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Image Generation Documentation

Complete documentation for Bike4Mind's AI image generation system, covering architecture, implementation, extension, and troubleshooting.

Documentation Overview​

This documentation suite provides comprehensive coverage of the image generation system:

📋 Architecture Overview​

Comprehensive system architecture and design patterns:

  • System overview and component interactions
  • Supported models (OpenAI, BFL, etc.)
  • Data flow and processing pipelines
  • Storage architecture and security
  • Real-time updates and error handling

🔧 Implementation Guide​

Detailed code examples and implementation patterns:

  • Model configuration and validation
  • Frontend React components and state management
  • Backend services and API endpoints
  • Provider implementations (OpenAI, BFL)
  • Usage examples and best practices

🚀 Extension Guide​

How to add new AI image providers:

  • Step-by-step provider integration
  • Parameter schemas and validation
  • Frontend UI components
  • API key management
  • Testing and deployment

🐛 Troubleshooting Guide​

Common issues and solutions:

  • Generation failures and timeouts
  • Parameter validation errors
  • Storage and URL issues
  • WebSocket and real-time problems
  • Credit and billing issues
  • Debugging tools and monitoring

Quick Start​

For Developers​

  1. Read the Architecture Overview to understand the system
  2. Follow the Implementation Guide for code examples
  3. Use the Troubleshooting Guide when issues arise

For Extension Development​

  1. Start with the Extension Guide
  2. Review existing provider implementations
  3. Follow the deployment checklist

For Operations​

  1. Review the monitoring section in Troubleshooting
  2. Set up health checks and alerts
  3. Monitor key metrics and error rates

System Capabilities​

Supported Providers​

  • OpenAI: GPT-Image-1
  • BlackForest Labs (BFL): FLUX PRO, FLUX PRO Ultra, FLUX PRO Fill
  • Extensible: Easy integration of new providers

Key Features​

  • Unified Interface: Single API for all providers
  • Real-time Updates: WebSocket progress notifications
  • Flexible Parameters: Model-specific parameter validation
  • Credit Management: Integrated billing and usage tracking
  • Image Editing: Advanced editing with masks and prompts
  • Storage Integration: S3 with signed URLs and public access

Technical Highlights​

  • Async Processing: Queue-based background generation
  • Provider Abstraction: Factory pattern for easy extension
  • Type Safety: Comprehensive TypeScript interfaces
  • Error Handling: Robust error recovery and user feedback
  • Performance: Optimized for scale and reliability

Common Workflows​

Basic Image Generation​

await handleImageGenerationCommand({
  params: "A beautiful landscape",
  model: ImageModels.FLUX_PRO_ULTRA,
  currentSession,
  workBenchFiles: [],
  queryClient,
});

Image Editing​

await handleImageEditCommand({
  params: "Change the sky to sunset",
  image: sourceImageUrl,
  workBenchFiles: [maskFile],
  model: ImageModels.FLUX_PRO_FILL,
  currentSession,
  queryClient,
});

Model Configuration​

const { setLLM } = useLLM();

// Configure for BFL FLUX PRO Ultra
setLLM({
  model: ImageModels.FLUX_PRO_ULTRA,
  aspect_ratio: '16:9',
  safety_tolerance: 4,
  prompt_upsampling: true,
});

Architecture Highlights​

Layered Design​

Frontend Layer    → React components, state management
API Layer         → Next.js routes, authentication
Service Layer     → Business logic, credit management  
Provider Layer    → AI service implementations
Infrastructure    → Queue, storage, WebSocket

Provider Pattern​

The system uses a factory pattern to abstract different AI providers:

const service = aiImageService(vendor, apiKey, logger);
const images = await service.generate(prompt, options);

This design makes it easy to:

  • Add new providers without changing existing code
  • Handle provider-specific parameters and constraints
  • Implement consistent error handling and monitoring
  • Scale to support multiple AI services

Best Practices​

Development​

  • Follow existing code patterns and interfaces
  • Implement comprehensive error handling
  • Add proper TypeScript types for all new features
  • Write tests for new functionality

Performance​

  • Use optimistic updates for immediate user feedback
  • Implement proper loading states and progress indicators
  • Optimize image storage and delivery
  • Monitor and cache frequently accessed data

Security​

  • Validate all user inputs comprehensively
  • Implement proper content moderation
  • Use secure API key management
  • Follow principle of least privilege for access control

User Experience​

  • Provide clear error messages and recovery options
  • Show real-time progress during generation
  • Implement proper loading and error states
  • Handle edge cases gracefully

Contributing​

When contributing to the image generation system:

  1. Read the Documentation: Understand the architecture and patterns
  2. Follow Conventions: Use existing code patterns and TypeScript interfaces
  3. Test Thoroughly: Write unit and integration tests
  4. Update Documentation: Keep documentation current with changes
  5. Monitor Impact: Ensure changes don't affect system performance

Support​

For questions or issues with the image generation system:

  1. Check Documentation: Start with this documentation suite
  2. Review Logs: Check server logs and browser console
  3. Use Debug Tools: Utilize the debugging utilities provided
  4. Test Isolation: Isolate issues to specific components or providers
  5. Monitor Metrics: Check system health and performance metrics

Next: Start with the Architecture Overview to understand the system design.