genitext 0.3.2

A CLI tool for generating high-quality image-text pairs for AI training

GenIText: Generative Image-Text Automated package

Overview

This repository is independently developed as a felxible framework to generate high-quality Image-Text pairs for finetuning Image-Generation models, such as Stable Diffusion, DALL-E, and other generative models. By leveraging open-source captioning models, GenIText automates the process of generating diverse captions for corresponding images, ensuring that the text data is well-suited for downstream applications such as style-specific generations or domain adaptation. This framework is designed to complement contemporary repositories or modules in the field, offering an additional option for flexibility and automation to create customized datasets.

GenIText will become distributable as a CLI tool once package is ready for testing across systems. Please support in any way you see fit!

Table of Contents

• Installation
• Benchmarks

Benchmarks

Model	Auto-Batch Memory Usage	Auto-Batch Seconds per Image	1 Batch Memory Usage	1 Batch Seconds per Image
LLaVA 7B	17,978 MB	3.25	7,014 MB	3.62
ViT-GPT2 0.27B	7,570 MB	0.08	914 MB	0.79
BLIPv2 2.7B	13,534 MB	0.25	4,590 MB	2.53

All models were tested on 502 random image from kaggle dataset found here. Images were resized based on their config files and tested on GeForce RTX 4090 Graphics Card with 24 Gb memory.

Installation

GenIText is available as a Python package and can be installed easily using pip.

To install GenIText, simply run:

pip install genitext

After installation, you can verify that the CLI tool is accessible by running:

genitext --help

To initiate the CLI tool, run:

genitext

GenIText incorporates LLMs from Ollama to assist with prompt refinement which means ollama has to be available on the device when running /refine in the CLI tool. You can download the software for Mac or Windows OS from here. For Linux OS, you can install directly via the following:

curl -fsSL https://ollama.com/install.sh | sh

After installing, pull the appropriate LLM you want to use in /refine. Currently, the default config is set to deepseek-r1:7b since it offers strong performance with it's reasoning capabilities while using relatively trivial memory. Options to switch the LLM in config will be made available soon.