AI RESEARCH
SynerDiff: Synergetic Continuous Batching for Fast and Parallel Diffusion Model Inference
arXiv CS.AI
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ArXi:2605.08835v1 Announce Type: new 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.