Merge branch 'main' into audio_r2v

.gitignore

@@ -25,3 +25,5 @@
 build/
 dist/
 .cache/
+server_cache/
+app/.gradio/

README.md

-# LightX2V: Light Video Generation Inference Framework
+<div align="center" style="font-family: charter;">
+  <h1>⚡️ LightX2V:<br> Lightweight Video Generation Inference Framework</h1>
 
-<div align="center" id="lightx2v">
 <img alt="logo" src="assets/img_lightx2v.png" width=75%></img>
 
 [![License](https://img.shields.io/badge/License-Apache_2.0-blue.svg)](https://opensource.org/licenses/Apache-2.0)
 [![Ask DeepWiki](https://deepwiki.com/badge.svg)](https://deepwiki.com/ModelTC/lightx2v)
 [![Doc](https://img.shields.io/badge/docs-English-99cc2)](https://lightx2v-en.readthedocs.io/en/latest)
 [![Doc](https://img.shields.io/badge/文档-中文-99cc2)](https://lightx2v-zhcn.readthedocs.io/zh-cn/latest)
+[![Papers](https://img.shields.io/badge/论文集-中文-99cc2)](https://lightx2v-papers-zhcn.readthedocs.io/zh-cn/latest)
 [![Docker](https://badgen.net/badge/icon/docker?icon=docker&label)](https://hub.docker.com/r/lightx2v/lightx2v/tags)
 
+**\[ English | [中文](README_zh.md) \]**
+
 </div>
 
 --------------------------------------------------------------------------------
 
-## Supported Model List
+**LightX2V** is an advanced lightweight video generation inference framework engineered to deliver efficient, high-performance video synthesis solutions. This unified platform integrates multiple state-of-the-art video generation techniques, supporting diverse generation tasks including text-to-video (T2V) and image-to-video (I2V). **X2V represents the transformation of different input modalities (X, such as text or images) into video output (V)**.
 
-✅ [HunyuanVideo-T2V](https://huggingface.co/tencent/HunyuanVideo)
+## 💡 Quick Start
 
-✅ [HunyuanVideo-I2V](https://huggingface.co/tencent/HunyuanVideo-I2V)
+For comprehensive usage instructions, please refer to our documentation: **[English Docs](https://lightx2v-en.readthedocs.io/en/latest/) | [中文文档](https://lightx2v-zhcn.readthedocs.io/zh-cn/latest/)**
 
-✅ [Wan2.1-T2V](https://huggingface.co/Wan-AI/Wan2.1-T2V-1.3B)
 
-✅ [Wan2.1-I2V](https://huggingface.co/Wan-AI/Wan2.1-I2V-14B-480P)
+## 🤖 Supported Model Ecosystem
 
-✅ [Wan2.1-T2V-StepDistill-CfgDistill](https://huggingface.co/lightx2v/Wan2.1-T2V-14B-StepDistill-CfgDistill)
+### Official Open-Source Models
+- ✅ [HunyuanVideo](https://huggingface.co/tencent/HunyuanVideo)
+- ✅ [Wan2.1](https://huggingface.co/Wan-AI/)
+- ✅ [SkyReels-V2-DF](https://huggingface.co/Skywork/SkyReels-V2-DF-14B-540P)
+- ✅ [CogVideoX1.5-5B-T2V](https://huggingface.co/THUDM/CogVideoX1.5-5B)
 
-✅ [Wan2.1-T2V-CausVid](https://huggingface.co/lightx2v/Wan2.1-T2V-14B-CausVid)
+### Quantized Models
+- ✅ [Wan2.1-T2V-1.3B-Lightx2v](https://huggingface.co/lightx2v/Wan2.1-T2V-1.3B-Lightx2v)
+- ✅ [Wan2.1-T2V-14B-Lightx2v](https://huggingface.co/lightx2v/Wan2.1-T2V-14B-Lightx2v)
+- ✅ [Wan2.1-I2V-14B-480P-Lightx2v](https://huggingface.co/lightx2v/Wan2.1-I2V-14B-480P-Lightx2v)
+- ✅ [Wan2.1-I2V-14B-720P-Lightx2v](https://huggingface.co/lightx2v/Wan2.1-I2V-14B-720P-Lightx2v)
 
