Minecraft AI Texture & Model Generator

An advanced AI-powered tool that generates Minecraft textures and models using deep learning. This project uses PyTorch to train and run generative models that can create custom Minecraft-compatible textures and block models from text prompts.

🌟 Features

• Texture Generation: Create 16x16 Minecraft-compatible textures from text descriptions
• Model Generation: Generate JSON models for Minecraft blocks and items
• Training Pipeline: Custom training system for fine-tuning the generators
• Minecraft Compatibility: All outputs are automatically formatted to work with Minecraft's resource pack system

🔧 Installation

1. Clone the repository:

git clone https://github.com/yourusername/minecraft-ai-generator.git cd minecraft-ai-generator

2. Install dependencies:

`` pip install torch torchvision pillow numpy

``

🚀 Usage

python src/minecraft_ai_generator/main.py --generate

Generating Textures

Then follow the prompts to enter your texture descriptions. For example:

• "A weathered oak plank texture"
• "Smooth polished granite"
• "Glowing ancient debris"

Training the Model

1. Prepare your dataset in the following structure:

dataset/ ├── textures/ │ ├── wood_planks.png │ ├── stone_brick.png │ └── ...

2. Start training: python src/minecraft_ai_generator/main.py --train --dataset dataset --epochs 100

🏗️ Project Structure

• src/minecraft_ai_generator/
  • main.py - Main entry point and CLI interface
  • texture_generator.py - Texture generation model
  • model_generator.py - 3D model generation
  • training.py - Training pipeline and dataset handling

🔍 Technical Details

Texture Generator

• Uses a GAN-based architecture
• 16x16 RGB output
• Conditional generation based on text prompts
• Built-in Minecraft compatibility checks

Model Generator

• Transformer-based architecture
• Generates Minecraft-compatible JSON models
• Includes validation for Minecraft's model format
• Supports custom display settings

Training System

• Custom dataset loader for Minecraft textures
• Automatic labeling based on texture names
• Checkpoint system for model saving
• Progress tracking and logging

📊 Model Architecture

Texture Generator

• Latent dimension: 100
• Multiple deconvolutional layers
• BatchNorm and LeakyReLU activations
• Final Tanh activation for [-1, 1] output range

Training Parameters

• Batch size: 32
• Learning rate: 0.0002
• Adam optimizer with β1=0.5, β2=0.999
• Checkpoints saved every 10 epochs

🔐 Requirements

• Python 3.8+
• PyTorch 1.8+
• PIL (Pillow)
• NumPy
• CUDA-capable GPU (optional, but recommended for training)

📝 License

[Your chosen license]

🤝 Contributing

Contributions are welcome! Please feel free to submit a Pull Request.

🐛 Known Issues

• Training requires significant GPU resources
• Generation time can vary based on hardware
• Limited to 16x16 textures currently