llava-caption 0.8.0

Automatically caption images using various LLaVA multimodal models. This tool processes images with state-of-the-art vision language models to generate accurate, high-quality captions.

LLaVA-Caption

By David "Zanshinmu" Van de Ven [e-mail]

Automatically caption images using various LLaVA multimodal models. This tool processes images with state-of-the-art vision language models to generate accurate, high-quality captions. Optimized for and primarily tested on Apple Silicon Macs, though cross-platform compatibility is available.

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Overview

LLaVA Caption was designed to solve a specific problem in AI training: when using generated images, the original prompts often contain elements that aren't present in the final images. Manual verification and captioning is time-consuming, but inaccurate captions make for bad training data. This tool provides higher quality captions than BLIP, with options ranging from basic processing to near-manual quality.

Llava Caption was built and tested on Apple Silicon. While cross-platform tools make it accessible to PCs, it hasn't been tested on Linux or Windows.

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Available Models

MLXModel (Recommended)(Default)

• Uses Qwen2-VL-7B-Instruct-8bit with Apple's MLX framework
• Apple Silicon only
• Fast processing with 16GB unified memory
• Accuracy comparable to VisionModel
• Requirements: Apple Silicon Mac, 16GB+ unified memory

VisionModel

• Uses Llama 3.2 Vision via Ollama
• High accuracy with moderate resource requirements
• Excellent results with secondary caption generation
• Ideal for training Flux/SD3
• Requirements: 24GB RAM, GPU recommended

DualModel (Experimental)

• Combines LLaVA 1.5 and Mixtral
• Highest potential accuracy but resource-intensive
• Supports distributed processing across machines
• Currently experimental: may need optimization
• Requirements: 64GB RAM, GPU strongly recommended

Additional Models

• OLModel: Basic Ollama-based processing
• HFModel: Hugging Face transformers-based processing (Note: MPS not supported on Apple Silicon)
• LCPModel: Direct LLaMA C++ processing

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Installation

Prerequisites

• Python 3.10 (python.org)
• Git (git-scm.com)
• Ollama (ollama.com/download) - Required for Ollama-based models

Installation with Poetry (Recommended)

# Clone repository
git clone https://github.com/yourusername/llava-caption.git
cd llava-caption

# Install with Poetry
poetry install

# Activate virtual environment
poetry shell

# Verify installation
llava-caption --help

Alternative Installation with pip

# Clone repository
git clone https://github.com/yourusername/llava-caption.git
cd llava-caption

# Create and activate virtual environment
python3 -m venv venv
source venv/bin/activate  # On Windows: venv\Scripts\activate

# Install package
pip install -e .

# Verify installation
llava-caption --help

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Usage

Basic Commands

# Basic usage with defaults
llava-caption /path/to/images/

# Specific model selection
llava-caption --model MLXModel /path/to/images/

# Direct captioning (no prompt comparison)
llava-caption --direct-caption /path/to/images/

Command Line Options

llava-caption [OPTIONS] DIRECTORY

Arguments:
  DIRECTORY                      Directory containing images

Model Selection:
  --model MODEL                  Model to use (default: MLXModel)
                                [env: LLAVA_PROCESSOR]

Processing Modes:
  --direct-caption              Enable direct captioning mode
  --secondary-caption           Enable secondary captioning
                                [env: SECONDARY_CAPTION]
  --no-preprocess              Disable text preprocessing
                                [env: PREPROCESSOR]

Model Parameters:
  --temperature FLOAT           Generation temperature (default: 0.0)
                                [env: TEMPERATURE]
  --gpu-layers INT             GPU layers (-1 for all)
                                [env: N_GPU_LAYERS]

Ollama Configuration:
  --ollama-address HOST:PORT    Ollama address (default: 127.0.0.1:11434)
                                [env: OLLAMA_REMOTEHOST]

Logging:
  --logging                     Enable detailed logging
  --sys-logging                Enable system logging
    
  

Example Usage Patterns

# MLXModel with direct captioning
llava-caption --model MLXModel --direct-caption /path/to/images/

# VisionModel with remote Ollama
llava-caption --model VisionModel --ollama-address 192.168.1.110:11434 /path/to/images/

# Secondary captioning with higher temperature
llava-caption --model VisionModel --secondary-caption --temperature 0.7 /path/to/images/

# Debug mode
llava-caption --logging --sys-logging /path/to/images/

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Important Notes

PC Users

• You may need to remove any mlx entries from requirements.txt to install successfully.

Model Downloads

• Models are automatically downloaded via Hugging Face Hub or Ollama
• Initial downloads may take time and significant disk space
• Models are selected for optimal performance and resource usage

Resource Requirements

• CPU Mode: Significant CPU and RAM usage, especially with HFModel
• GPU Usage: Set TORCH_DEVICE="cuda:0" for Nvidia GPU support
• Distributed Processing: Possible to run models across 2 hosts using DualModel

File Handling

• Expects matching .png and .txt files in target directory
• Existing text files will be overwritten with new captions
• In direct caption mode, creates new .txt files for each image

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License

This project is licensed under the GNU General Public License v3.0 - see the LICENSE file for details.