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Negative Prompt Guide

Learn how negative prompts work to remove unwanted elements from generated images.

1. What a negative prompt actually does

A negative prompt is the companion text that tells the model what not to include. Under the hood, it's implemented by classifier-free guidance (CFG): the model computes two predictions — one with the positive prompt, one with the negative — and steers away from the negative. That means your negative prompt isn't just a wishlist; it actively shapes the final sampling direction on every step.

Positive Prompt

“Draw this”

masterpiece, 1girl, smile

Negative Prompt

“Don't draw this”

low quality, blurry, bad anatomy

Fun fact: Many experienced users report that a good negative prompt has more impact on final quality than doubling the positive token count.

2. The basic set — universal baseline

Start every project with this set. It covers the three most common failure modes: low sample quality, anatomical errors, and unwanted overlays (watermarks, text).

Universal basics

low quality, worst quality, normal quality, poorly drawn, blurry, lowres, jpeg artifacts, watermark, signature, text, error, logo, username, artist name

Anatomy add-on (any character)

bad anatomy, bad hands, missing fingers, extra fingers, fused fingers, extra limbs, missing limbs, deformed, disfigured, mutation, mutated, cross-eyed, bad proportions

Sizing: Keep the basic set under 20 tokens. Anything more starts to restrict creativity — the model literally has fewer directions it can go.

🚫 Negative Prompt — Tag examples

poorly_drawn — Poorly Drawn

How much negative promptseed 777004 fixed · prompt differs per cut
Example image generated with the none tag — Safety guard only
noneSafety guard only
Example image generated with the guard only tag — + anatomy terms
guard only+ anatomy terms
Example image generated with the quality terms tag — + quality terms
quality terms+ quality terms
Example image generated with the full stack tag — Full stack
full stackFull stack

Model

rinFlanimeIllustrious_v30 · euler_ancestral/normal · steps 28 · CFG 5 · 896×1152 · seed 777004

This comparison was measured on a single Stable Diffusion family checkpoint. The same tags can behave differently on NovelAI, Midjourney or another checkpoint — read it as a reference point, not a verdict.

3. The advanced set — when the basic isn't enough

Add these when you consistently see specific problems. Don't stack everything at once — add one group at a time.

Face details

asymmetric eyes, cross-eyed, wonky eyes, misaligned eyes, ugly face, bad face, deformed face, distorted face, plastic face

Hands & feet

bad hands, mutated hands, poorly drawn hands, extra fingers, missing fingers, bad feet, extra toes, fused toes, long fingers, short arms, long arms

Framing / cropping

out of frame, cropped, cut off, off-center, bad framing, poorly cropped, head out of frame

Duplication

duplicate, clones, tiling, repeating pattern, multiple heads, twin body, fused bodies

Aesthetic cleanup

oversaturated, overexposed, underexposed, harsh shadows, motion blur (unintended), grainy, noisy, purple fringing

🚫 Negative Prompt — Tag examples

poorly_drawn — Poorly Drawn

4. Five situational templates

Copy these directly. Each template combines the basic set with the specific hazards of that scene type.

Template 1 — Portrait

low quality, worst quality, blurry, jpeg artifacts, watermark, text, bad anatomy, bad hands, missing fingers, extra fingers, cross-eyed, asymmetric eyes, plastic face, ugly, deformed, out of frame, cropped

Biggest risks: face anatomy, hand anatomy, cropping.

Template 2 — Landscape

low quality, worst quality, blurry, oversaturated, cartoon, anime, watermark, text, logo, people, person, human, buildings (unless intended), duplicate, tiling, deformed terrain

Biggest risks: unintended subjects, over-saturation, tiling.

Template 3 — Anime / Illustration

low quality, worst quality, normal quality, lowres, bad anatomy, bad hands, missing fingers, extra fingers, extra limbs, ugly, deformed, text, watermark, signature, artist name, username, realistic (if you want anime), photo, 3d

Biggest risks: style drift toward realism, artist signatures, common Booru-label junk.

Template 4 — Photorealism

low quality, worst quality, blurry, jpeg artifacts, watermark, text, cartoon, anime, illustration, painting, 3d render, plastic skin, waxy skin, airbrushed, deformed, bad anatomy, bad hands

Biggest risks: style drift toward illustration, plastic skin, airbrush look.

Template 5 — Cyberpunk / Neon

low quality, worst quality, blurry, watermark, text, daytime, clear sky, sunny, rural, nature (unless intended), medieval, fantasy armor, boring composition, flat lighting

Biggest risks: daytime/rural drift, flat lighting, wrong genre props.

5. Model-specific caveats

  • SDXL / A1111: All of the above works directly. Put the full negative prompt in the negative field.
  • NovelAI: Use the UC (Undesired Content) field. Their preset "Heavy" option covers most of the universal basic set.
  • Animagine XL / Pony V6: Include 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 (recommended preset).
  • Flux.1-dev / schnell: Negative prompts are not supported. Use positive phrasing instead ("clean skin" rather than negative "blemishes").
  • Midjourney: Use --no flag. --no text, watermark, blurry, low quality is the common shorthand.

6. Attribute leakage — when negatives backfire

A negative prompt can leak into the positive and remove things you wanted:

  • Color leak: If you want red hair, do not put red in the negative — the model may remove red altogether. Be specific: red dress only.
  • Subject leak: Putting person in the negative kills your character. Be specific: crowd, background people.
  • Style leak: painting in the negative will remove all painterly qualities — fine for photos, bad for illustrations.

7. Iteration workflow

  1. Start with the basic set + one situational template.
  2. Generate 4 images, identify the worst common problem.
  3. Add 1–2 specific negative tokens targeting that problem.
  4. Regenerate with the same seed. If the problem persists, the issue is probably in the positive prompt, not the negative.
  5. Save your winning combo in Promgrammer for reuse.

8. Related

For more context on when and why these tokens matter: Quality Tags Deep Dive, Model Comparison.

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