fix(lora): update config

configs/distill/wan_i2v_distill_4step_cfg_lora.json

 {
-    "infer_steps": 4,
-    "target_video_length": 81,
-    "target_height": 480,
-    "target_width": 832,
-    "self_attn_1_type": "flash_attn3",
-    "cross_attn_1_type": "flash_attn3",
-    "cross_attn_2_type": "flash_attn3",
-    "seed": 42,
-    "sample_guide_scale": 5,
-    "sample_shift": 5,
-    "enable_cfg": false,
-    "cpu_offload": false,
-    "denoising_step_list": [1000, 750, 500, 250],
-    "lora_path": [
-        "Wan2.1-I2V-14B-480P/loras/Wan21_I2V_14B_lightx2v_cfg_step_distill_lora_rank64.safetensors"
-    ]
+  "infer_steps": 4,
+  "target_video_length": 81,
+  "target_height": 480,
+  "target_width": 832,
+  "self_attn_1_type": "flash_attn3",
+  "cross_attn_1_type": "flash_attn3",
+  "cross_attn_2_type": "flash_attn3",
+  "seed": 42,
+  "sample_guide_scale": 5,
+  "sample_shift": 5,
+  "enable_cfg": false,
+  "cpu_offload": false,
+  "denoising_step_list": [1000, 750, 500, 250],
+  "lora_configs": [
+    {
+      "path": "Wan2.1-I2V-14B-480P/loras/Wan21_I2V_14B_lightx2v_cfg_step_distill_lora_rank64.safetensors",
+      "strength": 1.0
+    }
+  ]
 }

configs/distill/wan_t2v_distill_4step_cfg_lora.json

 {
-    "infer_steps": 4,
-    "target_video_length": 81,
-    "text_len": 512,
-    "target_height": 480,
-    "target_width": 832,
-    "self_attn_1_type": "flash_attn3",
-    "cross_attn_1_type": "flash_attn3",
-    "cross_attn_2_type": "flash_attn3",
-    "seed": 42,
-    "sample_guide_scale": 6,
-    "sample_shift": 5,
-    "enable_cfg": false,
-    "cpu_offload": false,
-    "denoising_step_list": [1000, 750, 500, 250],
-    "lora_path": [
-        "Wan2.1-T2V-14B/loras/Wan21_T2V_14B_lightx2v_cfg_step_distill_lora_rank64.safetensors"
-    ]
+  "infer_steps": 4,
+  "target_video_length": 81,
+  "text_len": 512,
+  "target_height": 480,
+  "target_width": 832,
+  "self_attn_1_type": "flash_attn3",
+  "cross_attn_1_type": "flash_attn3",
+  "cross_attn_2_type": "flash_attn3",
+  "seed": 42,
+  "sample_guide_scale": 6,
+  "sample_shift": 5,
+  "enable_cfg": false,
+  "cpu_offload": false,
+  "denoising_step_list": [1000, 750, 500, 250],
+  "lora_configs": [
+    {
+      "path": "Wan2.1-T2V-14B/loras/Wan21_T2V_14B_lightx2v_cfg_step_distill_lora_rank64.safetensors",
+      "strength": 1.0
+    }
+  ]
 }

docs/EN/source/deploy_guides/lora_deploy.md

@@ -17,14 +17,17 @@ Specify LoRA path in configuration file:
 
 ```json
 {
-  "lora_path": [
-    "/path/to/your/lora.safetensors"
-  ],
-  "strength_model": 1.0
+  "lora_configs": [
+    {
+      "path": "/path/to/your/lora.safetensors",
+      "strength": 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
 
@@ -137,6 +140,7 @@ python tools/extract/lora_extractor.py \
 ### Advanced Usage Examples
 
 **Extract High-Rank LoRA:**
+
 ```bash
 python tools/extract/lora_extractor.py \
   --source-model /path/to/base/model \
@@ -147,6 +151,7 @@ python tools/extract/lora_extractor.py \
 ```
 
 **Save Weight Differences Only:**
+
 ```bash
 python tools/extract/lora_extractor.py \
   --source-model /path/to/base/model \
@@ -185,6 +190,7 @@ python tools/extract/lora_merger.py \
 ### Advanced Usage Examples
 
