AI Image Generation API

A GraphQL API service for generating AI images with custom styles, sizes, and formats. Built with FastAPI, Strawberry GraphQL, and Poetry.

Features

• 🖼️ Generate AI images with customizable:
  • Sizes (256x256, 512x512, 1024x1024)
  • Formats (PNG, JPEG, WEBP)
  • Styles (Photorealistic, Artistic, Cartoon, Abstract)
• 🔒 API Key authentication
• 📚 Auto-generated documentation
• ✨ Type-safe GraphQL schema
• 🔄 SDK generation for multiple languages

Quick Start

# Install dependencies
poetry install

# Start the server
poetry run python app/main.py

# Server runs at http://localhost:8000
# Documentation at http://localhost:8000/docs
# Interactive GraphQL Query Builder (GraphiQL) at http://localhost:8000/graphql

API Usage

Authentication

Include your API key in requests:

curl -H "X-API-Key: sk_your_key" http://localhost:8000/

Example Query

mutation GenerateImages($input: ImageGenerationInput!) {
  generateImages(input: {
    prompt: "a beautiful sunset"
    width: 1024
    height: 1024
    style: PHOTOREALISTIC
    image_format: PNG
    numImages: 1
  }) {
    success
    results {
      id
      url
    }
  }
}

Development

# Run with auto-reload
poetry run uvicorn app.main:app --reload

# Generate SDKs
graphql-codegen

# Run tests (once implemented)
poetry run pytest

Project Structure

app/
├── __init__.py
├── main.py             # FastAPI app setup
├── schema.py           # GraphQL Schema - Dynamically loaded from resolvers
├── types/              # Type files used across resolvers
│   ├── enums.py        # Enum definitions
│   ├── inputs.py       # Input types
│   ├── responses.py    # Response types
│   └── scalars.py      # Custom scalars
├── resolvers/
│   ├── queries/        # Query resolvers
│   │   └── example-query/
│   │       └── example-query.py
│   └── mutations/    # Mutation resolvers
│       └── example-mutation/
│           └── example-mutation.py
└── services/
    ├── auth.py
    └── image_generator.py

Generated SDKs

SDKs are automatically generated for:

• Python
• TypeScript
• Go
• Java
• More coming soon!

Environment Variables

# Required
API_KEY_SECRET=your_secret_key

# Optional
PORT=8000
HOST=0.0.0.0

License

MIT

Contributing

We welcome contributions! To add a new model, follow these steps:

1. Fork the repository and clone it to your local machine.
2. Create a new branch for your feature:

   git checkout -b feature/add-new-model
   

3. Add your model:
   • Add a model repository from huggingface in enums.py:

   @strawberry.enum
   class ModelType(Enum):
       ...
       FLUX_1_SCHNELL = "black-forest-labs/FLUX.1-schnell"
   
       # Add your model
       YOUR_NEW_MODEL = "your-company/URMoDL.1"
   

   • Register your model pipeline in model_pipeline_registry.py:

   cls.register(
       ModelType.STABLE_V1_4,
       PipelineConfig(
           pipeline_class=StableDiffusionPipeline,
           default_params={"safety_checker": None},
           inference_params={"num_inference_steps": 50},
       ),
   )
   

   • Update app/schema.py to include your model in the GraphQL schema.
   • Create necessary resolvers in app/resolvers/ to handle queries and mutations for your model.
4. Write tests for your model in the tests/ directory.
5. Commit your changes and push your branch to GitHub:

   git add .
   git commit -m "Add new model"
   git push origin add-new-model
   

6. Create a Pull Request on GitHub:

• Go to your forked repository on GitHub.
• Click on the "New pull request" button.
• Ensure the base repository is set to the original repository and the base branch is master.
• Provide a specific and concise description of your changes.
• Submit the pull request for review.

Thank you for contributing!