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

SynerDiff is a new continuous batching system for diffusion model inference that addresses resource contention issues between UNet and VAE components. The system achieves 1.6× throughput improvement and up to 78.7% latency reduction through intra-level and inter-level optimization strategies, enabling faster AI-generated content services.

SynerDiff represents a meaningful engineering advancement in diffusion model serving infrastructure, addressing a critical bottleneck in AI-generated content delivery systems. The research tackles a genuine technical problem: existing continuous batching approaches create resource contention when UNet and VAE components operate concurrently, causing unpredictable latency spikes that degrade user experience in production environments. This directly impacts the viability of scalable generative AI services.

The solution employs a two-tiered optimization approach. At the intra-concurrency level, techniques like VAE Chunking and Adaptive Skip-CFG reduce resource competition between components. At the inter-concurrency level, the threshold-aware scheduler intelligently orchestrates task scheduling while a feedback controller dynamically adapts based on system load. This architectural sophistication demonstrates how infrastructure optimization can unlock significant performance gains.

For the AI infrastructure industry, SynerDiff's results matter substantially. A 1.6× throughput improvement and 78.7% latency reduction directly translate to lower operational costs and better user experience for services like image generation APIs. Companies operating diffusion models at scale—whether in creative tools, e-commerce, or content platforms—face real cost pressures from inference expenses. Such optimizations reduce the gap between research-grade performance and production requirements.

The work exemplifies the increasing focus on inference optimization as diffusion models become standard infrastructure. Beyond the specific technical contributions, the research highlights how careful system design around component-level dynamics can yield outsized performance gains. Future development will likely focus on extending these techniques across different model architectures and hardware configurations.

• →SynerDiff reduces diffusion model inference latency by up to 78.7% while improving throughput by 1.6× through dual-level optimization
• →The system addresses resource contention between UNet and VAE components using adaptive scheduling and component-specific pruning techniques
• →Threshold-aware scheduling and feedback control dynamically balance throughput requirements with latency constraints based on queue conditions
• →Results demonstrate that system-level architectural improvements can deliver substantial gains in AI infrastructure efficiency and cost-effectiveness
• →The approach maintains image generation fidelity while achieving production-grade performance metrics across both average and tail latencies