SynerDiff: Synergetic Continuous Batching for Fast and Parallel Diffusion Model Inference
Researchers have introduced SynerDiff, a novel continuous batching system designed to optimize the serving of diffusion models for AI-generated content. As demand for such services grows, existing methods struggle with resource contention between UNet and VAE components, causing latency spikes and throughput trade-offs. SynerDiff addresses these challenges through intra-inter level synergy. At the intra-concurrency level, it reduces bottlenecks using VAE Chunking and Adaptive Skip-CFG techniques. At the inter-concurrency level, a threshold-aware scheduler manages concurrent sequences to minimize VAE latency while maintaining high UNet throughput, supported by a feedback controller that dynamically adjusts thresholds based on queue loads. Experimental results demonstrate significant performance improvements, with SynerDiff achieving a 1.6x increase in throughput and reducing both average end-to-end and P99 tail latencies by up to 78.7% compared to benchmarks, all while preserving high image fidelity. This development represents a significant step forward in efficient AI model inference infrastructure.
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SynerDiff: Synergetic Continuous Batching for Fast and Parallel Diffusion Model Inference
Researchers have introduced SynerDiff, a novel continuous batching system designed to optimize the serving of diffusion models for AI-generated content. As demand for such services grows, existing methods struggle with resource contention between UNet and VAE components, causing latency spikes and throughput trade-offs. SynerDiff addresses these challenges through intra-inter level synergy. At the intra-concurrency level, it reduces bottlenecks using VAE Chunking and Adaptive Skip-CFG techniques. At the inter-concurrency level, a threshold-aware scheduler manages concurrent sequences to minimize VAE latency while maintaining high UNet throughput, supported by a feedback controller that dynamically adjusts thresholds based on queue loads. Experimental results demonstrate significant performance improvements, with SynerDiff achieving a 1.6x increase in throughput and reducing both average end-to-end and P99 tail latencies by up to 78.7% compared to benchmarks, all while preserving high image fidelity. This development represents a significant step forward in efficient AI model inference infrastructure.
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