Performance Optimization

This section covers the main performance levers for SGLang Diffusion: attention backends, caching acceleration, and profiling.

Overview

<table style={{width: "100%", borderCollapse: "collapse", tableLayout: "fixed"}}> <colgroup> <col style={{width: "22%"}} /> <col style={{width: "18%"}} /> <col style={{width: "60%"}} /> </colgroup> <thead> <tr style={{borderBottom: "2px solid #d55816"}}> <th style={{textAlign: "left", padding: "10px 12px", fontWeight: 700, whiteSpace: "nowrap", backgroundColor: "rgba(255,255,255,0.02)"}}>Optimization</th> <th style={{textAlign: "left", padding: "10px 12px", fontWeight: 700, whiteSpace: "nowrap", backgroundColor: "rgba(255,255,255,0.05)"}}>Type</th> <th style={{textAlign: "left", padding: "10px 12px", fontWeight: 700, whiteSpace: "nowrap", backgroundColor: "rgba(255,255,255,0.02)"}}>Description</th> </tr> </thead> <tbody> <tr> <td style={{padding: "9px 12px", fontWeight: 500, backgroundColor: "rgba(255,255,255,0.02)"}}>Cache-DiT</td> <td style={{padding: "9px 12px", backgroundColor: "rgba(255,255,255,0.05)"}}>Caching</td> <td style={{padding: "9px 12px", backgroundColor: "rgba(255,255,255,0.02)"}}>Block-level caching with DBCache, TaylorSeer, and SCM</td> </tr> <tr> <td style={{padding: "9px 12px", fontWeight: 500, backgroundColor: "rgba(255,255,255,0.02)"}}>TeaCache</td> <td style={{padding: "9px 12px", backgroundColor: "rgba(255,255,255,0.05)"}}>Caching</td> <td style={{padding: "9px 12px", backgroundColor: "rgba(255,255,255,0.02)"}}>Timestep-level caching based on temporal similarity</td> </tr> <tr> <td style={{padding: "9px 12px", fontWeight: 500, backgroundColor: "rgba(255,255,255,0.02)"}}>Attention Backends</td> <td style={{padding: "9px 12px", backgroundColor: "rgba(255,255,255,0.05)"}}>Kernel</td> <td style={{padding: "9px 12px", backgroundColor: "rgba(255,255,255,0.02)"}}>Optimized attention implementations (FlashAttention, SageAttention, etc.)</td> </tr> <tr> <td style={{padding: "9px 12px", fontWeight: 500, backgroundColor: "rgba(255,255,255,0.02)"}}>Profiling</td> <td style={{padding: "9px 12px", backgroundColor: "rgba(255,255,255,0.05)"}}>Diagnostics</td> <td style={{padding: "9px 12px", backgroundColor: "rgba(255,255,255,0.02)"}}>PyTorch Profiler and Nsight Systems guidance</td> </tr> </tbody> </table>

Start Here

• Use Attention Backends to choose the best backend for your model and hardware.
• Use Caching Acceleration to reduce denoising cost with Cache-DiT or TeaCache.
• Use Profiling when you need to diagnose a bottleneck rather than guess.

Caching at a Glance

• Cache-DiT is block-level caching for diffusers pipelines and higher speedup-oriented tuning.
• TeaCache is timestep-level caching built into SGLang model families.

Current Baseline Snapshot

For Ring SP benchmark details, see:

• Ring SP Performance

References

• Cache-DiT Repository
• TeaCache Paper