Why can a long negative prompt hurt quality?

Every token in the negative prompt consumes part of the model's limited attention budget and can pull the image in unintended directions. Piling on dozens of redundant or vague negatives like "bad", "ugly", "worst" often does little and can wash out the result. A short, targeted negative prompt usually outperforms a sprawling one.

Does SDXL need fewer negatives than SD 1.5?

Yes. SDXL was trained on higher-quality data and handles anatomy and composition far better out of the box, so the heavy "deformed, extra limbs, bad hands" negatives that helped SD 1.5 are mostly unnecessary. Many SDXL users run with a very short negative prompt or none at all.

What counts as a redundant or low-value token?

Duplicate tokens (the same word twice), near-synonyms that stack ("bad quality, low quality, worst quality, poor quality"), and vague aesthetic words that the model cannot meaningfully act on. The optimizer collapses synonym clusters to one representative and drops exact duplicates.

Is my prompt sent anywhere?

No. All analysis happens locally in your browser. Nothing is uploaded or stored — the tool only reads the text you paste and returns a cleaned version.

Negative Prompt Optimizer

Negative prompt optimizer

Negative prompts grow by accretion — people copy a long list from a forum, add a few more “bad, ugly, worst” tokens, and never trim it. But every negative token spends part of the model’s limited attention, and a bloated list of duplicates and vague terms can actually reduce quality. This tool analyzes your negative prompt, strips the dead weight, and returns a tight, high-impact version.

How it works

The optimizer splits your prompt into individual tokens, then applies three passes: it removes exact duplicates, collapses synonym clusters (for example the “bad/low/worst/poor quality” pile-up) down to a single representative, and flags low-value filler that rarely changes output. It also adjusts for model type — SDXL and Pony need far fewer negatives than SD 1.5, so the advice scales to your checkpoint. The result is a cleaned, reordered negative prompt ready to copy.

Tips for strong negatives

Less is more on SDXL. Modern models handle anatomy and composition well; start from a near-empty negative and add only what you actually see going wrong.
Target specific failures. “Extra fingers” earns its place when hands break; “ugly” almost never does.
Avoid synonym stacking. One quality term beats four — the extras just dilute attention.
Re-run after edits. Negative prompts drift over a project; periodically pass them through to clear accumulated cruft.