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Tool plugin for zk-rag to generate images with stable diffusion 3.5 medium

Project description

zk-rag-image-generator

A plugin for zk-rag that enables image generation using Stable Diffusion 3.5 Medium model.

Description

This plugin provides a tool to generate images from textual descriptions using the Stable Diffusion 3.5 Medium model. It can be used to enhance your RAG (Retrieval-Augmented Generation) applications with image generation capabilities.

Features

  • Generate images from textual descriptions
  • Save generated images as PNG files
  • Integration with mojentic LLM tools
  • Comprehensive error handling
  • Configurable through a gateway pattern

Installation

pip install zk-rag-image-generator

Usage

The plugin will be automatically discovered by zk-rag when installed. It provides the GenerateImage tool which can be used to create images from textual descriptions.

Example usage through zk-rag:

from zk_rag import get_tool

image_tool = get_tool("generate_image")
filename = image_tool.run(
    image_description="A serene mountain landscape at sunset with snow-capped peaks and a clear lake reflecting the orange sky",
    base_filename="mountain_sunset"
)
print(f"Image saved as: {filename}")  # Will print: Image saved as: mountain_sunset.png

Requirements

  • Python >= 3.11
  • mojentic (LLM tools framework)
  • torch, torchvision, torchaudio (PyTorch stack)
  • diffusers (Stable Diffusion implementation)
  • transformers (Hugging Face Transformers)
  • accelerate (Hardware acceleration support)
  • safetensors (Model weight handling)
  • Additional dependencies: scipy, protobuf, sentencepiece

For the complete list with specific versions, see requirements.txt.

Local Development

  1. Clone the repository
  2. Install dependencies:
    pip install -r requirements.txt
    pip install -r dev-requirements.txt
    
  3. Install pre-commit hooks (recommended):
    # Create a pre-commit hook that runs pytest
    cat > .git/hooks/pre-commit << 'EOL'
    #!/bin/sh
    
    # Run pytest
    echo "Running pytest..."
    python -m pytest
    
    # Store the exit code
    exit_code=$?
    
    # Exit with pytest's exit code
    exit $exit_code
    EOL
    
    # Make the hook executable
    chmod +x .git/hooks/pre-commit
    

Testing Guidelines

  • Tests are co-located with implementation files
  • Run tests: pytest
  • Linting: flake8 src
  • Code style:
    • Max line length: 127
    • Max complexity: 10
    • Follow numpy docstring style

Testing Best Practices

  • Use pytest for testing, with mocker if you require mocking
  • Do not use unittest or MagicMock directly
  • Use @fixture markers for pytest fixtures
  • Break up fixtures into smaller fixtures if they are too large
  • Separate test phases with a single blank line
  • Each test must fail for only one clear reason
  • Only write mocks for our own gateway classes

License

MIT License

Author

Stacey Vetzal (stacey@vetzal.com)

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