Merge branch 'main' of github.com:ModelTC/lightx2v into main

app/gradio_demo.py

@@ -403,6 +403,7 @@ def run_inference(
         "rotary_chunk": rotary_chunk,
         "rotary_chunk_size": rotary_chunk_size,
         "clean_cuda_cache": clean_cuda_cache,
+        "denoising_step_list": [1000, 750, 500, 250],
     }
 
     args = argparse.Namespace(
@@ -818,18 +819,22 @@ def main():
                                 randomize_btn.click(fn=generate_random_seed, inputs=None, outputs=seed)
 
                                 with gr.Column():
+                                    # Set default inference steps based on model class
+                                    default_infer_steps = 4 if model_cls == "wan2.1_distill" else 40
                                     infer_steps = gr.Slider(
                                         label="Inference Steps",
                                         minimum=1,
                                         maximum=100,
                                         step=1,
-                                        value=40,
+                                        value=default_infer_steps,
                                         info="Number of inference steps for video generation. Increasing steps may improve quality but reduce speed.",
                                     )
 
+                            # Set default CFG based on model class
+                            default_enable_cfg = False if model_cls == "wan2.1_distill" else True
                             enable_cfg = gr.Checkbox(
                                 label="Enable Classifier-Free Guidance",
-                                value=True,
+                                value=default_enable_cfg,
                                 info="Enable classifier-free guidance to control prompt strength",
                             )
                             cfg_scale = gr.Slider(
@@ -1149,7 +1154,7 @@ def main():
                 outputs=output_video,
             )
 
-    demo.launch(share=True, server_port=args.server_port, server_name=args.server_name)
+    demo.launch(share=True, server_port=args.server_port, server_name=args.server_name, inbrowser=True)
 
 
 if __name__ == "__main__":
@@ -1158,9 +1163,9 @@ if __name__ == "__main__":
     parser.add_argument(
         "--model_cls",
         type=str,
-        choices=["wan2.1"],
+        choices=["wan2.1", "wan2.1_distill"],
         default="wan2.1",
-        help="Model class to use",
+        help="Model class to use (wan2.1: standard model, wan2.1_distill: distilled model for faster inference)",
     )
     parser.add_argument("--model_size", type=str, required=True, choices=["14b", "1.3b"], help="Model type to use")
     parser.add_argument("--task", type=str, required=True, choices=["i2v", "t2v"], help="Specify the task type. 'i2v' for image-to-video translation, 't2v' for text-to-video generation.")

app/gradio_demo_zh.py

@@ -405,6 +405,7 @@ def run_inference(
         "rotary_chunk": rotary_chunk,
         "rotary_chunk_size": rotary_chunk_size,
         "clean_cuda_cache": clean_cuda_cache,
+        "denoising_step_list": [1000, 750, 500, 250],
     }
 
     args = argparse.Namespace(
@@ -818,18 +819,22 @@ def main():
                                 randomize_btn.click(fn=generate_random_seed, inputs=None, outputs=seed)
 
                                 with gr.Column():
+                                    # 根据模型类别设置默认推理步数
+                                    default_infer_steps = 4 if model_cls == "wan2.1_distill" else 40
                                     infer_steps = gr.Slider(
                                         label="推理步数",
                                         minimum=1,
                                         maximum=100,
                                         step=1,
-                                        value=40,
+                                        value=default_infer_steps,
                                         info="视频生成的推理步数。增加步数可能提高质量但降低速度。",
                                     )
 
+                            # 根据模型类别设置默认CFG
+                            default_enable_cfg = False if model_cls == "wan2.1_distill" else True
                             enable_cfg = gr.Checkbox(
                                 label="启用无分类器引导",
-                                value=True,
+                                value=default_enable_cfg,
                                 info="启用无分类器引导以控制提示词强度",
                             )
                             cfg_scale = gr.Slider(
@@ -1147,7 +1152,7 @@ def main():
                 outputs=output_video,
             )
 
-    demo.launch(share=True, server_port=args.server_port, server_name=args.server_name)
+    demo.launch(share=True, server_port=args.server_port, server_name=args.server_name, inbrowser=True)
 
 
 if __name__ == "__main__":
@@ -1156,9 +1161,9 @@ if __name__ == "__main__":
     parser.add_argument(
         "--model_cls",
         type=str,
-        choices=["wan2.1"],
+        choices=["wan2.1", "wan2.1_distill"],
         default="wan2.1",
-        help="要使用的模型类别",
+        help="要使用的模型类别 (wan2.1: 标准模型, wan2.1_distill: 蒸馏模型，推理更快)",
     )
     parser.add_argument("--model_size", type=str, required=True, choices=["14b", "1.3b"], help="模型大小：14b 或 1.3b")
     parser.add_argument("--task", type=str, required=True, choices=["i2v", "t2v"], help="指定任务类型。'i2v'用于图像到视频转换，'t2v'用于文本到视频生成。")

app/run_gradio.sh

@@ -28,6 +28,10 @@ t2v_model_path=/path/to/Wan2.1-T2V-1.3B
 # Default model size (14b, 1.3b)
 model_size="14b"
 
+# Model class configuration
+# Default model class (wan2.1, wan2.1_distill)
+model_cls="wan2.1"
+
 # Server configuration
 server_name="0.0.0.0"
 server_port=8032
@@ -72,6 +76,10 @@ while [[ $# -gt 0 ]]; do
             model_size="$2"
             shift 2
             ;;
+        --model_cls)
+            model_cls="$2"
+            shift 2
+            ;;
         --help)
             echo "🎬 Lightx2v Gradio Demo Startup Script"
             echo "=========================================="
@@ -90,6 +98,10 @@ while [[ $# -gt 0 ]]; do
             echo "                     Model size (default: 14b)"
             echo "                     14b: 14 billion parameters model"
             echo "                     1.3b: 1.3 billion parameters model"
+            echo "  --model_cls MODEL_CLASS"
+            echo "                     Model class (default: wan2.1)"
+            echo "                     wan2.1: Standard model variant"
+            echo "                     wan2.1_distill: Distilled model variant for faster inference"
             echo "  --help            Show this help message"
             echo ""
             echo "🚀 Usage examples:"
@@ -99,6 +111,7 @@ while [[ $# -gt 0 ]]; do
             echo "  $0 --task i2v --gpu 1 --port 8032     # Use GPU 1"
             echo "  $0 --task t2v --model_size 1.3b       # Use 1.3B model"
             echo "  $0 --task i2v --model_size 14b        # Use 14B model"
+            echo "  $0 --task i2v --model_cls wan2.1_distill  # Use distilled model"
             echo ""
             echo "📝 Notes:"
             echo "  - Edit script to configure model paths before first use"
@@ -132,6 +145,12 @@ if [[ "$model_size" != "14b" && "$model_size" != "1.3b" ]]; then
     exit 1
 fi
 
