[diffusion] doc: consolidate documentation (#21373)

docs/diffusion/performance/cache/cache_dit.md

@@ -1,4 +1,4 @@
-# Cache-DiT Acceleration
+# Cache-DiT
 
 SGLang integrates [Cache-DiT](https://github.com/vipshop/cache-dit), a caching acceleration engine for Diffusion Transformers (DiT), to achieve up to **1.69x inference speedup** with minimal quality loss.
 

docs/diffusion/performance/cache/index.md

@@ -1,6 +1,6 @@
-# Caching Acceleration for Diffusion Models
+# Caching Acceleration
 
-SGLang provides multiple caching acceleration strategies for Diffusion Transformer (DiT) models. These strategies can significantly reduce inference time by skipping redundant computation.
+SGLang provides two complementary caching strategies for Diffusion Transformer (DiT) models. Both reduce denoising cost by skipping redundant computation, but they operate at different levels.
 
 ## Overview
 
@@ -11,8 +11,6 @@ SGLang supports two complementary caching approaches:
 | **Cache-DiT** | Block-level | Skip individual transformer blocks dynamically | Advanced, higher speedup |
 | **TeaCache** | Timestep-level | Skip entire denoising steps based on L1 similarity | Simple, built-in |
 
-
-
 ## Cache-DiT
 
 [Cache-DiT](https://github.com/vipshop/cache-dit) provides block-level caching with
@@ -54,6 +52,13 @@ See [teacache.md](teacache.md) for detailed documentation.
 
 For Flux and Qwen models, TeaCache is automatically disabled when CFG is enabled.
 
+```{toctree}
+:maxdepth: 1
+
+cache_dit
+teacache
+```
+
 ## References
 
 - [Cache-DiT Repository](https://github.com/vipshop/cache-dit)

docs/diffusion/performance/cache/teacache.md

@@ -1,4 +1,4 @@
-# TeaCache Acceleration
+# TeaCache
 
 > **Note**: This is one of two caching strategies available in SGLang.
 > For an overview of all caching options, see [caching](../index.md).

docs/diffusion/performance/index.md

@@ -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)