ComfyUI Extension: Comfy Text Stats

Authored by scofano

Created 28 days ago

Updated 22 days ago

0 stars

Node: Text Stats (Char & Word Count). A lightweight ComfyUI custom node that takes a text string and outputs character and word counts (INT). Ideal for caption preprocessing, prompt analytics, and dataset workflows.

Custom Nodes (0)

README

🧩 Comfy Text Stats (Char & Word Count)

A lightweight ComfyUI custom node that receives a text string and outputs:

🧮 Character count (INT)
✍️ Word count (INT)

This node is designed for text preprocessing, caption analytics, or quick statistics inside your ComfyUI workflows.

🚀 Features

✅ Simple and fast — no dependencies
✅ Unicode-aware word detection (supports multilingual text)
✅ Deterministic results (not affected by seeds)
✅ Works with any STRING input from other nodes

📦 Installation

Option 1 — Via ComfyUI-Manager (recommended)

Download comfy-text-stats.zip
In ComfyUI-Manager, go to:
Install → Install from ZIP
Select the ZIP file and click Install
Then Reload Custom Nodes (or restart ComfyUI)

Option 2 — Manual Install

Extract the folder to your ComfyUI custom node directory:

ComfyUI/
└── custom_nodes/
    └── comfy-text-stats/
        ├── __init__.py
        ├── text_stats.py
        ├── README.md
        └── requirements.txt

Restart ComfyUI.

🧠 Usage

Node name

Text Stats (Char & Word Count)
Category: Text / Utils

Inputs

| Name | Type | Description | |------|------|--------------| | text | STRING | The text input to analyze. Supports multiline input. |

Outputs

| Name | Type | Description | |------|------|--------------| | char_count | INT | Number of characters (including spaces, punctuation, and newlines). | | word_count | INT | Number of words (using Unicode-aware regex). |

🧪 Example

Input text:

ComfyUI makes custom nodes easy!

Outputs:

char_count = 33
word_count = 5

🔧 Technical Details

Word detection uses the regex:
```
\b\w+\b
```
which matches Unicode word boundaries.
The node is deterministic (IS_CHANGED checks text length for caching).

💡 Use Cases

Text preprocessing for captioning or prompt tuning
Generating text metadata before feeding into LLM pipelines
Measuring caption sizes in dataset workflows