+# Validate model class
+if [[ "$model_cls" != "wan2.1" && "$model_cls" != "wan2.1_distill" ]]; then
+    echo "Error: Model class must be 'wan2.1' or 'wan2.1_distill'"
+    exit 1
+fi
+
 # Select model path based on task type
 if [[ "$task" == "i2v" ]]; then
     model_path=$i2v_model_path
@@ -181,6 +200,7 @@ echo "📁 Project path: $lightx2v_path"
 echo "🤖 Model path: $model_path"
 echo "🎯 Task type: $task"
 echo "🤖 Model size: $model_size"
+echo "🤖 Model class: $model_cls"
 echo "🌏 Interface language: $lang"
 echo "🖥️  GPU device: $gpu_id"
 echo "🌐 Server address: $server_name:$server_port"
@@ -208,6 +228,7 @@ echo "=========================================="
 # Start Python demo
 python $demo_file \
     --model_path "$model_path" \
+    --model_cls "$model_cls" \
     --task "$task" \
     --server_name "$server_name" \
     --server_port "$server_port" \

app/run_gradio_win.bat

+@echo off
+chcp 65001 >nul
+echo 🎬 LightX2V Gradio Windows Startup Script
+echo ==========================================
+
+REM ==================== Configuration Area ====================
+REM ⚠️  Important: Please modify the following paths according to your actual environment
+
+REM 🚨 Storage Performance Tips 🚨
+REM 💾 Strongly recommend storing model files on SSD solid-state drives!
+REM 📈 SSD can significantly improve model loading speed and inference performance
+REM 🐌 Using mechanical hard drives (HDD) may cause slow model loading and affect overall experience
+
+REM LightX2V project root directory path
+REM Example: D:\LightX2V
+set lightx2v_path=/path/to/LightX2V
+
+REM Model path configuration
+REM Image-to-video model path (for i2v tasks)
+REM Example: D:\models\Wan2.1-I2V-14B-480P-Lightx2v
+set i2v_model_path=/path/to/Wan2.1-I2V-14B-480P-Lightx2v
+
+REM Text-to-video model path (for t2v tasks)
+REM Example: D:\models\Wan2.1-T2V-1.3B
+set t2v_model_path=/path/to/Wan2.1-T2V-1.3B
+
+REM Model size configuration
+REM Default model size (14b, 1.3b)
+set model_size=14b
+
+REM Model class configuration
+REM Default model class (wan2.1, wan2.1_distill)
+set model_cls=wan2.1
+
+REM Server configuration
+set server_name=127.0.0.1
+set server_port=8032
+
+REM GPU configuration
+set gpu_id=0
+
+REM ==================== Environment Variables Setup ====================
+set CUDA_VISIBLE_DEVICES=%gpu_id%
+set PYTHONPATH=%lightx2v_path%;%PYTHONPATH%
+set ENABLE_PROFILING_DEBUG=true
+set PYTORCH_CUDA_ALLOC_CONF=expandable_segments:True
+
+REM ==================== Parameter Parsing ====================
+REM Default task type
+set task=i2v
+REM Default interface language
+set lang=zh
+
+REM Parse command line arguments
+:parse_args
+if "%1"=="" goto :end_parse
+if "%1"=="--task" (
+    set task=%2
+    shift
+    shift
+    goto :parse_args
+)
+if "%1"=="--lang" (
+    set lang=%2
+    shift
+    shift
+    goto :parse_args
+)
+if "%1"=="--port" (
+    set server_port=%2
+    shift
+    shift
+    goto :parse_args
+)
+if "%1"=="--gpu" (
+    set gpu_id=%2
+    set CUDA_VISIBLE_DEVICES=%gpu_id%
+    shift
+    shift
+    goto :parse_args
+)
+if "%1"=="--model_size" (
+    set model_size=%2
+    shift
+    shift
+    goto :parse_args
+)
+if "%1"=="--model_cls" (
+    set model_cls=%2
+    shift
+    shift
+    goto :parse_args
+)
+if "%1"=="--help" (
+    echo 🎬 LightX2V Gradio Windows Startup Script
+    echo ==========================================
+    echo Usage: %0 [options]
+    echo.
+    echo 📋 Available options:
+    echo   --task i2v^|t2v    Task type (default: i2v)
+    echo                      i2v: Image-to-video generation
+    echo                      t2v: Text-to-video generation
+    echo   --lang zh^|en      Interface language (default: zh)
+    echo                      zh: Chinese interface
+    echo                      en: English interface
+    echo   --port PORT        Server port (default: 8032)
+    echo   --gpu GPU_ID       GPU device ID (default: 0)
+    echo   --model_size MODEL_SIZE
+    echo                      Model size (default: 14b)
+    echo                      14b: 14B parameter model
+    echo                      1.3b: 1.3B parameter model
+    echo   --model_cls MODEL_CLASS
+    echo                      Model class (default: wan2.1)
+    echo                      wan2.1: Standard model variant
+    echo                      wan2.1_distill: Distilled model variant for faster inference
+    echo   --help             Show this help message
+    echo.
+    echo 🚀 Usage examples:
+    echo   %0                                    # Default startup for image-to-video mode
+    echo   %0 --task i2v --lang zh --port 8032   # Start with specified parameters
+    echo   %0 --task t2v --lang en --port 7860   # Text-to-video with English interface
+    echo   %0 --task i2v --gpu 1 --port 8032     # Use GPU 1
+    echo   %0 --task t2v --model_size 1.3b       # Use 1.3B model
+    echo   %0 --task i2v --model_size 14b        # Use 14B model
+    echo   %0 --task i2v --model_cls wan2.1_distill  # Use distilled model
+    echo.
+    echo 📝 Notes:
+    echo   - Edit script to configure model paths before first use
