---
title: "Performance Optimization"
description: "Optimize SGLang diffusion performance with caching, kernels, and profiling."
---
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%"}} />
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  <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](./attention_backends) to choose the best backend for your model and hardware.
- Use [Caching Acceleration](./caching-acceleration) to reduce denoising cost with Cache-DiT or TeaCache.
- Use [Profiling](./profiling) when you need to diagnose a bottleneck rather than guess.

## Caching at a Glance

- [Cache-DiT](./cache_dit) is block-level caching for diffusers pipelines and higher speedup-oriented tuning.
- [TeaCache](./teacache) is timestep-level caching built into SGLang model families.


## Current Baseline Snapshot

For Ring SP benchmark details, see:

- [Ring SP Performance](./ring_sp_performance)

## References

- [Cache-DiT Repository](https://github.com/vipshop/cache-dit)
- [TeaCache Paper](https://arxiv.org/abs/2411.14324)