Negative Prompts: Tell AI What NOT to Create

Negative Prompts: How to Tell AI What NOT to Create (Complete Guide)

You craft a perfect prompt. The AI generates your image. And then you notice the extra finger. Or the random text floating in the background. Or the face that's almost right but deeply unsettling.

This is where negative prompts come in. Telling an AI what you want is only half the equation. Professional results come from learning what to exclude.

This guide covers everything about negative prompts: what they are, how they work technically, which platforms support them, and most importantly—50+ negative prompt tokens organized by the problems they solve.

What Are Negative Prompts?

Negative prompts are instructions that tell AI models what to avoid generating. While your main prompt says "create this," negative prompts say "but don't include that."

In image generation, positive-only prompting often produces unwanted elements. Ask for "a portrait" and you might get watermarks, weird anatomy, background clutter, or stylistic elements you didn't want. The AI model doesn't know what you consider "wrong" unless you tell it.

Negative prompts work by pushing the generation away from unwanted patterns. Think of them as guardrails that keep the AI's creative process within acceptable boundaries.

Why positive prompts aren't enough:

AI models generate images through a process called diffusion—starting with random noise and progressively refining it. At each step, the model decides "does this look more like what the prompt describes, or less?" But without negative guidance, "what the prompt describes" can include unwanted elements that commonly appear in training data.

Example: If you prompt "a person," the model has seen millions of photos with people—some with text overlays, watermarks, professional photography signatures, extra background elements. Without negative prompts, any of these might appear because they're statistically associated with "a person" in the training set.

Negative prompts actively suppress those patterns.

How Negative Prompts Work Technically

Understanding the mechanism helps you write better negative prompts.

Classifier-free guidance (CFG):

Most modern image models use a technique called classifier-free guidance. During generation, the model runs two parallel processes:

1. A conditional generation (following your positive prompt)
2. An unconditional generation (random output with no guidance)

The final output is a combination where the model amplifies the difference between these two. Adding negative prompts creates a third process: a negatively-conditioned generation (following your negative prompt).

The model now produces:

Final output = Positive prompt output + (Positive - Negative) amplification

In simple terms: the model generates what you asked for, then pushes away from what you said not to generate.

CFG Scale impact:

The "CFG scale" parameter (common in Stable Diffusion) controls how strongly the model follows your prompts. Higher CFG = stronger adherence to positive AND negative prompts. Lower CFG = more creative freedom but weaker prompt following.

Typical ranges:

• CFG 4-6: Loose interpretation, creative, negative prompts have less power
• CFG 7-12: Balanced, most common for quality results
• CFG 13-20: Strong adherence, negative prompts very powerful, risk of over-correction
• CFG 20+: Often produces artifacts and over-fitted results

Weighting (Stable Diffusion):

Stable Diffusion supports weighted negative prompts:

Negative prompt: (extra fingers:1.3), (deformed hands:1.5), watermark

Higher weights = stronger suppression. Use weights above 1.0 for persistent problems.

Platform Support — Where Negative Prompts Work

Different AI platforms implement negative prompts differently.

Stable Diffusion: Full Support ✓

Implementation: Dedicated negative prompt field in all interfaces (AUTOMATIC1111, ComfyUI, web UIs)

Syntax: Plain comma-separated words, with optional weighting:

Positive: a beautiful landscape, mountains, lake, sunset
Negative: blurry, low quality, watermark, text, (deformed:1.2)

Effectiveness: Very strong. Negative prompts are essential for quality in Stable Diffusion.

Best practices:

• Start every session with a baseline negative prompt
• Use embedding shortcuts like EasyNegative or bad_prompt_version2
• Weight persistent problems higher (1.3-1.5)

Midjourney: Partial Support ✓

Implementation: --no parameter

Syntax:

a portrait --no glasses, hats, jewelry

Effectiveness: Moderate. Works better for objects and style elements than anatomy fixes. Less powerful than SD's negative prompts.

Limitations:

• Can't weight individual terms
• Best for excluding objects, style elements, or concepts
• Less effective for fixing anatomy issues (Midjourney's models just handle anatomy better by default)

Best practices:

• Use for style exclusions: --no cartoon, anime when you want photorealism
• Use for object exclusions: --no text, watermark, signature
• Don't rely on it for anatomy fixes—Midjourney v7 handles this natively

DALL-E 3: Limited Support ✓

Implementation: Include exclusions in main prompt

Syntax:

A portrait of a woman, no text, no watermarks, no extra fingers

Effectiveness: Weak to moderate. DALL-E 3 interprets "no X" but not as powerfully as dedicated negative prompt systems.

