PrismXL Image Generator

A high-performance, feature-rich desktop GUI for generating images using state-of-the-art diffusion models. Built with PySide6 for a responsive and native user experience, this application provides a robust interface for both casual users and advanced artists to create stunning visuals.

Table of Contents

• Introduction
• Key Features
• Technical Stack
• Installation
  • Prerequisites
  • Setup
• Usage
• Configuration and Data
• Contributing
• License
• Acknowledgments
• Disclaimer

Introduction

The PrismXL Image Generator is a standalone desktop application designed to harness the power of diffusion models, specifically leveraging the RunDiffusion/Juggernaut-XL-v9 model. It offers a comprehensive suite of tools for crafting the perfect image, from basic text-to-image generation to fine-grained control over advanced parameters. The application is built with a focus on performance, responsiveness, and user experience, featuring asynchronous generation to prevent UI freezes, real-time system resource monitoring, and a host of workflow-enhancing utilities.

Key Features

Generation Engine

• High-Quality Model: Natively integrates the powerful Juggernaut-XL-v9 diffusion model.
• Advanced Parameter Control: Adjust Inference Steps, CFG Scale, and CLIP Skip for precise creative control.
• Batch & Grid Generation: Generate multiple images from a single prompt to explore variations efficiently.
• Multiple Resolutions: Supports a wide range of resolutions, including standard and widescreen formats.
• Reproducibility: Use custom seeds to recreate previous results.
• Optimized Performance: Automatically enables VAE tiling for high-resolution images to conserve memory.
• Hardware Acceleration: Utilizes CUDA for GPU-accelerated generation with a fallback to CPU if needed.

User Interface

• Modern & Responsive UI: Built with PySide6 for a clean, fast, and native cross-platform experience.
• Custom Frameless Window: A sleek, modern design with a custom title bar.
• Light & Night Modes: Switch between themes for your visual comfort.
• Image Viewer: Displays generated images in a grid with thumbnails and a detailed main view.
• Magnifying Loupe: An interactive zoom tool to inspect image details directly in the viewer.
• Modular Layout: Re-arrange UI sections via drag-and-drop to customize your workspace.

Workflow & Tools

• Prompt Library: Save, categorize, search, and reuse your favorite prompts.
• Built-in Spell Checker: Catches typos in your prompts and offers corrections to improve generation quality.
• Real-time Progress: Monitor generation progress with a detailed progress bar, status updates, and time estimates.
• System Resource Monitor: Keep an eye on RAM and GPU memory usage directly within the application.
• Live Rendering: (Optional) Watch your image come to life with a real-time preview of the generation process for 512x512 images.
• Metadata Saving: Automatically saves generation parameters (prompt, seed, steps, etc.) in a JSON file alongside the saved image.
• Persistent Settings: The application remembers your UI layout, theme, and generation settings between sessions.

Technical Stack

• Backend: Python
• GUI Framework: PySide6 (The official Qt for Python project)
• AI/ML:
  • PyTorch
  • Hugging Face Diffusers
  • Transformers
  • Accelerate
• Image Processing: Pillow (PIL)
• System Utilities: Psutil, GPUtil
• Miscellaneous: Pyspellchecker

Installation

Prerequisites

1. Python: Version 3.9 or newer.
2. Git: For cloning the repository.
3. NVIDIA GPU (Recommended): For hardware-accelerated generation. Ensure you have the latest NVIDIA drivers and a compatible CUDA Toolkit version installed.

Setup

1. Clone the repository:

   git clone https://github.com/your-username/PrismXL-Image-Generator.git
   cd PrismXL-Image-Generator

2. Create a virtual environment (recommended):

   python -m venv venv
   # On Windows
   venv\Scripts\activate
   # On macOS/Linux
   source venv/bin/activate

3. Install the required Python packages: A requirements.txt file should be provided. To ensure PyTorch is installed with the correct CUDA support for your system, it is highly recommended to first visit the PyTorch website and install it using the command provided there.

   Example for CUDA 11.8:

   pip install torch torchvision torchaudio --index-url https://download.pytorch.org/whl/cu118

   Then, install the remaining dependencies:

   pip install -r requirements.txt

   If a requirements.txt is not available, install the core packages:

   pip install PySide6 diffusers transformers accelerate torch numpy Pillow psutil GPUtil pyspellchecker safetensors

Usage

Once the installation is complete, run the application from the root directory of the project:

python main.py

The main window will appear. Follow these steps to generate your first image:

1. Enter a Prompt: Type a description of the image you want to create in the "Prompt" text area.
2. Enter a Negative Prompt (Optional): Type concepts you wish to exclude in the "Negative Prompt" area.
3. Adjust Settings (Optional): Use the sliders and dropdowns in the "Advanced Options" section to fine-tune the generation process.
4. Generate: Click the "Generate" button. The UI will remain responsive while the image is being created.
5. View and Save: The generated image(s) will appear on the right. You can select a thumbnail to view it larger, and use the "Save" button to save the selected image to your computer. Right-click the "Save" button to save all images from the current grid.

Configuration and Data

• Settings: The application saves UI and user preferences automatically. On Windows, these settings are stored in the registry under HKEY_CURRENT_USER\Software\Sapphire\PrismXL.
• Image Autosaves: All generated images are automatically saved for your convenience in a user-specific directory:
  • Windows: C:\Users\<YourUsername>\.sapphire_prismxl\images\
  • macOS/Linux: /home/<YourUsername>/.sapphire_prismxl/images/
• Prompt Library: Your saved prompts are stored in prompt_library.json within the same .sapphire_prismxl directory.
• Logs: A detailed log file, image_generator.log, is created in the application's root directory for troubleshooting purposes.

Contributing

Contributions are welcome! If you would like to contribute to the project, please follow these steps:

1. Fork the repository.
2. Create a new branch (git checkout -b feature/your-feature-name).
3. Make your changes.
4. Commit your changes (git commit -m 'Add some feature').
5. Push to the branch (git push origin feature/your-feature-name).
6. Open a Pull Request.

Please ensure your code follows existing style conventions and includes comments where necessary.

License

This project is licensed under the MIT License. See the LICENSE file for details.

Acknowledgments

• This project is built upon the incredible work of the teams behind PyTorch, Hugging Face, and the Qt for Python (PySide6) project.
• The core image generation capabilities are powered by the diffusers library.
• The AI model Juggernaut-XL-v9 was created by RunDiffusion.

Disclaimer

This software provides an interface to a third-party generative AI model. The developer of this application is not the creator of the underlying image generation model.

• The developer of this tool takes zero responsibility or liability for any content or images generated by the user.
• All outputs are the sole responsibility of the user.
• It is the user's responsibility to adhere to all applicable local, national, and international laws, regulations, and ethical guidelines regarding the use of generative AI and the content they create.