LunarPLUS

LunarPLUS is an advanced AI-powered aimbot with enhanced features, performance optimizations, and training capabilities.

Features

Core Features

• Advanced AI Detection: Uses YOLOv8 with optimized performance
• CUDA Acceleration: Full CUDA support with mixed precision training
• TensorRT Optimization: Enhanced inference speed with TensorRT
• Smart Targeting:
  • Configurable aim height
  • Automatic closest target selection
  • Smooth aim interpolation
• TriggerBot: Auto-fire when crosshair is on target
• Real-time Performance: Uncapped FPS with performance optimizations
• Auto Screen Resolution: Automatic display configuration

Advanced Controls

• Hotkeys:
  • F1: Toggle aimbot on/off
  • F2: Exit program
  • Right Mouse Button: Activate targeting
  • C: Capture training data (in training mode)
• Training Mode:
  • python lunar.py collect_data: Enter training data collection mode
  • Press 'C' while targeting to capture training samples
  • python train.py: Train model on collected data

Performance Features

• CUDA Optimizations:
  • TensorRT acceleration
  • torch.compile optimization
  • Mixed precision inference
  • cuDNN benchmark mode
  • TF32 acceleration
• Memory Optimizations:
  • Pre-allocated frame buffers
  • Optimized numpy operations
  • Efficient screen capture
• Processing Optimizations:
  • Vectorized calculations
  • Bit-shift operations
  • Asynchronous key detection
  • Smooth FPS calculation

Technical Specifications

• PyTorch 2.5.1 with CUDA 11.8
• TorchVision 0.20.1
• Ultralytics 8.0.0+ (YOLOv8)
• OpenCV 4.9.0
• MSS for efficient screen capture
• CUDA-optimized neural network processing
• Custom Win32 API integration

Installation

Prerequisites

1. Python Requirements:

   • Python 3.10.5 or higher (Download)
   • Add Python to PATH during installation
   • Verify installation: python --version

2. NVIDIA Requirements:

   • NVIDIA GPU with CUDA support
   • NVIDIA Graphics Driver
     • Minimum driver version: 470.63.01
   • CUDA Toolkit 11.8
     • Select your OS and follow installation steps
     • Add CUDA to PATH
   • Verify CUDA: nvcc --version

3. Visual C++:

   • Visual C++ Redistributable 2019
   • Required for PyTorch and CUDA operations

Installation Steps

1. Clone Repository:

   git clone <repository-url>
   cd LunarPLUS

2. Create Virtual Environment (Recommended):

   python -m venv venv
   # Windows
   .\venv\Scripts\activate
   # Linux/Mac
   source venv/bin/activate

3. Install Dependencies:

   # Upgrade pip
   python -m pip install --upgrade pip
   
   # Install PyTorch with CUDA support
   pip install torch==2.5.1+cu118 torchvision==0.20.1+cu118 --index-url https://download.pytorch.org/whl/cu118
   
   # Install other requirements
   pip install -r requirements.txt

4. Verify CUDA Setup:

   python -c "import torch; print('CUDA available:', torch.cuda.is_available())"

   Should output: CUDA available: True

5. Configure Settings:

   python lunar.py setup

   Follow the prompts to configure your sensitivity settings.

Common Issues and Solutions

1. CUDA Not Found:

   • Ensure NVIDIA drivers are up to date
   • Verify CUDA installation: nvcc --version
   • Check PATH environment variables
   • Try reinstalling PyTorch with CUDA support

2. DLL Load Failed:

   • Install Visual C++ Redistributable 2019
   • Restart your computer
   • Verify Python architecture matches CUDA architecture (64-bit)

3. Import Error: No module named 'x':

   pip install --force-reinstall -r requirements.txt

4. Low FPS Issues:

   • Enable CUDA acceleration
   • Update NVIDIA drivers
   • Close background applications
   • Reduce detection box size if needed

Updating

To update to the latest version:

git pull
pip install -r requirements.txt --upgrade

First Run

After installation:

1. Run python lunar.py to start
2. Press F1 to toggle the aimbot
3. Hold right-click to activate targeting
4. Press F2 to quit

Training Custom Models

Collecting Training Data

1. Run python lunar.py collect_data
2. Hold right-click to target enemies
3. Press 'C' to capture training samples
4. Collect at least 100 diverse samples

Tips for quality training data:

• Capture various angles and distances
• Include different lighting conditions
• Mix positive (player visible) and negative (no player) samples
• Keep crosshair precisely on target when capturing

Training Process

1. After collecting data, run python train.py
2. Training process:
   • Uses collected images from lib/data/images/
   • Trains for 50 epochs
   • Uses CUDA acceleration
   • Automatically saves best model

Training Requirements

• NVIDIA GPU with CUDA support
• At least 100 training images
• ~30-60 minutes training time

Performance Tips

1. Ensure latest NVIDIA drivers are installed
2. Close unnecessary background applications
3. Run in "Performance" power mode
4. Keep training data diverse and clean
5. Adjust confidence threshold if needed (default: 0.45)

Troubleshooting

If you encounter issues:

1. Ensure CUDA is properly installed
2. Verify all dependencies are installed
3. Check your sensitivity settings
4. Make sure you have a compatible GPU

For detailed error messages, run:

python lunar.py

About

LunarPLUS uses advanced AI object detection to provide high-performance targeting assistance. It operates purely through screen capture and does not modify any game memory. The system is designed to be efficient and accurate while maintaining high FPS.