Best practices:

• Phrase as explicit instructions: "without any text" works better than "no text"
• Focus on style/content exclusions rather than anatomy (DALL-E 3 has strong anatomy by default)
• Use ChatGPT to expand your negative instructions into natural language

Adobe Firefly: No Support ✗

Implementation: None. Positive prompts only.

Workarounds:

• Use very explicit positive prompts that crowd out unwanted elements
• Rely on Firefly's built-in safety and quality filters
• Generate multiple variations and select the best

Other Platforms

• Leonardo.ai: Full support (SD-based)
• DreamStudio (Stability AI): Full support
• Playground AI: Full support
• NightCafe: Full support (SD option)
• Bing Image Creator (DALL-E): Limited (include in main prompt)

The Essential Baseline Negative Prompt

Every image generation session should start with a baseline negative prompt that prevents common quality issues.

For Stable Diffusion (copy-paste ready):

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

This baseline prevents:

• Low resolution outputs
• Anatomy errors (malformed hands, wrong finger count)
• Text and watermarks
• JPEG compression artifacts
• Blurriness and low-quality generations
• Photography signatures

Expanded version for portraits:

lowres, bad anatomy, bad hands, extra fingers, missing fingers, deformed hands, text, error, cropped, worst quality, low quality, jpeg artifacts, signature, watermark, blurry, deformed, disfigured, poorly drawn face, mutation, mutated, extra limbs, ugly, fat, missing limbs, floating limbs, disconnected limbs, long neck, cross-eyed

For photorealism specifically:

lowres, bad anatomy, text, watermark, blurry, 3d render, cartoon, anime, sketch, painting, illustration, cgi, fake, unrealistic

This excludes artistic styles when you need pure photorealism.

For Midjourney (minimalist):

--no text, watermark, signature, blurry

Midjourney needs less because its models handle quality better by default.

Negative Prompts by Problem

Here's how to fix specific issues with targeted negative prompts.

50+ Negative Prompt Tokens by Category

Copy these into your negative prompts to prevent specific issues.

Anatomy Errors

bad anatomy, bad hands, extra fingers, missing fingers, extra digit, fewer digits, deformed hands, mutated hands, poorly drawn hands, extra limbs, extra arms, extra legs, missing limbs, floating limbs, disconnected limbs, malformed limbs, long neck, elongated body, disproportionate, asymmetric

Facial Issues

poorly drawn face, bad eyes, cross-eyed, lazy eye, asymmetric eyes, asymmetric face, malformed face, ugly face, deformed face, bad teeth, missing teeth

Quality Problems

lowres, low quality, worst quality, low resolution, blurry, blur, out of focus, hazy, soft focus, bokeh (when unwanted), noise, grainy, jpeg artifacts, compression, pixelated, low detail

Text and Overlays

text, watermark, signature, username, logo, letters, words, caption, subtitle, title, stamp, copyright, watermarked, signed, autograph, typography, font

Artistic Style Exclusions (for photorealism)

cartoon, anime, illustration, painting, drawing, sketch, comic, manga, 3d render, cgi, digital art, stylized, artistic, abstract, vector art, clipart

Content Type Exclusions

duplicate, multiple, clone, copy, two heads, two faces, conjoined, disfigured, mutation, mutated, extra, excess

Lighting/Exposure

overexposed, underexposed, too bright, too dark, washed out, blown out, high contrast, low contrast, flat lighting, harsh lighting (when unwanted)

Skin/Texture Issues

plastic skin, waxy skin, shiny skin, oily skin, fake skin, artificial, mannequin, doll-like, unrealistic skin, smooth skin (for realism), airbrushed

Composition Problems

cropped, cut off, out of frame, partial, incomplete, zoomed too close, bad composition, unbalanced, off-center (when center wanted), tilted, distorted

Background Issues

cluttered background, messy background, busy, distracting, chaotic background, random objects, complex background (when simple wanted)

Model-Specific (Stable Diffusion)

error, artifacts, glitches, distorted, warped, deformed, disfigured, morbid, ugly, gross, disgusting

Professional Photography Exclusions

amateur, snapshot, phone photo, selfie, low production value, unpolished, unprofessional

Negative Prompt Strategy for Portraits

Portraits are where negative prompts matter most. Human faces trigger uncanny valley responses when slightly off.

