SynerDiff: Synergetic Continuous Batching for Fast and Parallel Diffusion Model Inference

• URL: http://arxiv.org/abs/2605.08835v1
• Date: Sat, 09 May 2026 09:39:39 GMT
• Title: SynerDiff: Synergetic Continuous Batching for Fast and Parallel Diffusion Model Inference
• Abstract summary: Existing continuous methods suffer from severe resource contention during UNet-VAE.<n>We propose SynerDiff, an efficient continuous system built on intra-inter level synergy.
• Score: 8.251374398589906
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• Abstract: 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 bottlenecks via VAE Chunking and Adaptive Skip-CFG. At the inter-concurrency level, leveraging components' differential sensitivity to scheduling granularities, a threshold-aware scheduler plans concurrent sequences and tunes intra-concurrency decisions to minimize VAE latency while maintaining UNet within high-throughput threshold. Additionally, a feedback controller dynamically adjusts this threshold based on queue loads to boost system capacity ceiling. Experimental results show that, SynerDiff improves throughput by 1.6$\times$ and decreases both average E2E and P99 tail latencies by up to 78.7\%, compared to benchmarks while guaranteeing high image fidelity.

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