> ## Documentation Index > Fetch the complete documentation index at: https://docs.sglang.io/llms.txt > Use this file to discover all available pages before exploring further. # 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 [caching](./caching-acceleration). 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 theme={null} rel_l1 = |current - previous|.mean() / |previous|.mean() ``` This distance is then rescaled using polynomial coefficients and accumulated: ```text theme={null} 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 theme={null} 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](https://arxiv.org/abs/2411.14324)