psdfy 0.1.5

PSD layer converter and processor

🎨 psdfy

Convert any image into an editable Adobe Photoshop file with AI-powered layer segmentation.

Upload a photo, and psdfy automatically detects objects, creates separate layers for each one, and exports a ready-to-edit .psd file. Perfect for designers, photographers, and anyone who needs to work with layered images.

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✨ Features

• 🤖 AI-Powered Segmentation - Uses SAM 2 to automatically detect and separate objects
• 📦 Multi-Layer PSD Export - Each detected object becomes an editable layer
• 🎯 Text-Prompted Detection - Optional GroundingDINO integration for specific object detection
• 🌐 Web UI - Simple browser interface for uploading and downloading
• 💻 CLI Tool - Command-line interface for automation and scripting
• ☁️ Cloud Ready - S3 storage backend support for cloud deployments
• 🐳 Docker Support - CPU and GPU Docker images included
• ⚡ Fast Processing - Optimized for 1080p images (< 5 seconds on GPU)

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🚀 Quick Start

Option 1: Web UI (Easiest)

# Install psdfy
pip install psdfy

# Run installation wizard
psdfy install

# Start the service
psdfy start

# Open browser and go to http://localhost:3457

Then:

1. Login with password (default: 123456)
2. Upload an image
3. Click "Convert to PSD"
4. Download your layered PSD file

Option 1b: Install Without Weights (Lightweight)

If you want to set up psdfy without downloading the large model weights (5GB+), use:

# Install psdfy
pip install psdfy

# Run installation wizard without downloading weights
psdfy install --no-weights

# Later, download weights when ready
psdfy install --download-weights-only

# Start the service
psdfy start

This is useful for:

• Setting up on servers with limited bandwidth
• Testing the UI before committing to full installation
• Downloading weights on a separate machine
• Running without SAM2 segmentation (basic functionality only)

Option 2: Docker (Recommended)

# CPU version
docker build -f docker/Dockerfile -t psdfy:latest .
docker run -p 3456:3456 -p 3457:3457 psdfy:latest

# GPU version (CUDA 12.1)
docker build -f docker/Dockerfile.gpu -t psdfy:gpu .
docker run --gpus all -p 3456:3456 -p 3457:3457 psdfy:gpu

Option 3: Python API

from app.utils.io import load_image
from app.services.segmenter import get_segmenter
from app.services.psd_writer import get_psd_writer

# Load image
image_array, (width, height), fmt = load_image(image_bytes)

# Segment objects
segmenter = get_segmenter()
masks = segmenter.segment_auto(image_array)

# Write PSD
psd_writer = get_psd_writer()
psd_bytes = psd_writer.write_psd(layers, width, height)

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📖 Usage

Web UI

1. Login - Enter password (default: 123456)
2. Upload - Drag & drop or click to select image
3. Configure - Choose segmentation mode:
   • Automatic - Detects all objects
   • Text Prompt - Specify objects (e.g., "person . table . book")
4. Convert - Click "Convert to PSD"
5. Download - Get your layered PSD file

CLI Commands

# Show version and system info
psdfy version

# Install/configure psdfy
psdfy install --password mypassword

# Start API and UI servers
psdfy start

# Stop servers
psdfy stop

# Diagnose and repair installation
psdfy fix --dry-run

# Update to latest version
psdfy update

# Check for issues
psdfy fix --redownload-weights

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🏗️ Architecture

┌─────────────────────────────────────────────────────────┐
│                    Browser (Web UI)                      │
└────────────────────────┬────────────────────────────────┘
                         │ (Cookie Auth)
                         ▼
┌─────────────────────────────────────────────────────────┐
│         GUI Web Server (Port 3457)                       │
│  - Login page                                            │
│  - Upload interface                                      │
│  - Server-side proxy to API                             │
└────────────────────────┬────────────────────────────────┘
                         │ (X-Session-Id, X-Client-Signature)
                         ▼
┌─────────────────────────────────────────────────────────┐
│         Proxy API Server (Port 3456)                     │
│  - /convert - Image to PSD conversion                   │
│  - /files - Download results                            │
│  - /auth - Session management                           │
└────────────────────────┬────────────────────────────────┘
                         │
        ┌────────────────┼────────────────┐
        ▼                ▼                ▼
    ┌────────┐      ┌────────┐      ┌────────┐
    │ SAM 2  │      │ Mask   │      │ PSD    │
    │ Loader │      │ Post-  │      │ Writer │
    │        │      │ process│      │        │
    └────────┘      └────────┘      └────────┘

