Negative Prompts Masterclass: Complete Guide to Better AI Images 2025

Negative prompts are your quality control tool for AI image generation. They specify what shouldn't appear in your images—filtering out distortions, unwanted objects, and artifacts that degrade results.

Quick Answer: Negative prompts tell AI models what to avoid in generated images. Effective negative prompts are specific, start small and iterate, avoid contradicting your positive prompt, and focus on common AI failure modes like "bad hands" or "blurry."

How Negative Prompts Actually Work

When you add a negative prompt, the AI model actively steers away from generating those concepts. The model creates an internal representation of what you don't want, then pushes the generation in the opposite direction.

This is why vague terms like "bad" or "ugly" are ineffective—they don't give the model a specific concept to avoid. "Deformed fingers" gives the model something concrete to steer away from.

Proper negative prompts help achieve flawless portraits

The Foundation: Universal Negative Prompts

These terms work across most models and should be your starting point:

Quality Issues:

worst quality, low quality, normal quality, lowres,
jpeg artifacts, compression artifacts, blurry

Anatomical Issues:

bad anatomy, bad hands, extra fingers, missing fingers,
extra limbs, missing limbs, fused fingers, too many fingers

Face Issues:

poorly drawn face, mutation, mutated, ugly, disfigured,
deformed, bad proportions, gross proportions

Composition Issues:

cropped, out of frame, watermark, signature, text,
username, artist name, logo

Model-Specific Negative Prompts

Different models respond to different negative prompts:

Stable Diffusion 1.5: Responds well to detailed anatomical negatives. Use extensive hand-related negatives as SD 1.5 struggles with hands.

SDXL: Needs fewer negatives overall. Focus on quality terms and specific issues rather than long lists.

Flux: Uses a different architecture—negative prompts have less impact. Focus on positive prompt quality instead.

Pony Diffusion: Responds to quality tags: score_4, score_3, score_2, score_1 in negatives for higher quality output.

Common Mistakes to Avoid

Mistake 1: Overloading Negatives

Too many restrictions confuse the model and can flatten details or create unpredictable results.

Bad:

bad, ugly, worst, terrible, awful, disgusting, horrible,
poor quality, low quality, bad quality...

Better:

worst quality, bad anatomy, blurry

Mistake 2: Contradicting Your Prompt

If you want "moody dark lighting," don't put "dark shadows" in negatives.

Mistake 3: Using Positive Concepts

"A room without furniture" forces the model to conceptualize furniture then negate it. Instead, prompt for "an empty room."

Mistake 4: Vague Terms

"Bad quality" doesn't give the model anything specific. "Jpeg artifacts, pixelated, low resolution" does.

Negative Prompts by Category

Portraits

bad anatomy, poorly drawn face, mutation, mutated,
extra limb, ugly, poorly drawn hands, missing limb,
floating limbs, disconnected limbs, malformed hands,
out of focus, long neck, long body

Landscapes

oversaturated, ugly, blurry, low quality,
watermark, signature, out of frame,
poorly drawn, bad composition

Anime/Illustration

bad anatomy, bad hands, missing fingers, extra digit,
fewer digits, cropped, worst quality, low quality,
normal quality, jpeg artifacts, blurry, bad feet

Photorealistic

cartoon, anime, illustration, painting, drawing,
render, 3d, cgi, worst quality, low quality,
normal quality, bad anatomy, bad hands

Targeted negative prompts eliminate common AI artifacts

Using Weights in Negative Prompts

Emphasize specific exclusions with weights:

Standard Weight:

bad hands

Increased Emphasis (1.3x):

(bad hands:1.3)

Strong Emphasis (1.5x):

(bad hands:1.5)

Use higher weights for persistent problems. If hands consistently appear wrong, increase the weight on hand-related negatives.

The Iteration Approach

Instead of copy-pasting massive negative prompt lists, build yours iteratively:

1. Start Minimal: Just worst quality, blurry
2. Generate Test Images: See what issues appear
3. Add Specific Fixes: If hands are bad, add hand negatives
4. Repeat: Keep only what's necessary

This approach gives you a lean, effective negative prompt tailored to your specific use case.

Advanced Techniques

Scheduled Negatives

In ComfyUI, you can apply different negatives at different steps:

• Early steps (0-30%): Focus on composition negatives
• Middle steps (30-70%): Focus on quality negatives
• Late steps (70-100%): Focus on detail negatives

Embedding-Based Negatives

Use textual inversion embeddings as negative prompts:

• EasyNegative
• BadDream
• UnrealisticDream

These pack effective negatives into single tokens.

Conditional Negatives

Only apply certain negatives when specific content is present. If your prompt includes a person, activate anatomical negatives.

Frequently Asked Questions

Do negative prompts affect generation speed?

Minimally. The model processes both prompts, but the overhead is small. Quality improvements outweigh any speed impact.

Can negative prompts completely prevent unwanted content?

No, they reduce probability but don't guarantee exclusion. For NSFW filtering, use model-level restrictions.

Should I use the same negatives for every generation?

No. Customize based on subject matter. Portrait negatives differ from landscape negatives.

Why do my negative prompts sometimes not work?

The positive prompt may be stronger, the model may not understand the concept, or the negative may be too vague.

How do negative prompts interact with LoRAs?

LoRAs can override negative prompts if strongly trained on specific concepts. Test your LoRA with different negatives.

Negative Prompt Templates

General Purpose:

worst quality, low quality, normal quality, lowres,
bad anatomy, bad hands, error, missing fingers,
extra digit, fewer digits, cropped, jpeg artifacts,
signature, watermark, username, blurry

Portrait Photography:

deformed, ugly, mutilated, disfigured, text, extra limbs,
face cut, head cut, extra fingers, extra arms, poorly drawn face,
mutation, bad proportions, cropped head, bad anatomy,
out of frame, bad art, beginner, amateur, distorted face

Anime/Digital Art:

lowres, bad anatomy, bad hands, text, error, missing fingers,
extra digit, fewer digits, cropped, worst quality, low quality,
normal quality, jpeg artifacts, signature, watermark, username,
blurry, bad feet, artist name, poorly drawn

Conclusion

Negative prompts are powerful but require restraint. Start minimal, iterate based on actual issues, and customize for your specific use case.

Remember that different models respond differently—what works for SD 1.5 may be unnecessary for SDXL. Test and refine rather than copy-pasting massive lists.

The goal is targeted quality improvement, not comprehensive exclusion. A few well-chosen negative terms outperform long lists of vague restrictions.