SynerDiff: Synergetic Continuous Batching for Fast and Parallel Diffusion Model Inference
The expansion of Artificial Intelligence-generated content service requires diffusion model serving to simultaneously achieve high throughput and low task end-to-end (E2E) latency. However, existing continuous batching methods suffer from severe resource contention during UNet-VAE concurrency, leading to latency spikes. Furthermore, concurrent multi-task scheduling entails a trade-off between UNet throughput and VAE latency across varying scheduling strategies. To address these, we propose SynerDiff, an efficient continuous batching system built on intra-inter level synergy. At the intra-concurrency level, SynerDiff alleviates resource contention by pruning component-specific resource bot...
| Authors | Ziqi Zhou, Peng Yang, Yuxin Liang et al. |
|---|---|
| Primary category | cs.AI |
| Published | 9 May 2026 |
| Last updated at source | 9 May 2026 |
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