Update doc (#387)

docs/EN/source/deploy_guides/model_structure.md

@@ -179,7 +179,16 @@ huggingface-cli download lightx2v/Wan2.1-Distill-Models \
     --include "wan2.1_i2v_720p_lightx2v_4step.safetensors"
 ```
 
-**Step 2: Configure Launch Script**
+**Step 2: Manually Organize Other Components**
+
+Directory structure as follows:
+```
+wan2.1_i2v_720p/
+├── wan2.1_i2v_720p_lightx2v_4step.safetensors                    # Original precision
+└── t5/clip/vae/config.json/xlm-roberta-large/google and other components       # Need manual organization
+```
+
+**Step 3: Configure Launch Script**
 
 ```bash
 # Set in launch script (point to directory containing model file)
@@ -219,17 +228,19 @@ huggingface-cli download lightx2v/Wan2.1-Distill-Models \
     --include "wan2.1_i2v_720p_int8_lightx2v_4step.safetensors"
 ```
 
-**Directory Structure**:
+**Step 2: Manually Organize Other Components**
+
+Directory structure as follows:
 
 ```
 wan2.1_i2v_720p_multi/
 ├── wan2.1_i2v_720p_lightx2v_4step.safetensors                    # Original precision
 ├── wan2.1_i2v_720p_scaled_fp8_e4m3_lightx2v_4step.safetensors   # FP8 quantization
 └── wan2.1_i2v_720p_int8_lightx2v_4step.safetensors              # INT8 quantization
-└── t5/clip/vae/config.json/xlm-roberta-large/google and other components  # Manually organized
+└── t5/clip/vae/config.json/xlm-roberta-large/google and other components       # Need manual organization
 ```
 
-**Step 2: Specify Model in Configuration File**
+**Step 3: Specify Model in Configuration File**
 
 Edit configuration file (e.g., `configs/distill/wan_i2v_distill_4step_cfg.json`):
 
@@ -257,13 +268,15 @@ Edit configuration file (e.g., `configs/distill/wan_i2v_distill_4step_cfg.json`)
 > - **dit_original_ckpt**: Used to specify the path to original precision models (BF16/FP32/FP16)
 > - **dit_quantized_ckpt**: Used to specify the path to quantized models (FP8/INT8), must be used with `dit_quantized` and `dit_quant_scheme` parameters
 
-**Step 3: Start Inference**
+**Step 4: Start Inference**
 
 ```bash
 cd LightX2V/scripts
 bash wan/run_wan_i2v_distill_4step_cfg.sh
 ```
 
+> 💡 **Tip**: Other components (T5, CLIP, VAE, tokenizer, etc.) need to be manually organized into the model directory
+
 ### Wan2.2 Single-File Models
 
 #### Directory Structure Requirements
@@ -273,10 +286,10 @@ When using Wan2.2 single-file models, you need to manually create a specific dir
 ```
 wan2.2_models/
 ├── high_noise_model/                                    # High-noise model directory (required)
-│   └── wan2.2_i2v_A14b_high_noise_lightx2v_4step.safetensors  # High-noise model file
-└── low_noise_model/                                     # Low-noise model directory (required)
-│   └── wan2.2_i2v_A14b_low_noise_lightx2v_4step.safetensors  # Low-noise model file
-└── t5/vae/config.json/xlm-roberta-large/google and other components  # Manually organized
+│   └── wan2.2_i2v_A14b_high_noise_lightx2v_4step.safetensors
+├── low_noise_model/                                     # Low-noise model directory (required)
+│   └── wan2.2_i2v_A14b_low_noise_lightx2v_4step.safetensors
+└── t5/clip/vae/config.json/...                          # Other components (manually organized)
 ```
 
 #### Scenario A: Only One Model File Per Directory
@@ -336,9 +349,10 @@ wan2.2_models_multi/
 │   ├── wan2.2_i2v_A14b_high_noise_fp8_e4m3_lightx2v_4step.safetensors    # FP8 quantization
 │   └── wan2.2_i2v_A14b_high_noise_int8_lightx2v_4step.safetensors   # INT8 quantization
 └── low_noise_model/
-    ├── wan2.2_i2v_A14b_low_noise_lightx2v_4step.safetensors         # Original precision
-    ├── wan2.2_i2v_A14b_low_noise_fp8_e4m3_lightx2v_4step.safetensors     # FP8 quantization
-    └── wan2.2_i2v_A14b_low_noise_int8_lightx2v_4step.safetensors    # INT8 quantization
+│    ├── wan2.2_i2v_A14b_low_noise_lightx2v_4step.safetensors         # Original precision
+│    ├── wan2.2_i2v_A14b_low_noise_fp8_e4m3_lightx2v_4step.safetensors     # FP8 quantization
+│    └── wan2.2_i2v_A14b_low_noise_int8_lightx2v_4step.safetensors    # INT8 quantization
+└── t5/vae/config.json/xlm-roberta-large/google and other components       # Need manual organization
 ```
 
 **Configuration File Settings**:
@@ -450,16 +464,17 @@ Dynamically load LoRA weights during inference without modifying the base model.
 
 ```python
 # LoRA weight application formula
-# W' = W + (alpha/rank) * B @ A
+# lora_scale = (alpha / rank)
+# W' = W + lora_scale * B @ A
 # Where: B = up_proj (out_features, rank)
 #        A = down_proj (rank, in_features)
 
 if weights_dict["alpha"] is not None:
-    lora_alpha = weights_dict["alpha"] / lora_down.shape[0]
+    lora_scale = weights_dict["alpha"] / lora_down.shape[0]
 elif alpha is not None:
-    lora_alpha = alpha / lora_down.shape[0]
+    lora_scale = alpha / lora_down.shape[0]
 else:
-    lora_alpha = 1.0
+    lora_scale = 1.0
 ```
 
