Remote MCP Server

# AI Forge Studio ## RTX 50-Series Edition     **AI Forge Studio** is a professional desktop application designed specifically for NVIDIA RTX 50-Series GPU users. It combines cutting-edge artificial intelligence with advanced graphics processing to deliver high-quality image generation, real-time hardware monitoring, and AI model management. 🌐 **Official Website:** [aiforgestudio.net](https://aiforgestudio.net) 📖 **Documentation:** [aiforgestudio.net/docs](https://aiforgestudio.net/docs/getting-started.html) 💾 **Download:** [aiforgestudio.net/#download](https://aiforgestudio.net/#download) --- ## 🚀 Features ### Core Capabilities - **Real-Time Hardware Monitoring** - GPU utilization, temperature, and power consumption - VRAM usage tracking - Multi-GPU support - CPU and RAM monitoring - Live performance graphs - **AI Model Management** - Support for multiple model formats (SafeTensors, GGUF, ONNX, PyTorch) - Text-to-Image generation (Stable Diffusion, SDXL) - Image upscaling (Real-ESRGAN) - LLM integration - One-click model loading and optimization - HuggingFace model import - **Advanced Rendering Engine** - 4K+ real-time preview - NVIDIA DLSS 3.5 integration - Hardware ray tracing support - HDR rendering - Vulkan/DirectX 12 backend - **Optimization Features** - TensorRT model optimization - FP16/INT8 precision modes - VRAM ↔ RAM off-loading - Automatic memory management - Multi-GPU link support ### User Interface - **Glass-morphism Design** - Modern UI with blur effects and Neon Cyan/Purple accents - **Dark Mode** - Easy on the eyes for extended sessions - **Responsive Layout** - Adapts to different screen sizes - **Real-Time Updates** - Live metrics and progress indicators - **Intuitive Navigation** - Easy-to-use multi-panel interface --- ## 📋 Requirements ### Hardware Requirements **Minimum:** - NVIDIA RTX 4060 or higher - 16 GB RAM - 50 GB free storage - 1920x1080 display **Recommended:** - NVIDIA RTX 5080/5090 - 32 GB+ RAM - 100 GB+ free SSD storage - 4K display ### Software Requirements **Windows:** - Windows 10/11 (64-bit) - NVIDIA Driver 550.0 or newer - Visual Studio 2019/2022 (for building) **Linux:** - Ubuntu 22.04 LTS or newer (or equivalent) - NVIDIA Driver 550.0 or newer - GCC 9.0+ or Clang 10.0+ **Common:** - Qt 6.5+ - CUDA Toolkit 12.0+ - Python 3.10+ (optional, for Python bridge) - Vulkan SDK 1.3+ (optional, for rendering) --- ## 🛠️ Building from Source ### 1. Install Dependencies #### Windows ```powershell # Install Qt (via online installer or vcpkg) vcpkg install qt6 # Install CUDA Toolkit # Download from: https://developer.nvidia.com/cuda-downloads # Install Vulkan SDK # Download from: https://vulkan.lunarg.com/ ``` #### Linux (Ubuntu/Debian) ```bash # Install Qt6 sudo apt install qt6-base-dev qt6-declarative-dev qt6-tools-dev \ qml6-module-qtquick-controls qml6-module-qtcharts # Install CUDA Toolkit wget https://developer.download.nvidia.com/compute/cuda/repos/ubuntu2204/x86_64/cuda-ubuntu2204.pin sudo mv cuda-ubuntu2204.pin /etc/apt/preferences.d/cuda-repository-pin-600 wget https://developer.download.nvidia.com/compute/cuda/12.3.0/local_installers/cuda-repo-ubuntu2204-12-3-local_12.3.0-545.23.06-1_amd64.deb sudo dpkg -i cuda-repo-ubuntu2204-12-3-local_12.3.0-545.23.06-1_amd64.deb sudo cp /var/cuda-repo-ubuntu2204-12-3-local/cuda-*-keyring.gpg /usr/share/keyrings/ sudo apt-get update sudo apt-get -y install cuda # Install Vulkan SDK wget -qO - https://packages.lunarg.com/lunarg-signing-key-pub.asc | sudo apt-key add - sudo wget -qO /etc/apt/sources.list.d/lunarg-vulkan-jammy.list https://packages.lunarg.com/vulkan/lunarg-vulkan-jammy.list sudo apt update sudo apt install vulkan-sdk # Install Python dependencies (optional) pip install torch torchvision torchaudio --index-url https://download.pytorch.org/whl/cu121 pip install diffusers transformers accelerate safetensors ``` ### 2. Clone and Build ```bash # Clone repository git clone https://github.com/yourusername/AI-Forge-Studio.git cd AI-Forge-Studio # Create build directory mkdir build && cd build # Configure with CMake cmake .. -DCMAKE_BUILD_TYPE=Release # Build cmake --build . --config Release -j$(nproc) # Run ./AIForgeStudio ``` ### 3. Optional: Build with TensorRT ```bash # Download TensorRT from NVIDIA website # Extract and set environment variable export TensorRT_DIR=/path/to/TensorRT # Rebuild with TensorRT support cmake .. -DCMAKE_BUILD_TYPE=Release -DTensorRT_DIR=$TensorRT_DIR cmake --build . --config Release -j$(nproc) ``` --- ## 📖 Usage Guide ### Getting Started 1. **Launch the Application** ```bash ./AIForgeStudio ``` 2. **Check Dashboard** - View real-time GPU/CPU metrics - Monitor VRAM usage - Check system status 3. **Load a Model** - Navigate to "Models" tab - Click "Load Model" or "Import from HF" - Select your AI model file or HuggingFace model ID - Wait for loading to complete 