+    echo   - Ensure required Python dependencies are installed
+    echo   - Recommended to use GPU with 8GB+ VRAM
+    echo   - 🚨 Strongly recommend storing models on SSD for better performance
+    pause
+    exit /b 0
+)
+echo Unknown parameter: %1
+echo Use --help to see help information
+pause
+exit /b 1
+
+:end_parse
+
+REM ==================== Parameter Validation ====================
+if "%task%"=="i2v" goto :valid_task
+if "%task%"=="t2v" goto :valid_task
+echo Error: Task type must be 'i2v' or 't2v'
+pause
+exit /b 1
+
+:valid_task
+if "%lang%"=="zh" goto :valid_lang
+if "%lang%"=="en" goto :valid_lang
+echo Error: Language must be 'zh' or 'en'
+pause
+exit /b 1
+
+:valid_lang
+if "%model_size%"=="14b" goto :valid_size
+if "%model_size%"=="1.3b" goto :valid_size
+echo Error: Model size must be '14b' or '1.3b'
+pause
+exit /b 1
+
+:valid_size
+if "%model_cls%"=="wan2.1" goto :valid_cls
+if "%model_cls%"=="wan2.1_distill" goto :valid_cls
+echo Error: Model class must be 'wan2.1' or 'wan2.1_distill'
+pause
+exit /b 1
+
+:valid_cls
+
+REM Select model path based on task type
+if "%task%"=="i2v" (
+    set model_path=%i2v_model_path%
+    echo 🎬 Starting Image-to-Video mode
+) else (
+    set model_path=%t2v_model_path%
+    echo 🎬 Starting Text-to-Video mode
+)
+
+REM Check if model path exists
+if not exist "%model_path%" (
+    echo ❌ Error: Model path does not exist
+    echo 📁 Path: %model_path%
+    echo 🔧 Solutions:
+    echo   1. Check model path configuration in script
+    echo   2. Ensure model files are properly downloaded
+    echo   3. Verify path permissions are correct
+    echo   4. 💾 Recommend storing models on SSD for faster loading
+    pause
+    exit /b 1
+)
+
+REM Select demo file based on language
+if "%lang%"=="zh" (
+    set demo_file=gradio_demo_zh.py
+    echo 🌏 Using Chinese interface
+) else (
+    set demo_file=gradio_demo.py
+    echo 🌏 Using English interface
+)
+
+REM Check if demo file exists
+if not exist "%demo_file%" (
+    echo ❌ Error: Demo file does not exist
+    echo 📄 File: %demo_file%
+    echo 🔧 Solutions:
+    echo   1. Ensure script is run in the correct directory
+    echo   2. Check if file has been renamed or moved
+    echo   3. Re-clone or download project files
+    pause
+    exit /b 1
+)
+
+REM ==================== System Information Display ====================
+echo ==========================================
+echo 🚀 LightX2V Gradio Starting...
+echo ==========================================
+echo 📁 Project path: %lightx2v_path%
+echo 🤖 Model path: %model_path%
+echo 🎯 Task type: %task%
+echo 🤖 Model size: %model_size%
+echo 🤖 Model class: %model_cls%
+echo 🌏 Interface language: %lang%
+echo 🖥️  GPU device: %gpu_id%
+echo 🌐 Server address: %server_name%:%server_port%
+echo ==========================================
+
+REM Display system resource information
+echo 💻 System resource information:
+wmic OS get TotalVisibleMemorySize,FreePhysicalMemory /format:table
+
+REM Display GPU information
+nvidia-smi --query-gpu=name,memory.total,memory.free --format=csv,noheader,nounits 2>nul
+if errorlevel 1 (
+    echo 🎮 GPU information: Unable to get GPU info
+) else (
+    echo 🎮 GPU information:
+    nvidia-smi --query-gpu=name,memory.total,memory.free --format=csv,noheader,nounits
+)
+
+REM ==================== Start Demo ====================
+echo 🎬 Starting Gradio demo...
+echo 📱 Please access in browser: http://%server_name%:%server_port%
+echo ⏹️  Press Ctrl+C to stop service
+echo 🔄 First startup may take several minutes to load resources...
+echo ==========================================
+
+REM Start Python demo
+python %demo_file% ^
+    --model_path "%model_path%" ^
+    --model_cls %model_cls% ^
+    --task %task% ^
+    --server_name %server_name% ^
+    --server_port %server_port% ^
+    --model_size %model_size%
+
+REM Display final system resource usage
+echo.
+echo ==========================================
+echo 📊 Final system resource usage:
+wmic OS get TotalVisibleMemorySize,FreePhysicalMemory /format:table
+
+pause

configs/bench/lightx2v_3.json

@@ -13,5 +13,6 @@
     "cpu_offload": false,
     "mm_config": {
         "mm_type": "W-fp8-channel-sym-A-fp8-channel-sym-dynamic-Sgl-ActVllm"
-    }
+    },
+    "use_tiling_vae": true
 }

configs/bench/lightx2v_3_distill.json

 {
-    "infer_steps": 40,
+    "infer_steps": 4,
     "target_video_length": 81,
     "target_height": 480, // 720
     "target_width": 832, // 1280
@@ -9,9 +9,11 @@
     "seed": 42, //1234
     "sample_guide_scale": 5,
     "sample_shift": 5,
-    "enable_cfg": true,
+    "enable_cfg": false,
     "cpu_offload": false,
+    "denoising_step_list": [1000, 750, 500, 250],
     "mm_config": {
         "mm_type": "W-fp8-channel-sym-A-fp8-channel-sym-dynamic-Sgl-ActVllm"
-    }
+    },
+    "use_tiling_vae": true
 }

configs/bench/lightx2v_4.json

@@ -24,5 +24,6 @@
     // [-114.36346466, 65.26524496, -18.82220707, 4.91518089, -0.23412683]
     // ],
     "use_ret_steps": false,
-    "teacache_thresh": 0.2
+    "teacache_thresh": 0.2,
+    "use_tiling_vae": true
 }

