Negative prompt guide for AI video
Generative video has signature artifacts: temporal flicker, morphing faces, jittery motion, stray text and watermarks. A good negative prompt steers the model away from them. This guide assembles a curated, platform-aware negative prompt from the exact issues you’re fighting.
How negative prompts work in video AI
A negative prompt lists what you do not want. The model down-weights those concepts during generation, nudging the output away from those patterns. This is different from simply omitting something from the positive prompt — explicitly naming what to avoid tends to have stronger and more reliable effect on the output.
This tool keeps a vetted vocabulary per artifact and combines the terms you select. For example:
| Artifact | Effective negative terms |
|---|---|
| Temporal flicker | temporal flicker, strobing, flashing frames, inconsistent lighting |
| Morphing faces | morphing, distorted face, melting features, inconsistent identity |
| Jitter / shake | camera shake, jitter, unstable motion, jerky movement |
| Watermarks / text | watermark, text overlay, logo, caption, subtitle |
| Extra limbs | extra limbs, deformed hands, mutated fingers, anatomically incorrect |
Where a platform has no dedicated negative field (for example Sora), the tool phrases the list as "no flickering, no watermarks, no morphing" for inclusion in the main prompt instead.
Platform behaviour differences
Different tools handle negatives differently:
- Runway — has a dedicated negative prompt field; paste the list directly.
- Kling — separate negative prompt input; works well with comma-separated terms.
- Pika — limited negative support; fold key terms into the main prompt.
- Sora — no negative field; add “avoid:” phrasing to the main prompt.
- Stable Video Diffusion — full negative conditioning, similar to image diffusion.
Why some artifacts persist despite negatives
Negatives reduce the probability of an artifact but do not guarantee its absence. Some causes are in the model itself or in the generation settings:
- Baked watermarks — if a watermark appears on every single output, it is likely baked into the model’s training or output pipeline. A negative will not remove it; you need to switch to a clean model or use inpainting.
- High motion commands — asking for fast action or camera pans dramatically increases flicker and morphing. Lower the commanded motion level first; negatives alone cannot compensate for extreme motion settings.
- Short clip length — some artifacts appear mostly in the first and last frames. Generating slightly longer clips and trimming the ends often produces cleaner results.
Tips for cleaner results
- Pair negatives with calmer prompts. Reducing commanded motion and locking the camera prevents most flicker and morphing at the source.
- Keep negatives focused. A list of 5–8 targeted terms beats a 30-term dump that dilutes the signal.
- Match terms to the symptom. Morphing and jitter need different terms — fix what you actually see rather than throwing in everything.
- Iterate one variable at a time. Change the negative prompt, regenerate, and compare before also changing the positive prompt — otherwise you cannot tell which change caused the improvement.