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MixFusion: A Patch-Level Parallel Serving System for Mixed-Resolution Diffusion Models
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AI
DOI：10.1145/3774934.3786420.png)
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Abstract
En 中文
Text-to-Image (T2I) diffusion models have recently attracted significant attention due to their ability to synthesize highfidelity photorealistic images. However, serving diffusion models would suffer from hardware underutilization in realworld settings due to highly variable request resolutions. To this end, we present MixFusion, a parallel serving System that exploits fine-grained patch-level parallelism to enable efficient batching of mixed-resolution requests. Specifically, MixFusion introduces a novel patch-based processing workflow, significantly enabling concurrent processing across heterogeneous requests. Furthermore, MixFusion incorporates a patch-tailored cache management policy to exploit the patch-level locality benefits. In addition, MixFusion features an SLO-aware scheduling strategy with lightweight online latency prediction. Extensive evaluation demonstrates that MixFusion achieves 30.1% higher SLO satisfaction compared to the state-of-the-art solutions on average. Our code is available at https://github.com/desenSunUBW/mixfusion.
Keywords:
Patch Management
Diffusion Model Serving
Mixed-Resolution Batching
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