minke.yu/sglang
1
0
Fork 0
Code Issues Pull Requests Actions 12 Packages Projects Releases Wiki Activity

[diffusion] doc: consolidate documentation (#21373)

Browse Source
This commit is contained in:
Mick
2026-03-25 16:01:32 +08:00
committed by GitHub
parent f5c225eeba
commit 6425df5c8a
15 changed files with 204 additions and 376 deletions
Download Patch File Download Diff File Expand all files Collapse all files
docs/diffusion/performance/index.md
+16 -52
View File
@@ -1,71 +1,35 @@
# Performance Optimization
# Performance
SGLang-Diffusion provides multiple performance optimization strategies to accelerate inference. This section covers all available performance tuning options.
This section covers the main performance levers for SGLang Diffusion: attention backends, caching acceleration, and profiling.
## Overview
| Optimization | Type | Description |
|--------------|------|-------------|
| **Cache-DiT** | Caching | Block-level caching with DBCache, TaylorSeer, and SCM |
| **TeaCache** | Caching | Timestep-level caching using L1 similarity |
| **TeaCache** | Caching | Timestep-level caching based on temporal similarity |
| **Attention Backends** | Kernel | Optimized attention implementations (FlashAttention, SageAttention, etc.) |
| **Profiling** | Diagnostics | PyTorch Profiler and Nsight Systems guidance |
## Caching Strategies
## Start Here
SGLang supports two complementary caching approaches:
- Use [Attention Backends](attention_backends.md) to choose the best backend for your model and hardware.
- Use [Caching Acceleration](cache/index.md) to reduce denoising cost with Cache-DiT or TeaCache.
- Use [Profiling](profiling.md) when you need to diagnose a bottleneck rather than guess.
### Cache-DiT
## Caching at a Glance
[Cache-DiT](https://github.com/vipshop/cache-dit) provides block-level caching with advanced strategies. It can achieve up to **1.69x speedup**.
- [Cache-DiT](cache/cache_dit.md) is block-level caching for diffusers pipelines and higher speedup-oriented tuning.
- [TeaCache](cache/teacache.md) is timestep-level caching built into SGLang model families.
**Quick Start:**
```bash
SGLANG_CACHE_DIT_ENABLED=true \
sglang generate --model-path Qwen/Qwen-Image \
--prompt "A beautiful sunset over the mountains"
```{toctree}
:maxdepth: 1
attention_backends
cache/index
profiling
```
**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)