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.
Why negative prompts accumulate junk
The typical lifecycle of a negative prompt: you start with a minimal list, see an artifact, add a token to fix it, copy the list to another project, someone else posts their improved version on a forum, you merge it with yours, repeat. After six months the prompt looks like:
bad quality, low quality, worst quality, poor quality, blurry, blur, out of focus,
ugly, deformed, bad anatomy, bad hands, extra fingers, extra limbs, missing fingers,
missing limbs, watermark, text, logo, signature, nsfw, nude, censored ...Several problems appear here. “Bad quality”, “low quality”, “worst quality”, and “poor quality” are near-synonyms occupying four slots. “Blur” and “blurry” are the same concept. “Watermark”, “text”, “logo”, and “signature” all address the same failure mode. Each redundant entry consumes attention budget without adding new information to the model’s guidance.
Model-specific guidance
SD 1.5 was trained on noisier data and benefits from more explicit quality and anatomy negatives. A list of 15 to 25 focused tokens is reasonable.
SDXL uses a larger model trained on cleaner, curated data. Anatomy and composition tend to be better out of the box, so the heavy-handed anatomy negatives from SD 1.5 workflows are mostly wasted attention. Many experienced SDXL users find that 5 to 10 focused tokens outperform sprawling legacy lists.
Pony Diffusion has its own training conventions — quality scoring tokens in the positive prompt often do more work than negative prompts, and anatomy negatives carry over from SDXL behavior in practice.
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.
- Test by removing. If removing a token does not change your output, it was doing nothing — drop it permanently.