configs/bench/lightx2v_5.json

+{
+    "infer_steps": 40,
+    "target_video_length": 81,
+    "target_height": 480, // 720
+    "target_width": 832, // 1280
+    "self_attn_1_type": "sage_attn2",
+    "cross_attn_1_type": "sage_attn2",
+    "cross_attn_2_type": "sage_attn2",
+    "seed": 42,
+    "sample_guide_scale": 5,
+    "sample_shift": 5,
+    "enable_cfg": true,
+    "cpu_offload": true,
+    "offload_granularity": "block",
+    "offload_ratio": 0.8, //1
+    "t5_cpu_offload": true,
+    "mm_config": {
+        "mm_type": "W-fp8-channel-sym-A-fp8-channel-sym-dynamic-Q8F"
+    },
+    "use_tiling_vae": true
+}

configs/bench/lightx2v_5_distill.json

+{
+    "infer_steps": 4,
+    "target_video_length": 81,
+    "target_height": 480, // 720
+    "target_width": 832, // 1280
+    "self_attn_1_type": "sage_attn2",
+    "cross_attn_1_type": "sage_attn2",
+    "cross_attn_2_type": "sage_attn2",
+    "seed": 42,
+    "sample_guide_scale": 5,
+    "sample_shift": 5,
+    "enable_cfg": false,
+    "cpu_offload": true,
+    "offload_granularity": "block",
+    "offload_ratio": 0.8, //1
+    "t5_cpu_offload": true,
+    "denoising_step_list": [1000, 750, 500, 250],
+    "mm_config": {
+        "mm_type": "W-fp8-channel-sym-A-fp8-channel-sym-dynamic-Q8F"
+    },
+    "use_tiling_vae": true
+}

configs/bench/lightx2v_6.json

+{
+    "infer_steps": 40,
+    "target_video_length": 81,
+    "target_height": 480, // 720
+    "target_width": 832, // 1280
+    "self_attn_1_type": "sage_attn2",
+    "cross_attn_1_type": "sage_attn2",
+    "cross_attn_2_type": "sage_attn2",
+    "seed": 42,
+    "sample_guide_scale": 5,
+    "sample_shift": 5,
+    "enable_cfg": true,
+    "cpu_offload": true,
+    "offload_granularity": "block",
+    "offload_ratio": 0.8, //1
+    "t5_cpu_offload": true,
+    "mm_config": {
+        "mm_type": "W-fp8-channel-sym-A-fp8-channel-sym-dynamic-Q8F"
+    },
+    "use_tiling_vae": true
+}

configs/bench/lightx2v_6_distill.json

+{
+    "infer_steps": 4,
+    "target_video_length": 81,
+    "target_height": 480, // 720
+    "target_width": 832, // 1280
+    "self_attn_1_type": "sage_attn2",
+    "cross_attn_1_type": "sage_attn2",
+    "cross_attn_2_type": "sage_attn2",
+    "seed": 42,
+    "sample_guide_scale": 5,
+    "sample_shift": 5,
+    "enable_cfg": false,
+    "cpu_offload": true,
+    "offload_granularity": "block",
+    "offload_ratio": 0.8, //1
+    "t5_cpu_offload": true,
+    "denoising_step_list": [1000, 750, 500, 250],
+    "mm_config": {
+        "mm_type": "W-fp8-channel-sym-A-fp8-channel-sym-dynamic-Q8F"
+    },
+    "use_tiling_vae": true
+}

configs/changing_resolution/wan_i2v.json

+{
+    "infer_steps": 40,
+    "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": 442,
+    "sample_guide_scale": 5,
+    "sample_shift": 3,
+    "enable_cfg": true,
+    "cpu_offload": false,
+    "changing_resolution": true,
+    "resolution_rate": [0.75],
+    "changing_resolution_steps": [20]
+}

configs/changing_resolution/wan_i2v_U.json

+{
+    "infer_steps": 40,
+    "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": 442,
+    "sample_guide_scale": 5,
+    "sample_shift": 3,
+    "enable_cfg": true,
+    "cpu_offload": false,
+    "changing_resolution": true,
+    "resolution_rate": [1.0, 0.75],
+    "changing_resolution_steps": [5, 25]
+}

configs/changing_resolution/wan_t2v.json

@@ -13,6 +13,6 @@
     "enable_cfg": true,
     "cpu_offload": false,
     "changing_resolution": true,
-    "resolution_rate": 0.75,
-    "changing_resolution_steps": 25
+    "resolution_rate": [0.75],
+    "changing_resolution_steps": [25]
 }

configs/changing_resolution/wan_t2v_U.json

+{
+    "infer_steps": 50,
+    "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": 8,
+    "enable_cfg": true,
+    "cpu_offload": false,
+    "changing_resolution": true,
+    "resolution_rate": [1.0, 0.75],
+    "changing_resolution_steps": [10, 35]
+}

configs/quantization/wan_i2v_torchao.json

+{
+    "infer_steps": 4,
+    "target_video_length": 81,
+    "target_height": 480,
+    "target_width": 832,
+    "self_attn_1_type": "sage_attn2",
+    "cross_attn_1_type": "sage_attn2",
+    "cross_attn_2_type": "sage_attn2",
+    "seed": 42,
+    "sample_guide_scale": 5,
+    "sample_shift": 5,
+    "enable_cfg": false,
+    "cpu_offload": false,
+    "mm_config": {
+        "mm_type": "W-int8-channel-sym-A-int8-channel-sym-dynamic-Torchao"
+    },
+    "t5_quantized": true,
+    "t5_quant_scheme": "int8-torchao",
+    "clip_quantized": true,
+    "clip_quant_scheme": "int8-torchao"
+}

docs/EN/source/deploy_guides/deploy_gradio.md

-# Gradio Deployment
+# Gradio Deployment Guide
 
 ## 📖 Overview
 
 Lightx2v is a lightweight video inference and generation engine that provides a web interface based on Gradio, supporting both Image-to-Video and Text-to-Video generation modes.
 
