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---
title: "TeaCache Acceleration"
description: "Configure TeaCache for temporal similarity-based diffusion acceleration."
---
> **Note**: This is one of two caching strategies available in SGLang.
> For an overview of all caching options, see [SGLang diffusion overview](../../sglang-diffusion/intro).
TeaCache (Temporal similarity-based caching) accelerates diffusion inference by detecting when consecutive denoising steps are similar enough to skip computation entirely.
## Overview
TeaCache works by:
1. Tracking the L1 distance between modulated inputs across consecutive timesteps
2. Accumulating the rescaled L1 distance over steps
3. When accumulated distance is below a threshold, reusing the cached residual
4. Supporting CFG (Classifier-Free Guidance) with separate positive/negative caches
## How It Works
### L1 Distance Tracking
At each denoising step, TeaCache computes the relative L1 distance between the current and previous modulated inputs:
```text
rel_l1 = |current - previous|.mean() / |previous|.mean()
```
This distance is then rescaled using polynomial coefficients and accumulated:
```text
accumulated += poly(coefficients)(rel_l1)
```
### Cache Decision
- If `accumulated >= threshold`: Force computation, reset accumulator
- If `accumulated < threshold`: Skip computation, use cached residual
### CFG Support
For models that support CFG cache separation (Wan, Hunyuan, Z-Image), TeaCache maintains separate caches for positive and negative branches:
- `previous_modulated_input` / `previous_residual` for positive branch
- `previous_modulated_input_negative` / `previous_residual_negative` for negative branch
For models that don't support CFG separation (Flux, Qwen), TeaCache is automatically disabled when CFG is enabled.
## Configuration
TeaCache is configured via `TeaCacheParams` in the sampling parameters:
```python
from sglang.multimodal_gen.configs.sample.teacache import TeaCacheParams
params = TeaCacheParams(
teacache_thresh=0.1, # Threshold for accumulated L1 distance
coefficients=[1.0, 0.0, 0.0], # Polynomial coefficients for L1 rescaling
)
```
### Parameters
<table style={{width: "100%", borderCollapse: "collapse", tableLayout: "fixed"}}>
<colgroup>
<col style={{width: "28%"}} />
<col style={{width: "14%"}} />
<col style={{width: "58%"}} />
</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)"}}>Parameter</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)"}}>`teacache_thresh`</td>
<td style={{padding: "9px 12px", backgroundColor: "rgba(255,255,255,0.05)"}}>float</td>
<td style={{padding: "9px 12px", backgroundColor: "rgba(255,255,255,0.02)"}}>Threshold for accumulated L1 distance. Lower = more caching, faster but potentially lower quality</td>
</tr>
<tr>
<td style={{padding: "9px 12px", fontWeight: 500, backgroundColor: "rgba(255,255,255,0.02)"}}>`coefficients`</td>
<td style={{padding: "9px 12px", backgroundColor: "rgba(255,255,255,0.05)"}}>list[float]</td>
<td style={{padding: "9px 12px", backgroundColor: "rgba(255,255,255,0.02)"}}>Polynomial coefficients for L1 rescaling. Model-specific tuning</td>
</tr>
</tbody>
</table>
### Model-Specific Configurations
Different models may have different optimal configurations. The coefficients are typically tuned per-model to balance speed and quality.
## Supported Models
TeaCache is built into the following model families:
<table style={{width: "100%", borderCollapse: "collapse", tableLayout: "fixed"}}>
<colgroup>
<col style={{width: "34%"}} />
<col style={{width: "28%"}} />
<col style={{width: "38%"}} />
</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)"}}>Model Family</th>
<th style={{textAlign: "left", padding: "10px 12px", fontWeight: 700, whiteSpace: "nowrap", backgroundColor: "rgba(255,255,255,0.05)"}}>CFG Cache Separation</th>
<th style={{textAlign: "left", padding: "10px 12px", fontWeight: 700, whiteSpace: "nowrap", backgroundColor: "rgba(255,255,255,0.02)"}}>Notes</th>
</tr>
</thead>
<tbody>
<tr>
<td style={{padding: "9px 12px", fontWeight: 500, backgroundColor: "rgba(255,255,255,0.02)"}}>Wan (wan2.1, wan2.2)</td>
<td style={{padding: "9px 12px", backgroundColor: "rgba(255,255,255,0.05)"}}>Yes</td>
<td style={{padding: "9px 12px", backgroundColor: "rgba(255,255,255,0.02)"}}>Full support</td>
</tr>
<tr>
<td style={{padding: "9px 12px", fontWeight: 500, backgroundColor: "rgba(255,255,255,0.02)"}}>Hunyuan (HunyuanVideo)</td>
<td style={{padding: "9px 12px", backgroundColor: "rgba(255,255,255,0.05)"}}>Yes</td>
<td style={{padding: "9px 12px", backgroundColor: "rgba(255,255,255,0.02)"}}>To be supported</td>
</tr>
<tr>
<td style={{padding: "9px 12px", fontWeight: 500, backgroundColor: "rgba(255,255,255,0.02)"}}>Z-Image</td>
<td style={{padding: "9px 12px", backgroundColor: "rgba(255,255,255,0.05)"}}>Yes</td>
<td style={{padding: "9px 12px", backgroundColor: "rgba(255,255,255,0.02)"}}>To be supported</td>
</tr>
<tr>
<td style={{padding: "9px 12px", fontWeight: 500, backgroundColor: "rgba(255,255,255,0.02)"}}>Flux</td>
<td style={{padding: "9px 12px", backgroundColor: "rgba(255,255,255,0.05)"}}>No</td>
<td style={{padding: "9px 12px", backgroundColor: "rgba(255,255,255,0.02)"}}>To be supported</td>
</tr>
<tr>
<td style={{padding: "9px 12px", fontWeight: 500, backgroundColor: "rgba(255,255,255,0.02)"}}>Qwen</td>
<td style={{padding: "9px 12px", backgroundColor: "rgba(255,255,255,0.05)"}}>No</td>
<td style={{padding: "9px 12px", backgroundColor: "rgba(255,255,255,0.02)"}}>To be supported</td>
</tr>
</tbody>
</table>
## References
- [TeaCache: Accelerating Diffusion Models with Temporal Similarity](https://arxiv.org/abs/2411.14324)