Artifact control

Negative Prompts For Image To Video

Use negative prompts to name the specific failure modes that image-to-video models often introduce.

Try it here

Mini prompt studio

Change the image summary or motion and copy a focused prompt.

Prompt

Create a Kling-ready image-to-video prompt from the reference image. Subject anchor: a close-up portrait of a runner tying a shoe before sunrise. Motion: the runner tightens the laces while sunrise light moves across the pavement. Camera: tracking movement with a clear beginning, middle, and ending. Visual style: cinematic lighting, natural depth of field, controlled color contrast. Constraints: use the provided image as the first frame; keep the main subject visually anchored through the full shot; do not invent new major objects; keep the background layout consistent unless motion is requested; compose for 16:9 and 8s. Model rewrite: Prioritize the subject motion, camera path, and what must stay locked.

negative prompts for image to video

What matters

Negative prompts work best when they match the visible risk in the image.
People images need face, hand, and identity protections.
Product images need logo, label, text, shape, and material protections.

Runner close-up

Before and after prompt

Before

No bad quality, no blur.

After

Negative prompt: face drift, identity change, extra fingers, warped hands, changing shoe shape, melted laces, sliding pavement, camera jitter, flicker, low-detail fabric.

Why it works

It names the exact anatomy and object risks.
It replaces generic quality language.
It keeps background and camera failures in scope.

FAQ

Common questions

Can I use the prompt in Seedance, Veo, Kling, or Runway?

Yes. Pick the target model in the generator and it rewrites the same image idea with model-specific emphasis.

Does the tool analyze uploaded images?

The first version works from your written image description, image URL, and motion notes. The upload is for preview while you write.

Why include a negative prompt?

Image-to-video outputs often fail through identity drift, warped text, unstable backgrounds, or jitter. The negative prompt names those failure modes directly.