+## 📁 File Structure
+
+```
+LightX2V/app/
+├── gradio_demo.py          # English interface demo
+├── gradio_demo_zh.py       # Chinese interface demo
+├── run_gradio.sh          # Startup script
+├── README.md              # Documentation
+├── saved_videos/          # Generated video save directory
+└── inference_logs.log     # Inference logs
+```
+
 This project contains two main demo files:
 - `gradio_demo.py` - English interface version
 - `gradio_demo_zh.py` - Chinese interface version
 
 ## 🚀 Quick Start
 
-### System Requirements
+### Environment Requirements
 
-- Python 3.10+ (recommended)
-- CUDA 12.4+ (recommended)
-- At least 8GB GPU VRAM
-- At least 16GB system memory (preferably at least 32GB)
-- At least 128GB SSD solid-state drive (**💾 Strongly recommend using SSD solid-state drives to store model files! During "lazy loading" startup, significantly improves model loading speed and inference performance**)
-
-### Install Dependencies
-
-```bash
-# Install basic dependencies
-pip install -r requirements.txt
-pip install gradio
-```
+Follow the [Quick Start Guide](../getting_started/quickstart.md) to install the environment
 
 #### Recommended Optimization Library Configuration
 
@@ -34,6 +34,8 @@ pip install gradio
 - ✅ [sglang-kernel](https://github.com/sgl-project/sglang/tree/main/sgl-kernel)
 - ✅ [q8-kernel](https://github.com/KONAKONA666/q8_kernels) (only supports ADA architecture GPUs)
 
+Install according to the project homepage tutorials for each operator as needed
+
 ### 🤖 Supported Models
 
 #### 🎬 Image-to-Video Models
@@ -54,15 +56,21 @@ pip install gradio
 | ✅ [Wan2.1-T2V-14B-StepDistill-CfgDistill-Lightx2v](https://huggingface.co/lightx2v/Wan2.1-T2V-14B-StepDistill-CfgDistill-Lightx2v) | 14B | Distilled optimized version | High quality + fast inference |
 
 **💡 Model Selection Recommendations**:
-- **First-time use**: Recommend choosing distilled versions
+- **First-time use**: Recommend choosing distilled versions (`wan2.1_distill`)
 - **Pursuing quality**: Choose 720p resolution or 14B parameter models
-- **Pursuing speed**: Choose 480p resolution or 1.3B parameter models
+- **Pursuing speed**: Choose 480p resolution or 1.3B parameter models, prioritize distilled versions
 - **Resource-constrained**: Prioritize distilled versions and lower resolutions
+- **Real-time applications**: Strongly recommend using distilled models (`wan2.1_distill`)
+
+**🎯 Model Category Description**:
+- **`wan2.1`**: Standard model, provides the best video generation quality, suitable for scenarios with extremely high quality requirements
+- **`wan2.1_distill`**: Distilled model, optimized through knowledge distillation technology, significantly improves inference speed, maintains good quality while greatly reducing computation time, suitable for most application scenarios
 
 ### Startup Methods
 
 #### Method 1: Using Startup Script (Recommended)
 
+**Linux Environment:**
 ```bash
 # 1. Edit the startup script to configure relevant paths
 cd app/
@@ -79,41 +87,84 @@ vim run_gradio.sh
 # 2. Run the startup script
 bash run_gradio.sh
 
-# 3. Or start with parameters (recommended)
-bash run_gradio.sh --task i2v --lang en --model_size 14b --port 8032
-# bash run_gradio.sh --task i2v --lang en --model_size 14b --port 8032
-# bash run_gradio.sh --task i2v --lang en --model_size 1.3b --port 8032
+# 3. Or start with parameters (recommended using distilled models)
+bash run_gradio.sh --task i2v --lang en --model_cls wan2.1 --model_size 14b --port 8032
+bash run_gradio.sh --task t2v --lang en --model_cls wan2.1 --model_size 1.3b --port 8032
+bash run_gradio.sh --task i2v --lang en --model_cls wan2.1_distill --model_size 14b --port 8032
+bash run_gradio.sh --task t2v --lang en --model_cls wan2.1_distill --model_size 1.3b --port 8032
+```
+
+**Windows Environment:**
+```cmd
+# 1. Edit the startup script to configure relevant paths
+cd app\
+notepad run_gradio_win.bat
+
+# Configuration items that need to be modified:
+# - lightx2v_path: Lightx2v project root directory path
+# - i2v_model_path: Image-to-video model path
+# - t2v_model_path: Text-to-video model path
+
+# 💾 Important note: Recommend pointing model paths to SSD storage locations
+# Example: D:\models\ or E:\models\
+
+# 2. Run the startup script
+run_gradio_win.bat
+
+# 3. Or start with parameters (recommended using distilled models)
+run_gradio_win.bat --task i2v --lang en --model_cls wan2.1 --model_size 14b --port 8032
+run_gradio_win.bat --task t2v --lang en --model_cls wan2.1 --model_size 1.3b --port 8032
+run_gradio_win.bat --task i2v --lang en --model_cls wan2.1_distill --model_size 14b --port 8032
+run_gradio_win.bat --task t2v --lang en --model_cls wan2.1_distill --model_size 1.3b --port 8032
 ```
 
 #### Method 2: Direct Command Line Startup
 
+**Linux Environment:**
+
 **Image-to-Video Mode:**
 ```bash
 python gradio_demo.py \
-    --model_path /path/to/Wan2.1-I2V-14B-720P-Lightx2v \
+    --model_path /path/to/Wan2.1-I2V-14B-480P-Lightx2v \
+    --model_cls wan2.1 \
     --model_size 14b \
     --task i2v \
     --server_name 0.0.0.0 \
     --server_port 7862
 ```
 
-**Text-to-Video Mode:**
+**English Interface Version:**
 ```bash
 python gradio_demo.py \
-    --model_path /path/to/Wan2.1-T2V-1.3B \
-    --model_size 1.3b \
+    --model_path /path/to/Wan2.1-T2V-14B-StepDistill-CfgDistill-Lightx2v \
+    --model_cls wan2.1_distill \
+    --model_size 14b \
     --task t2v \
     --server_name 0.0.0.0 \
     --server_port 7862
 ```
 
