Why longer negative prompts make things worse
Beginners copy a wall-of-text negative prompt and assume more is better. Negative prompts also consume model attention. Listing "asymmetric eyes, cross-eyed, deformed pupils, wrong eye color, extra eyes" five times does not make eyes more accurate — it makes the eye region weighted up, sometimes producing odd makeup, oversaturated irises or unnatural highlights.
A more reliable approach: pick the 3–5 problems your current prompt is genuinely prone to and ignore everything else. A side-profile prompt does not need asymmetric eyes. A silhouette in a wide shot does not need bad hands. Targeting beats coverage.
SDXL and Flux reduce the need for negative prompts dramatically compared to SD 1.5. Flux Dev and Schnell, built on a Transformer architecture, essentially ignore negative prompts; you control quality through positive prompting alone. Knowing your model generation is the prerequisite for everything that follows.
5 scenario templates
| Scenario | Negative prompt template (copy-paste) |
|---|---|
| Realistic portrait | deformed face, asymmetric eyes, plastic skin, over-smoothed, low-res, blurry, watermark, signature |
| Hand close-up | extra fingers, fused fingers, missing fingers, malformed hand, wrong proportions, blurry hand |
| Poster with text | misspelled text, broken letters, gibberish, random characters, distorted font, low-res text |
| Low-res / repair | low-res, jpeg artifacts, blurry, noisy, oversharpened, compression artifact, banding |
| Video stability | camera shake, flicker, jitter, frame skipping, ghosting, motion blur on static subject |
Each template caps at eight tokens. Need both portraits and text in the same image? Merge the two, but stay under 12 tokens total.
Weight syntax: (token:1.3) and [token:0.7]
Pushing weights above 1.5 backfires: the model may avoid the concept entirely (hide the hand behind a sleeve) rather than fix it. Keep negative weights between 0.6 and 1.4.
Wrong vs. right examples
✗ Wrong (mega list)
worst quality, low quality, normal quality, lowres, monochrome, grayscale, watermark, signature, ugly, deformed, mutated, mutation, bad anatomy, bad proportions, gross proportions, text, error, missing fingers, extra digits, fewer digits, cropped, jpeg artifacts, signature, watermark, username, blurry, bad feet, cloned face, fused fingers, too many fingers, long neck, malformed limbs, asymmetric eyes
30+ tokens, duplicated entries (watermark, signature appear twice). Result: worse than the 6-token targeted template below.
✓ Right (scenario-targeted)
(deformed face:1.3), asymmetric eyes, plastic skin, blurry, watermark, low-res
Targets the five failures most likely on realistic portraits. Short, no duplicates, weights only on the highest priority.
SDXL vs. Flux: a generational shift
SD 1.5 made negative prompts essential — every image used a long list. SDXL halves the dependency. Flux Dev and Schnell, built differently, treat negative prompts as low-priority noise. On Flux, the better move is to phrase the requirement positively: "clean composition, single subject, no extra elements" reads more strongly than a negative list.
4 common mistakes
That wall-of-text template was tuned for SD 1.5. On SDXL or Flux it drags the result down.
"Low quality, bad quality" in negative + "high quality, masterpiece" in positive cancel each other. Send one signal per concept.
White-background product shots do not need NSFW negatives. Each negative token should target a failure the prompt is actually prone to.
A vague "a woman" cannot be rescued by "not ugly, not old". Specify the subject first; negatives are insurance, not a corrective layer.