update docs

docs/EN/source/deploy_guides/lora_deploy.md

-# Lora模型部署
+# LoRA Model Deployment and Related Tools
 
-xxx
+LoRA (Low-Rank Adaptation) is an efficient model fine-tuning technique that significantly reduces the number of trainable parameters through low-rank matrix decomposition. LightX2V fully supports LoRA technology, including LoRA inference, LoRA extraction, and LoRA merging functions.
+
+## 🎯 LoRA Technical Features
+
+- **Efficient Fine-tuning**: Dramatically reduces training parameters through low-rank adaptation
+- **Flexible Deployment**: Supports dynamic loading and removal of LoRA weights
+- **Multiple Formats**: Supports various LoRA weight formats and naming conventions
+- **Comprehensive Tools**: Provides complete LoRA extraction and merging toolchain
+
+## 📜 LoRA Inference Deployment
+
+### Configuration File Method
+
+Specify LoRA path in configuration file:
+
+```json
+{
+  "lora_path": [
+    "/path/to/your/lora.safetensors"
+  ],
+  "strength_model": 1.0
+}
+```
+
+**Configuration Parameter Description:**
+- `lora_path`: LoRA weight file path list, supports loading multiple LoRAs simultaneously
+- `strength_model`: LoRA strength coefficient (alpha), controls LoRA's influence on the original model
+
+### Command Line Method
+
+Specify LoRA path directly in command line (supports loading single LoRA only):
+
+```bash
+python -m lightx2v.infer \
+  --model_cls wan2.1 \
+  --task t2v \
+  --model_path /path/to/model \
+  --config_json /path/to/config.json \
+  --lora_path /path/to/your/lora.safetensors \
+  --prompt "Your prompt here"
+```
+
+### Supported LoRA Formats
+
+LightX2V supports multiple LoRA weight naming conventions:
+
+| Format Type | Weight Naming | Description |
+|-------------|---------------|-------------|
+| **Standard LoRA** | `lora_A.weight`, `lora_B.weight` | Standard LoRA matrix decomposition format |
+| **Down/Up Format** | `lora_down.weight`, `lora_up.weight` | Another common naming convention |
+| **Diff Format** | `diff` | `weight` difference values |
+| **Bias Diff** | `diff_b` | `bias` weight difference values |
+| **Modulation Diff** | `diff_m` | `modulation` weight difference values |
+
+### Inference Script Examples
+
+**Step Distillation LoRA Inference:**
+
+```bash
+# T2V LoRA Inference
+bash scripts/wan/run_wan_t2v_distill_4step_cfg_lora.sh
+
+# I2V LoRA Inference
+bash scripts/wan/run_wan_i2v_distill_4step_cfg_lora.sh
+```
+
+**Audio-Driven LoRA Inference:**
+
+```bash
+bash scripts/wan/run_wan_i2v_audio.sh
+```
+
+### Using LoRA in API Service
+
+Specify through [config file](wan_t2v_distill_4step_cfg_lora.json), 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_lora.json \
+  --port 8000 \
+  --nproc_per_node 1
+```
+
+## 🔧 LoRA Extraction Tool
+
+Use `tools/extract/lora_extractor.py` to extract LoRA weights from the difference between two models.
+
+### Basic Usage
+
+```bash
+python tools/extract/lora_extractor.py \
+  --source-model /path/to/base/model \
+  --target-model /path/to/finetuned/model \
+  --output /path/to/extracted/lora.safetensors \
+  --rank 32
+```
+
+### Parameter Description
+
+| Parameter | Type | Required | Default | Description |
+|-----------|------|----------|---------|-------------|
+| `--source-model` | str | ✅ | - | Base model path |
+| `--target-model` | str | ✅ | - | Fine-tuned model path |
+| `--output` | str | ✅ | - | Output LoRA file path |
+| `--source-type` | str | ❌ | `safetensors` | Base model format (`safetensors`/`pytorch`) |
+| `--target-type` | str | ❌ | `safetensors` | Fine-tuned model format (`safetensors`/`pytorch`) |
+| `--output-format` | str | ❌ | `safetensors` | Output format (`safetensors`/`pytorch`) |
+| `--rank` | int | ❌ | `32` | LoRA rank value |
+| `--output-dtype` | str | ❌ | `bf16` | Output data type |
+| `--diff-only` | bool | ❌ | `False` | Save weight differences only, without LoRA decomposition |
+
+### Advanced Usage Examples
+
+**Extract High-Rank LoRA:**
+```bash
+python tools/extract/lora_extractor.py \
+  --source-model /path/to/base/model \
+  --target-model /path/to/finetuned/model \
+  --output /path/to/high_rank_lora.safetensors \
+  --rank 64 \
+  --output-dtype fp16
+```
+
+**Save Weight Differences Only:**
+```bash
+python tools/extract/lora_extractor.py \
+  --source-model /path/to/base/model \
+  --target-model /path/to/finetuned/model \
+  --output /path/to/weight_diff.safetensors \
+  --diff-only
+```
+
+## 🔀 LoRA Merging Tool
+
+Use `tools/extract/lora_merger.py` to merge LoRA weights into the base model for subsequent quantization and other operations.
+
+### Basic Usage
+
+```bash
+python tools/extract/lora_merger.py \
+  --source-model /path/to/base/model \
+  --lora-model /path/to/lora.safetensors \
+  --output /path/to/merged/model.safetensors \
+  --alpha 1.0
+```
+
+### Parameter Description
+
+| Parameter | Type | Required | Default | Description |
+|-----------|------|----------|---------|-------------|
+| `--source-model` | str | ✅ | - | Base model path |
+| `--lora-model` | str | ✅ | - | LoRA weights path |
+| `--output` | str | ✅ | - | Output merged model path |
+| `--source-type` | str | ❌ | `safetensors` | Base model format |
+| `--lora-type` | str | ❌ | `safetensors` | LoRA weights format |
+| `--output-format` | str | ❌ | `safetensors` | Output format |
+| `--alpha` | float | ❌ | `1.0` | LoRA merge strength |
+| `--output-dtype` | str | ❌ | `bf16` | Output data type |
+
+### Advanced Usage Examples
+
+**Partial Strength Merging:**
+```bash
+python tools/extract/lora_merger.py \
+  --source-model /path/to/base/model \
+  --lora-model /path/to/lora.safetensors \
+  --output /path/to/merged_model.safetensors \
+  --alpha 0.7 \
+  --output-dtype fp32
+```
+
+**Multi-Format Support:**
+```bash
+python tools/extract/lora_merger.py \
+  --source-model /path/to/base/model.pt \
+  --source-type pytorch \
+  --lora-model /path/to/lora.safetensors \
+  --lora-type safetensors \
+  --output /path/to/merged_model.safetensors \
+  --output-format safetensors \
+  --alpha 1.0
+```

