🎌 Kanji Diffusion Model - New Conji Generation

A complete pipeline for training and generating novel Kanji characters using diffusion models, built with PyTorch.

🌟 Features

• Complete Stable Diffusion Pipeline: Full implementation with VAE, UNet, and CLIP
• Text-to-Kanji Generation: Generate Kanji from English descriptions
• Modern Concept Support: YouTube, Gundam, AI, Crypto, Internet
• Semantic Interpolation: Blend between different concepts
• Advanced Training: DDPM noise scheduling and cross-attention
• Dataset Management: Automatic processing of KANJIDIC2 and KanjiVG data
• Multiple Training Modes: Quick test, full training, and concept-specific generation
• Visualization Tools: Comprehensive analysis and comparison scripts
• GPU/CPU Optimized: Efficient training on both GPU and CPU resources

📁 Project Structure

Question2/
├── configs/                 # Training configurations
├── data/                    # Dataset storage
├── docs/                    # Documentation
├── scripts/                 # Core scripts
├── models/                  # Model storage
└── results/                 # Training results

🚀 Quick Start

1. Setup Environment

# Clone the repository
git clone https://github.com/CrystalHu940106/Stable_Diffusion_model_new_conji_generation.git
cd Stable_Diffusion_model_new_conji_generation

# Install dependencies
pip install -r requirements.txt

2. Prepare Dataset

# Build the Kanji dataset
python3 scripts/fix_kanji_dataset.py

3. Train Stable Diffusion Model

# Train the complete Stable Diffusion model
python3 scripts/train_stable_diffusion.py

4. Generate Modern Concept Kanji

# Generate Kanji for modern concepts (YouTube, Gundam, AI, etc.)
python3 scripts/advanced_concept_generation.py

5. Google Colab Training (Recommended)

🚀 Get 5-10x faster training with free GPU!

# Option 1: Upload files to Colab and run directly
# 1. Upload improved_stable_diffusion.py to Colab
# 2. Upload colab_training.py to Colab
# 3. Run: !python colab_training.py

# Option 2: Use the provided notebook
# 1. Open colab_training_notebook.ipynb in Colab
# 2. Follow the step-by-step instructions
# 3. Start training with optimized parameters

Colab Benefits:

• 🆓 Free T4 GPU (3-5x faster than local)
• 💎 Pro V100/P100 GPU (8-10x faster than local)
• ☁️ Cloud-based - no local setup required
• 💾 Auto-save checkpoints every 5 epochs
• 🔄 Resume training from any checkpoint
• 📊 Real-time monitoring of GPU usage

Expected Training Time:

• Colab Free (T4): 50 epochs in 2-3 hours
• Colab Pro (V100/P100): 50 epochs in 1-1.5 hours

6. Legacy Training (Optional)

🔧 Core Scripts

• fix_kanji_dataset.py: Builds the complete Kanji dataset from KANJIDIC2 and KanjiVG
• stable_diffusion_kanji.py: Complete Stable Diffusion implementation
• improved_stable_diffusion.py: Enhanced model based on official Stable Diffusion best practices
• train_stable_diffusion.py: Full Stable Diffusion training pipeline
• colab_training.py: Google Colab optimized training script with GPU acceleration
• advanced_concept_generation.py: Generate Kanji for modern concepts (YouTube, Gundam, etc.)
• quick_train_test.py: Quick validation of the training pipeline
• full_train_kanji.py: Legacy training (simple UNet)
• generate_concept_kanji.py: Legacy concept generation
• compare_generated_kanji.py: Compare generated vs. existing Kanji

📊 Dataset

• Source: KANJIDIC2 (meanings) + KanjiVG (stroke data)
• Size: 6,410 Kanji characters
• Format: 128x128 PNG images with black strokes on white background
• Quality: High-quality vector-to-raster conversion

🏗️ Model Architecture

• VAE: Variational Autoencoder for image compression (4x downsampling)
• UNet: 2D conditional model with cross-attention and time embedding
• Text Encoder: CLIP for semantic text understanding
• Scheduler: DDPM noise scheduling with 1000 timesteps
• Input/Output: 128x128 RGB images with text conditioning
• Optimization: AdamW with cosine annealing and gradient clipping

⚙️ Configuration

Training parameters can be customized in configs/optimized_training_config.py:

• Image resolution: 64x64 to 256x256
• Batch size: 2-8 (CPU optimized)
• Learning rate: 2e-4 (default)
• Epochs: 3-10 (configurable)

📈 Training Results

The model generates:

• Concept-specific Kanji: Success, failure, novel, funny, culturally meaningful
• Visual patterns: Learned representations of Kanji stroke structures
• Quality metrics: Training/validation loss tracking

🔍 Analysis Tools

• Image Quality Analysis: Pixel distribution, contrast, complexity
• Training Progress: Loss curves and convergence analysis
• Generation Comparison: Side-by-side analysis of generated vs. existing Kanji

🎯 Use Cases

• Educational: Teaching Kanji stroke patterns
• Creative: Generating novel character designs
• Research: Studying AI understanding of written language
• Cultural: Exploring AI interpretation of cultural symbols

🛠️ Technical Details

• Framework: PyTorch
• Image Processing: PIL, NumPy
• Data Loading: Custom Dataset classes with DataLoader
• Optimization: Gradient clipping, learning rate scheduling
• Checkpointing: Automatic model saving and loading

📝 Requirements

• Python 3.7+
• PyTorch 1.8+
• PIL (Pillow)
• NumPy
• Matplotlib
• rsvg-convert (for SVG processing)

🤝 Contributing

This project is open for contributions! Areas for improvement:

• Model architecture enhancements
• Additional training strategies
• More concept-specific generation
• Performance optimizations

📄 License

This project is open source and available under the MIT License.

🙏 Acknowledgments

• KANJIDIC2 project for Kanji meanings
• KanjiVG project for stroke data
• PyTorch community for the deep learning framework

📚 Additional Documentation

• IMPROVEMENTS_ANALYSIS.md: Detailed analysis of model improvements based on official Stable Diffusion
• COLAB_USAGE_GUIDE.md: Complete guide for Google Colab training with step-by-step instructions
• colab_training_notebook.ipynb: Jupyter notebook ready for Colab with all cells pre-configured

📞 Contact

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Note: This project demonstrates AI's potential in understanding and generating written language, particularly in the context of Japanese Kanji characters. While the current model is a foundation, it opens exciting possibilities for future research in AI-generated writing systems.