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🛠️ Development

Setup

# Clone repository
git clone https://github.com/Mattel-Limbo/psdfy.git
cd psdfy

# Create virtual environment
python -m venv venv
source venv/bin/activate  # On Windows: venv\Scripts\activate

# Install in development mode
pip install -e ".[dev]"

Code Quality

# Run linter
ruff check app psdfy tests

# Format code
black app psdfy tests

# Type checking
mypy --strict app psdfy

# Run tests
pytest tests/ -v

Project Structure

psdfy/
├── app/                    # FastAPI application
│   ├── api_app/           # Proxy API server (port 3456)
│   ├── ui_app/            # Web UI server (port 3457)
│   ├── services/          # Business logic (segmentation, PSD writing, etc.)
│   ├── models/            # AI model loaders (SAM 2, GroundingDINO)
│   ├── storage/           # Storage backends (local, S3)
│   └── utils/             # Helper utilities
├── psdfy/                 # CLI tool
│   ├── commands/          # CLI commands (install, start, stop, etc.)
│   ├── config.py          # Configuration management
│   └── weights.py         # Model weights downloader
├── web/                   # Web UI assets
│   └── templates/         # HTML templates
├── tests/                 # Test suite
├── docker/                # Docker configurations
└── scripts/               # Utility scripts (benchmarking, etc.)

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📋 Requirements

• Python: 3.11 or higher
• RAM: 8GB minimum (16GB recommended)
• GPU (optional): NVIDIA GPU with CUDA 12.1+ for faster processing
• Disk: 5GB for model weights

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🔧 Configuration

Configuration is stored in ~/.psdfy/config.toml:

[app]
host = "localhost"
api_port = 3456
ui_port = 3457
device = "cpu"  # or "cuda", "mps"

[auth]
ui_password_hash = "..."
client_secret = "..."

[models]
sam2_weights_path = "~/.psdfy/weights/sam2_hiera_large.pt"
enable_grounding_dino = false

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🐛 Troubleshooting

Port Already in Use

# Change ports
psdfy start --api-port 3500 --ui-port 3501

Model Weights Not Found

# Re-download weights
psdfy fix --redownload-weights

Reset Password

# Reset to default (123456)
psdfy fix --reset-password

Check System Health

# Run diagnostics
psdfy fix --dry-run

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📊 Performance

Resolution	GPU (RTX 3080)	CPU (i7-12700)
512x512	~0.5s	~3s
1080x1080	~1.5s	~8s
2160x2160	~4s	~20s

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📝 API Examples

Convert Image (cURL)

# Get session
SESSION=$(curl -X POST http://localhost:3456/auth/client-signature \
  -H "Content-Type: application/json" \
  -d '{"clientSecret":"your-secret"}' | jq -r '.sessionId')

# Convert image
curl -X POST http://localhost:3456/convert \
  -H "X-Session-Id: $SESSION" \
  -H "X-Client-Signature: your-signature" \
  -F "file=@image.jpg" \
  -F "mode=auto" \
  -o output.psd

Convert Image (Python)

import requests

# Get session
response = requests.post(
    "http://localhost:3456/auth/client-signature",
    json={"clientSecret": "your-secret"}
)
session_id = response.json()["sessionId"]

# Convert image
with open("image.jpg", "rb") as f:
    response = requests.post(
        "http://localhost:3456/convert",
        headers={
            "X-Session-Id": session_id,
            "X-Client-Signature": "your-signature"
        },
        files={"file": f},
        data={"mode": "auto"}
    )

# Save PSD
with open("output.psd", "wb") as f:
    f.write(response.content)

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📄 License

MIT License - see LICENSE file for details

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🤝 Contributing

Contributions are welcome! Please:

1. Fork the repository
2. Create a feature branch (git checkout -b feature/amazing-feature)
3. Commit changes (git commit -m 'Add amazing feature')
4. Push to branch (git push origin feature/amazing-feature)
5. Open a Pull Request

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📞 Support

• Issues: GitHub Issues
• Discussions: GitHub Discussions
• Documentation: See plan.md for detailed technical documentation

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🎯 Roadmap

• Batch processing support
• Advanced layer ordering heuristics
• Real-time preview in browser
• Multi-user support with API keys
• Webhook notifications
• Custom model fine-tuning

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Made with ❤️ for designers and developers