System Design: Image Generation and Multimodal Generation

Part 1 — End-to-End Image Generation System

Design an end-to-end image generation system. Cover the following:

1. Data collection and curation
   • Sources and licensing strategy
   • Deduplication and near-duplicate removal
   • Content filtering (NSFW, violence, watermarks, PII)
   • Captioning/annotations and multilingual support
2. Model architecture choices
   • Diffusion vs. autoregressive (AR) vs. hybrid
   • Conditioning (text, style, ControlNet-like signals) and resolution scaling
3. Training objectives and losses
4. Compute and throughput planning
5. Safety and content filtering (pre-, in-, and post-training)
6. Evaluation metrics (quality, diversity, prompt adherence, bias/fairness)
7. Inference optimization and deployment
   • Caching, batching, quantization, distillation/acceleration
8. Cost controls (tiers, rate limits, autoscaling)
9. Monitoring and observability

Part 2 — Extend to Multimodal Text-and-Image Generation

Extend the design to a system that can accept and produce both modalities (text and images). Address:

1. Multimodal data collection and alignment
2. Architectures for cross-modal fusion
3. Training strategies (pretraining, instruction tuning, RLHF/RLAIF)
4. Knowledge updating and retrieval augmentation
5. Product constraints (latency targets, guardrails, feedback loops)

Paper Deep-Dive

Pick a recent, relevant paper and walk through:

• Key idea and architecture
• Experimental setup and datasets
• Metrics and results
• Trade-offs and limitations
• How you would adapt or productionize the approach in a real system