shot | Studio photo of a white sneaker on a gray background, soft light | text, watermark, logo, shadows, clutter |
| Realistic portrait | Close-up portrait of an older fisherman, natural light | cartoon, plastic skin, extra fingers, deformed hands |
| Stable video clip | Slow drone shot over a pine forest at sunrise | flicker, shaky camera, warping, low resolution |
| Claymation character | Clay figure of a chef in a tiny kitchen, stop-motion style | photorealistic, smooth CGI, glossy plastic |
Where negative prompts apply
- AI image generation, to remove common artifacts (malformed hands, extra limbs, text) or unwanted styles.
- AI video generation, to reduce flicker, morphing, jitter, or on-screen text, when the model exposes the option.
- Style control, keeping a stop-motion or claymation look from drifting toward glossy 3D.
- Brand-safe content, excluding logos, watermarks, or specific objects from generated b-roll.
Models that ignore or do not expose negative prompts
Not every model supports negative prompts, and support is changing quickly. Some newer models are guidance-distilled, meaning they learned to follow the prompt in a single pass without the separate unconditional prediction that negative prompts rely on. FLUX.1 [dev] is a well-known example: it does not natively use a negative prompt, and the usual advice is to describe what you want more precisely in the main prompt instead.
Many hosted image and video tools also hide the field entirely, rewrite your prompt with a language model before generation, or accept a negative prompt only through their API. As of 2026, the safest assumption is to check each model's documentation. If there is no negative prompt option, rephrase positively: instead of "no people," describe "an empty beach at dawn."
Why negative prompts matter
For creators, negative prompts are one of the cheapest ways to cut down on rerolls. Each discarded generation costs time and, on paid tools, credits. Removing a recurring artifact with a few words can make a model usable for a whole series of clips.
They also help with consistency. If you are producing many shots in the same style, a shared negative prompt keeps unwanted looks out of every clip. Just remember that a negative prompt narrows what the model avoids; it does not replace a clear, detailed positive prompt. When you generate footage with an AI video tool, it is worth testing a short negative prompt on a few clips first to see whether that model actually responds to it.