Essential portrait negative prompt (SD):

bad anatomy, bad hands, extra fingers, missing fingers, poorly drawn face, bad eyes, asymmetric eyes, asymmetric face, deformed, mutation, extra limbs, lowres, blurry, text, watermark, long neck, cross-eyed

For photorealistic portraits:

bad anatomy, poorly drawn face, asymmetric eyes, deformed, mutation, lowres, blurry, text, watermark, cartoon, anime, illustration, plastic skin, waxy skin, unrealistic, 3d render, cgi, fake, airbrushed, oversaturated

For artistic portraits (less strict):

deformed, disfigured, extra limbs, blurry, text, watermark, lowres, worst quality

Key focus areas for portrait negatives:

1. Eyes — symmetry is critical; even slight misalignment is unsettling
2. Hands — most common AI failure point; always exclude hand problems
3. Facial symmetry — humans are hyper-sensitive to facial asymmetry
4. Skin texture — avoid plastic/waxy appearance in photorealism

Negative Prompts for Specific Styles

Different styles need different negative prompts.

Photorealism

Exclude artistic styles:

cartoon, anime, illustration, painting, drawing, sketch, 3d render, cgi, artistic, stylized, vector art, comic, manga, abstract

Anime/Manga

Exclude realism:

photorealistic, realistic, photography, photo, real, 3d, render, lowres, blurry, text

Oil Painting

Exclude digital and photo:

photograph, photo, digital, 3d render, screenshot, text, watermark, lowres

Technical/Scientific Illustration

Exclude artistic interpretation:

artistic, stylized, dramatic lighting, shadow, abstract, painterly, watercolor, sketchy, rough

Fantasy Concept Art

Exclude modern/mundane:

modern, contemporary, realistic, photograph, mundane, everyday, plain, simple, minimalist

Product Photography

Exclude distractions:

person, people, face, hands, cluttered, messy background, text, watermark, shadow (sometimes), dramatic lighting (for clean shots)

When Negative Prompts Backfire

Negative prompts aren't magic. Sometimes they cause problems.

The Forbidden Word Effect

Problem: Mentioning something in a negative prompt can make the model think about it more.

Example:

Positive: a peaceful forest
Negative: no bears, no dangerous animals

Result: You might get more wildlife than if you hadn't mentioned it. The model activates bear-related patterns even when told to avoid them.

Solution: Only use negative prompts for things that appear without being prompted for. Don't pre-emptively exclude things unlikely to appear anyway.

Over-Correction

Problem: Too many negative prompts or too high negative weights can produce bland, generic results.

Example:

Negative: (bad anatomy:2.0), (deformed:2.0), (mutation:2.0), (extra limbs:2.0), (bad proportions:2.0)

With CFG scale 15+, this might produce stiff, unnatural poses as the model avoids anything remotely unusual.

Solution:

• Keep negative weights moderate (1.0-1.5)
• Use CFG scale 7-12
• Only exclude actual problems, not creative variations

Conflicting Instructions

Problem: Positive and negative prompts that contradict each other.

Example:

Positive: dynamic action pose, movement, motion blur
Negative: blur, blurry, motion

The model gets conflicting signals about blur/motion.

Solution: Be specific. "Blurry face" vs. "motion blur" are different. Or accept that some blur comes with motion.

Platform Limitations

Midjourney: --no is weaker than SD negative prompts. Don't expect anatomy-fixing miracles.

DALL-E 3: Natural language negatives are inconsistent. It might interpret "no red" as "avoid red" or might ignore it entirely.

Solution: Test platform-specific effectiveness. What works in SD might not work in Midjourney.

Advanced: Prompt Weighting in Stable Diffusion

Stable Diffusion supports weighted negative prompts for precise control.

Syntax:

Negative prompt: (extra fingers:1.3), (bad anatomy:1.2), text, watermark

Weight ranges:

• (word:0.5) = weak negative (50% normal strength)
• (word:1.0) = normal negative (can omit the weight)
• (word:1.3) = strong negative
• (word:1.5) = very strong negative
• (word:2.0) = maximum negative (risk of over-correction)

When to use higher weights:

• Persistent problems that won't go away (extra fingers is the classic case)
• Style exclusions when the model strongly leans toward that style
• Quality issues that appear despite negative prompts

When to use lower weights:

• Subtle adjustments
• Avoiding over-correction
• Elements you want to discourage but not eliminate

Example (portrait with stubborn hand problems):

Positive: a portrait of a woman, hands visible, elegant pose
Negative: (extra fingers:1.4), (bad hands:1.4), (deformed hands:1.3), blurry, text, watermark, bad anatomy

The hand-related negatives get extra weight because hands are the most common failure point.

