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This page assumes you’re comfortable with basic prompt structure. See Prompting Basics first if you haven’t.

Token weights

Add emphasis to a specific term by wrapping it in parentheses with a multiplier: (term:1.3)
  • Default weight is 1.0
  • Above 1.0 increases influence — (big breast:1.4)
  • Below 1.0 reduces influence — (clothing:0.6)
  • Stay between 0.5 and 1.5 — outside that range the model produces artifacts
You can stack weights: (long red hair:1.3), (pale skin:1.2), (black lingerie:0.8) Good: a woman, (detailed face:1.3), soft studio light, photorealistic Bad: a woman, (beautiful:2.0), (perfect:2.0), (amazing:2.0) — stacking high weights on vague sentiment terms produces oversaturated, distorted output.

Negative prompts

Open the Advanced toggle to access the negative prompt field. Negatives tell the model what to exclude from the output. Use them to fix recurring problems rather than as a general quality filter. Useful negatives by problem:
ProblemNegative terms
Deformed handsdeformed hands, extra fingers, missing fingers, fused fingers
Bad anatomybad anatomy, malformed body, extra limbs
Flat lightingflat lighting, overexposed, washed out
Unwanted texttext, watermark, signature, logo
Blurry outputblurry, out of focus, low resolution
Clothing when unwantedclothing, dressed, covered
Keep the negative prompt under ten terms. Beyond that the model treats the entire negative as low-priority and ignores it.

Token order

The model weights tokens that appear earlier in the prompt more heavily. Use this deliberately:
  • Put the subject and the most important visual elements first
  • Put style, quality tags, and camera settings last
  • If two elements conflict, whichever appears first usually wins
Example — subject first: a woman, red hair, pale skin, black lingerie, sitting on a bed, soft window light, shallow depth of field, photorealistic, high detail Example — quality tags first (wrong): photorealistic, high detail, 8k, a woman, red hair — the model treats the quality tags as the primary subject and the actual subject as secondary.

Combining LoRAs with weighted prompts

When using a LoRA, add trigger words at the start of the prompt before other descriptors, then use weights to balance LoRA influence against the rest: <lora:64:0.6>, big breast, pink nipples, (a woman:1.0), dark hair, hotel room, soft light, photorealistic If the LoRA overpowers the composition, reduce its weight. If it’s not visible, increase it — but stay below 0.9.

Common failure modes

Add deformed hands, extra fingers, missing fingers, fused fingers to the negative prompt. For close-up hand shots, also add (hands:0.8) in the positive prompt to reduce the model’s focus on them.
The prompt is too long or has conflicting terms. Cut it down to the ten most important elements and remove any redundant quality tags. Check token order — critical elements should be first.
Token weights are too high. Bring everything above 1.3 back down to 1.1–1.2 and regenerate.
The model and the style tags disagree. If you’re using Realism, drop anime or illustration style tags entirely — they confuse the model rather than blending styles.

Prompting Basics

Prompt structure and element order.

Model-specific tips

Per-model prompting quirks.