Cost-Aware Routing for Efficient Text-To-Image Generation

• URL: http://arxiv.org/abs/2506.14753v2
• Date: Mon, 23 Jun 2025 00:44:17 GMT
• Title: Cost-Aware Routing for Efficient Text-To-Image Generation
• Authors: Qinchan Li, Kenneth Chen, Changyue Su, Wittawat Jitkrittum, Qi Sun, Patsorn Sangkloy,
• Abstract summary: We propose a framework to allow the amount of computation to vary for each prompt, depending on its complexity.<n>We empirically demonstrate on COCO and DiffusionDB that by learning to route to nine already-trained text-to-image models, our approach is able to deliver an average quality that is higher than that achievable by any of these models alone.
• Score: 19.848723289971208
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• 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 complex prompts, and employ more economical choices (e.g., small distilled model) for less sophisticated prompts. We empirically demonstrate on COCO and DiffusionDB that by learning to route to nine already-trained text-to-image models, our approach is able to deliver an average quality that is higher than that achievable by any of these models alone.

Related papers

• FlexiDiT: Your Diffusion Transformer Can Easily Generate High-Quality Samples with Less Compute [25.151209708074134]
  Our framework enables pre-trained DiT models to be converted into emphflexible ones -- dubbed FlexiDiT.<n>We demonstrate how a single emphflexible model can generate images without any drop in quality.<n>We show how our approach can be readily extended for video generation, where FlexiDiT models generate samples with up to $75$% less compute.
  arXiv  Detail & Related papers  (2025-02-27T14:16:56Z)
• Prompt-Aware Scheduling for Efficient Text-to-Image Inferencing System [6.305230222189566]
  This work introduces a novel text-to-image inference system that optimally matches prompts across multiple instances of the same model operating at various approximation levels to deliver high-quality images under high loads and fixed budgets.
  arXiv  Detail & Related papers  (2025-01-29T03:17:48Z)
• PassionSR: Post-Training Quantization with Adaptive Scale in One-Step Diffusion based Image Super-Resolution [95.98801201266099]
  Diffusion-based image super-resolution (SR) models have shown superior performance at the cost of multiple denoising steps.<n>We propose a novel post-training quantization approach with adaptive scale in one-step diffusion (OSD) image SR, PassionSR.<n>Our PassionSR achieves significant advantages over recent leading low-bit quantization methods for image SR.
  arXiv  Detail & Related papers  (2024-11-26T04:49:42Z)
• Fast constrained sampling in pre-trained diffusion models [77.21486516041391]
  We propose an algorithm that enables fast and high-quality generation under arbitrary constraints.<n>During inference, we can interchange between gradient updates computed on the noisy image and updates computed on the final, clean image.<n>Our approach produces results that rival or surpass the state-of-the-art training-free inference approaches.
  arXiv  Detail & Related papers  (2024-10-24T14:52:38Z)
• A Simple Approach to Unifying Diffusion-based Conditional Generation [63.389616350290595]
  We introduce a simple, unified framework to handle diverse conditional generation tasks.<n>Our approach enables versatile capabilities via different inference-time sampling schemes.<n>Our model supports additional capabilities like non-spatially aligned and coarse conditioning.
  arXiv  Detail & Related papers  (2024-10-15T09:41:43Z)
• MMAR: Towards Lossless Multi-Modal Auto-Regressive Probabilistic Modeling [64.09238330331195]
  We propose a novel Multi-Modal Auto-Regressive (MMAR) probabilistic modeling framework.<n>Unlike discretization line of method, MMAR takes in continuous-valued image tokens to avoid information loss in an efficient way.<n>We also propose a theoretically proven technique that addresses the numerical stability issue and a training strategy that balances the generation and understanding task goals.
  arXiv  Detail & Related papers  (2024-10-14T17:57:18Z)
• Step Saver: Predicting Minimum Denoising Steps for Diffusion Model Image Generation [0.6906005491572401]
  This paper introduces an innovative NLP model to determine the minimal number of denoising steps required for any given text prompt. It is designed to work seamlessly with the Diffusion model, ensuring that images are produced with superior quality in the shortest possible time.
  arXiv  Detail & Related papers  (2024-08-04T15:01:23Z)
• One-Step Diffusion Distillation via Deep Equilibrium Models [64.11782639697883]
  We introduce a simple yet effective means of distilling diffusion models directly from initial noise to the resulting image. Our method enables fully offline training with just noise/image pairs from the diffusion model. We demonstrate that the DEQ architecture is crucial to this capability, as GET matches a $5times$ larger ViT in terms of FID scores.
  arXiv  Detail & Related papers  (2023-12-12T07:28:40Z)
• AdaDiff: Adaptive Step Selection for Fast Diffusion Models [82.78899138400435]
  We introduce AdaDiff, a lightweight framework designed to learn instance-specific step usage policies.<n>AdaDiff is optimized using a policy method to maximize a carefully designed reward function.<n>We conduct experiments on three image generation and two video generation benchmarks and demonstrate that our approach achieves similar visual quality compared to the baseline.
  arXiv  Detail & Related papers  (2023-11-24T11:20:38Z)
• BOOT: Data-free Distillation of Denoising Diffusion Models with Bootstrapping [64.54271680071373]
  Diffusion models have demonstrated excellent potential for generating diverse images. Knowledge distillation has been recently proposed as a remedy that can reduce the number of inference steps to one or a few. We present a novel technique called BOOT, that overcomes limitations with an efficient data-free distillation algorithm.
  arXiv  Detail & Related papers  (2023-06-08T20:30:55Z)

This list is automatically generated from the titles and abstracts of the papers in this site.

This site does not guarantee the quality of this site (including all information) and is not responsible for any consequences.