-✅ [SkyReels-V2-DF](https://huggingface.co/Skywork/SkyReels-V2-DF-14B-540P)
+### Distilled Models (**🚀 Recommended: 4-step inference**)
+- ✅ [Wan2.1-T2V-14B-StepDistill-CfgDistill-Lightx2v](https://huggingface.co/lightx2v/Wan2.1-T2V-14B-StepDistill-CfgDistill-Lightx2v)
+- ✅ [Wan2.1-I2V-14B-480P-StepDistill-CfgDistill-Lightx2v](https://huggingface.co/lightx2v/Wan2.1-I2V-14B-480P-StepDistill-CfgDistill-Lightx2v)
+- ✅ [Wan2.1-I2V-14B-720P-StepDistill-CfgDistill-Lightx2v](https://huggingface.co/lightx2v/Wan2.1-I2V-14B-720P-StepDistill-CfgDistill-Lightx2v)
 
-✅ [CogVideoX1.5-5B-T2V](https://huggingface.co/THUDM/CogVideoX1.5-5B)
+### Autoregressive Models
+- ✅ [Wan2.1-T2V-CausVid](https://huggingface.co/lightx2v/Wan2.1-T2V-14B-CausVid)
 
-## How to Run
 
-Please refer to the [documentation](https://github.com/ModelTC/lightx2v/tree/main/docs) in lightx2v.
+## 🚀 Core Features
 
-## Contributing Guidelines
+### 🎯 **Ultimate Performance Optimization**
+- **🔥 SOTA Inference Speed**: Achieve **~15x** acceleration via step distillation and system optimization (single GPU)
+- **⚡️ Revolutionary 4-Step Distillation**: Compress original 40-50 step inference to just 4 steps without CFG requirements
+- **🛠️ Advanced Operator Support**: Integrated with cutting-edge operators including [Sage Attention](https://github.com/thu-ml/SageAttention), [Flash Attention](https://github.com/Dao-AILab/flash-attention), [Radial Attention](https://github.com/mit-han-lab/radial-attention), [q8-kernel](https://github.com/KONAKONA666/q8_kernels), [sgl-kernel](https://github.com/sgl-project/sglang/tree/main/sgl-kernel), [vllm](https://github.com/vllm-project/vllm)
 
-We have prepared a `pre-commit` hook to enforce consistent code formatting across the project.
+### 💾 **Resource-Efficient Deployment**
+- **💡 Breaking Hardware Barriers**: Run 14B models for 480P/720P video generation with only **8GB VRAM + 16GB RAM**
+- **🔧 Intelligent Parameter Offloading**: Advanced disk-CPU-GPU three-tier offloading architecture with phase/block-level granular management
+- **⚙️ Comprehensive Quantization**: Support for `w8a8-int8`, `w8a8-fp8`, `w4a4-nvfp4` and other quantization strategies
 
-1. Install the required dependencies:
+### 🎨 **Rich Feature Ecosystem**
+- **📈 Smart Feature Caching**: Intelligent caching mechanisms to eliminate redundant computations
+- **🔄 Parallel Inference**: Multi-GPU parallel processing for enhanced performance
+- **📱 Flexible Deployment Options**: Support for Gradio, service deployment, ComfyUI and other deployment methods
+- **🎛️ Dynamic Resolution Inference**: Adaptive resolution adjustment for optimal generation quality
 
