Another important open source app in our ecosystem that I'm building, very much in alpha and the training module still has errors but rest is working https://github.com/compiling-org/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.
🌟 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
Rust License: MIT
🚀 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
🧠 EEG & Neuroscience
Signal Processing: Filtering, frequency analysis, artifact removal
Brain Wave Analysis: Alpha, Beta, Theta, Delta, Gamma band extraction
Real-time Processing: Circular buffers for streaming EEG data
Neurofeedback: Real-time brain state monitoring and feedback
Multisensorial Fusion: EEG + biometric + environmental data integration
Cross-Modal Translation: Brain waves to visual/audio/artistic expressions
📊 ML Research Tools
Data Loading: CSV support, batching, shuffling, cross-validation
Preprocessing: Normalization, augmentation, feature scaling
Metrics: MSE, MAE, accuracy, IoU, F1-score, AUC-ROC
Experiment Tracking: Parameter logging, artifact storage, dashboard
🎨 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
🎓 Training Framework
Multiple Optimizers: SGD, Adam, RMSProp with configurable parameters
Loss Functions: MSE, Cross-Entropy, Binary Cross-Entropy, Huber loss
Model Interface: Trainable trait with forward/backward passes
Checkpointing: Automatic model saving and loading
🌐 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
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