# DiffServe: Efficiently Serving Text-to-Image Diffusion Models with Query-Aware Model Scaling

arXiv: https://arxiv.org/abs/2411.15381 · HTML: https://mlsanity.com/paper/2411.15381/

## Key facts

- Cited by 4 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 22 Nov 2024.

## Built on by (most cited first, 4 of 4)

- [db-SP: Accelerating Sparse Attention for Visual Generative Models with Dual-Balanced Sequence Parallelism](https://mlsanity.com/paper/2511.23113/) (2025)
- [GF-DiT: Scheduling Parallelism for Diffusion Transformer Serving](https://mlsanity.com/paper/2606.13501/) (2026)
- [DiLaServe: High SLO Attainment Serving for Diffusion Language Models](https://mlsanity.com/paper/2606.29094/) (2026)
- [StreamWise: Serving Multi-Modal Generation in Real-Time at Scale](https://mlsanity.com/paper/2603.05800/) (2026)

## Abstract (arXiv)

Text-to-image generation using diffusion models has gained increasing popularity due to their ability to produce high-quality, realistic images based on text prompts. However, efficiently serving these models is challenging due to their computation-intensive nature and the variation in query demands. In this paper, we aim to address both problems simultaneously through query-aware model scaling. The core idea is to construct model cascades so that easy queries can be processed by more lightweight diffusion models without compromising image generation quality. Based on this concept, we develop an end-to-end text-to-image diffusion model serving system, DiffServe, which automatically constructs model cascades from available diffusion model variants and allocates resources dynamically in response to demand fluctuations. Our empirical evaluations demonstrate that DiffServe achieves up to 24% improvement in response quality while maintaining 19-70% lower latency violation rates compared to state-of-the-art model serving systems.