Cost-Aware Routing for Efficient Text-To-Image Generation
Qinchan Li, Kenneth Chen, Changyue Su, Wittawat Jitkrittum, Qi Sun, Patsorn Sangkloy

TL;DR
This paper introduces a cost-aware routing framework for text-to-image diffusion models that dynamically allocates computational resources based on prompt complexity, balancing image quality and efficiency.
Contribution
It proposes a novel routing approach that selects among multiple models or denoising steps to optimize quality and cost for each prompt, outperforming single-model methods.
Findings
Achieves higher average image quality than individual models on COCO and DiffusionDB.
Effectively reserves high-cost computation for complex prompts, saving resources on simpler ones.
Demonstrates the benefit of multi-model routing over uniform cost reduction techniques.
Abstract
Diffusion models are well known for their ability to generate a high-fidelity image for an input prompt through an iterative denoising process. Unfortunately, the high fidelity also comes at a high computational cost due the inherently sequential generative process. In this work, we seek to optimally balance quality and computational cost, and propose a framework to allow the amount of computation to vary for each prompt, depending on its complexity. Each prompt is automatically routed to the most appropriate text-to-image generation function, which may correspond to a distinct number of denoising steps of a diffusion model, or a disparate, independent text-to-image model. Unlike uniform cost reduction techniques (e.g., distillation, model quantization), our approach achieves the optimal trade-off by learning to reserve expensive choices (e.g., 100+ denoising steps) only for a few…
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Taxonomy
MethodsDiffusion