docs/ZH_CN/source/deploy_guides/lora_deploy.md

-# Lora模型部署
+# LoRA 模型部署与相关工具
 
-xxx
+LoRA (Low-Rank Adaptation) 是一种高效的模型微调技术，通过低秩矩阵分解显著减少可训练参数数量。LightX2V 全面支持 LoRA 技术，包括 LoRA 推理、LoRA 提取和 LoRA 合并等功能。
+
+## 🎯 LoRA 技术特性
+
+- **灵活部署**：支持动态加载和移除 LoRA 权重
+- **多种格式**：支持多种 LoRA 权重格式和命名约定
+- **工具完善**：提供完整的 LoRA 提取、合并工具链
+
+## 📜 LoRA 推理部署
+
+### 配置文件方式
+
+在配置文件中指定 LoRA 路径：
+
+```json
+{
+  "lora_path": [
+    "/path/to/your/lora.safetensors"
+  ],
+  "strength_model": 1.0
+}
+```
+
+**配置参数说明：**
+- `lora_path`: LoRA 权重文件路径列表，支持多个 LoRA 同时加载
+- `strength_model`: LoRA 强度系数 (alpha)，控制 LoRA 对原模型的影响程度
+
+### 命令行方式
+
+直接在命令行中指定 LoRA 路径（仅支持加载单个 LoRA）：
+
+```bash
+python -m lightx2v.infer \
+  --model_cls wan2.1 \
+  --task t2v \
+  --model_path /path/to/model \
+  --config_json /path/to/config.json \
+  --lora_path /path/to/your/lora.safetensors \
+  --prompt "Your prompt here"
+```
+
+### 支持的 LoRA 格式
+
+LightX2V 支持多种 LoRA 权重命名约定：
+
+| 格式类型 | 权重命名 | 说明 |
+|----------|----------|------|
+| **标准 LoRA** | `lora_A.weight`, `lora_B.weight` | 标准的 LoRA 矩阵分解格式 |
+| **Down/Up 格式** | `lora_down.weight`, `lora_up.weight` | 另一种常见的命名约定 |
+| **差值格式** | `diff` | `weight` 权重差值 |
+| **偏置差值** | `diff_b` | `bias` 权重差值 |
+| **调制差值** | `diff_m` | `modulation` 权重差值 |
+
+### 推理脚本示例
+
+**步数蒸馏 LoRA 推理：**
+
+```bash
+# T2V LoRA 推理
+bash scripts/wan/run_wan_t2v_distill_4step_cfg_lora.sh
+
+# I2V LoRA 推理
+bash scripts/wan/run_wan_i2v_distill_4step_cfg_lora.sh
+```
+
+**音频驱动 LoRA 推理：**
+
+```bash
+bash scripts/wan/run_wan_i2v_audio.sh
+```
+
+### API 服务中使用 LoRA
+
+在 API 服务中通过 [config 文件](wan_t2v_distill_4step_cfg_lora.json) 指定，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_lora.json \
+  --port 8000 \
+  --nproc_per_node 1
+```
+
+## 🔧 LoRA 提取工具
+
+使用 `tools/extract/lora_extractor.py` 从两个模型的差异中提取 LoRA 权重。
+
+### 基本用法
+
+```bash
+python tools/extract/lora_extractor.py \
+  --source-model /path/to/base/model \
+  --target-model /path/to/finetuned/model \
+  --output /path/to/extracted/lora.safetensors \
+  --rank 32
+```
+
+### 参数说明
+
+| 参数 | 类型 | 必需 | 默认值 | 说明 |
+|------|------|------|--------|------|
+| `--source-model` | str | ✅ | - | 基础模型路径 |
+| `--target-model` | str | ✅ | - | 微调后模型路径 |
+| `--output` | str | ✅ | - | 输出 LoRA 文件路径 |
+| `--source-type` | str | ❌ | `safetensors` | 基础模型格式 (`safetensors`/`pytorch`) |
+| `--target-type` | str | ❌ | `safetensors` | 微调模型格式 (`safetensors`/`pytorch`) |