-**Chinese Interface Version:**
-```bash
-python gradio_demo_zh.py \
-    --model_path /path/to/model \
-    --model_size 14b \
-    --task i2v \
-    --server_name 0.0.0.0 \
+**Windows Environment:**
+
+**Image-to-Video Mode:**
+```cmd
+python gradio_demo.py ^
+    --model_path D:\models\Wan2.1-I2V-14B-480P-Lightx2v ^
+    --model_cls wan2.1 ^
+    --model_size 14b ^
+    --task i2v ^
+    --server_name 127.0.0.1 ^
+    --server_port 7862
+```
+
+**English Interface Version:**
+```cmd
+python gradio_demo.py ^
+    --model_path D:\models\Wan2.1-T2V-14B-StepDistill-CfgDistill-Lightx2v ^
+    --model_cls wan2.1_distill ^
+    --model_size 14b ^
+    --task t2v ^
+    --server_name 127.0.0.1 ^
     --server_port 7862
 ```
 
@@ -122,8 +173,8 @@ python gradio_demo_zh.py \
 | Parameter | Type | Required | Default | Description |
 |-----------|------|----------|---------|-------------|
 | `--model_path` | str | ✅ | - | Model folder path |
-| `--model_cls` | str | ❌ | wan2.1 | Model class (currently only supports wan2.1) |
-| `--model_size` | str | ✅ | - | Model size: `14b(t2v or i2v)` or `1.3b(t2v)` |
+| `--model_cls` | str | ❌ | wan2.1 | Model class: `wan2.1` (standard model) or `wan2.1_distill` (distilled model, faster inference) |
+| `--model_size` | str | ✅ | - | Model size: `14b (image-to-video or text-to-video)` or `1.3b (text-to-video)` |
 | `--task` | str | ✅ | - | Task type: `i2v` (image-to-video) or `t2v` (text-to-video) |
 | `--server_port` | int | ❌ | 7862 | Server port |
 | `--server_name` | str | ❌ | 0.0.0.0 | Server IP address |
@@ -197,23 +248,11 @@ After enabling "Auto-configure Inference Options", the system will automatically
 
 **💡 For devices with insufficient VRAM or performance constraints**:
 
-- **🎯 Model Selection**: Prioritize using distilled version models (StepDistill-CfgDistill)
+- **🎯 Model Selection**: Prioritize using distilled version models (`wan2.1_distill`)
 - **⚡ Inference Steps**: Recommend setting to 4 steps
 - **🔧 CFG Settings**: Recommend disabling CFG option to improve generation speed
 - **🔄 Auto-Configuration**: Enable "Auto-configure Inference Options"
-
-
-## 📁 File Structure
-
-```
-lightx2v/app/
-├── gradio_demo.py          # English interface demo
-├── gradio_demo_zh.py       # Chinese interface demo
-├── run_gradio.sh          # Startup script
-├── README.md              # Documentation
-├── saved_videos/          # Generated video save directory
-└── inference_logs.log     # Inference logs
-```
+- **💾 Storage Optimization**: Ensure models are stored on SSD for optimal loading performance
 
 ## 🎨 Interface Description
 
@@ -278,5 +317,6 @@ nvidia-smi
 htop
 ```
 
+Welcome to submit Issues and Pull Requests to improve this project!
 
 **Note**: Please comply with relevant laws and regulations when using videos generated by this tool, and do not use them for illegal purposes.

docs/EN/source/deploy_guides/deploy_local_windows.md

-# Local Windows Deployment Guide
+# Windows Local Deployment Guide
 
-This document provides detailed instructions for deploying LightX2V locally on Windows environments.
+## 📖 Overview
 
-## System Requirements
+This document provides detailed instructions for deploying LightX2V locally on Windows environments, including batch file inference, Gradio Web interface inference, and other usage methods.
 
-Before getting started, please ensure your system meets the following requirements:
+## 🚀 Quick Start
 
-- **Operating System**: Windows 10/11
-- **Graphics Card**: NVIDIA GPU (with CUDA support)
-- **VRAM**: At least 8GB VRAM
-- **Memory**: At least 16GB RAM
-- **Storage**: 20GB+ available disk space
-- **Environment Manager**: Anaconda or Miniconda installed
-- **Network Tools**: Git (for cloning repositories)
-
-## Deployment Steps
-
-### Step 1: Check CUDA Version
-
-First, verify your GPU driver and CUDA version by running the following command in Command Prompt:
-
-```bash
-nvidia-smi
-```
-
-Note the **CUDA Version** displayed in the output, as you'll need to match this version during subsequent installations.
-
-### Step 2: Create Python Environment
+### Environment Requirements
 
-Create an isolated conda environment, we recommend using Python 3.12:
+#### Hardware Requirements
+- **GPU**: NVIDIA GPU, recommended 8GB+ VRAM
+- **Memory**: Recommended 16GB+ RAM
+- **Storage**: Strongly recommended to use SSD solid-state drives, mechanical hard drives will cause slow model loading
 
-```bash
-# Create new environment (using Python 3.12 as example)
-conda create -n lightx2v python=3.12 -y
+#### Software Requirements
+- **Operating System**: Windows 10/11
+- **Python**: 3.12 or higher version
+- **CUDA**: 12.4 or higher version
+- **Dependencies**: Refer to LightX2V project's requirements_win.txt
 
-# Activate environment
-conda activate lightx2v
-```
+## 🎯 Usage Methods
 
-> 💡 **Tip**: Python 3.10 or higher is recommended for optimal compatibility.
+### Method 1: Using Batch File Inference
 
-### Step 3: Install PyTorch Framework
+Refer to [Quick Start Guide](../getting_started/quickstart.md) to install environment, and use [batch files](https://github.com/ModelTC/LightX2V/tree/main/scripts/win) to run.
 
