Stream Diffusion RS 🧠⚡

High-performance multimodal AI and ML research framework in Rust - A comprehensive toolkit for diffusion models, EEG analysis, multisensorial processing, and real-time neurofeedback systems.

⚠️ WORK IN PROGRESS - Active Development

Current Status & Recent Updates

✅ Completed Features

• EEG Processing: Real-time brain wave analysis with frequency band extraction
• Diffusion Models: UNet architecture with DDIM scheduler implementation
• ONNX Integration: Model conversion and inference with hardware acceleration
• Web Interface: REST API and interactive dashboard for experiments
• Visualization: Plotting engine for EEG data and training curves
• Training Framework: Multiple optimizers and loss functions

🔄 In Development

• MCP Server Integration: Model Context Protocol servers for AI-brain interfaces
• Gesture Tracking: MediaPipe and LeapMotion integration for gesture control
• Multimodal Fusion: Cross-sensory data integration and synthesis
• Real-time Streaming: Sub-10ms latency optimization for neurofeedback
• BCI Integration: Brain-computer interface protocols and hardware support

🚧 Known Issues

• Compilation Errors: Some modules have incomplete implementations
• Missing Dependencies: Additional crates needed for full functionality
• Integration Testing: Cross-module compatibility needs verification
• Performance Optimization: GPU acceleration and memory management tuning

📈 Next Development Phase

1. Fix Compilation Issues: Resolve all build errors and missing implementations
2. Complete MCP Integration: Finish BCI and scientific computing server setup
3. Gesture Control: Implement full gesture-EEG correlation system
4. Performance Testing: Benchmark real-time processing capabilities
5. Documentation: Update API docs and usage examples

🌟 Vision: Multimodal & Multisensorial AI

Combining StreamDiffusion with multimodal and multisensorial AI describes an emerging field of high-speed, interactive generative AI that can process and create content using multiple senses at once. While StreamDiffusion focuses on the real-time processing of images and video, combining it with multimodal inputs (e.g., text, images, audio) and multisensorial AI (tactile, thermal, EEG, biometric) creates more contextually rich and responsive AI systems.

Stream Diffusion RS extends this vision by providing:

• Multimodal Fusion: Text, image, audio, and biometric data integration
• Multisensorial Processing: EEG, tactile, thermal, and physiological signals
• Real-time Streaming: Sub-10ms latency across multiple data dimensions
• Neuro-Emotive AI: Brain-computer interfaces with emotional intelligence
• Cross-Modal Generation: Converting between different sensory modalities

🚀 Features

🌈 Multimodal AI Core

• Cross-Modal Fusion: Text, image, audio, and biometric data integration
• Multisensorial Processing: EEG, tactile, thermal, physiological signal analysis
• Real-time Streaming: Sub-10ms latency across multiple sensory dimensions
• Neuro-Emotive Intelligence: Brain-computer interfaces with emotional context

🤖 Diffusion Models

• UNet Architecture: Complete implementation with attention blocks, resnet blocks, and time embeddings
• DDIM Scheduler: Advanced denoising diffusion implicit models for fast inference
• Text-to-Image: CLIP text encoding integration
• Streaming Support: Real-time image generation with progress callbacks
• Multimodal Generation: EEG-to-visual, audio-to-image cross-modal synthesis

🔄 ONNX Integration

• Model Conversion: PyTorch, TensorFlow, JAX, and HuggingFace model support
• ONNX Runtime: High-performance inference with hardware acceleration
• Model Registry: Management system for multiple models
• Burn Compatibility: Seamless integration with Burn tensor operations

🎭 Performance Control

• Gesture Control: Real-time motion capture for parameter modulation
• EEG Integration: Brain wave analysis for interactive control
• Audio-Reactive: Spectrum analysis and beat detection
• Real-time Processing: Low-latency parameter mapping
• Multimodal Control: Combined gesture and EEG input processing

