ComfyUI Prompt Engineering

CLIP Text Encoding Fundamentals

ComfyUI uses CLIP (Contrastive Language-Image Pre-training) text encoders to convert text prompts into conditioning tensors. The CLIPTextEncode node takes a text string and a CLIP model, producing a CONDITIONING output for the KSampler.

Token Limit

CLIP processes text in 77-token chunks. Each word is typically 1-3 tokens. Prompts exceeding 77 tokens are silently truncated unless you use the BREAK token or a multi-clip encoding node.

Weight Syntax

Emphasis (Attention Weights)

Adjust how strongly the model attends to specific words or phrases:

Syntax	Effect	Equivalent Weight
(word:1.3)	Increase emphasis by 30%	Explicit weight 1.3
(word:0.7)	Decrease emphasis by 30%	Explicit weight 0.7
(word)	Slight increase	(word:1.1)
((word))	Moderate increase	(word:1.21), 1.1^2
(((word)))	Strong increase	(word:1.331), 1.1^3
[word]	Slight decrease	(word:0.9091), 1/1.1
[[word]]	Moderate decrease	(word:0.8264), 1/1.1^2

Weight Rules

• Valid range: 0.0 to 2.0 (going beyond 1.5 often causes artifacts)
• Default weight: 1.0 for unmodified tokens
• Nesting stacks multiplicatively: ((word)) = 1.1 * 1.1 = (word:1.21)
• Phrases: (red sports car:1.3) applies weight to the entire phrase
• Mixing: (detailed face:1.4), (blurry background:0.6), combined in one prompt

Examples

a (beautiful:1.3) woman with (flowing red hair:1.2), wearing a blue dress, (sharp focus:1.1)

(masterpiece:1.4), (best quality:1.3), a knight in (ornate armor:1.2), standing on a cliff, (dramatic lighting:1.1), cinematic

BREAK Token

The BREAK keyword forces CLIP to end the current 77-token chunk and start processing subsequent text in a new chunk. This is critical for long prompts.

When to Use BREAK