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Design an Image Synthesis System

Build a comprehensive image synthesis platform supporting text-to-image, style transfer, inpainting, and controllable generation with commercial-grade quality and safety.

GenAI SystemsDiffusion ModelsComputer Vision
Q: What types of image synthesis do we need to support?
A: Text-to-image generation, style transfer, image inpainting/outpainting, super-resolution upscaling, and controlled image editing with fine-grained attribute manipulation.
Engineering Implications: Different synthesis types require different model architectures: diffusion models for text-to-image, encoder-decoder for style transfer, masked autoencoders for inpainting, and GANs for super-resolution. Each has distinct computational and quality requirements.
Q: What scale and quality requirements must we meet?
A: Generate 500K images daily, support up to 4K resolution, maintain sub-2 minute generation time, and achieve 90%+ user satisfaction with first generation attempt.
Engineering Implications: Scale drives model selection, distributed inference architecture, and caching strategies. Quality requirements determine model size, generation steps, and post-processing pipelines. User satisfaction metrics guide prompt understanding and output ranking systems.
Q: How do users interact with and control the generation process?
A: Support natural language prompts, reference image uploads, style presets, fine-grained parameter controls, and iterative refinement workflows with history tracking.
Engineering Implications: Control mechanisms require multi-modal conditioning, parameter embedding spaces, version management, and real-time preview systems. Balance between simplicity for casual users and advanced controls for professionals.
Q: What content safety and quality standards must we enforce?
A: Block NSFW content, prevent copyright infringement, ensure diverse representation, maintain aesthetic quality standards, and provide content provenance tracking.
Engineering Implications: Safety requires multi-stage filtering (prompt analysis, generation guidance, output classification), copyright detection systems, bias monitoring, quality scoring models, and blockchain-based provenance for commercial use.
Q: What are the business model and cost considerations?
A: Freemium with 50 generations/month free, $30/month pro tier with commercial rights, enterprise API at $0.15/image. Target 70% gross margin while competing with Midjourney and DALL-E pricing.
Engineering Implications: Business model drives cost optimization strategies, quality tiers, commercial licensing, and enterprise features. Competitive pricing requires efficient model serving, spot instance usage, and advanced batching techniques.
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🎯 Interview Practice Questions

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

1. Multi-Task Architecture Design

"Design a unified model architecture that can handle text-to-image, image-to-image, inpainting, and super-resolution tasks. How do you design the conditioning mechanisms, shared representations, and task-specific components while maintaining quality across all use cases?"

2. Commercial Licensing and Rights Management

"Build a system that tracks usage rights, prevents copyright infringement, and enables commercial licensing of generated images. How do you handle training data provenance, similarity detection, and automated licensing while scaling to millions of daily generations?"

3. Style Consistency and Brand Control

"Enterprise customers need consistent brand imagery across campaigns. Design a system that learns and maintains brand styles, ensures visual consistency across different prompts, and provides fine-grained control over brand elements while preventing style drift."

4. Real-time Collaborative Editing

"Design a collaborative image synthesis platform where multiple users can edit the same image simultaneously. Handle real-time updates, conflict resolution, version control, and seamless merging of different editing operations while maintaining generation quality."

5. Multi-Resolution Progressive Generation

"Implement progressive generation that starts with low-resolution previews and iteratively increases quality. How do you design the multi-scale architecture, handle user interactions during generation, and optimize for both speed and final quality?"

6. Cross-Platform Model Optimization

"Your image synthesis needs to run on web browsers, mobile apps, and edge devices with varying computational capabilities. Design model optimization strategies, adaptive quality settings, and seamless cloud-edge hybrid processing for consistent user experience."