Prompt-Aware Text-to-Image Inference Routing for Quality-Throughput Balance
Find Innovative SolutionsGenerate Solutions
Solution Overview
Problem
Existing systems face inefficiencies in handling high volumes of input requests from generative models, leading to latency issues, quality degradation, and inflexibility in output generation, particularly when scaling to larger networks.
Innovation Solution
The prompt-aware accuracy-scaling content inference system dynamically manages input prompts by utilizing varying approximation levels and historical affinities to direct prompts to appropriate generative model variants, minimizing quality degradation and reducing model switching overhead.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Manufacturing precision
If high approximation levels are used for all prompts, then output quality is maintained, but throughput decreases and latency increases
Solution Approach 1:
The patent applies local quality by differentiating approximation levels based on prompt characteristics. Prompts are classified into different categories (e.g., complex vs. simple, high-quality vs. acceptable-quality) and assigned different approximation levels accordingly. This allows the system to maintain high output quality for prompts that require it while using lower approximation levels for prompts where quality can be compromised, thereby increasing overall throughput.
Solution Approach 2:
The system dynamically adjusts the approximation level assigned to each prompt based on real-time factors including prompt load distribution, historical prompt affinities, and current system state. The approximation parameter is not fixed but adapts dynamically to balance quality and throughput requirements for each individual prompt based on its characteristics and the current system state.
2Productivity
If multiple model variants are used to handle high loads, then throughput increases, but model switching overhead increases
Solution Approach 1:
Instead of switching between different model variants, the patent changes the approximation parameter of a single generative model dynamically. By adjusting parameters such as the number of denoising iterations or other controllable model settings, the system achieves different performance levels without the overhead of model switching. This allows throughput scaling while avoiding the complexity of managing multiple model variants.
3Speed
If approximation levels are increased to speed up inference, then latency decreases, but output quality degrades
Solution Approach 1:
The system applies partial action by using lower approximation levels (which perform fewer denoising iterations or use simpler processing) for prompts where maximum quality is not critical. This partial processing achieves sufficient quality for many use cases while significantly reducing latency, accepting that not all prompts will achieve maximum quality but that the average quality-throughput tradeoff is optimized.
4Device complexity
If prompts are distributed uniformly across all model variants, then system simplicity is maintained, but efficiency decreases under high load
Solution Approach 1:
The system performs preliminary classification of prompts before processing, using historical prompt affinity data and prompt characteristics to pre-determine the optimal approximation level. This preliminary action allows the system to make intelligent routing decisions in advance, assigning prompts to appropriate processing levels before execution, thereby improving efficiency without requiring complex real-time decision-making during inference.
Data Source
AI summary
This disclosure describes one or more implementations of systems that utilizes prompt-aware, accuracy-scaling inference serving to serve prompts into generative models. For instance, the disclosed systems utilize varying approximation levels in generative models to speed up model output inferences. For example, the disclosed systems determine a set of approximation parameters for a set of generative models based on a predicted input prompt load. The disclosed systems generate an input prompt distribution mapping utilizing a historical prompt affinity mapping to the set of generative models and a prompt load distribution for the set of generative models. The disclosed systems select, for an input prompt, a generative model corresponding to a particular approximation parameter based on the input prompt distribution mapping and an approximation parameter assignment for the input prompt. The disclosed systems generate an inference output for the input prompt by utilizing the input prompt with the generative model.