-```shell
-pip install ruff pre-commit
-```
 
-2. Then, run the following command before commit:
+## 🏆 Performance Benchmarks
+
+For detailed performance metrics and comparisons, please refer to our [benchmark documentation](https://github.com/ModelTC/LightX2V/blob/main/docs/EN/source/getting_started/benchmark_source.md).
+
+[Detailed Service Deployment Guide →](https://lightx2v-en.readthedocs.io/en/latest/deploy_guides/deploy_service.html)
+
+## 📚 Technical Documentation
+
+### 📖 **Method Tutorials**
+- [Model Quantization](https://lightx2v-en.readthedocs.io/en/latest/method_tutorials/quantization.html) - Comprehensive guide to quantization strategies
+- [Feature Caching](https://lightx2v-en.readthedocs.io/en/latest/method_tutorials/cache.html) - Intelligent caching mechanisms
+- [Attention Mechanisms](https://lightx2v-en.readthedocs.io/en/latest/method_tutorials/attention.html) - State-of-the-art attention operators
+- [Parameter Offloading](https://lightx2v-en.readthedocs.io/en/latest/method_tutorials/offload.html) - Three-tier storage architecture
+- [Parallel Inference](https://lightx2v-en.readthedocs.io/en/latest/method_tutorials/parallel.html) - Multi-GPU acceleration strategies
+- [Step Distillation](https://lightx2v-en.readthedocs.io/en/latest/method_tutorials/step_distill.html) - 4-step inference technology
+
+### 🛠️ **Deployment Guides**
+- [Low-Resource Deployment](https://lightx2v-en.readthedocs.io/en/latest/deploy_guides/for_low_resource.html) - Optimized 8GB VRAM solutions
+- [Low-Latency Deployment](https://lightx2v-en.readthedocs.io/en/latest/deploy_guides/for_low_latency.html) - Ultra-fast inference optimization
+- [Gradio Deployment](https://lightx2v-en.readthedocs.io/en/latest/deploy_guides/deploy_gradio.html) - Web interface setup
+- [Service Deployment](https://lightx2v-en.readthedocs.io/en/latest/deploy_guides/deploy_service.html) - Production API service deployment
+
+## 🧾 Contributing Guidelines
+
+We maintain code quality through automated pre-commit hooks to ensure consistent formatting across the project.
 
