Design image and multimodal generation systems

Q: Design image and multimodal generation systems

This question evaluates a candidate's competence in designing end-to-end image and multimodal generation systems, covering data collection and curation, model architecture and conditioning choices, training objectives, safety and content filtering, evaluation metrics, deployment, monitoring, and critical analysis of relevant research.

Q: How do I approach ML System Design interview questions?

ML System Design questions require understanding of core concepts and practice. PracHub provides solutions with explanations to help you master ml system design interviews.

Question

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:

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

Multimodal data collection and alignment
Architectures for cross-modal fusion
Training strategies (pretraining, instruction tuning, RLHF/RLAIF)
Knowledge updating and retrieval augmentation
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

Design image and multimodal generation systems

System Design: Image Generation and Multimodal Generation

Part 1 — End-to-End Image Generation System

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

Paper Deep-Dive

Solution

Comments (0)

Design image and multimodal generation systems

Overview

System Design: Image Generation and Multimodal Generation

Part 1 — End-to-End Image Generation System

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

Paper Deep-Dive

Solution

Comments (0)