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SGLang-Diffusion provides multiple performance optimization strategies to accelerate inference. This section covers all available performance tuning options.

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Overview

OptimizationTypeDescription
Cache-DiTCachingBlock-level caching with DBCache, TaylorSeer, and SCM
TeaCacheCachingTimestep-level caching using L1 similarity
Attention BackendsKernelOptimized attention implementations (FlashAttention, SageAttention, etc.)
ProfilingDiagnosticsPyTorch Profiler and Nsight Systems guidance

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Caching Strategies

SGLang supports two complementary caching approaches:

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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.

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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.

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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.

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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.

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References