SynerDiff: New System for Fast Diffusion Model Inference
A new system called SynerDiff aims to improve the efficiency of diffusion model serving for AI-generated content. It addresses resource contention during UNet-VAE concurrency and optimizes multi-task scheduling. SynerDiff uses intra-inter level synergy, including VAE Chunking and Adaptive Skip-CFG at the intra-concurrency level, and a threshold-aware scheduler at the inter-concurrency level. The system is described in a paper on arXiv (2605.08835).
Key facts
- SynerDiff is a continuous batching system for diffusion model inference.
- It targets high throughput and low end-to-end latency.
- It addresses resource contention during UNet-VAE concurrency.
- It uses intra-inter level synergy.
- Intra-concurrency level includes VAE Chunking and Adaptive Skip-CFG.
- Inter-concurrency level uses a threshold-aware scheduler.
- The paper is available on arXiv with ID 2605.08835.
- The system is designed for AI-generated content services.
Entities
Institutions
- arXiv