update docs

docs/EN/source/method_tutorials/step_distill.md

-# 步数蒸馏
+# 🚀 Step Distillation
 
-xxx
+Step distillation is an important optimization technique in LightX2V. By training distilled models, it significantly reduces inference steps from the original 40-50 steps to **4 steps**, dramatically improving inference speed while maintaining video quality. LightX2V implements step distillation along with CFG distillation to further enhance inference speed.
+
+## 🔍 Technical Principle
+
+Step distillation is implemented through [Self-Forcing](https://github.com/guandeh17/Self-Forcing) technology. Self-Forcing performs step distillation and CFG distillation on 1.3B autoregressive models. LightX2V extends it with a series of enhancements:
+
+1. **Larger Models**: Supports step distillation training for 14B models;
+2. **More Model Types**: Supports standard bidirectional models and I2V model step distillation training;
+
+For detailed implementation, refer to [Self-Forcing-Plus](https://github.com/GoatWu/Self-Forcing-Plus).
+
+## 🎯 Technical Features
+
+- **Inference Acceleration**: Reduces inference steps from 40-50 to 4 steps without CFG, achieving approximately **20-24x** speedup
+- **Quality Preservation**: Maintains original video generation quality through distillation techniques
+- **Strong Compatibility**: Supports both T2V and I2V tasks
+- **Flexible Usage**: Supports loading complete step distillation models or loading step distillation LoRA on top of native models
+
+## 🛠️ Configuration Files
+
+### Basic Configuration Files
+
+Multiple configuration options are provided in the [configs/distill/](https://github.com/ModelTC/lightx2v/tree/main/configs/distill) directory:
+
+| Configuration File | Purpose | Model Address |
+|-------------------|---------|---------------|
+| [wan_t2v_distill_4step_cfg.json](https://github.com/ModelTC/lightx2v/blob/main/configs/distill/wan_t2v_distill_4step_cfg.json) | Load T2V 4-step distillation complete model | TODO |
+| [wan_i2v_distill_4step_cfg.json](https://github.com/ModelTC/lightx2v/blob/main/configs/distill/wan_i2v_distill_4step_cfg.json) | Load I2V 4-step distillation complete model | TODO |
+| [wan_t2v_distill_4step_cfg_lora.json](https://github.com/ModelTC/lightx2v/blob/main/configs/distill/wan_t2v_distill_4step_cfg_lora.json) | Load Wan-T2V model and step distillation LoRA | TODO |
+| [wan_i2v_distill_4step_cfg_lora.json](https://github.com/ModelTC/lightx2v/blob/main/configs/distill/wan_i2v_distill_4step_cfg_lora.json) | Load Wan-I2V model and step distillation LoRA | TODO |
+
+### Key Configuration Parameters
+
+```json
+{
+  "infer_steps": 4,                              // Inference steps
+  "denoising_step_list": [999, 750, 500, 250],   // Denoising timestep list
+  "enable_cfg": false,                           // Disable CFG for speed improvement
+  "lora_path": [                                 // LoRA weights path (optional)
+    "path/to/distill_lora.safetensors"
+  ]
+}
+```
+
+## 📜 Usage
+
+### Complete Step Distillation Model Inference Scripts
+
+**T2V:**
+```bash
+bash scripts/wan/run_wan_t2v_distill_4step_cfg.sh
+```
+
+**I2V:**
+```bash
+bash scripts/wan/run_wan_i2v_distill_4step_cfg.sh
+```
+
+### Step Distillation LoRA Inference Scripts
+
+**T2V LoRA:**
+```bash
+bash scripts/wan/run_wan_t2v_distill_4step_cfg_lora.sh
+```
+
+**I2V LoRA:**
+```bash
+bash scripts/wan/run_wan_i2v_distill_4step_cfg_lora.sh
+```
+
+## 🔧 Service Deployment
+
+### Start Distillation Model Service
+
+Modify the startup command in [scripts/server/start_server.sh](https://github.com/ModelTC/lightx2v/blob/main/scripts/server/start_server.sh):
+
+```bash
+python -m lightx2v.api_server \
+  --model_cls wan2.1_distill \
+  --task t2v \
+  --model_path $model_path \
+  --config_json ${lightx2v_path}/configs/distill/wan_t2v_distill_4step_cfg.json \
+  --port 8000 \
+  --nproc_per_node 1
+```
+
+Run the service startup script:
+
+```bash
+scripts/server/start_server.sh
+```
+
+For more details, see [Service Deployment](https://lightx2v-en.readthedocs.io/en/latest/deploy_guides/deploy_service.html).
+
+### Usage in Gradio Interface
+
+See [Gradio Documentation](https://lightx2v-en.readthedocs.io/en/latest/deploy_guides/deploy_gradio.html)

