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Python Integration for Stream Diffusion RS

This document explains how to set up and use the Python integration features in Stream Diffusion RS.

Overview

Stream Diffusion RS includes Python interop capabilities that allow the Rust application to leverage Python-based AI/ML libraries such as:

  • PyTorch for deep learning models
  • Diffusers for diffusion models
  • Transformers for NLP models
  • MNE/SciPy for EEG processing
  • NumPy for numerical computations

Setup Instructions

Prerequisites

  1. Python 3.8 or later
  2. pip package manager
  3. Virtual environment support (venv module)

Automatic Setup

Run the setup script for your platform:

Linux/macOS:

./scripts/setup_python_env.sh

Windows:

scripts\setup_python_env.bat

Manual Setup

  1. Create a virtual environment:

    python3 -m venv venv

  2. Activate the virtual environment:

    # Linux/macOS
source venv/bin/activate

# Windows
venv\Scripts\activate.bat

  3. Upgrade pip:

    pip install --upgrade pip

  4. Install dependencies:

    pip install -r scripts/requirements.txt

Testing the Integration

After setting up the Python environment, you can test the integration:

  1. Activate the virtual environment

  2. Run the Python test script:

    python scripts/test_python_integration.py

  3. Run the Rust test binary:

    cargo run --bin test_python_integration

Usage Examples

Image Generation

The Python integration allows for real image generation using Stable Diffusion models:

from diffusers import StableDiffusionPipeline
import torch

# Load model
pipe = StableDiffusionPipeline.from_pretrained("runwayml/stable-diffusion-v1-5")
pipe = pipe.to("cuda" if torch.cuda.is_available() else "cpu")

# Generate image
image = pipe("a beautiful landscape").images[0]

EEG Processing

The integration also supports EEG data processing:

import numpy as np
from scipy import signal

# Process EEG data
eeg_data = np.load("eeg_data.npy")
filtered_data = signal.butterworth(eeg_data, 4, [8, 13], btype='band')

API Reference

PythonEnvironment

Manages the Python execution environment.

Methods:

  • new(python_path: &str) - Create new environment
  • install_dependencies() - Install required packages
  • execute_script(script: &str, args: Option<&[&str]>) - Execute Python script
  • execute_script_file(script_path: &Path, args: Option<&[&str]>) - Execute Python script file

PythonModel

Interface for Python-based AI/ML models.

Methods:

  • new(model_path: &str, model_type: &str, python_env: PythonEnvironment) - Create new model
  • load_model() - Load model
  • generate_image(prompt: &str, steps: usize) - Generate image from prompt
  • process_eeg(eeg_data_path: &str) - Process EEG data

Troubleshooting

Common Issues

  1. Python not found: Ensure Python 3.8+ is installed and in PATH
  2. Import errors: Make sure all dependencies are installed
  3. CUDA issues: Verify CUDA drivers and PyTorch CUDA version compatibility

Debugging

Enable verbose logging by setting the environment variable:

export RUST_LOG=debug

Contributing

To extend the Python integration:

  1. Add new methods to the PythonModel struct in src/python.rs
  2. Create corresponding Python functions in scripts/diffusion_model.py
  3. Update the requirements.txt file if new dependencies are needed
  4. Add tests to verify new functionality

License

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