Caching Acceleration 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 provides block-level caching with advanced strategies like DBCache and TaylorSeer. It can achieve up to 1.69x speedup.
See cache_dit.md for detailed configuration.
Quick Start
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.md 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.