-```shell
-pre-commit run --all-files
+> [!TIP]
+> **Setup Instructions:**
+>
+> 1. Install required dependencies:
+> ```shell
+> pip install ruff pre-commit
+> ```
+>
+> 2. Run before committing:
+> ```shell
+> pre-commit run --all-files
+> ```
+
+We appreciate your contributions to making LightX2V better!
+
+## 🤝 Acknowledgments
+
+We extend our gratitude to all the model repositories and research communities that inspired and contributed to the development of LightX2V. This framework builds upon the collective efforts of the open-source community.
+
+## 🌟 Star History
+
+[![Star History Chart](https://api.star-history.com/svg?repos=ModelTC/lightx2v&type=Timeline)](https://star-history.com/#ModelTC/lightx2v&Timeline)
+
+## ✏️ Citation
+
+If you find LightX2V useful in your research, please consider citing our work:
+
+```bibtex
+@misc{lightx2v,
+ author = {LightX2V Contributors},
+ title = {LightX2V: Light Video Generation Inference Framework},
+ year = {2025},
+ publisher = {GitHub},
+ journal = {GitHub repository},
+ howpublished = {\url{https://github.com/ModelTC/lightx2v}},
+}
 ```
 
-Thank you for your contributions!
+## 📞 Contact & Support
 
+For questions, suggestions, or support, please feel free to reach out through:
+- 🐛 [GitHub Issues](https://github.com/ModelTC/lightx2v/issues) - Bug reports and feature requests
+- 💬 [GitHub Discussions](https://github.com/ModelTC/lightx2v/discussions) - Community discussions and Q&A
 
-## Acknowledgments
+---
 
-We built the code for this repository by referencing the code repositories involved in all the models mentioned above.
+<div align="center">
+Built with ❤️ by the LightX2V team
+</div>

README_zh.md

+<div align="center" style="font-family: charter;">
+  <h1>⚡️ LightX2V:<br> 轻量级视频生成推理框架</h1>
+
+<img alt="logo" src="assets/img_lightx2v.png" width=75%></img>
+
+[![License](https://img.shields.io/badge/License-Apache_2.0-blue.svg)](https://opensource.org/licenses/Apache-2.0)
+[![Ask DeepWiki](https://deepwiki.com/badge.svg)](https://deepwiki.com/ModelTC/lightx2v)
+[![Doc](https://img.shields.io/badge/docs-English-99cc2)](https://lightx2v-en.readthedocs.io/en/latest)
+[![Doc](https://img.shields.io/badge/文档-中文-99cc2)](https://lightx2v-zhcn.readthedocs.io/zh-cn/latest)
+[![Papers](https://img.shields.io/badge/论文集-中文-99cc2)](https://lightx2v-papers-zhcn.readthedocs.io/zh-cn/latest)
+[![Docker](https://badgen.net/badge/icon/docker?icon=docker&label)](https://hub.docker.com/r/lightx2v/lightx2v/tags)
+
+**\[ [English](README.md) | 中文 \]**
+
+</div>
+
+--------------------------------------------------------------------------------
+
+**LightX2V** 是一个先进的轻量级视频生成推理框架，专为提供高效、高性能的视频合成解决方案而设计。该统一平台集成了多种前沿的视频生成技术，支持文本生成视频(T2V)和图像生成视频(I2V)等多样化生成任务。**X2V 表示将不同的输入模态(X，如文本或图像)转换为视频输出(V)**。
+
+## 💡 快速开始
+
+详细使用说明请参考我们的文档：**[英文文档](https://lightx2v-en.readthedocs.io/en/latest/) | [中文文档](https://lightx2v-zhcn.readthedocs.io/zh-cn/latest/)**
+
+## 🤖 支持的模型生态
+
+### 官方开源模型
+- ✅ [HunyuanVideo](https://huggingface.co/tencent/HunyuanVideo)
+- ✅ [Wan2.1](https://huggingface.co/Wan-AI/)
+- ✅ [SkyReels-V2-DF](https://huggingface.co/Skywork/SkyReels-V2-DF-14B-540P)
+- ✅ [CogVideoX1.5-5B-T2V](https://huggingface.co/THUDM/CogVideoX1.5-5B)
+
+### 量化模型
+- ✅ [Wan2.1-T2V-1.3B-Lightx2v](https://huggingface.co/lightx2v/Wan2.1-T2V-1.3B-Lightx2v)
+- ✅ [Wan2.1-T2V-14B-Lightx2v](https://huggingface.co/lightx2v/Wan2.1-T2V-14B-Lightx2v)
+- ✅ [Wan2.1-I2V-14B-480P-Lightx2v](https://huggingface.co/lightx2v/Wan2.1-I2V-14B-480P-Lightx2v)