 **Partial Strength Merging:**
+
 ```bash
 python tools/extract/lora_merger.py \
   --source-model /path/to/base/model \
@@ -195,6 +201,7 @@ python tools/extract/lora_merger.py \
 ```
 
 **Multi-Format Support:**
+
 ```bash
 python tools/extract/lora_merger.py \
   --source-model /path/to/base/model.pt \

docs/EN/source/method_tutorials/step_distill.md

@@ -38,8 +38,11 @@ Multiple configuration options are provided in the [configs/distill/](https://gi
   "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"
+  "lora_configs": [                              // LoRA weights path (optional)
+    {
+      "path": "path/to/distill_lora.safetensors",
+      "strength": 1.0
+    }
   ]
 }
 ```
@@ -50,21 +53,25 @@ Multiple configuration options are provided in the [configs/distill/](https://gi
 
 **Complete Model:**
 Place the downloaded model (`distill_model.pt` or `distill_model.safetensors`) in the `distill_models/` folder under the Wan model root directory:
+
 - For T2V: `Wan2.1-T2V-14B/distill_models/`
 - For I2V-480P: `Wan2.1-I2V-14B-480P/distill_models/`
 
 **LoRA:**
+
 1. Place the downloaded LoRA in any location
 2. Modify the `lora_path` parameter in the configuration file to the LoRA storage path
 
 ### Inference Scripts
 
 **T2V Complete Model:**
+
 ```bash
 bash scripts/wan/run_wan_t2v_distill_4step_cfg.sh
 ```
 
 **I2V Complete Model:**
+
 ```bash
 bash scripts/wan/run_wan_i2v_distill_4step_cfg.sh
 ```
@@ -72,11 +79,13 @@ 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
 ```

docs/ZH_CN/source/deploy_guides/lora_deploy.md

@@ -16,14 +16,17 @@ LoRA (Low-Rank Adaptation) 是一种高效的模型微调技术，通过低秩
 
 ```json
 {
-  "lora_path": [
-    "/path/to/your/lora.safetensors"
-  ],
-  "strength_model": 1.0
+  "lora_configs": [
+    {
+      "path": "/path/to/your/lora.safetensors",
+      "strength": 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
 
@@ -136,6 +139,7 @@ python tools/extract/lora_extractor.py \
 ### 高级用法示例
 
 **提取高秩 LoRA：**
+
 ```bash
 python tools/extract/lora_extractor.py \
   --source-model /path/to/base/model \
@@ -146,6 +150,7 @@ python tools/extract/lora_extractor.py \
 ```
 
 **仅保存权重差值：**
+
 ```bash
 python tools/extract/lora_extractor.py \
   --source-model /path/to/base/model \
@@ -184,6 +189,7 @@ python tools/extract/lora_merger.py \
 ### 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 \
@@ -194,6 +200,7 @@ python tools/extract/lora_merger.py \
 ```
 
 **多格式支持：**
+
 ```bash
 python tools/extract/lora_merger.py \
   --source-model /path/to/base/model.pt \

docs/ZH_CN/source/method_tutorials/step_distill.md

@@ -38,8 +38,11 @@
   "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"
+  "lora_configs": [                              // LoRA权重路径（可选）
+    {
+      "path": "path/to/distill_lora.safetensors",
+      "strength": 1.0
+    }
   ]
 }
 ```
@@ -50,21 +53,25 @@
 
 **完整模型：**
 将下载好的模型（`distill_model.pt` 或者 `distill_model.safetensors`）放到 Wan 模型根目录的 `distill_models/` 文件夹下即可
+
 - 对于 T2V：`Wan2.1-T2V-14B/distill_models/`
 - 对于 I2V-480P：`Wan2.1-I2V-14B-480P/distill_models/`
 
 **LoRA：**
+
 1. 将下载好的 LoRA 放到任意位置
 2. 修改配置文件中的 `lora_path` 参数为 LoRA 存放路径即可
 
 ### 推理脚本
 
 **T2V 完整模型：**
+
 ```bash
 bash scripts/wan/run_wan_t2v_distill_4step_cfg.sh
 ```
 
 **I2V 完整模型：**
+
 ```bash
 bash scripts/wan/run_wan_i2v_distill_4step_cfg.sh
 ```
@@ -72,11 +79,13 @@ 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
 ```