Negative Prompt Generator
Build a negative prompt for Midjourney, Stable Diffusion, or DALL-E from preset categories in one click.
Image models often need to be told what to avoid just as much as what to include, since a positive prompt alone won't stop common artifacts like extra fingers, warped text, or a mismatched art style from creeping in. This tool generates a negative prompt matched to your positive prompt's specific subject and style, not a generic exclusion list reused for everything.
How to use it
The negative prompt is generated to match its specific subject and style, not as a generic list.
Negative prompt syntax and common failure modes differ between Midjourney, Stable Diffusion, and other models.
Most models take positive and negative prompts as separate fields or parameters.
Tips for better results
- Regenerate the negative prompt if you change the positive one significantly. A negative prompt matched to a portrait won't cover the right failure modes for a landscape.
- Don't stack unrelated exclusions just because they're common. Excluding artifacts irrelevant to your specific image wastes prompt budget some models actually have.
- Check your model's negative prompt syntax before pasting. Some models expect a separate field; others expect it appended to the main prompt with specific syntax.
Example output
“For a positive prompt describing a realistic portrait, generates: deformed hands, extra fingers, blurry, asymmetrical eyes, cartoon, illustration, low resolution — matched to portrait-specific failure modes, not a generic list.”
TL;DR
Negative prompts tell image models what to avoid — extra fingers, watermarks, blurry results — and retyping the right terms for your style every time gets old fast. Pick a preset or check the terms you need, then copy the result into Midjourney, Stable Diffusion, DALL-E, or any tool with a negative prompt field.
Why it helps
Not every model has a negative prompt field, and the ones that do handle it slightly differently, but the underlying terms are consistent across most diffusion-based generators. This groups the common ones by category — anatomy, quality, composition, text and branding, style, and color — so you’re not retyping “extra fingers, blurry, watermark” from memory every session, and so you don’t forget a category entirely just because it wasn’t top of mind.
A negative prompt works differently from a regular prompt: instead of describing what you want, you’re describing what to suppress, which shifts the model’s attention away from the failure modes most common to its training. That’s why a generic negative prompt copy-pasted from somewhere else often underperforms one tuned to the specific kind of image you’re generating — a portrait and a landscape fail in different ways, and their negative prompts should reflect that.
What each category actually fixes
- Anatomy (extra fingers, deformed hands, disfigured face) — the single most common failure mode in AI-generated people, especially hands and eyes.
- Quality (blurry, low resolution, jpeg artifacts, oversaturated) — general image-fidelity issues that show up regardless of subject matter.
- Composition (cropped, out of frame, bad proportions, tiling) — framing and layout problems, particularly common with wide aspect ratios.
- Text & branding (watermark, signature, logo, username) — unwanted marks that diffusion models sometimes hallucinate from training data.
- Style (cartoon, illustration, 3d render, photorealistic) — used to exclude a style you don’t want, which matters most when your positive prompt could be read multiple ways.
- Color (washed out colors, dull colors, black and white) — corrects color and contrast issues that positive prompts alone often don’t fully control.
Choosing the right preset
The six presets aren’t arbitrary groupings — each one targets the failure modes most common to that type of shot. Portrait excludes anatomy and facial issues heavily, since that’s where portraits fail most. Product Shot leans on composition and cropping terms, since commercial images live or die on framing. Photorealistic and Anime/Illustration presets exclude each other’s style — a photorealistic preset excludes “cartoon” and “illustration,” while the anime preset excludes “photorealistic” and “3d render,” since the biggest risk with either is the model drifting toward the wrong rendering style entirely.
Who this is for
Anyone generating images regularly with Midjourney, Stable Diffusion, or DALL-E benefits from having a maintained negative-prompt library instead of rebuilding one from memory each time — designers producing product mockups, marketers generating social content, or hobbyists refining a consistent visual style across a series of images. It’s most useful paired with a positive prompt that’s already specific; a strong negative prompt narrows failure modes, but it can’t compensate for a vague description of what you actually want to see.
FAQ
Does every image model support negative prompts?
No. Check your tool’s documentation — Midjourney, Stable Diffusion, and most SD-based tools support them; some newer models handle unwanted elements differently or don’t expose a separate negative prompt field at all.
Can I combine terms from more than one preset?
Yes. Clicking a preset sets the checkboxes for that preset, but you can still check or uncheck individual terms afterward, or combine terms from two different presets manually.
Will this guarantee the AI avoids these elements?
No prompt guarantees a result. Negative prompts reduce the likelihood of these elements appearing — they don’t eliminate it, and results still vary by model and seed.
Why do photorealistic and anime presets exclude opposite terms?
Each preset excludes the style it’s most likely to accidentally drift into. A photorealistic prompt risks drifting toward illustration or 3D render; an anime prompt risks drifting toward photorealism — so each preset’s negative terms target its own most common failure.
Should I use the same negative prompt for every image?
Not ideally. A negative prompt tuned to your specific shot type — portrait versus landscape versus product photo — will generally outperform one generic list reused everywhere.