+- ✅ [Wan2.1-I2V-14B-720P-Lightx2v](https://huggingface.co/lightx2v/Wan2.1-I2V-14B-720P-Lightx2v)
+
+### 蒸馏模型 (**🚀 推荐：4步推理**)
+- ✅ [Wan2.1-T2V-14B-StepDistill-CfgDistill-Lightx2v](https://huggingface.co/lightx2v/Wan2.1-T2V-14B-StepDistill-CfgDistill-Lightx2v)
+- ✅ [Wan2.1-I2V-14B-480P-StepDistill-CfgDistill-Lightx2v](https://huggingface.co/lightx2v/Wan2.1-I2V-14B-480P-StepDistill-CfgDistill-Lightx2v)
+- ✅ [Wan2.1-I2V-14B-720P-StepDistill-CfgDistill-Lightx2v](https://huggingface.co/lightx2v/Wan2.1-I2V-14B-720P-StepDistill-CfgDistill-Lightx2v)
+
+### 自回归模型
+- ✅ [Wan2.1-T2V-CausVid](https://huggingface.co/lightx2v/Wan2.1-T2V-14B-CausVid)
+
+## 🚀 核心特性
+
+### 🎯 **极致性能优化**
+- **🔥 SOTA推理速度**: 通过步数蒸馏和系统优化实现**15倍**极速加速(单GPU)
+- **⚡️ 革命性4步蒸馏**: 将原始40-50步推理压缩至仅需4步，且无需CFG配置
+- **🛠️ 先进算子支持**: 集成顶尖算子，包括[Sage Attention](https://github.com/thu-ml/SageAttention)、[Flash Attention](https://github.com/Dao-AILab/flash-attention)、[Radial Attention](https://github.com/mit-han-lab/radial-attention)、[q8-kernel](https://github.com/KONAKONA666/q8_kernels)、[sgl-kernel](https://github.com/sgl-project/sglang/tree/main/sgl-kernel)、[vllm](https://github.com/vllm-project/vllm)
+
+### 💾 **资源高效部署**
+- **💡 突破硬件限制**: **仅需8GB显存 + 16GB内存**即可运行14B模型生成480P/720P视频
+- **🔧 智能参数卸载**: 先进的磁盘-CPU-GPU三级卸载架构，支持阶段/块级别的精细化管理
+- **⚙️ 全面量化支持**: 支持`w8a8-int8`、`w8a8-fp8`、`w4a4-nvfp4`等多种量化策略
+
+### 🎨 **丰富功能生态**
+- **📈 智能特征缓存**: 智能缓存机制，消除冗余计算，提升效率
+- **🔄 并行推理加速**: 多GPU并行处理，显著提升性能表现
+- **📱 灵活部署选择**: 支持Gradio、服务化部署、ComfyUI等多种部署方式
+- **🎛️ 动态分辨率推理**: 自适应分辨率调整，优化生成质量
+
+
+## 🏆 性能基准测试
+
+详细的性能指标和对比分析，请参考我们的[基准测试文档](https://github.com/ModelTC/LightX2V/blob/main/docs/ZH_CN/source/getting_started/benchmark_source.md)。
+
+[详细服务部署指南 →](https://lightx2v-zhcn.readthedocs.io/zh-cn/latest/deploy_guides/deploy_service.html)
+
+## 📚 技术文档
+
+### 📖 **方法教程**
+- [模型量化](https://lightx2v-zhcn.readthedocs.io/zh-cn/latest/method_tutorials/quantization.html) - 量化策略全面指南
+- [特征缓存](https://lightx2v-zhcn.readthedocs.io/zh-cn/latest/method_tutorials/cache.html) - 智能缓存机制详解
+- [注意力机制](https://lightx2v-zhcn.readthedocs.io/zh-cn/latest/method_tutorials/attention.html) - 前沿注意力算子
+- [参数卸载](https://lightx2v-zhcn.readthedocs.io/zh-cn/latest/method_tutorials/offload.html) - 三级存储架构
+- [并行推理](https://lightx2v-zhcn.readthedocs.io/zh-cn/latest/method_tutorials/parallel.html) - 多GPU加速策略
+- [步数蒸馏](https://lightx2v-zhcn.readthedocs.io/zh-cn/latest/method_tutorials/step_distill.html) - 4步推理技术
+
+### 🛠️ **部署指南**
+- [低资源场景部署](https://lightx2v-zhcn.readthedocs.io/zh-cn/latest/deploy_guides/for_low_resource.html) - 优化的8GB显存解决方案
+- [低延迟场景部署](https://lightx2v-zhcn.readthedocs.io/zh-cn/latest/deploy_guides/for_low_latency.html) - 极速推理优化
+- [Gradio部署](https://lightx2v-zhcn.readthedocs.io/zh-cn/latest/deploy_guides/deploy_gradio.html) - Web界面搭建
+- [服务化部署](https://lightx2v-zhcn.readthedocs.io/zh-cn/latest/deploy_guides/deploy_service.html) - 生产级API服务部署
+
+## 🧾 代码贡献指南
+
+我们通过自动化的预提交钩子来保证代码质量，确保项目代码格式的一致性。
+
+> [!TIP]
+> **安装说明：**
+>
+> 1. 安装必要的依赖：
+> ```shell
+> pip install ruff pre-commit
+> ```
+>
+> 2. 提交前运行：
+> ```shell
+> pre-commit run --all-files
+> ```
+
+感谢您为LightX2V的改进做出贡献！
+
+## 🤝 致谢
+
+我们向所有启发和促进LightX2V开发的模型仓库和研究社区表示诚挚的感谢。此框架基于开源社区的集体努力而构建。
+
+## 🌟 Star 历史
+
+[![Star History Chart](https://api.star-history.com/svg?repos=ModelTC/lightx2v&type=Timeline)](https://star-history.com/#ModelTC/lightx2v&Timeline)
+
+## ✏️ 引用
+
+如果您发现LightX2V对您的研究有用，请考虑引用我们的工作：
+
+```bibtex
+@misc{lightx2v,
+ author = {LightX2V Contributors},
+ title = {LightX2V: Light Video Generation Inference Framework},
+ year = {2025},
+ publisher = {GitHub},