+| `--output-format` | str | ❌ | `safetensors` | 输出格式 (`safetensors`/`pytorch`) |
+| `--rank` | int | ❌ | `32` | LoRA 秩值 |
+| `--output-dtype` | str | ❌ | `bf16` | 输出数据类型 |
+| `--diff-only` | bool | ❌ | `False` | 仅保存权重差值，不进行 LoRA 分解 |
+
+### 高级用法示例
+
+**提取高秩 LoRA：**
+```bash
+python tools/extract/lora_extractor.py \
+  --source-model /path/to/base/model \
+  --target-model /path/to/finetuned/model \
+  --output /path/to/high_rank_lora.safetensors \
+  --rank 64 \
+  --output-dtype fp16
+```
+
+**仅保存权重差值：**
+```bash
+python tools/extract/lora_extractor.py \
+  --source-model /path/to/base/model \
+  --target-model /path/to/finetuned/model \
+  --output /path/to/weight_diff.safetensors \
+  --diff-only
+```
+
+## 🔀 LoRA 合并工具
+
+使用 `tools/extract/lora_merger.py` 将 LoRA 权重合并到基础模型中，以进行后续量化等操作。
+
+### 基本用法
+
+```bash
+python tools/extract/lora_merger.py \
+  --source-model /path/to/base/model \
+  --lora-model /path/to/lora.safetensors \
+  --output /path/to/merged/model.safetensors \
+  --alpha 1.0
+```
+
+### 参数说明
+
+| 参数 | 类型 | 必需 | 默认值 | 说明 |
+|------|------|------|--------|------|
+| `--source-model` | str | ✅ | 无 | 基础模型路径 |
+| `--lora-model` | str | ✅ | 无 | LoRA 权重路径 |
+| `--output` | str | ✅ | 无 | 输出合并模型路径 |
+| `--source-type` | str | ❌ | `safetensors` | 基础模型格式 |
+| `--lora-type` | str | ❌ | `safetensors` | LoRA 权重格式 |
+| `--output-format` | str | ❌ | `safetensors` | 输出格式 |
+| `--alpha` | float | ❌ | `1.0` | LoRA 合并强度 |
+| `--output-dtype` | str | ❌ | `bf16` | 输出数据类型 |
+
+### 高级用法示例
+
+**部分强度合并：**
+```bash
+python tools/extract/lora_merger.py \
+  --source-model /path/to/base/model \
+  --lora-model /path/to/lora.safetensors \
+  --output /path/to/merged_model.safetensors \
+  --alpha 0.7 \
+  --output-dtype fp32
+```
+
+**多格式支持：**
+```bash
+python tools/extract/lora_merger.py \
+  --source-model /path/to/base/model.pt \
+  --source-type pytorch \
+  --lora-model /path/to/lora.safetensors \
+  --lora-type safetensors \
+  --output /path/to/merged_model.safetensors \
+  --output-format safetensors \
+  --alpha 1.0
+```

docs/ZH_CN/source/method_tutorials/autoregressive_distill.md

@@ -9,7 +9,7 @@
 自回归蒸馏通过 [CausVid](https://github.com/tianweiy/CausVid) 技术实现。CausVid 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 模型的自回归蒸馏训练；
-2. **更完整的数据处理流程**：生成50000个提示词-视频对的训练数据集；
+2. **更完整的数据处理流程**：生成 50,000 个提示词-视频对的训练数据集；
 
 具体实现可参考 [CausVid-Plus](https://github.com/GoatWu/CausVid-Plus)。