vLLM Prompt Node for ComfyUI

A ComfyUI custom node that generates Stable Diffusion prompts using a locally running vLLM server. Supports wildcard expansion and a fixed prefix for quality tags or style anchors.

https://www.theoath.studio/projects/comfy-vllm-node

Requirements

• A running vLLM server (see vLLM docs)
• Python package: requests (pip install requests)
• ComfyUI

Installation

1. Clone or copy this folder into your ComfyUI/custom_nodes/ directory:

cd ComfyUI/custom_nodes
git clone https://github.com/OATH-Studio/comfy-vLLM

2. Restart ComfyUI.

Setup

Start your local vLLM server. The node will automatically detect whichever model is currently loaded no need to specify it in the node.

Example launch:

vllm serve ./models/Qwen2.5-3B \
    --host 0.0.0.0 \
    --port 8765 \
    --served-model-name Qwen2.5-3B

Note: The node queries /v1/models on each generation and uses the first model returned. If you change models, just restart your vLLM server the node picks it up automatically.

Node Inputs

Input	Type	Default	Description
prompt	STRING		Generation instruction. Supports {wild|card} syntax.
prefix	STRING	masterpiece, best quality, highres	Fixed tags prepended to the output. Not sent to the model.
host	STRING	localhost	vLLM server host.
port	INT	8765	vLLM server port.
max_tokens	INT	128	Maximum tokens to generate.
temperature	FLOAT	0.7	Sampling temperature. Higher = more creative.
retries	INT	3	How many times to retry on empty or failed responses.

Node Output

Output	Type	Description
combined_prompt	STRING	prefix + generated text, ready to wire into CLIPTextEncode

The node displays a live preview on the node face after each generation showing the prefix, the raw generated text, and the final combined string separately so you can see exactly what was assembled.

Wildcard Syntax

Use {option1|option2|option3} anywhere in your prompt. One option is chosen at random each run. Multiple wildcards are each resolved independently.

A {red|blue|green} dragon, {breathing fire into the sky|coiled around a mountain peak in a storm|diving into a glowing ocean abyss|rearing up against a blood moon}

Wildcards are expanded before the prompt is sent to the model, so the model always receives a fully resolved string.

Example Workflow

VLLMPromptNode ──→ CLIPTextEncode (positive) ──→ KSampler
                                                      ↑
                         CLIPTextEncode (negative) ───┘

Prompt Format

The node uses the completions endpoint with a structured format that instructs the model to return comma-separated tags only, with no conversational filler:

### Stable Diffusion prompt tags (comma separated, no sentences):
Input: <your expanded prompt>
Output:

Generation stops at the first newline, so the model cannot ramble past a single line of tags. If you still get conversational responses, try:

• Lowering temperature to 0.3–0.5
• Increasing to a larger model (1.5B or 3B recommended minimum)
• Reducing max_tokens to leave less room for filler

Model Recommendations

Model	Quality	Notes
Qwen2.5-0.5B	⚠️ Unreliable	Too small for consistent instruction following
Qwen2.5-1.5B	✓ Usable	Occasional filler, mostly clean
Qwen2.5-3B	✓✓ Recommended	Clean output, follows format reliably
Qwen2.5-32B	✓✓✓ Best	Overkill for this task but flawless

Tested With

• vLLM 0.4+
• Qwen2.5-0.5B, Qwen2.5-1.5B, Qwen2.5-3B
• ComfyUI (latest)