SwiftDiffusion: Efficient Diffusion Model Serving with Add-on Modules
<|reference_start|>SwiftDiffusion: Efficient Diffusion Model Serving with Add-on Modules: This paper documents our characterization study and practices for serving text-to-image requests with stable diffusion models in production. We first comprehensively analyze inference request traces for commercial text-to-image applications. It commences with our observation that add-on modules, i.e., ControlNets and LoRAs, that augment the base stable diffusion models, are ubiquitous in generating images for commercial applications. Despite their efficacy, these add-on modules incur high loading overhead, prolong the serving latency, and swallow up expensive GPU resources. Driven by our characterization study, we present SwiftDiffusion, a system that efficiently generates high-quality images using stable diffusion models and add-on modules. To achieve this, SwiftDiffusion reconstructs the existing text-to-image serving workflow by identifying the opportunities for parallel computation and distributing ControlNet computations across multiple GPUs. Further, SwiftDiffusion thoroughly analyzes the dynamics of image generation and develops techniques to eliminate the overhead associated with LoRA loading and patching while preserving the image quality. Last, SwiftDiffusion proposes specialized optimizations in the backbone architecture of the stable diffusion models, which are also compatible with the efficient serving of add-on modules. Compared to state-of-the-art text-to-image serving systems, SwiftDiffusion reduces serving latency by up to 5x and improves serving throughput by up to 2x without compromising image quality.<|reference_end|>
@article{li2024swiftdiffusion:,
    title={SwiftDiffusion: Efficient Diffusion Model Serving with Add-on Modules},
    author={Suyi Li, Lingyun Yang, Xiaoxiao Jiang, Hanfeng Lu, Zhipeng Di, Weiyi
  Lu, Jiawei Chen, Kan Liu, Yinghao Yu, Tao Lan, Guodong Yang, Lin Qu, Liping
  Zhang, Wei Wang},
    journal={arXiv preprint arXiv:2407.02031},
    year={2024},
    archivePrefix={arXiv},
    eprint={2407.02031},
    primaryClass={cs.DC cs.AI cs.LG}
}