MayaGen: Enterprise Synthetic Image Data Platform

MayaGen is a scalable, full-stack platform designed for generating high-quality synthetic image datasets for Machine Learning and Computer Vision applications. Built on top of Stable Diffusion (via ComfyUI), it automates the prompt engineering and bulk generation process, enabling researchers to create diverse, labeled datasets with ease.

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🚀 Key Features

• 🏭 Bulk Generation Pipeline: Orchestrate large-scale image generation jobs (1000+ images) with customizable batch sizes and concurrency control.
• 🎨 Dynamic Prompt Engineering: Template-based prompt construction system that automatically injects variations (lighting, camera angles, styles, colors) to ensure dataset diversity.
• 📂 Structured Asset Management:
  • Global Gallery: Public showcase of community-generated assets.
  • My Collections: Private, secure workspace for managing user-specific datasets.
  • Smart Filtering: Organize by category, model type, and processing status.
• ⚡ Modern Event-Driven Architecture: Non-blocking asynchronous job queue (Producer/Consumer model) ensuring reliable generation even under heavy loads.
• 🔌 Model Agnostic: Supports standard checkpoints (SD1.5, SDXL) and specialized models (LCM, Flux) via extensible provider interface.
• 🔐 Enterprise Security: Role-Based Access Control (RBAC), JWT Authentication, and secure asset isolation.

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🛠️ Technology Stack

Component	Technology	Description
Frontend		High-performance React framework with Shadcn UI components.
Backend		Async Python API handling job orchestration and DB interactions.
Database		Robust relational storage for users, jobs, and image metadata.
Inference		Node-based Stable Diffusion backend for flexible workflow design.
Infrastructure		Containerized deployment for consistent environments.

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🏗️ Architecture Overview

graph TD
    User[User / Data Scientist] -->|UI Operations| FE[Next.js Frontend]
    FE -->|REST API| API[FastAPI Backend]
    
    subgraph "Backend Services"
        API -->|CRUD| DB[(PostgreSQL)]
        API -->|Queue Job| Worker[Async Worker]
        Worker -->|Websocket/API| Comfy[ComfyUI Node]
    end
    
    Comfy -->|Save Image| Storage[Local/Cloud Storage]
    Worker -->|Update Status| DB

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⚡ Getting Started

Prerequisites

• Python 3.10+ (with uv package manager recommended)
• Node.js 18+ & npm
• Docker & Docker Compose (optional, for full stack)
• ComfyUI instance (running locally or remotely)

1. Backend Setup

cd mayagen-be

# Create virtual environment & install dependencies
uv venv
source .venv/bin/activate
uv pip install -r pyproject.toml

# Configuration
cp .env.example .env.development
# Edit .env.development with your ComfyUI URL and DB credentials

# Run Server (Development)
uv run run_server.py

2. Frontend Setup

cd mayagen-fe

# Install dependencies
npm install

# Run Development Server
npm run dev

The application will be available at http://localhost:3000.

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📖 Usage Guide

Creating a Bulk Dataset

1. Navigate to Bulk Generate in the sidebar.
2. Define your Base Prompt (e.g., "A futuristic cyberpunk city").
3. Select Variations:
   • Lighting: Neon, Daylight, Foggy
   • Style: Realistic, Anime, Oil Painting
4. Set Batch Size (e.g., 50 images).
5. Click Queue Batch. The system will generate unique permutations of your prompt and begin processing.

Exporting Data

Generated images are stored in mayagen-be/synthetic_dataset/{Category}/. Metadata is available via the GET /api/images endpoint for easy integration with training pipelines.

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🤝 Contribution

Contributions are welcome! Please verify that your changes pass the linting checks and include tests for any new API endpoints.

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

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📄 License

Distributed under the MIT License. See LICENSE for more information.