--- title: "Performance Optimization" description: "Optimize SGLang diffusion performance with caching, kernels, and profiling." --- SGLang-Diffusion provides multiple performance optimization strategies to accelerate inference. This section covers all available performance tuning options. ## Overview

Optimization	Type	Description
Cache-DiT	Caching	Block-level caching with DBCache, TaylorSeer, and SCM
TeaCache	Caching	Timestep-level caching using L1 similarity
Attention Backends	Kernel	Optimized attention implementations (FlashAttention, SageAttention, etc.)
Profiling	Diagnostics	PyTorch Profiler and Nsight Systems guidance

## Caching Strategies SGLang supports two complementary caching approaches: ### Cache-DiT [Cache-DiT](https://github.com/vipshop/cache-dit) provides block-level caching with advanced strategies. It can achieve up to **1.69x speedup**. **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 See [Cache-DiT documentation](./cache-dit) for detailed configuration. ### TeaCache TeaCache (Temporal similarity-based caching) accelerates diffusion inference by detecting when consecutive denoising steps are similar enough to skip computation entirely. **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 See [TeaCache documentation](./tea-cache) for detailed configuration. ## Attention Backends Different attention backends offer varying performance characteristics depending on your hardware and model: - **FlashAttention**: Fastest on NVIDIA GPUs with fp16/bf16 - **SageAttention**: Alternative optimized implementation - **xformers**: Memory-efficient attention - **SDPA**: PyTorch native scaled dot-product attention See [Attention backends](./attention-backends) for platform support and configuration options. ## Profiling To diagnose performance bottlenecks, SGLang-Diffusion supports profiling tools: - **PyTorch Profiler**: Built-in Python profiling - **Nsight Systems**: GPU kernel-level analysis See [Profiling guide](./profiling) for detailed instructions. ## References - [Cache-DiT Repository](https://github.com/vipshop/cache-dit) - [TeaCache Paper](https://arxiv.org/abs/2411.14324)