-#### Method 1: Download Official Wheel Packages (Recommended)
+### Method 2: Using Gradio Web Interface Inference
 
-1. Visit the [PyTorch Official Wheel Download Page](https://download.pytorch.org/whl/torch/)
-2. Select the appropriate wheel package, ensuring you match:
-   - **Python Version**: Must match your environment (cp312 means Python 3.12)
-   - **CUDA Version**: Must match your GPU driver
-   - **Platform**: Choose Windows version (win_amd64)
+#### Manual Gradio Configuration
 
-**Example for Python 3.12 + PyTorch 2.6 + CUDA 12.4:**
+Refer to [Quick Start Guide](../getting_started/quickstart.md) to install environment, refer to [Gradio Deployment Guide](./deploy_gradio.md)
 
-```
-torch-2.6.0+cu124-cp312-cp312-win_amd64.whl
-```
+#### One-Click Gradio Startup (Recommended)
 
-After downloading, install the packages:
+**📦 Download Software Package**
+- [Baidu Cloud](https://pan.baidu.com/s/1ef3hEXyIuO0z6z9MoXe4nQ?pwd=7g4f)
+- [Quark Cloud](https://pan.quark.cn/s/36a0cdbde7d9)
 
-```bash
-# Install PyTorch (replace with actual file path)
-pip install torch-2.6.0+cu124-cp312-cp312-win_amd64.whl
+**📁 Directory Structure**
+After extraction, ensure the directory structure is as follows:
 
-# Install accompanying vision and audio packages
-pip install torchvision==0.21.0 torchaudio==2.6.0
 ```
-
-#### Method 2: Direct pip Installation
-
-If you prefer direct installation, use the following command:
-
-```bash
-# Example: CUDA 12.4 version
-pip install torch==2.6.0+cu124 torchvision==0.21.0+cu124 torchaudio==2.6.0+cu124 --index-url https://download.pytorch.org/whl/cu124
+├── env/                        # LightX2V environment directory
+├── LightX2V/                   # LightX2V project directory
+├── start_lightx2v.bat          # One-click startup script
+├── lightx2v_config.txt         # Configuration file
+├── LightX2V使用说明.txt         # LightX2V usage instructions
+└── models/                     # Model storage directory
+    ├── 说明.txt                       # Model documentation
+    ├── Wan2.1-I2V-14B-480P-Lightx2v/  # Image-to-video model (480P)
+    ├── Wan2.1-I2V-14B-720P-Lightx2v/  # Image-to-video model (720P)
+    ├── Wan2.1-I2V-14B-480P-StepDistill-CfgDistil-Lightx2v/  # Image-to-video model (4-step distillation, 480P)
+    ├── Wan2.1-I2V-14B-720P-StepDistill-CfgDistil-Lightx2v/  # Image-to-video model (4-step distillation, 720P)
+    ├── Wan2.1-T2V-1.3B-Lightx2v/      # Text-to-video model (1.3B parameters)
+    ├── Wan2.1-T2V-14B-Lightx2v/       # Text-to-video model (14B parameters)
+    └── Wan2.1-T2V-14B-StepDistill-CfgDistill-Lightx2v/      # Text-to-video model (4-step distillation)
 ```
 
-### Step 4: Install Windows Version vLLM
+**📋 Configuration Parameters**
 
-Download the corresponding wheel package from the [vllm-windows releases page](https://github.com/SystemPanic/vllm-windows/releases).
+Edit the `lightx2v_config.txt` file and modify the following parameters as needed:
 
-**Version Matching Requirements:**
-- Python version must match (e.g., cp312)
-- PyTorch version must match
-- CUDA version must match
+```ini
+# Task type (i2v: image-to-video, t2v: text-to-video)
+task=i2v
 
-**Recommended v0.9.1 Installation:**
+# Interface language (zh: Chinese, en: English)
+lang=en
 
-```bash
-pip install vllm-0.9.1+cu124-cp312-cp312-win_amd64.whl
-```
-
-> ⚠️ **Note**: Please select the appropriate wheel package filename based on your specific environment.
+# Server port
+port=8032
 
-### Step 5: Install Attention Mechanism Operators
+# GPU device ID (0, 1, 2...)
+gpu=0
 
-You can choose to install either Flash Attention 2 or SageAttention 2. **SageAttention 2 is strongly recommended**.
+# Model size (14b: 14B parameter model, 1.3b: 1.3B parameter model)
+model_size=14b
 
-#### Option A: Flash Attention 2
-
-```bash
-pip install flash-attn==2.7.2.post1
+# Model class (wan2.1: standard model, wan2.1_distill: distilled model)
+model_cls=wan2.1
 ```
 
-#### Option B: SageAttention 2 (Recommended)
-
-**Download Sources:**
-- [Windows Version 1](https://github.com/woct0rdho/SageAttention/releases)
-- [Windows Version 2](https://github.com/sdbds/SageAttention-for-windows/releases)
+**⚠️ Important Note**: If using distilled models (model names containing StepDistill-CfgDistil field), please set `model_cls` to `wan2.1_distill`
 
-**Version Selection Guidelines:**
-- Python version must match
-- PyTorch version must match
-- **CUDA version can be flexible** (SageAttention doesn't use breaking APIs yet)
+**🚀 Start Service**
 
-**Recommended Installation Version:**
+Double-click to run the `start_lightx2v.bat` file, the script will:
+1. Automatically read configuration file
+2. Verify model paths and file integrity
+3. Start Gradio Web interface
+4. Automatically open browser to access service
 
-```bash
-pip install sageattention-2.1.1+cu126torch2.6.0-cp312-cp312-win_amd64.whl
-```
+**💡 Usage Suggestion**: After opening the Gradio Web page, it's recommended to check "Auto-configure Inference Options", the system will automatically select appropriate optimization configurations for your machine. When reselecting resolution, you also need to re-check "Auto-configure Inference Options".
 
-**Verify SageAttention Installation:**
+**⚠️ Important Note**: On first run, the system will automatically extract the environment file `env.zip`, which may take several minutes. Please be patient. Subsequent launches will skip this step. You can also manually extract the `env.zip` file to the current directory to save time on first startup.
 
-After installation, we recommend running a verification script to ensure proper functionality:
+### Method 3: Using ComfyUI Inference
 
-> 📝 **Testing**: You can also run the [official test script](https://github.com/woct0rdho/SageAttention/blob/main/tests/test_sageattn.py) for more detailed functionality verification.
+This guide will instruct you on how to download and use the portable version of the Lightx2v-ComfyUI environment, so you can avoid manual environment configuration steps. This is suitable for users who want to quickly start experiencing accelerated video generation with Lightx2v on Windows systems.
 
-### Step 6: Get LightX2V Project Code
+#### Download the Windows Portable Environment:
 
-Clone the LightX2V project from GitHub and install Windows-specific dependencies:
+- [Baidu Cloud Download](https://pan.baidu.com/s/1FVlicTXjmXJA1tAVvNCrBw?pwd=wfid), extraction code: wfid
 
-```bash
-# Clone project code
-git clone https://github.com/ModelTC/LightX2V.git
+The portable environment already packages all Python runtime dependencies, including the code and dependencies for ComfyUI and LightX2V. After downloading, simply extract to use.
 
-# Enter project directory
-cd LightX2V
+After extraction, the directory structure is as follows:
 
-# Install Windows-specific dependencies
-pip install -r requirements_win.txt
+```shell
+lightx2v_env
+├──📂 ComfyUI                    # ComfyUI code
+├──📂 portable_python312_embed   # Standalone Python environment
+└── run_nvidia_gpu.bat            # Windows startup script (double-click to start)
 ```
 
