Performance Optimization
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 provides block-level caching with advanced strategies. It can achieve up to 1.69x speedup.
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
See Cache-DiT Documentation 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 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 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 for detailed instructions.