🎨 Creative Tools

• Interactive Generation: Real-time parameter control and feedback
• Style Transfer: Dynamic visual style modulation
• Composition Tools: Multi-layer image synthesis and blending
• Export Options: High-resolution image and video output

🎨 Visualization

• Plotting Engine: Line plots, scatter plots, histograms, confusion matrices
• EEG Visualizations: Signal plots, topographic maps, spectrograms
• Training Curves: Loss and accuracy monitoring over epochs
• Real-time Dashboard: Live experiment monitoring

🎛️ Control Interface

• Parameter Mapping: Intuitive control over generation parameters
• Preset System: Save and recall favorite settings
• Automation: External control via OSC, MIDI, and API
• Feedback Loop: Real-time visual feedback during generation

🌐 Web Interface

• Gradio-like UI: Modern tabbed interface with JavaScript
• REST API: Full API for image generation, EEG analysis, training
• Interactive Features: Real-time plotting, model management
• File Upload: Support for EEG data and model files

🏗️ Architecture

stream-diffusion-rs/
├── src/
│   ├── lib.rs              # Main library interface
│   ├── diffusion.rs        # Diffusion model implementation
│   ├── onnx.rs            # ONNX model integration
│   ├── ml.rs              # ML research utilities
│   ├── eeg.rs             # EEG analysis and processing
│   ├── visualization.rs   # Data visualization tools
│   ├── training.rs        # Model training framework
│   └── web.rs             # Web interface and API
├── examples/
│   ├── basic_usage.rs              # Basic usage example
│   ├── eeg_neurofeedback.rs        # Real-time neurofeedback
│   └── model_training_example.rs   # Custom model training
└── Cargo.toml

📦 Installation

Add to your Cargo.toml:

[dependencies]
stream-diffusion-rs = "0.1.0"

Dependencies

[dependencies]
# Core ML
ndarray = "0.15"
ndarray-rand = "0.14"
rand = "0.8"

# ONNX Runtime
ort = "1.16"

# Visualization
plotters = { version = "0.3", default-features = false, features = ["svg_backend", "bitmap_backend"] }

# Web interface
axum = "0.7"
tokio = { version = "1.0", features = ["full"] }
serde = { version = "1.0", features = ["derive"] }

# Audio processing
rodio = "0.17"
hound = "3.5"

# Image processing
image = "0.24"

# Utils
anyhow = "1.0"
thiserror = "1.0"
log = "0.4"
env_logger = "0.10"

🚀 Quick Start

Basic Image Generation

use stream_diffusion_rs::*;

#[tokio::main]
async fn main() -> Result<(), Box<dyn std::error::Error>> {
    env_logger::init();

    // Initialize the engine
    let mut engine = StreamDiffusionRs::new();

    // Load a diffusion model
    engine.load_model("stable-diffusion", "models/sd.onnx")?;

    // Configure streaming parameters
    engine.set_stream_parameters(30.0, 0.8); // 30 FPS, 80% quality

    // Generate an image
    let prompt = "A beautiful sunset over mountains, digital art style";
    let image_data = engine.generate_image(prompt, "stable-diffusion")?;

    println!("Generated image with {} bytes", image_data.len());

    Ok(())
}

EEG Neurofeedback

use stream_diffusion_rs::*;

#[tokio::main]
async fn main() -> Result<(), Box<dyn std::error::Error>> {
    // Initialize EEG processor
    let mut processor = EEGProcessor::new();
    processor.add_filter("bandpass", DigitalFilter::new(FilterType::BandPass, 4, 1.0, 40.0));

    // Initialize visualizer
    let visualizer = EEGVisualizer::new(std::path::Path::new("output"));

    // Simulate real-time EEG processing
    for frame in 0..1000 {
        let eeg_data = generate_simulated_eeg_data();

        // Process EEG data
        let mut processed_data = eeg_data.clone();
        processor.remove_dc_offset(&mut processed_data);

        // Extract frequency bands
        let alpha_power = processor.extract_band_power(&processed_data, FrequencyBand::Alpha)?;
        let beta_power = processor.extract_band_power(&processed_data, FrequencyBand::Beta)?;

