sglang/docs_new/docs/sglang-diffusion/caching-acceleration.mdx

---
title: "Caching Acceleration"
description: "Compare caching acceleration strategies for diffusion models."
---

SGLang provides multiple caching acceleration strategies for Diffusion Transformer (DiT) models. These strategies can significantly reduce inference time by skipping redundant computation.

## Overview

SGLang supports two complementary caching approaches:

<table style={{width: "100%", borderCollapse: "collapse", tableLayout: "fixed"}}>
  <colgroup>
    <col style={{width: "18%"}} />
    <col style={{width: "18%"}} />
    <col style={{width: "42%"}} />
    <col style={{width: "22%"}} />
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    <tr style={{borderBottom: "2px solid #d55816"}}>
      <th style={{textAlign: "left", padding: "10px 12px", fontWeight: 700, whiteSpace: "nowrap", backgroundColor: "rgba(255,255,255,0.02)"}}>Strategy</th>
      <th style={{textAlign: "left", padding: "10px 12px", fontWeight: 700, whiteSpace: "nowrap", backgroundColor: "rgba(255,255,255,0.05)"}}>Scope</th>
      <th style={{textAlign: "left", padding: "10px 12px", fontWeight: 700, whiteSpace: "nowrap", backgroundColor: "rgba(255,255,255,0.02)"}}>Mechanism</th>
      <th style={{textAlign: "left", padding: "10px 12px", fontWeight: 700, whiteSpace: "nowrap", backgroundColor: "rgba(255,255,255,0.05)"}}>Best For</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)"}}>Block-level</td>
      <td style={{padding: "9px 12px", backgroundColor: "rgba(255,255,255,0.02)"}}>Skip individual transformer blocks dynamically</td>
      <td style={{padding: "9px 12px", backgroundColor: "rgba(255,255,255,0.05)"}}>Advanced, higher speedup</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)"}}>Timestep-level</td>
      <td style={{padding: "9px 12px", backgroundColor: "rgba(255,255,255,0.02)"}}>Skip entire denoising steps based on L1 similarity</td>
      <td style={{padding: "9px 12px", backgroundColor: "rgba(255,255,255,0.05)"}}>Simple, built-in</td>
    </tr>
  </tbody>
</table>


## Cache-DiT

[Cache-DiT](https://github.com/vipshop/cache-dit) provides block-level caching with
advanced strategies like DBCache and TaylorSeer. It can achieve up to **1.69x speedup**.

See [Cache-DiT](./cache-dit) for detailed configuration.

### Quick Start

```bash
SGLANG_CACHE_DIT_ENABLED=true \
sglang generate --model-path Qwen/Qwen-Image \
    --prompt "A beautiful sunset over the mountains"
```

### Key Features

- **DBCache**: Dynamic block-level caching based on residual differences
- **TaylorSeer**: Taylor expansion-based calibration for optimized caching
- **SCM**: Step-level computation masking for additional speedup

## TeaCache

TeaCache (Temporal similarity-based caching) accelerates diffusion inference by detecting when consecutive denoising steps are similar enough to skip computation entirely.

See [TeaCache](./tea-cache) for detailed documentation.

### Quick Overview

- Tracks L1 distance between modulated inputs across timesteps
- When accumulated distance is below threshold, reuses cached residual
- Supports CFG with separate positive/negative caches

### Supported Models

- Wan (wan2.1, wan2.2)
- Hunyuan (HunyuanVideo)
- Z-Image

For Flux and Qwen models, TeaCache is automatically disabled when CFG is enabled.

## References

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