[diffusion] docs: consolidate diffusion documentation into docs (#18095)

Co-authored-by: gemini-code-assist[bot] <176961590+gemini-code-assist[bot]@users.noreply.github.com> Co-authored-by: JiaxinD <djx2048@gmail.com>
2026-02-11 16:55:07 -08:00
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+# 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](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/cache_dit.md) 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](cache/teacache.md) 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.md) 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.md) for detailed instructions.
+
+## References
+
+- [Cache-DiT Repository](https://github.com/vipshop/cache-dit)
+- [TeaCache Paper](https://arxiv.org/abs/2411.14324)