Negative Prompt Embeddings (Stable Diffusion)

Stable Diffusion supports "textual inversion embeddings"—pre-trained negative prompts that act as shortcuts.

Popular negative embeddings:

EasyNegative:

• Most popular negative embedding
• Trained specifically to avoid common quality issues
• Usage: Just type EasyNegative in negative prompt
• Equivalent to a comprehensive quality negative prompt

bad_prompt_version2:

• Another common quality embedding
• Focuses on anatomy and composition issues
• Usage: bad_prompt_version2

bad-hands-5:

• Specialized for hand anatomy
• Usage: bad-hands-5 (add to other negatives)

How to use embeddings:

Negative prompt: EasyNegative, bad-hands-5, text, watermark

Embeddings plus specific exclusions = comprehensive negative prompting with minimal tokens.

Where to get embeddings:

• Civitai (huggingface.co/models)
• AUTOMATIC1111 embedding browser
• Pre-packaged in some SD distributions

Install embeddings to your SD /embeddings folder. They appear as available tokens in your negative prompt field.

Negative Prompts for Different Model Types

SD 1.5 models:

• Need comprehensive negative prompts
• Hand problems common—always exclude
• EasyNegative highly effective

SDXL models:

• Better base quality, less aggressive negatives needed
• Still need anatomy and quality negatives
• More artistic freedom with fewer restrictions

SD 3.5 / 4.0 models:

• Significantly better anatomy by default
• Focus negatives on style and content rather than anatomy
• Minimal baseline usually sufficient

Anime/manga models:

• Different negative prompt conventions
• Often benefit from: lowres, bad anatomy, bad hands, text, error, missing fingers, extra digit, cropped, worst quality, low quality, normal quality, jpeg artifacts
• Style exclusions less necessary (they're trained on anime)

Practical Negative Prompt Workflow

Step 1: Start with baseline

lowres, bad anatomy, text, watermark, blurry

Step 2: Generate and evaluate

Look for specific problems in the output.

Step 3: Add targeted negatives

If you see extra fingers: add extra fingers, deformed hands

If you see style issues: add style exclusions

If you see background clutter: add cluttered background, messy

Step 4: Weight persistent problems

If adding to negative prompt doesn't fix it:

(extra fingers:1.3), (deformed hands:1.3)

Step 5: Adjust CFG scale

If negatives aren't working: increase CFG (8 → 10 → 12)

If results are over-corrected: decrease CFG (12 → 10 → 8)

Step 6: Save successful combinations

When you find a negative prompt that works for a specific style/subject, save it. Build a library:

• Portrait negative prompt
• Landscape negative prompt
• Product shot negative prompt
• Fantasy art negative prompt

Summary: Negative Prompt Best Practices

✓ Do:

• Start every session with a baseline negative prompt
• Add specific negatives for problems that actually appear
• Use embeddings (SD) for efficiency
• Weight persistent problems higher (1.3-1.5)
• Test platform-specific effectiveness
• Save successful negative prompts for reuse

✗ Don't:

• Pre-emptively exclude things unlikely to appear
• Use weights above 1.5 without testing (risk of over-correction)
• Mix contradictory positive and negative prompts
• Assume negatives work the same across all platforms
• Write 200-word negative prompts (signal-to-noise ratio matters)

Platform summary:

• Stable Diffusion: Negative prompts are essential—always use them
• Midjourney: Use --no for style/object exclusion, not anatomy fixes
• DALL-E 3: Include negatives in main prompt, don't over-rely on them

Next steps:

• Prompt Anatomy Guide — Learn positive prompt structure
• Stable Diffusion Prompting Guide — Platform-specific negative prompt strategies
• Midjourney Prompts Guide — When to use --no and when not to

Browse tools:

• AI Tools Directory — Compare negative prompt support across platforms

Master negative prompts and you'll eliminate 80% of frustrating AI generation issues. They're the difference between amateur random outputs and professional consistent quality.

📚 For structured learning beyond this guide: Stable Diffusion and AI art books cover prompting in detail alongside model theory.