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Design a Text-to-Image Generation System

Build a large-scale DALL-E-style system that generates 1024×1024 images from text prompts, serving 100M+ monthly generations with robust safety controls.

Diffusion ModelsCreative AIContent Safety
Q: What's the expected scale and user base for the image generation platform?
A: 10M monthly active users generating 100M images/month. Peak traffic during evenings/weekends (4x baseline). Consumer and professional use cases.
Engineering Implications: Massive scale requires robust queue management and auto-scaling. Consumer users expect simple prompts and fast turnaround. Professionals need advanced controls, batch generation, and API access. Geographic distribution matters for latency.
Q: What image quality and generation capabilities are required?
A: 1024×1024 high-resolution images as standard, with 512×512 option for speed. Photorealistic and artistic styles. Support for style conditioning, aspect ratios, and negative prompts.
Engineering Implications: High resolution demands significant VRAM (24GB+ per generation) and longer inference time (15-25 seconds). Style conditioning requires specialized model training. Multiple resolutions need different serving strategies.
Q: What are the safety, content policy, and regulatory requirements?
A: Family-friendly platform: no NSFW, violence, hate speech, copyrighted characters, or real people without consent. GDPR compliance, content transparency, user appeals process.
Engineering Implications: Multi-layered safety pipeline: prompt filtering (pre-generation), content classification (post-generation), human review (edge cases). Requires specialized safety classifiers, ongoing model updates, and compliance tracking.
Q: What are the latency expectations and user experience requirements?
A: Async generation acceptable (15-30 seconds), but users need progress updates, queue position, and accurate ETAs. WebSocket real-time updates essential.
Engineering Implications: Queue-based architecture with sophisticated job scheduling. Need distributed processing, load balancing across GPU clusters, and graceful handling of failures. User experience depends on transparent progress communication.
Q: What business model and cost constraints drive the technical architecture?
A: Freemium model: 10 free generations/month, unlimited for $20/month. API pricing at $0.02/image. Target 60% gross margins.
Engineering Implications: Cost optimization critical: efficient model serving, smart batching, spot instance utilization. Free tier needs rate limiting and quota management. API tier requires dedicated infrastructure and SLAs.

Complete Text-to-Image Generation ML Systems Framework

🏗️ Section 8: Production ML System Design

  • • Async queue architecture: Redis-based job management with WebSocket progress updates
  • • Multi-region GPU clusters: 200 A100s globally with auto-scaling and spot integration
  • • Safety pipeline: Pre-generation prompt filtering + post-generation content classification
  • • Inference optimization: TensorRT + dynamic batching for 20s generation time

⚖️ Key Trade-offs & Decisions

  • • Latent vs pixel diffusion: 64x memory reduction with 8×8 VAE compression
  • • Quality vs speed: 50 DDIM steps for quality, 25 DPM steps for speed options
  • • Safety vs creativity: 99% precision safety filters with 10% artistic false positives
  • • Cost vs availability: $2.85M/month for 100M generations with 4x peak scaling

🔧 Implementation Challenges

  • • Scale challenges: 150 QPS peak with 20s generation time = 3K concurrent jobs
  • • Memory constraints: 24GB VRAM per generation limits parallel processing
  • • Safety complexity: Multi-modal content requires specialized classifiers per violation type
  • • Quality consistency: Maintaining FID < 15 across diverse prompts and styles

🚀 Alternative Approaches

  • • GAN-based generation: 2s inference but training instability and mode collapse
  • • Autoregressive (DALL-E 1): High control but 5+ minute generation time
  • • Pixel-space diffusion: Direct generation but 64x higher memory requirements
  • • Retrieval-augmented: Fast but limited creativity and novel concept generation
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🎯 Interview Practice Questions

Practice these follow-up questions to demonstrate deep understanding of text-to-image generation systems in interviews.

1. Cost Optimization at Scale

"Your text-to-image service costs are growing rapidly ($3M/month) as usage scales to 100M generations. Design a comprehensive cost optimization strategy including model compression, spot instances, request batching, and caching while maintaining generation quality and user experience."

2. Multi-Modal Safety Pipeline

"Design a comprehensive safety system that prevents generation of harmful content (NSFW, violence, copyrighted characters) while minimizing false positives on legitimate creative content. Include both prompt filtering and image classification with appeal mechanisms."

3. Fine-Grained Control System

"Build advanced controls allowing users to specify style, composition, lighting, camera angle, and specific object attributes. How do you design the conditioning mechanisms and training pipeline to support detailed creative control without requiring extensive prompt engineering skills?"

4. Personalization and Style Consistency

"Design a system where users can create and maintain consistent characters, art styles, or brand aesthetics across multiple generations. Address training data requirements, style encoding, and preventing style leakage between users in a multi-tenant environment."

5. Real-time Generation Pipeline

"A customer wants near real-time image generation (under 3 seconds) for their interactive application. Design modifications to your architecture including model distillation, specialized hardware, caching strategies, and quality trade-offs to achieve this latency target."

6. Quality Consistency and Evaluation

"Build a comprehensive evaluation framework that can detect quality regressions, measure improvement over time, and guide model retraining decisions. Include both automated metrics (FID, CLIP score) and human evaluation systems with proper statistical significance testing."