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metadata
title: Pepe Meme Generator
emoji: 🐸
colorFrom: green
colorTo: blue
sdk: streamlit
sdk_version: 1.31.0
app_file: src/app.py
python_version: '3.11'

🐸 Pepe the Frog AI Meme Generator

Create custom Pepe memes using AI-powered Stable Diffusion with LoRA fine-tuning

Python 3.10+ Streamlit License: MIT Hugging Face

Demo • Documentation • Training Guide • Report Bug

📖 Table of Contents

✨ Features

🎨 Multiple AI Models

  • Pepe Fine-tuned LoRA - Custom trained on Pepe dataset (1600 steps)
  • Pepe + LCM (FAST) - 8x faster generation with LCM technology
  • Tiny SD - Lightweight model for faster CPU generation
  • Small SD - Balanced speed and quality
  • Base SD 1.5 - Standard Stable Diffusion
  • Dreamlike Photoreal 2.0 - Photorealistic style
  • Openjourney v4 - Artistic Midjourney-inspired style

⚡ Performance Features

  • LCM Support: Generate images in 6 steps (~30 seconds on CPU)
  • GPU Acceleration: Automatic CUDA detection with xformers support
  • Memory Efficient: Attention slicing and VAE slicing enabled

🎭 Generation Features

  • Style Presets: Happy, sad, smug, angry, crying, and more
  • Raw Prompt Mode: Use exact prompts without automatic enhancements
  • Text Overlays: Add meme text with Impact font
  • Batch Generation: Create multiple variations
  • Progress Tracking: Real-time generation progress bar
  • Seed Control: Reproducible generations with fixed seeds
  • Gallery System: View and manage all generated memes

🎯 User Experience

  • Model Hot-Swapping: Switch models without restart
  • Interactive UI: Clean Streamlit interface
  • Example Prompts: Built-in inspiration gallery
  • Download Support: Save images with one click
  • Responsive Design: Works on desktop and mobile

🚀 Quick Start

Try Online (No Installation)

🌐 Open in Hugging Face Spaces - Run instantly in your browser!

Local Installation 

# 1. Clone the repository
git clone https://github.com/YOUR_USERNAME/pepe-meme-generator.git
cd pepe-meme-generator

# 2. Create virtual environment (recommended)
python -m venv venv
source venv/bin/activate  # On Windows: venv\Scripts\activate

# 3. Install dependencies
pip install -r requirements.txt

# 4. Run the app
streamlit run src/app.py

The app will open in your browser at http://localhost:8501

📦 Installation

System Requirements

  • Python: 3.10 or higher
  • RAM: 8GB minimum, 16GB recommended
  • GPU: Optional (NVIDIA with CUDA for faster generation)
  • Storage: ~5GB for models and dependencies

Dependencies 

# Core dependencies
pip install torch torchvision  # PyTorch
pip install diffusers transformers accelerate  # Diffusion models
pip install streamlit  # Web interface
pip install pillow numpy scipy  # Image processing
pip install peft safetensors  # LoRA support

Or install everything at once:

pip install -r requirements.txt

GPU Setup (Optional but Recommended)

For NVIDIA GPUs with CUDA:

# Install PyTorch with CUDA support
pip install torch torchvision --index-url https://download.pytorch.org/whl/cu118

# Install xformers for memory-efficient attention
pip install xformers

🎮 Usage

Basic Usage

  1. Select a Model: Choose from the dropdown (try "Pepe + LCM (FAST)" for speed)
  2. Enter a Prompt: e.g., "pepe the frog as a wizard casting spells"
  3. Adjust Settings: Steps (6 for LCM, 25 for normal), guidance scale, etc.
  4. Generate: Click "Generate Meme" and wait
  5. Download: Save your creation!

Example Prompts 

pepe_style_frog, wizard casting magical spells, detailed
pepe_style_frog, programmer coding on laptop, cyberpunk style
pepe_style_frog, drinking coffee at sunrise, peaceful
pepe_style_frog, wearing sunglasses, smug expression
pepe_style_frog, crying with rain, emotional, dramatic lighting

Advanced Features

Using LCM for Fast Generation

  1. Select "Pepe + LCM (FAST)" model
  2. Use 6 steps (optimal for LCM)
  3. Set guidance scale to 1.5
  4. Generate in ~30 seconds!