4. **Generate Images** - Go to "Render" tab - Select loaded model - Enter your prompt - Adjust settings (steps, guidance, resolution) - Click "Generate Image" ### Model Management **Supported Formats:** - `.safetensors` - HuggingFace safe format (recommended) - `.gguf` - GPT-Generated Unified Format - `.onnx` - Open Neural Network Exchange - `.pt`/`.pth` - PyTorch models **Optimization:** - Click "Optimize" on any loaded model - Select precision mode (FP16 recommended for RTX 50-series) - Wait for TensorRT optimization (one-time process) - Enjoy faster inference! ### Tips for Best Performance 1. **Enable TensorRT Optimization** - 2-3x faster inference 2. **Use FP16 Precision** - Optimal for RTX 50-series 3. **Enable DLSS** - Better preview quality with less VRAM 4. **Monitor VRAM** - Unload unused models to free memory 5. **Adjust Batch Size** - Lower for high-resolution images --- ## 🏗️ Architecture ### Project Structure ``` AI-Forge-Studio/ ├── core/ # C++ backend modules │ ├── hardware_monitor.cpp # GPU/CPU monitoring (NVML) │ ├── ai_engine.cpp # AI inference (CUDA/TensorRT) │ ├── render_engine.cpp # Graphics rendering (Vulkan) │ └── logger.cpp # Logging system │ ├── python_bridge/ # Python integration │ ├── bridge.cpp # C++ ↔ Python bridge (Pybind11) │ └── model_runner.py # Python AI model runner │ ├── ui/ # QML user interface │ ├── main.qml # Main window │ ├── dashboard.qml # Hardware monitoring dashboard │ ├── model_manager.qml # Model management interface │ ├── render_view.qml # Image generation view │ └── settings.qml # Application settings │ ├── tests/ # Test suite ├── docs/ # Documentation ├── models/ # Model storage directory ├── output/ # Generated images output └── main.cpp # Application entry point ``` ### Technology Stack - **Core:** C++17 - **UI Framework:** Qt 6 (QML + QtQuick) - **Graphics API:** Vulkan 1.3 / DirectX 12 - **AI Framework:** CUDA 12 + TensorRT 8 - **Python Integration:** Pybind11 - **Hardware Monitoring:** NVIDIA NVML - **Build System:** CMake 3.16+ ### Component Interaction ``` ┌─────────────────────────────────────────────────────────┐ │ QML Frontend │ │ (Dashboard, Model Manager, Render View, Settings) │ └────────────────┬────────────────────────────────────────┘ │ Qt Signals/Slots ┌────────────────▼────────────────────────────────────────┐ │ BackendController (C++) │ │ - Exposes C++ API to QML │ │ - Manages component lifecycle │ └─────┬──────────┬──────────┬──────────┬─────────────────┘ │ │ │ │ ┌─────▼────┐ ┌──▼─────┐ ┌──▼─────┐ ┌──▼──────────┐ │ Hardware │ │ AI │ │ Render │ │ Python │ │ Monitor │ │ Engine │ │ Engine │ │ Bridge │ │ (NVML) │ │(CUDA/ │ │(Vulkan)│ │ (Pybind11) │ │ │ │TensorRT│ │ │ │ │ └──────────┘ └────────┘ └────────┘ └──────┬──────┘ │ ┌──────▼──────┐ │ Python │ │ Model Runner│ │ (PyTorch) │ └─────────────┘ ``` --- ## 🧪 Testing ### Running Tests ```bash # Build with tests enabled cmake .. -DBUILD_TESTS=ON cmake --build . --config Release # Run all tests ctest --output-on-failure # Run specific test ./tests/gpu_benchmark ``` ### Benchmark Suite The included benchmark suite tests: - GPU inference performance - Memory allocation efficiency - Model loading times - Rendering frame rates --- ## 🐛 Troubleshooting ### Common Issues **Issue: "CUDA not found"** - Ensure CUDA Toolkit is installed - Set `CUDA_PATH` environment variable - Verify NVIDIA driver version **Issue: "Failed to initialize NVML"** - Update NVIDIA drivers to latest version - Check if GPU is properly detected by OS - Try running with administrator/sudo privileges **Issue: "QML module not found"** - Install Qt6 QuickControls and Charts modules - Set `QML_IMPORT_PATH` environment variable - Verify Qt installation **Issue: "Python bridge failed"** - Install required Python packages: `torch`, `diffusers`, `transformers` - Ensure Python 3.10+ is in PATH - Check `python_bridge` directory is accessible ### Debug Mode ```bash # Build in debug mode cmake .. -DCMAKE_BUILD_TYPE=Debug cmake --build . # Run with verbose logging ./AIForgeStudio --log-level=DEBUG ``` --- ## 📝 License This project is licensed under the MIT License - see the [LICENSE](LICENSE) file for details. --- ## 🤝 Contributing Contributions are welcome! Please feel free to submit pull requests. 1. Fork the repository 2. Create your feature branch (`git checkout -b feature/amazing-feature`) 3. Commit your changes (`git commit -m 'Add amazing feature'`) 4. Push to the branch (`git push origin feature/amazing-feature`) 5. Open a Pull Request --- ## 📧 Contact Project Link: [https://github.com/yourusername/AI-Forge-Studio](https://github.com/yourusername/AI-Forge-Studio) --- ## 🙏 Acknowledgments - NVIDIA for CUDA, TensorRT, and DLSS technologies - Qt Project for the excellent UI framework - HuggingFace for AI model hosting - The open-source community for various libraries and tools --- **Built with ❤️ for RTX 50-Series GPUs**