Stable Diffusion ControlNet Pipeline for Custom Print Artwork

A complete AI-powered design engine that generates custom artwork for printed physical products using Stable Diffusion and ControlNet technology.

🎯 Project Overview

This pipeline transforms structured prompts into:

• High-quality visual previews with product mockups
• Flattened 2D print files optimized for UV printing workflows

📐 Design Constraints

All artwork is confined to a printable band wrapping around a sphere:

• Band Dimensions: 134mm wide × 25mm tall
• Object: 42.67mm diameter spherical object
• Requirements: Clean, centered, undistorted, print-ready for UV RIP software

🚀 Features

• Stable Diffusion Integration: Support for SD 1.5, 2.1, and XL models
• ControlNet Implementation: Layout-constrained generation with image masks
• Product Mockup Generation: Realistic 3D object rendering
• Print File Extraction: High-resolution flattened designs (300+ DPI)
• Style Variations: Photorealistic, illustrative, graphic, minimalist styles
• Batch Generation: 2x2 design variant previews

🛠️ Technology Stack

• Python 3.9+
• PyTorch - Deep learning framework
• Diffusers - Hugging Face Stable Diffusion library
• ControlNet - Layout and structure control
• Pillow - Image processing
• OpenCV - Computer vision operations
• NumPy - Numerical computations

📁 Project Structure

stable-diffusion-controlnet-pipeline/
├── src/
│   ├── models/           # Model definitions and loading
│   ├── generators/       # Image generation pipelines
│   ├── processors/       # Image processing utilities
│   ├── mockups/          # Product mockup generation
│   └── utils/            # Helper functions
├── configs/              # Configuration files
├── assets/               # Base images and templates
├── outputs/              # Generated images
├── tests/                # Unit tests
├── requirements.txt      # Python dependencies
└── main.py              # Main application entry point

🚀 Quick Start

1. Install Dependencies:

   pip install -r requirements.txt

2. Download Models:

   python scripts/download_models.py

3. Run Generation:

   python main.py --prompt "A bold design with lightning bolts and flames"

📋 Usage Examples

Basic Generation

from src.generators.controlnet_pipeline import ControlNetPipeline

pipeline = ControlNetPipeline()
result = pipeline.generate(
    prompt="BEAST MODE in heavy blackletter font with lightning bolts",
    style="graphic",
    output_format="both"  # mockup + flattened
)

Batch Generation

variations = pipeline.generate_variations(
    prompt="Minimalist geometric pattern",
    num_variations=4,
    style="minimalist"
)

🎨 Supported Styles

• Photorealistic: High-detail, realistic rendering
• Illustrative: Artistic, hand-drawn aesthetic
• Graphic: Bold, geometric, modern designs
• Minimalist: Clean, simple, elegant patterns
• Tattoo-inspired: Intricate, detailed artwork

📊 Output Formats

1. Product Mockup: PNG with transparent background
2. Flattened Design: 134mm × 25mm @ 300 DPI
3. Preview Grid: 2x2 variation layout
4. Print-Ready: CMYK optimized for UV printing

🔧 Configuration

Edit configs/pipeline_config.yaml to customize:

• Model selection and parameters
• Generation settings
• Output formats and quality
• Style presets

🧪 Testing

Run the test suite:

python -m pytest tests/

📈 Performance

• Generation Time: 15-30 seconds per image
• Memory Usage: 8-16GB VRAM recommended
• Output Quality: 300+ DPI print-ready
• Batch Processing: Up to 4 variations simultaneously

🤝 Contributing

1. Fork the repository
2. Create a feature branch
3. Make your changes
4. Add tests
5. Submit a pull request

📄 License

MIT License - see LICENSE file for details

🆘 Support

For issues and questions:

• Create an issue in the repository
• Check the documentation in /docs
• Review example configurations

🎯 Roadmap

• LoRA fine-tuning for brand consistency
• Web UI interface
• Real-time preview generation
• Advanced style transfer
• Multi-object support