+ journal = {GitHub repository},
+ howpublished = {\url{https://github.com/ModelTC/lightx2v}},
+}
+```
+
+## 📞 联系与支持
+
+如有任何问题、建议或需要支持，欢迎通过以下方式联系我们：
+- 🐛 [GitHub Issues](https://github.com/ModelTC/lightx2v/issues) - 错误报告和功能请求
+- 💬 [GitHub Discussions](https://github.com/ModelTC/lightx2v/discussions) - 社区讨论和问答
+
+---
+
+<div align="center">
+由 LightX2V 团队用 ❤️ 构建
+</div>

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app/README.md

+# Gradio Demo
+
+Please refer our gradio deployment doc:
+
+[English doc: Gradio Deployment](https://lightx2v-en.readthedocs.io/en/latest/deploy_guides/deploy_gradio.html)
+
+[中文文档: Gradio 部署](https://lightx2v-zhcn.readthedocs.io/zh-cn/latest/deploy_guides/deploy_gradio.html)

app/run_gradio.sh

 #!/bin/bash
 
-lightx2v_path=/mtc/gushiqiao/llmc_workspace/lightx2v_new/lightx2v
-model_path=/data/nvme0/gushiqiao/models/I2V/Wan2.1-I2V-14B-720P-Lightx2v-Step-Distill
+# Lightx2v Gradio Demo Startup Script
+# Supports both Image-to-Video (i2v) and Text-to-Video (t2v) modes
 
-export CUDA_VISIBLE_DEVICES=7
+# ==================== Configuration Area ====================
+# ⚠️  Important: Please modify the following paths according to your actual environment
+
+# 🚨 Storage Performance Tips 🚨
+# 💾 Strongly recommend storing model files on SSD solid-state drives!
+# 📈 SSD can significantly improve model loading speed and inference performance
+# 🐌 Using mechanical hard drives (HDD) may cause slow model loading and affect overall experience
+
+
+# Lightx2v project root directory path
+# Example: /home/user/lightx2v or /data/video_gen/lightx2v
+lightx2v_path=/path/to/lightx2v
+# Model path configuration
+# Image-to-video model path (for i2v tasks)
+# Example: /path/to/Wan2.1-I2V-14B-720P-Lightx2v
+i2v_model_path=/path/to/Wan2.1-I2V-14B-720P-Lightx2v-Step-Distill
+
+# Text-to-video model path (for t2v tasks)
+# Example: /path/to/Wan2.1-T2V-1.3B
+t2v_model_path=/path/to/Wan2.1-T2V-1.3B
+
+# Model size configuration
+# Default model size (14b, 1.3b)
+model_size="14b"
+
+# Server configuration
+server_name="0.0.0.0"
+server_port=8032
+
+# GPU configuration
+gpu_id=0
+
+# ==================== Environment Variables Setup ====================
+export CUDA_VISIBLE_DEVICES=$gpu_id
 export CUDA_LAUNCH_BLOCKING=1
 export PYTHONPATH=${lightx2v_path}:$PYTHONPATH
 export ENABLE_PROFILING_DEBUG=true
 export PYTORCH_CUDA_ALLOC_CONF=expandable_segments:True
 
-python gradio_demo.py \
-    --model_path $model_path \
-    --server_name 0.0.0.0 \
-    --server_port 8005
+# ==================== Parameter Parsing ====================
+# Default task type
+task="i2v"
+# Default interface language
+lang="zh"
+
+# 解析命令行参数
+while [[ $# -gt 0 ]]; do
+    case $1 in
+        --task)
+            task="$2"
+            shift 2
+            ;;
+        --lang)
+            lang="$2"
+            shift 2
+            ;;
+        --port)
+            server_port="$2"
+            shift 2
+            ;;
+        --gpu)
+            gpu_id="$2"
+            export CUDA_VISIBLE_DEVICES=$gpu_id
+            shift 2
+            ;;
+        --model_size)
+            model_size="$2"
+            shift 2
+            ;;
+        --help)
+            echo "🎬 Lightx2v Gradio Demo 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: 14 billion parameters model"
+            echo "                     1.3b: 1.3 billion parameters model"
+            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 ""
+            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"
+            exit 0
+            ;;
