--- 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:

Strategy	Scope	Mechanism	Best For
Cache-DiT	Block-level	Skip individual transformer blocks dynamically	Advanced, higher speedup
TeaCache	Timestep-level	Skip entire denoising steps based on L1 similarity	Simple, built-in

## 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)