docs/ZH_CN/source/method_tutorials/step_distill.md

-# 步数蒸馏
+# 🚀 步数蒸馏
 
-xxx
+步数蒸馏是 LightX2V 中的一项重要优化技术，通过训练蒸馏模型将推理步数从原始的 40-50 步大幅减少到 **4 步**，在保持视频质量的同时显著提升推理速度。LightX2V 在实现步数蒸馏的同时也加入了 CFG 蒸馏，进一步提升推理速度。
+
+## 🔍 技术原理
+
+步数蒸馏通过 [Self-Forcing](https://github.com/guandeh17/Self-Forcing) 技术实现。Self-Forcing 针对 1.3B 的自回归模型进行步数蒸馏、CFG蒸馏。LightX2V 在其基础上，进行了一系列扩展：
+
+1. **更大的模型**：支持 14B 模型的步数蒸馏训练；
+2. **更多的模型**：支持标准的双向模型，以及 I2V 模型的步数蒸馏训练；
+
+具体实现可参考 [Self-Forcing-Plus](https://github.com/GoatWu/Self-Forcing-Plus)。
+
+## 🎯 技术特性
+
+- **推理加速**：推理步数从 40-50 步减少到 4 步且无需 CFG，速度提升约 **20-24x**
+- **质量保持**：通过蒸馏技术保持原有的视频生成质量
+- **兼容性强**：支持 T2V 和 I2V 任务
+- **使用灵活**：支持加载完整步数蒸馏模型，或者在原生模型的基础上加载步数蒸馏LoRA
+
+## 🛠️ 配置文件说明
+
+### 基础配置文件
+
+在 [configs/distill/](https://github.com/ModelTC/lightx2v/tree/main/configs/distill) 目录下提供了多种配置选项：
+
+| 配置文件 | 用途 | 模型地址 |
+|----------|------|------------|
+| [wan_t2v_distill_4step_cfg.json](https://github.com/ModelTC/lightx2v/blob/main/configs/distill/wan_t2v_distill_4step_cfg.json) | 加载 T2V 4步蒸馏完整模型 | TODO |
+| [wan_i2v_distill_4step_cfg.json](https://github.com/ModelTC/lightx2v/blob/main/configs/distill/wan_i2v_distill_4step_cfg.json) | 加载 I2V 4步蒸馏完整模型 | TODO |
+| [wan_t2v_distill_4step_cfg_lora.json](https://github.com/ModelTC/lightx2v/blob/main/configs/distill/wan_t2v_distill_4step_cfg_lora.json) | 加载 Wan-T2V 模型和步数蒸馏 LoRA | TODO |
+| [wan_i2v_distill_4step_cfg_lora.json](https://github.com/ModelTC/lightx2v/blob/main/configs/distill/wan_i2v_distill_4step_cfg_lora.json) | 加载 Wan-I2V 模型和步数蒸馏 LoRA | TODO |
+
+### 关键配置参数
+
+```json
+{
+  "infer_steps": 4,                              // 推理步数
+  "denoising_step_list": [999, 750, 500, 250],   // 去噪时间步列表
+  "enable_cfg": false,                           // 关闭CFG以提升速度
+  "lora_path": [                                 // LoRA权重路径（可选）
+    "path/to/distill_lora.safetensors"
+  ]
+}
+```
+
+## 📜 使用方法
+
+### 完整步数蒸馏模型推理脚本
+
+**T2V：**
+```bash
+bash scripts/wan/run_wan_t2v_distill_4step_cfg.sh
+```
+
+**I2V：**
+```bash
+bash scripts/wan/run_wan_i2v_distill_4step_cfg.sh
+```
+
+### 步数蒸馏 LoRA 推理脚本
+
+**T2V LoRA：**
+```bash
+bash scripts/wan/run_wan_t2v_distill_4step_cfg_lora.sh
+```
+
+**I2V LoRA：**
+```bash
+bash scripts/wan/run_wan_i2v_distill_4step_cfg_lora.sh
+```
+
+## 🔧 服务化部署
+
+### 启动蒸馏模型服务
+
+对 [scripts/server/start_server.sh](https://github.com/ModelTC/lightx2v/blob/main/scripts/server/start_server.sh) 中的启动命令进行修改：
+
+```bash
+python -m lightx2v.api_server \
+  --model_cls wan2.1_distill \
+  --task t2v \
+  --model_path $model_path \
+  --config_json ${lightx2v_path}/configs/distill/wan_t2v_distill_4step_cfg.json \
+  --port 8000 \
+  --nproc_per_node 1
+```
+
+运行服务启动脚本：
+
+```bash
+scripts/server/start_server.sh
+```
+
+更多详细信息见[服务化部署](https://lightx2v-zhcn.readthedocs.io/zh-cn/latest/deploy_guides/deploy_service.html)。
+
+### 在 Gradio 界面中使用
+
+见 [Gradio 文档](https://lightx2v-zhcn.readthedocs.io/zh-cn/latest/deploy_guides/deploy_gradio.html)