Glossary
Negative Prompts Explained
A complete guide to writing negative prompts that eliminate artefacts, bad anatomy, and unwanted styles from your AI images.
A negative prompt is a list of words or phrases that you want the diffusion model to steer away from during generation. It works because classifier-free guidance (CFG) evaluates the model's output against both a positive prompt and a negative prompt simultaneously, and pushes the result toward the positive while pulling it away from the negative.
Common universal negatives for SDXL: 'blurry, out of focus, low quality, jpeg artefacts, watermark, text, logo, duplicate, extra limbs, deformed, ugly, bad anatomy, bad hands, missing fingers, cropped, worst quality, low resolution'. These address the most frequent failure modes of diffusion models.
Flux-specific considerations: Flux's transformer architecture is significantly better at anatomy than SD, so the long SD-era negative prompts often hurt quality on Flux. Use shorter, targeted negatives: 'blurry, watermark, text'. Over-negating with a Flux model can suppress detail.
Anatomy-specific negatives: 'extra fingers, missing fingers, fused fingers, malformed hands' specifically target the hand-generation problem that plagues all diffusion models to varying degrees.
Style negatives: if you are generating photorealism but keep getting painterly or anime results, add 'illustration, drawing, painting, cartoon, anime, sketch' to your negative prompt.
CFG interaction: negative prompts have more impact at higher CFG scales. At CFG 3.5 (typical Flux), negatives have subtle effect. At CFG 9–12 (typical SDXL), they are powerful.
Frequently Asked Questions
- Do negative prompts work on Flux the same way as Stable Diffusion?
- No. Flux responds to shorter, more targeted negatives. Long SDXL-style negative prompt templates often degrade Flux outputs. Use 3–8 specific terms.
- Should I always use a negative prompt?
- For SDXL: yes, a standard negative prompt significantly raises output quality. For Flux: a short, targeted negative is helpful but less critical than on older architectures.
- Can I use parentheses to weight negative prompts?
- Yes. (bad hands:1.4) pushes the model 40% harder away from bad hand anatomy. Syntax varies by inference engine.
- What is embedding-based negatives?
- Textual inversion embeddings like 'EasyNegative' or 'bad-hands-5' encode a complex anti-concept into a single token, giving more de-artefacting power than plain text. They are SD-only and not applicable to Flux.