SynerDiff: Synergetic Continuous Batching for Fast and Parallel Diffusion Model Inference
In the rapidly evolving field of artificial intelligence, the demand for efficient content generation is surging. A recent paper, titled “SynerDiff: Synergetic Continuous Batching for Fast and Parallel Diffusion Model Inference,” introduces a novel approach to enhance the performance of diffusion models, addressing critical challenges in throughput and latency.
The paper, available on arXiv (2605.08835v1), highlights the limitations of existing continuous batching methods, which often encounter significant resource contention during the concurrent operation of UNet and VAE components. These limitations can lead to increased end-to-end (E2E) latency, undermining the efficiency required for real-time applications. As AI-generated content services expand, the need for a solution that ensures high throughput while minimizing latency has become paramount.
Challenges in Current Systems
Current continuous batching techniques struggle with two primary issues:
- Resource Contention: During concurrent UNet-VAE operations, resource contention can lead to latency spikes that disrupt service quality.
- Multi-Task Scheduling Trade-offs: Balancing UNet throughput and VAE latency across various scheduling strategies presents a significant challenge, often resulting in suboptimal performance.
The SynerDiff Solution
SynerDiff addresses these challenges through a dual-level synergy approach, incorporating both intra-concurrency and inter-concurrency strategies to optimize resource allocation and scheduling.
Intra-Concurrency Improvements
At the intra-concurrency level, SynerDiff employs two key innovations:
- VAE Chunking: This technique involves segmenting the VAE workload into manageable chunks, thereby reducing resource bottlenecks and improving processing efficiency.
- Adaptive Skip-CFG: By dynamically adjusting the configuration of VAE components, this method enhances throughput while keeping latency at an acceptable level.
Inter-Concurrency Enhancements
At the inter-concurrency level, SynerDiff implements a threshold-aware scheduler that considers the differential sensitivity of components to various scheduling granularities. This scheduler:
- Plans concurrent sequences of operations to optimize performance.
- Tunes intra-concurrency decisions to ensure VAE latency is minimized while maintaining UNet throughput above a high-threshold level.
Additionally, a feedback controller is included to dynamically adjust the throughput threshold based on real-time queue loads, effectively enhancing the system’s capacity without compromising performance.
Experimental Results
Experimental evaluations of SynerDiff demonstrate its efficacy in improving performance metrics significantly:
- Throughput increased by 1.6 times compared to existing benchmarks.
- Average E2E latency and P99 tail latencies reduced by up to 78.7%.
- Consistent high image fidelity maintained throughout the process.
Conclusion
SynerDiff represents a significant advancement in the field of AI-driven content generation, effectively tackling the challenges of resource contention and latency in diffusion model inference. By leveraging intra-inter level synergies, this system not only enhances throughput but also ensures rapid and reliable performance, paving the way for more robust AI applications in the future.
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