        // Calculate focus index
        let focus_index = calculate_focus_index(&beta_power, &alpha_power);

        // Generate feedback visualization
        visualizer.plot_eeg_topography(&values, &electrode_names, &format!("neurofeedback_{}.png", frame))?;
    }

    Ok(())
}

Custom Model Training

use stream_diffusion_rs::*;

// Define your model
struct MyModel {
    weights: ndarray::Array2<f32>,
    biases: ndarray::Array1<f32>,
}

impl TrainableModel for MyModel {
    fn forward(&self, input: &ndarray::Array4<f32>) -> Result<ndarray::Array2<f32>, Box<dyn std::error::Error>> {
        // Implement forward pass
        Ok(input.sum_axis(ndarray::Axis(3)).sum_axis(ndarray::Axis(2)).sum_axis(ndarray::Axis(1)))
    }

    fn get_parameters(&self) -> Vec<&ndarray::Array2<f32>> {
        vec![&self.weights]
    }
}

#[tokio::main]
async fn main() -> Result<(), Box<dyn std::error::Error>> {
    // Create training configuration
    let config = TrainingConfig {
        epochs: 50,
        batch_size: 32,
        learning_rate: 0.001,
        ..Default::default()
    };

    // Initialize trainer
    let mut trainer = ModelTrainer::new(config, Optimizer::Adam, LossFunction::MSE);

    // Train your model
    trainer.train(&mut model, &train_features, &train_labels, Some(&val_features), Some(&val_labels))?;

    Ok(())
}

🌐 Web Interface

Start the web server:

use stream_diffusion_rs::web::*;

#[tokio::main]
async fn main() -> Result<(), Box<dyn std::error::Error>> {
    start_default_server().await
}

Then visit http://localhost:3000 for the interactive interface.

📊 EEG Analysis Pipeline

use stream_diffusion_rs::*;

// Load EEG data
let eeg_data = EEGData::load_from_file("eeg_data.edf")?;

// Process signals
let mut processor = EEGProcessor::new();
processor.apply_filter(&mut eeg_data, "bandpass");

// Extract features
let alpha_power = processor.extract_band_power(&eeg_data, FrequencyBand::Alpha)?;
let connectivity = processor.compute_connectivity(&eeg_data);

// Convert to audiovisual
let converter = EEGToAudiovisualConverter::new("output");
let audiovisual = converter.convert(&eeg_data)?;

🔧 ONNX Model Integration

use stream_diffusion_rs::*;

// Initialize converter
let converter = OnnxConverter::new()?;

// Convert PyTorch model to ONNX
converter.convert_pytorch_to_onnx(
    "model.pth",
    "model.onnx",
    &[1, 3, 224, 224],
    11
)?;

// Load and run inference
let mut bridge = OnnxBridge::new();
bridge.load_onnx_model("model.onnx", &converter)?;
bridge.convert_to_burn()?;

// Run inference
let inputs = HashMap::new();
// ... populate inputs
let outputs = bridge.run_inference_burn(&input_tensor)?;

🎯 Performance Goals

• Real-time generation with interactive control
• High-quality output at various resolutions
• Efficient processing with GPU acceleration
• Responsive interface for live performance
• Cross-platform compatibility for different workflows

🤝 Contributing

Contributions welcome! Areas of interest:

• Multimodal fusion algorithms for cross-sensory data integration
• Additional sensor support (thermal, tactile, biometric)
• Burn framework integration for GPU acceleration
• Advanced EEG signal processing and artifact removal
• Real-time audio synthesis and cross-modal generation
• Web interface enhancements for multisensorial visualization
• ONNX model optimization for edge devices
• Neuro-emotive AI models for emotional intelligence

📄 License

MIT License - see LICENSE file for details.

🙏 Acknowledgements

• Built with ❤️ using Rust
• Inspired by the need for high-performance ML in neuroscience
• Thanks to the Burn, ONNX Runtime, and ndarray communities