Negative Prompts Explained: What They Are and How to Use Them

What Are Negative Prompts?

Negative prompts tell image generation models what to exclude from the output. They're supported by Stable Diffusion, SDXL, and other diffusion-based models (but NOT by Midjourney or DALL-E, which handle exclusion differently).

How They Work

During the diffusion process, the model uses your negative prompt to steer away from certain concepts. Think of it as:

• Positive prompt → "Generate images that look like THIS"
• Negative prompt → "...but NOT like THIS"

Universal Negative Prompt Template

A good starting point for most image generation:

ugly, deformed, noisy, blurry, distorted, out of focus, bad anatomy,
extra limbs, poorly drawn face, poorly drawn hands, missing fingers,
watermark, signature, text, logo, username, low quality, worst quality,
normal quality, jpeg artifacts, cropped

Category-Specific Negatives

Photorealistic portraits

cartoon, illustration, anime, drawing, painting, 3d render, CGI,
plastic skin, doll-like, uncanny valley, cross-eyed, asymmetric eyes,
extra fingers, mutated hands, bad teeth, unnatural pose

Landscapes

people, humans, figures, text, watermark, frame, border, oversaturated,
HDR artifacts, chromatic aberration, lens flare, vignette

Product photography

blurry, low resolution, distorted, warped, cluttered background,
shadows on product, reflections, text overlay, watermark, frame

Tips for Effective Negatives

1. Don't over-specify — Too many negatives can make images flat and lifeless
2. Weight important terms — In SDXL: (blurry:1.3) increases the weight
3. Be specific to your task — Generic negatives aren't always helpful
4. Test without negatives first — Sometimes the model does fine on its own
5. Negative prompts ≠ magic — They help but won't fix a bad positive prompt

Models That Support Negative Prompts

Model	Negative Prompt Support
SDXL 1.0	Full support
SD 3.5	Full support
Flux	Partial (via guidance)
Midjourney	Use --no parameter instead
DALL-E 3	Not supported

Common Mistakes

• Adding "don't" to positive prompts — Most models ignore negation in positive prompts
• Repeating positive content in negatives — If you say "red car" positively and "red" negatively, you'll confuse the model
• Copy-pasting massive negative templates — Tailor them to your specific use case