TeaCache Acceleration

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.

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:

rel_l1 = |current - previous|.mean() / |previous|.mean()

This distance is then rescaled using polynomial coefficients and accumulated:

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:

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

Parameter	Type	Description
teacache_thresh	float	Threshold for accumulated L1 distance. Lower = more caching, faster but potentially lower quality
coefficients	list[float]	Polynomial coefficients for L1 rescaling. Model-specific tuning

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:

Model Family	CFG Cache Separation	Notes
Wan (wan2.1, wan2.2)	Yes	Full support
Hunyuan (HunyuanVideo)	Yes	To be supported
Z-Image	Yes	To be supported
Flux	No	To be supported
Qwen	No	To be supported

References

• TeaCache: Accelerating Diffusion Models with Temporal Similarity