[Docs] Sync docs_new with legacy docs and update migration redirects (#23337)

Co-authored-by: Mingyi <wisclmy0611@gmail.com>

docs_new/docs/sglang-diffusion/performance-optimization.mdx

@@ -2,8 +2,7 @@
 title: "Performance Optimization"
 description: "Optimize SGLang diffusion performance with caching, kernels, and profiling."
 ---
-
-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
 
@@ -29,7 +28,7 @@ SGLang-Diffusion provides multiple performance optimization strategies to accele
     <tr>
       <td style={{padding: "9px 12px", fontWeight: 500, backgroundColor: "rgba(255,255,255,0.02)"}}>TeaCache</td>
       <td style={{padding: "9px 12px", backgroundColor: "rgba(255,255,255,0.05)"}}>Caching</td>
-      <td style={{padding: "9px 12px", backgroundColor: "rgba(255,255,255,0.02)"}}>Timestep-level caching using L1 similarity</td>
+      <td style={{padding: "9px 12px", backgroundColor: "rgba(255,255,255,0.02)"}}>Timestep-level caching based on temporal similarity</td>
     </tr>
     <tr>
       <td style={{padding: "9px 12px", fontWeight: 500, backgroundColor: "rgba(255,255,255,0.02)"}}>Attention Backends</td>
@@ -44,60 +43,23 @@ SGLang-Diffusion provides multiple performance optimization strategies to accele
   </tbody>
 </table>
 
-## Caching Strategies
+## Start Here
 
-SGLang supports two complementary caching approaches:
+- Use [Attention Backends](./attention_backends) to choose the best backend for your model and hardware.
+- Use [Caching Acceleration](./caching-acceleration) to reduce denoising cost with Cache-DiT or TeaCache.
+- Use [Profiling](./profiling) 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_dit) is block-level caching for diffusers pipelines and higher speedup-oriented tuning.
+- [TeaCache](./teacache) 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"
-```
 
-**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
+## Current Baseline Snapshot
 
-See [Cache-DiT documentation](./cache-dit) for detailed configuration.
+For Ring SP benchmark details, see:
 
-### 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](./tea-cache) 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) 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) for detailed instructions.
+- [Ring SP Performance](./ring_sp_performance)
 
 ## References