Adding Text Overlays

  1. Expand "Add Text" section
  2. Enter top and bottom text
  3. Text automatically styled in Impact font
  4. Signature "MJ" added to corner

Reproducible Generations

  1. Enable "Fixed Seed" in Advanced Settings
  2. Set a seed number (e.g., 42)
  3. Same seed + prompt = same image

🤖 Model Information

Fine-Tuned LoRA Model

Model ID: MJaheen/Pepe_The_Frog_model_v1_lora

Training Details:

  • Base Model: Stable Diffusion v1.5
  • Method: LoRA (Low-Rank Adaptation)
  • Dataset: iresidentevil/pepe_the_frog
  • Training Steps: 2000
  • Resolution: 512x512
  • Batch Size: 1 (4 gradient accumulation)
  • Learning Rate: 1e-4 (cosine schedule)
  • LoRA Rank: 16
  • Precision: Mixed FP16
  • Trigger Word: pepe_style_frog

Performance:

  • Quality: ⭐⭐⭐ (Good)
  • Speed (CPU): ~4 minutes (25 steps)
  • Speed (GPU): ~15 seconds (25 steps)

📁 Project Structure 

pepe-meme-generator/
├── src/                          # Source code
│   ├── app.py                    # Main Streamlit application
│   ├── model/                    # Model management
│   │   ├── __init__.py
│   │   ├── config.py             # Model configurations
│   │   └── generator.py          # Image generation logic
│   └── utils/                    # Utility functions
│       ├── __init__.py
│       └── image_processor.py    # Image processing utilities
├── docs/                         # Documentation
│   └──TRAINING.md               # Model training guide
├── models/                       # Downloaded models (gitignored)
├── outputs/                      # Generated images (gitignored)
├── scripts/                      # Utility scripts
├── tests/                        # Test files
├── diffusion_model_finetuning.ipynb  # Training notebook
├── requirements.txt              # Python dependencies
├── .gitignore                    # Git ignore rules
├── .dockerignore                 # Docker ignore rules
├── Dockerfile                    # Docker configuration
├── LICENSE                       # MIT License
└── README.md                     # This file

🎓 Training Your Own Model

Want to fine-tune your own Pepe model or create a different character?

Quick Training Overview 

# 1. Prepare your dataset (images + captions)
# 2. Run the training script
accelerator launch train_text_to_image_lora.py \
  --pretrained_model_name_or_path="runwayml/stable-diffusion-v1-5" \
  --train_data_dir="./your-data" \
  --resolution=512 \
  --train_batch_size=1 \
  --gradient_accumulation_steps=4 \
  --max_train_steps=2000 \
  --learning_rate=1e-4 \
  --lr_scheduler="cosine" \
  --output_dir="./output" \
  --rank=16

Complete Training Guide

See **[docs/TRAINING.md] for:

  • Dataset preparation
  • Training configuration
  • Hyperparameter tuning
  • Validation and testing
  • Model upload to Hugging Face

Or check out the diffusion_model_finetuning.ipynb notebook!

🛠️ Technology Stack

Core Technologies

AI/ML Components

  • Stable Diffusion 1.5 - Base diffusion model
  • LoRA - Low-Rank Adaptation for efficient fine-tuning
  • LCM - Latent Consistency Model for fast inference
  • DPM Solver - Fast diffusion sampling

Image Processing

  • Pillow (PIL) - Image manipulation
  • NumPy - Numerical operations
  • SciPy - Scientific computing

🤝 Contributing

Contributions are welcome! Here's how you can help:

Ways to Contribute

  • 🐛 Report bugs
  • 💡 Suggest new features
  • 📝 Improve documentation
  • 🎨 Add new style presets
  • ⚡ Optimize performance
  • 🧪 Add tests

Development Setup 

# Clone and setup
git clone https://github.com/YOUR_USERNAME/pepe-meme-generator.git
cd pepe-meme-generator
python -m venv venv
source venv/bin/activate
pip install -r requirements.txt

# Make your changes
# Test locally
streamlit run src/app.py

# Submit pull request

---

## 🐛 Troubleshooting

### Common Issues

**Issue**: Out of memory error  
**Solution**: Reduce resolution to 512x512, use CPU mode, or enable memory optimizations

**Issue**: Slow generation on CPU  
**Solution**: Use "Pepe + LCM (FAST)" model with 6 steps

**Issue**: Model not loading  
**Solution**: Clear Streamlit cache with "Clear Cache & Reload" button

**Issue**: Import errors  
**Solution**: Reinstall dependencies: `pip install -r requirements.txt --force-reinstall`


---

## 📜 License

This project is licensed under the **MIT License** - see the [LICENSE](LICENSE) file for details.

### Model Licenses
- **Stable Diffusion 1.5**: CreativeML Open RAIL-M License
- **Pepe LoRA**: MIT License
- **Training Dataset**: Check [iresidentevil/pepe_the_frog](https://huggingface.co/datasets/iresidentevil/pepe_the_frog)

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## 🙏 Acknowledgments

### Special Thanks
- **[WorldQuant University](https://www.wqu.edu/ai-lab-computer-vision)** - AI/ML education and resources
- **[Hugging Face](https://huggingface.co/)** - Model hosting and diffusers library
- **[Stability AI](https://stability.ai/)** - Stable Diffusion model
- **[Microsoft](https://github.com/microsoft/LoRA)** - LoRA technique
- **[iresidentevil](https://huggingface.co/iresidentevil)** - Pepe dataset


## 📞 Contact & Support

- **Issues**: [email protected]

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## 🌟 Star History

If you find this project useful, please consider giving it a ⭐ star on GitHub!

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<div align="center">

**Made with ❤️ by MJaheen**

*Generate Pepes responsibly! 🐸*

</div>