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Design a Face Generation System

Build a production-scale face generation system using diffusion models or GANs for creating photorealistic synthetic faces with controllable attributes.

GenAI SystemsComputer VisionDiffusion Models
Q: What are the primary use cases and quality requirements?
A: Professional headshots for business (95%+ photorealism), gaming avatars (stylized consistency), research applications (demographic diversity), and creative content (artistic control).
Engineering Implications: Different use cases require different model architectures: photorealism needs high-resolution diffusion models, gaming needs consistent style generation, research needs bias mitigation, and creative needs controllable generation parameters.
Q: What scale and performance requirements do we need to meet?
A: Handle 100K face generations per day, sub-30 second generation time, support 4K resolution output, and serve 10K concurrent users globally.
Engineering Implications: Scale requires distributed GPU clusters, intelligent batching, multi-region deployment, and caching strategies. Different resolution tiers allow trading speed for quality based on user needs.
Q: What safety and ethical constraints must the system enforce?
A: Prevent deepfake abuse, detect and block generation of real public figures, ensure demographic fairness, comply with GDPR/privacy laws, and maintain content safety filters.
Engineering Implications: Safety requires multi-layer filtering: prompt analysis, real person detection, generated image classification, watermarking for provenance tracking, and comprehensive audit logging for regulatory compliance.
Q: How do we handle user control and customization?
A: Support age, gender, ethnicity, hairstyle, and expression control while maintaining natural appearance. Enable style transfer, lighting adjustment, and background customization.
Engineering Implications: Control requires conditional generation models, disentangled latent spaces, and hierarchical conditioning. Balance between user control and natural generation requires careful model architecture and training strategies.
Q: What are the business model and cost constraints?
A: Freemium model: 10 generations/day free, $20/month unlimited professional tier. Target 60% profit margin on compute costs while maintaining quality competitive with manual photography.
Engineering Implications: Cost optimization drives model selection, inference batching, GPU utilization strategies, and quality-cost trade-offs. Premium tiers justify higher-quality models and faster generation times.
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🎯 Interview Practice Questions

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

1. Multi-Modal Safety Pipeline

"Design a comprehensive safety system that prevents generation of real people, detects deepfake attempts, and ensures responsible use. How do you balance safety with user creativity, handle edge cases, and maintain low false positive rates?"

2. Controllable Generation Architecture

"Build fine-grained control over facial attributes (age, gender, expression, lighting) while maintaining photorealism. How do you design the conditioning mechanism, handle attribute interactions, and ensure generated faces remain natural-looking?"

3. Bias Mitigation and Fairness

"Your face generation system shows demographic bias in output quality and diversity. Design a comprehensive approach to detect, measure, and mitigate bias while ensuring fair representation across all demographic groups and use cases."

4. Quality-Latency Trade-offs

"Users want both instant previews and high-quality final results. Design a progressive generation system that shows quick previews (<5 seconds) while rendering final quality in the background. Handle user interactions and cancellations efficiently."

5. Cross-Platform Consistency

"Your face generation needs to work consistently across mobile apps, web browsers, and API integrations. How do you handle different device capabilities, ensure identical outputs across platforms, and optimize for various network conditions?"

6. Personalization and Style Transfer

"Enable users to generate faces in consistent artistic styles or adapt existing photos into different styles while preserving identity. Design the style encoding, user preference learning, and cross-style consistency mechanisms."