+        *)
+            echo "Unknown parameter: $1"
+            echo "Use --help to see help information"
+            exit 1
+            ;;
+    esac
+done
+
+# ==================== Parameter Validation ====================
+if [[ "$task" != "i2v" && "$task" != "t2v" ]]; then
+    echo "Error: Task type must be 'i2v' or 't2v'"
+    exit 1
+fi
+
+if [[ "$lang" != "zh" && "$lang" != "en" ]]; then
+    echo "Error: Language must be 'zh' or 'en'"
+    exit 1
+fi
+
+# Validate model size
+if [[ "$model_size" != "14b" && "$model_size" != "1.3b" ]]; then
+    echo "Error: Model size must be '14b' or '1.3b'"
+    exit 1
+fi
+
+# Select model path based on task type
+if [[ "$task" == "i2v" ]]; then
+    model_path=$i2v_model_path
+    echo "🎬 Starting Image-to-Video mode"
+else
+    model_path=$t2v_model_path
+    echo "🎬 Starting Text-to-Video mode"
+fi
+
+# Check if model path exists
+if [[ ! -d "$model_path" ]]; then
+    echo "❌ Error: Model path does not exist"
+    echo "📁 Path: $model_path"
+    echo "🔧 Solutions:"
+    echo "  1. Check model path configuration in script"
+    echo "  2. Ensure model files are properly downloaded"
+    echo "  3. Verify path permissions are correct"
+    echo "  4. 💾 Recommend storing models on SSD for faster loading"
+    exit 1
+fi
+
+# Select demo file based on language
+if [[ "$lang" == "zh" ]]; then
+    demo_file="gradio_demo_zh.py"
+    echo "🌏 Using Chinese interface"
+else
+    demo_file="gradio_demo.py"
+    echo "🌏 Using English interface"
+fi
+
+# Check if demo file exists
+if [[ ! -f "$demo_file" ]]; then
+    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"
+    exit 1
+fi
+
+# ==================== System Information Display ====================
+echo "=========================================="
+echo "🚀 Lightx2v Gradio Demo Starting..."
+echo "=========================================="
+echo "📁 Project path: $lightx2v_path"
+echo "🤖 Model path: $model_path"
+echo "🎯 Task type: $task"
+echo "🤖 Model size: $model_size"
+echo "🌏 Interface language: $lang"
+echo "🖥️  GPU device: $gpu_id"
+echo "🌐 Server address: $server_name:$server_port"
+echo "=========================================="
+
+# Display system resource information
+echo "💻 System resource information:"
+free -h | grep -E "Mem|Swap"
+echo ""
+
+# Display GPU information
+if command -v nvidia-smi &> /dev/null; then
+    echo "🎮 GPU information:"
+    nvidia-smi --query-gpu=name,memory.total,memory.free --format=csv,noheader,nounits | head -1
+    echo ""
+fi
+
+# ==================== 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 model..."
+echo "=========================================="
+
+# Start Python demo
+python $demo_file \
+    --model_path "$model_path" \
+    --task "$task" \
+    --server_name "$server_name" \
+    --server_port "$server_port" \
+    --model_size "$model_size"
 
-# python gradio_demo_zh.py \
-#     --model_path $model_path \
-#     --server_name 0.0.0.0 \
-#     --server_port 8005
+# Display final system resource usage
+echo ""
+echo "=========================================="
+echo "📊 Final system resource usage:"
+free -h | grep -E "Mem|Swap"

configs/attentions/wan_i2v_radial.json

+{
+    "infer_steps": 40,
+    "target_video_length": 81,
+    "target_height": 480,
+    "target_width": 832,
+    "self_attn_1_type": "radial_attn",
+    "cross_attn_1_type": "flash_attn3",
+    "cross_attn_2_type": "flash_attn3",
+    "seed": 42,
+    "sample_guide_scale": 5,
+    "sample_shift": 5,
+    "enable_cfg": true,
+    "cpu_offload": false
+}