 **Configuration Method**:
@@ -507,7 +522,7 @@ Since Wan2.2 uses a dual-model architecture (high-noise/low-noise), LoRA needs t
 |------|------|--------|
 | `path` | LoRA model file path | Required |
 | `strength` | LoRA strength coefficient, range [0.0, 1.0] | 1.0 |
-| `alpha` | LoRA scaling factor, uses model's built-in value when `null`, defaults to 1 if no built-in value | null |
+| `alpha` | LoRA scaling factor, uses model's built-in value when `null` | null |
 | `name` | (Wan2.2 only) Specifies which model to apply to | Required |
 
 **Advantages**:

docs/ZH_CN/source/deploy_guides/model_structure.md

@@ -179,7 +179,16 @@ huggingface-cli download lightx2v/Wan2.1-Distill-Models \
     --include "wan2.1_i2v_720p_lightx2v_4step.safetensors"
 ```
 
-**步骤 2：配置启动脚本**
+**步骤 2：手动组织其他模块**
+
+目录结构如下
+```
+wan2.1_i2v_720p/
+├── wan2.1_i2v_720p_lightx2v_4step.safetensors                    # 原始精度
+└── t5/clip/vae/config.json/xlm-roberta-large/google等其他组件       # 需要手动组织
+```
+
+**步骤 3：配置启动脚本**
 
 ```bash
 # 在启动脚本中设置（指向包含模型文件的目录）
@@ -219,7 +228,9 @@ huggingface-cli download lightx2v/Wan2.1-Distill-Models \
     --include "wan2.1_i2v_720p_int8_lightx2v_4step.safetensors"
 ```
 
-**目录结构**：
+**步骤 2：手动组织其他模块**
+
+目录结构如下：
 
 ```
 wan2.1_i2v_720p_multi/
@@ -229,7 +240,7 @@ wan2.1_i2v_720p_multi/
 └── t5/clip/vae/config.json/xlm-roberta-large/google等其他组件       # 需要手动组织
 ```
 
-**步骤 2：在配置文件中指定模型**
+**步骤 3：在配置文件中指定模型**
 
 编辑配置文件（如 `configs/distill/wan_i2v_distill_4step_cfg.json`）：
 
@@ -257,7 +268,7 @@ wan2.1_i2v_720p_multi/
 > - **dit_original_ckpt**：用于指定原始精度模型（BF16/FP32/FP16）的路径
 > - **dit_quantized_ckpt**：用于指定量化模型（FP8/INT8）的路径，需配合 `dit_quantized` 和 `dit_quant_scheme` 参数使用
 
-**步骤 3：启动推理**
+**步骤 4：启动推理**
 
 ```bash
 cd LightX2V/scripts
@@ -336,9 +347,10 @@ wan2.2_models_multi/
 │   ├── wan2.2_i2v_A14b_high_noise_fp8_e4m3_lightx2v_4step.safetensors    # FP8 量化
 │   └── wan2.2_i2v_A14b_high_noise_int8_lightx2v_4step.safetensors   # INT8 量化
 └── low_noise_model/
-    ├── wan2.2_i2v_A14b_low_noise_lightx2v_4step.safetensors         # 原始精度
-    ├── wan2.2_i2v_A14b_low_noise_fp8_e4m3_lightx2v_4step.safetensors     # FP8 量化
-    └── wan2.2_i2v_A14b_low_noise_int8_lightx2v_4step.safetensors    # INT8 量化
+│    ├── wan2.2_i2v_A14b_low_noise_lightx2v_4step.safetensors         # 原始精度
+│    ├── wan2.2_i2v_A14b_low_noise_fp8_e4m3_lightx2v_4step.safetensors     # FP8 量化
+│    └── wan2.2_i2v_A14b_low_noise_int8_lightx2v_4step.safetensors    # INT8 量化
+└── t5/vae/config.json/xlm-roberta-large/google等其他组件       # 需要手动组织
 ```
 
 **配置文件设置**：
@@ -450,16 +462,17 @@ LoRA（Low-Rank Adaptation）模型提供了一种轻量级的模型微调方案
 
 ```python
 # LoRA 权重应用公式
-# W' = W + (alpha/rank) * B @ A
+# lora_scale = (alpha / rank)
+# W' = W + lora_scale * B @ A
 # 其中：B = up_proj (out_features, rank)
 #      A = down_proj (rank, in_features)
 
 if weights_dict["alpha"] is not None:
-    lora_alpha = weights_dict["alpha"] / lora_down.shape[0]
+    lora_scale = weights_dict["alpha"] / lora_down.shape[0]
 elif alpha is not None:
-    lora_alpha = alpha / lora_down.shape[0]
+    lora_scale = alpha / lora_down.shape[0]
 else:
-    lora_alpha = 1.0
+    lora_scale = 1.0
 ```
 
 **配置方法**：
@@ -507,7 +520,7 @@ else:
 |------|------|--------|
 | `path` | LoRA 模型文件路径 | 必填 |
 | `strength` | LoRA 强度系数，范围 [0.0, 1.0] | 1.0 |
-| `alpha` | LoRA 缩放因子，`null` 时使用模型内置值，如果没有内置值默认1 | null |
+| `alpha` | LoRA 缩放因子，`null` 时使用模型内置值 | null |
 | `name` | （仅 Wan2.2）指定应用到哪个模型 | 必填 |
 
 **优点**：