-> 🔍 **Note**: We use `requirements_win.txt` instead of the standard `requirements.txt` because Windows environments may require specific package versions or additional dependencies.
-
-
-## Troubleshooting
-
-### 1. CUDA Version Mismatch
-
-**Symptoms**: CUDA-related errors occur
-
-**Solutions**:
-- Verify GPU driver supports required CUDA version
-- Re-download matching wheel packages
-- Use `nvidia-smi` to check maximum supported CUDA version
-
-### 2. Dependency Conflicts
-
-**Symptoms**: Package version conflicts or import errors
-
-**Solutions**:
-- Remove existing environment: `conda env remove -n lightx2v`
-- Recreate environment and install dependencies strictly by version requirements
-- Use virtual environments to isolate dependencies for different projects
-
-### 3. Wheel Package Download Issues
-
-**Symptoms**: Slow download speeds or connection failures
+#### Start ComfyUI
 
-**Solutions**:
-- Use download tools or browser for direct downloads
-- Look for domestic mirror sources
-- Check network connections and firewall settings
+Directly double-click the run_nvidia_gpu.bat file. The system will open a Command Prompt window and run the program. The first startup may take a while, please be patient. After startup is complete, the browser will automatically open and display the ComfyUI frontend interface.
 
-## Next Steps
+![i2v example workflow](../../../../assets/figs/portabl_windows/pic1.png)
 
-After completing the environment setup, you can:
+The plugin used by LightX2V-ComfyUI is [ComfyUI-Lightx2vWrapper](https://github.com/ModelTC/ComfyUI-Lightx2vWrapper). Example workflows can be obtained from this project.
 
-- 📚 Check the [Quick Start Guide](../getting_started/quickstart.md) (skip environment installation steps)
-- 🌐 Use the [Gradio Web Interface](./deploy_gradio.md) for visual operations (skip environment installation steps)
+#### Tested Graphics Cards (offload mode)
 
-## Version Compatibility Reference
+- Tested model: `Wan2.1-I2V-14B-480P`
 
-| Component | Recommended Version |
-|-----------|-------------------|
-| Python | 3.12 |
-| PyTorch | 2.6.0+cu124 |
-| vLLM | 0.9.1+cu124 |
-| SageAttention | 2.1.1+cu126torch2.6.0 |
-| CUDA | 12.4+ |
+| GPU Model   | Task Type   | VRAM Capacity | Actual Max VRAM Usage | Actual Max RAM Usage |
+|:-----------|:------------|:--------------|:---------------------|:---------------------|
+| 3090Ti     | I2V         | 24G           | 6.1G                 | 7.1G                 |
+| 3080Ti     | I2V         | 12G           | 6.1G                 | 7.1G                 |
+| 3060Ti     | I2V         | 8G            | 6.1G                 | 7.1G                 |
 
----
 
-💡 **Pro Tip**: If you encounter other issues, we recommend first checking whether all component versions match properly, as most problems stem from version incompatibilities.
+#### Environment Packaging and Usage Reference
+- [ComfyUI](https://github.com/comfyanonymous/ComfyUI)
+- [Portable-Windows-ComfyUI-Docs](https://docs.comfy.org/zh-CN/installation/comfyui_portable_windows#portable-%E5%8F%8A%E8%87%AA%E9%83%A8%E7%BD%B2)

docs/EN/source/deploy_guides/for_low_latency.md

-# 低延迟场景部署
+# Deployment for Low Latency Scenarios
 
-xxx
+In low latency scenarios, we pursue faster speed, ignoring issues such as video memory and RAM overhead. We provide two solutions:
+
+## 💡 Solution 1: Inference with Step Distillation Model
+
+This solution can refer to the [Step Distillation Documentation](https://lightx2v-en.readthedocs.io/en/latest/method_tutorials/step_distill.html)
+
+🧠 **Step Distillation** is a very direct acceleration inference solution for video generation models. By distilling from 50 steps to 4 steps, the time consumption will be reduced to 4/50 of the original. At the same time, under this solution, it can still be combined with the following solutions:
+1. [Efficient Attention Mechanism Solution](https://lightx2v-en.readthedocs.io/en/latest/method_tutorials/attention.html)
+2. [Model Quantization](https://lightx2v-en.readthedocs.io/en/latest/method_tutorials/quantization.html)
+
+## 💡 Solution 2: Inference with Non-Step Distillation Model
+
+Step distillation requires relatively large training resources, and the model after step distillation may have degraded video dynamic range.
+
+For the original model without step distillation, we can use the following solutions or a combination of multiple solutions for acceleration:
+
+1. [Parallel Inference](https://lightx2v-en.readthedocs.io/en/latest/method_tutorials/parallel.html) for multi-GPU parallel acceleration.
+2. [Feature Caching](https://lightx2v-en.readthedocs.io/en/latest/method_tutorials/cache.html) to reduce the actual inference steps.
+3. [Efficient Attention Mechanism Solution](https://lightx2v-en.readthedocs.io/en/latest/method_tutorials/attention.html) to accelerate Attention inference.
+4. [Model Quantization](https://lightx2v-en.readthedocs.io/en/latest/method_tutorials/quantization.html) to accelerate Linear layer inference.
+5. [Variable Resolution Inference](https://lightx2v-en.readthedocs.io/en/latest/method_tutorials/changing_resolution.html) to reduce the resolution of intermediate inference steps.
+
+## ⚠️ Note
+
+Some acceleration solutions currently cannot be used together, and we are working to resolve this issue.
+
+If you have any questions, feel free to report bugs or request features in [🐛 GitHub Issues](https://github.com/ModelTC/lightx2v/issues)