airunner 3.0.0

A Stable Diffusion GUI

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AI RUNNER

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Run AI models on your own hardware

Stable Diffusion

Customizable Chatbots with Moods and Personalities

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⭐ Features

AI Runner is an AI interface which allows you to run open-source large language models (LLM) and AI image generators (Stable Diffusion) on your own hardware.

Feature	Description
🗣️ LLMs and communication
✅ Voice-based chatbot conversations	Have conversations with a chatbot using your voice
✅ Text-to-speech	Convert text to spoken audio
✅ Speech-to-text	Convert spoken audio to text
✅ Customizable chatbots with LLMs	Generate text using large language models
✅ RAG on local documents and websites	Interact with your local documents using an LLM
🎨 Image Generation
✅ Stable Diffusion (all versions)	Generate images using Stable Diffusion
✅ Drawing tools	Turn sketches into art
✅ Text-to-Image	Generate images from textual descriptions
✅ Image-to-Image	Generate images based on input images
🖼️ Image Manipulation
✅ Inpaint and Outpaint	Modify parts of an image while maintaining context
✅ Controlnet	Control image generation with additional input
✅ LoRA	Efficiently fine-tune models with LoRA
✅ Textual Embeddings	Use textual embeddings for image generation control
✅ Image Filters	Blur, film grain, pixel art and more
🔧 Utility
✅ Run offline, locally	Run on your own hardware without internet
✅ Fast generation	Generate images in ~2 seconds (RTX 2080s)
✅ Run multiple models at once	Utilize multiple models simultaneously
✅ Dark mode	Comfortable viewing experience in low-light environments
✅ Infinite scrolling canvas	Seamlessly scroll through generated images
✅ NSFW filter toggle	Help control the visibility of NSFW content
✅ NSFW guardrails toggle	Help prevent generation of LLM harmful content
✅ Fully customizable	Easily adjust all parameters
✅ Fast load time, responsive interface	Enjoy a smooth and responsive user experience
✅ Pure python	No reliance on a webserver, pure python implementation

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💻 System Requirements

Minimum system requirements

• OS: Linux
• Processor: Intel i5 or equivalent
• Memory: 16 GB RAM
• Graphics: 2080s RTX or higher
• Network: Broadband Internet connection required for setup
• Storage: 130 GB available space

Recommended system specs

• OS: Linux
• Processor: Intel i7 or equivalent
• Memory: 30 GB RAM
• Graphics: 4090 RTX or higher
• Network: Broadband Internet connection required for setup
• Storage: 130 GB available space

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🔧 Installation

Linux

1. Open your file explorer and navigate to the directory containing the install.sh script
2. Open the terminal using the keyboard shortcut Ctrl + Alt + T
3. Drag the install.sh script into the terminal and press Enter
4. Follow the on-screen instructions

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🚀 Running AI Runner

Linux

1. Open the terminal using the keyboard shortcut Ctrl + Alt + T
2. Navigate to the directory containing the run.sh script (cd ~/airunner for example)
3. Run the bin/run.sh script by typing ./bin/run.sh and pressing Enter
4. AI Runner will start and you can begin using it after following the on-screen setup instructions

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✏️ Using AI Runner

Instructions on how to use AI Runner can be found in the wiki

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💾 Compiling AI Runner

Clone this repository

git clone https://github.com/Capsize-Games/airunner.git
cd airunner

Build from source

pip install -e .
pip install pyinstaller
bash build.dev.sh

🔬 Unit tests

Run a specific test

python -m unittest src/airunner/tests/test_draggable_pixmap.py

Test coverage is currently low, but the existing tests can be run using the following command:

python -m unittest discover tests

Test coverage

Run tests with coverage tracking:

coverage run --source=src/airunner --omit=__init__.py,*/data/*,*/tests/*,*_ui.py,*/enums.py,*/settings.py -m unittest discover src/airunner/tests

To see a report in the terminal, use:

coverage report

For a more detailed HTML report, run:

coverage html

View results in htmlcov/index.html.

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Privacy and Security

Although AI Runner v3.0 is built with Huggingface libraries, we have taken care to strip the application of any telemetry or tracking features.

The main application itself is unable to access the internet, and we are working towards properly sandboxing certain features to ensure user privacy and security.

As this application evolves we will migrate away from the Huggingface libraries.

Internet access

The core application is incapable of accessing the internet. However there are two features which require internet access. These two features are the setup wizard and the model manager.

Each of these tools are isolated in their own application windows which are capable of directly accessing and downloading files on Huggingface.co and civitai.com (depending on the given URL). Any other URL will be blocked.

The Huggingface Hub library is not used to access these downloads.

For more information see the Darklock and Facehuggershield libraries.

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Disc access

Write access for the transformers library has been disabled, preventing it from creating a huggingface cache directory at runtime.

The application itself may still access the disc for reading and writing, however we have restricted reads and writes to the user provided airunner directory (by default this is located at ~/.local/share/airunner).

All other attempts to access the disc are blocked and logged for your review.

For more information see src/security/restrict_os_access.py.

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Huggingface Hub

The Huggingface Hub is installed so that Transformers, Diffusers and other Huggingface libraries will continue to function as expected, however it has been neutered to prevent it from accessing the internet.

The security measures taken for this library are as follows

• Prevented from accessing the internet
• Prevented from accessing the disc
• All environment variables set for maximum security
• All telemetry disabled

See Facehuggershield for more information.

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Planned security measures for Huggingface Libraries

We plant o remove the Huggingface libraries from the application in the future. Although the architecture is currently dependent on these libraries, we will migrate to a better solution in the future.

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Improving performance

To profile various functions in an effort to improve performance, you can install line_profiler

pip install line_profiler

To profile a function, add the @profile decorator to the function you wish to profile.

Then run the following command:

kernprof -l -v main.py

To view the results after

python display_profile_data.py