This one is massive and I underestimated the scope going in.
Start by clarifying requirements and constraints, then propose a high-level architecture that separates the text encoder, video generation model, and serving infrastructure. Dive into key components like diffusion transformers, spatiotemporal compression, and scalable inference, while discussing trade-offs in quality, latency, and cost.
Pro tip: Emphasize how you would handle the massive compute and memory demands of video generation, such as using latent diffusion and model parallelism, and discuss how to ensure safety and alignment in generated content.
Ask about expected video length, resolution, generation time, and scale (e.g., number of requests per day). Clarify if the system needs to support fine-tuning or customization.
Outline the main components: text encoder (e.g., CLIP), video generation model (e.g., diffusion transformer), and serving layer. Explain how they interact and the data flow.
Discuss the model architecture, such as using a diffusion model in latent space with spatiotemporal attention. Cover training data, compute requirements, and techniques like progressive distillation for faster inference.
Describe how to deploy the model for low-latency inference, including optimizations like model parallelism, quantization, and caching. Address load balancing and autoscaling.
Discuss trade-offs between quality, speed, and cost. Mention safety measures like content filtering and watermarking to prevent misuse.
AI-generated suggestions, not part of the candidate's original notes. May be inaccurate — verify before relying on them.