Update benchmark

Update benchmark

docs/EN/source/getting_started/benchmark.md

 # Benchmark
 
-xxx
+For a better display of video playback effects and detailed performance comparisons, you can get better presentation and corresponding documentation content on this [🔗 page](https://github.com/ModelTC/LightX2V/blob/main/docs/EN/source/getting_started/benchmark_source.md).

docs/EN/source/getting_started/benchmark_source.md

+# Benchmark
+
+---
+
+## H200 (~140GB VRAM)
+
+**Software Environment:**
+- Python 3.11
+- PyTorch 2.7.1+cu128
+- SageAttention 2.2.0
+- vLLM 0.9.2
+- sgl-kernel 0.1.8
+
+### 480P 5s Video
+
+**Test Configuration:**
+- **Model**: [Wan2.1-I2V-14B-480P-Lightx2v](https://huggingface.co/lightx2v/Wan2.1-I2V-14B-480P-Lightx2v)
+- **Parameters**: infer_steps=40, seed=42, enable_cfg=True
+
+#### Performance Comparison
+
+| Configuration | Model Load Time(s) | Inference Time(s) | GPU Memory(GB) | Speedup | Video Effect |
+|:-------------|:------------------:|:-----------------:|:--------------:|:-------:|:------------:|
+| Wan2.1 Official(baseline) | 68.26 | 366.04 | 71 | 1.0x | <video src="PATH_TO_BASELINE_480P_VIDEO" width="200px"></video> |
+| **LightX2V_1** | 37.28 | 249.54 | 53 | **1.47x** | <video src="PATH_TO_LIGHTX2V_1_480P_VIDEO" width="200px"></video> |
+| **LightX2V_2** | 37.24 | 216.16 | 50 | **1.69x** | <video src="PATH_TO_LIGHTX2V_2_480P_VIDEO" width="200px"></video> |
+| **LightX2V_3** | 23.62 | 190.73 | 35 | **1.92x** | <video src="PATH_TO_LIGHTX2V_3_480P_VIDEO" width="200px"></video> |
+| **LightX2V_4** | 23.62 | 107.19 | 35 | **3.41x** | <video src="PATH_TO_LIGHTX2V_4_480P_VIDEO" width="200px"></video> |
+
+### 720P 5s Video
+
+**Test Configuration:**
+- **Model**: [Wan2.1-I2V-14B-720P-Lightx2v](https://huggingface.co/lightx2v/Wan2.1-I2V-14B-720P-Lightx2v)
+- **Parameters**: infer_steps=40, seed=42, enable_cfg=True
+
+*Coming soon...*
+
+---
+
+## RTX 4090 (~24GB VRAM)
+
+### 480P 5s Video
+
+*Coming soon...*
+
+### 720P 5s Video
+
+*Coming soon...*
+
+---
+
+## Table Descriptions
+
+- **Wan2.1 Official(baseline)**: Baseline implementation based on [Wan2.1 official repository](https://github.com/Wan-Video/Wan2.1)
+- **LightX2V_1**: Uses SageAttention2 to replace native attention mechanism with DIT BF16+FP32 mixed precision (sensitive layers), improving computational efficiency while maintaining precision
+- **LightX2V_2**: Unified BF16 precision computation to further reduce memory usage and computational overhead while maintaining generation quality
+- **LightX2V_3**: Quantization optimization introducing FP8 quantization technology to significantly reduce computational precision requirements, combined with Tiling VAE technology to optimize memory usage
+- **LightX2V_4**: Ultimate optimization adding TeaCache (teacache_thresh=0.2) caching reuse technology on top of LightX2V_3 to achieve maximum acceleration by intelligently skipping redundant computations

docs/ZH_CN/source/getting_started/benchmark.md

 # 基准测试
 
-xxx
+由于要展示一些视频的播放效果和详细的性能对比，您可以在这个[🔗 页面](https://github.com/ModelTC/LightX2V/blob/main/docs/ZH_CN/source/getting_started/benchmark_source.md)获得更好的展示效果以及相对应的文档内容。

docs/ZH_CN/source/getting_started/benchmark_source.md

+# 基准测试
+
+---
+
+## H200 (~140GB显存)
+
+**软件环境配置:**
+- Python 3.11
+- PyTorch 2.7.1+cu128
+- SageAttention 2.2.0
+- vLLM 0.9.2
+- sgl-kernel 0.1.8
+
+### 480P 5s视频
+
+**测试配置:**
+- **模型**: [Wan2.1-I2V-14B-480P-Lightx2v](https://huggingface.co/lightx2v/Wan2.1-I2V-14B-480P-Lightx2v)
+- **参数**: infer_steps=40, seed=42, enable_cfg=True
+
+#### 性能对比
+
+| 配置 | 模型加载时间(s) | 推理时间(s) | GPU显存占用(GB) | 加速比 | 视频效果 |
+|:-----|:---------------:|:----------:|:---------------:|:------:|:--------:|
+| Wan2.1 Official(baseline) | 68.26 | 366.04 | 71 | 1.0x | <video src="PATH_TO_BASELINE_480P_VIDEO" width="200px"></video> |
+| **LightX2V_1** | 37.28 | 249.54 | 53 | **1.47x** | <video src="PATH_TO_LIGHTX2V_1_480P_VIDEO" width="200px"></video> |
+| **LightX2V_2** | 37.24 | 216.16 | 50 | **1.69x** | <video src="PATH_TO_LIGHTX2V_2_480P_VIDEO" width="200px"></video> |
+| **LightX2V_3** | 23.62 | 190.73 | 35 | **1.92x** | <video src="PATH_TO_LIGHTX2V_3_480P_VIDEO" width="200px"></video> |
+| **LightX2V_4** | 23.62 | 107.19 | 35 | **3.41x** | <video src="PATH_TO_LIGHTX2V_4_480P_VIDEO" width="200px"></video> |
+
+### 720P 5s视频
+
+**测试配置:**
+- **模型**: [Wan2.1-I2V-14B-720P-Lightx2v](https://huggingface.co/lightx2v/Wan2.1-I2V-14B-720P-Lightx2v)
+- **参数**: infer_steps=40, seed=42, enable_cfg=True
+
+*即将更新...*
+
+---
+
+## RTX 4090 (~24GB显存)
+
+### 480P 5s视频
+
+*即将更新...*
+
+### 720P 5s视频
+
+*即将更新...*
+
+---
+
+## 表格说明
+
+- **Wan2.1 Official(baseline)**: 基于[Wan2.1官方仓库](https://github.com/Wan-Video/Wan2.1)的基线实现
+- **LightX2V_1**: 使用SageAttention2替换原生注意力机制，采用DIT BF16+FP32(部分敏感层)混合精度计算，在保持精度的同时提升计算效率
+- **LightX2V_2**: 统一使用BF16精度计算，进一步减少显存占用和计算开销，同时保持生成质量
+- **LightX2V_3**: 引入FP8量化技术显著减少计算精度要求，结合Tiling VAE技术优化显存使用
+- **LightX2V_4**: 在LightX2V_3基础上加入TeaCache(teacache_thresh=0.2)缓存复用技术，通过智能跳过冗余计算实现最大加速