Designing GenAI Infrastructure: How to Scale Video Generation

Key Insights on Scaling Generative AI Systems

Scaling a generative AI system, particularly one that involves video generation using large models like Stable Diffusion or DALL-E, presents unique challenges and opportunities. Here are the key takeaways from this deep dive into the architecture, data flow, and scaling strategies for such systems.

Architecture Overview

1. Job-Queue-Worker Pattern:

   • Never use synchronous APIs: Implement a Job-Queue-Worker pattern to manage asynchronous requests efficiently.
   • Components:
     • Prompt Processor: Cleans input, applies safety filters, and expands prompts if necessary.
     • Sampling Loop: Iteratively removes noise from latent representations using techniques like FlashAttention for memory optimization.
     • VAE Decoder: Converts latent space back into pixel data.

2. Data Flow Lifecycle:

   • Input Processing: Clean and filter the input prompt.
   • Diffusion Process: Iterative sampling to remove noise from a latent representation.
   • Decoding: Use VAE to decode latents into actual pixels.

Scaling Strategies

1. Latency vs. Throughput:
   • Continuous Batching: Slot new requests into the GPU's processing

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