What is prompt weighting?
Every word in a prompt competes for the model's attention. Weighting lets you turn the dial up or down on specific tokens so the model emphasises — or ignores — them. A weight above1.0 strengthens a concept; below 1.0 weakens it.
Numeric weights: (token:1.2)
The clearest syntax is an explicit multiplier. Wrap the text in parentheses and add a colon plus a number:
(detailed eyes:1.3)— about 30% more emphasis.(background:0.6)— pushed well into the background.1.0is neutral; most useful values sit between0.7and1.5. Past ~1.6 images tend to distort.
Bracket emphasis: (((this))) and [[this]]
Automatic1111 also supports bare brackets. Each layer of ( ) multiplies a token's weight by roughly 1.1, and each [ ] divides by roughly 1.1. So ((vivid)) ≈ 1.21× and[muted] ≈ 0.91×. The numeric form is easier to reason about, so prefer it once you're past one or two layers.
LoRA syntax: <lora:name:weight>
A LoRA is a small add-on model that injects a style, character or concept. In Automatic1111 and Forge you load it inline:
<lora:add-detail:0.8>— load the add-detail LoRA at 0.8 strength.- Typical strengths are
0.6–1.0; stack multiple LoRAs by adding more tags. - Many LoRAs also need trigger words in the prompt to activate — check the model card.
In ComfyUI you usually apply a LoRA with a Load LoRA node instead of inline text, with separate model and CLIP strength sliders.
BREAK: isolating concepts
CLIP reads prompts in chunks of 75 tokens. Putting BREAK between two groups starts a fresh chunk so they don't bleed into each other — handy when colours or attributes are leaking across subjects. ComfyUI achieves the same with Conditioning (Concat) nodes.
Automatic1111 vs ComfyUI: what carries over
- Works in both: comma-separated tags and the
(token:1.2)numeric form. - A1111 / Forge only: nested
( )/[ ]emphasis and inline<lora:…>. - ComfyUI: LoRAs and conditioning go through nodes; positive and negative prompts are separate inputs.
See the companion ComfyUI & Automatic1111 prompt guide for engine-specific tips.
Frequently asked questions
What does (token:1.2) mean in a prompt?▾
It multiplies the attention weight of “token” by 1.2, making the model emphasise it about 20% more. Values below 1.0 de-emphasise it; 1.0 is neutral.
How do I weight a prompt in Automatic1111?▾
Wrap the text in parentheses with a number — (detailed eyes:1.3) — or use bare parentheses, where each ( ) layer multiplies weight by ~1.1 and each [ ] divides by ~1.1.
What is LoRA syntax?▾
A LoRA is loaded inline with <lora:filename:weight>, for example <lora:add-detail:0.8>. The weight scales the LoRA’s strength; 0.6–1.0 is typical.
Does ComfyUI use the same weighting as Automatic1111?▾
ComfyUI supports the (token:1.2) numeric form, but not A1111’s nested ( ) / [ ] multipliers by default, and LoRAs are usually applied with a Load LoRA node rather than inline <lora:…> text.