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+# Hunyuan3D 2.0: AI-Powered High-Resolution 3D Asset Generation Platform
+
+## Project Overview
+
+Hunyuan3D 2.0 is an advanced AI-powered system for generating high-resolution, textured 3D assets through state-of-the-art machine learning techniques. The project aims to democratize 3D content creation by providing a powerful, user-friendly platform for generating and manipulating 3D models from images or text inputs.
+
+### Key Features
+
+#### Advanced 3D Generation
+- **Two-Stage Generation Pipeline**: Seamlessly creates 3D assets by first generating a base mesh and then applying high-quality textures
+- Supports multiple generation modes:
+  - Image-to-3D conversion
+  - Multi-view 3D generation
+  - Texture synthesis for existing meshes
+
+#### Cutting-Edge AI Models
+- **Hunyuan3D-DiT**: A large-scale shape generation model built on a scalable flow-based diffusion transformer
+- **Hunyuan3D-Paint**: A texture synthesis model that produces vibrant, high-resolution texture maps
+- Multiple model variants including mini, multiview, and turbo versions
+
+#### Versatile Capabilities
+- Generate 3D assets from a single reference image
+- Create textured 3D models with high geometric detail and condition alignment
+- Supports various input types and generation scenarios
+
+### Core Benefits
+- Lowers the barrier to 3D content creation
+- Provides professional-grade 3D asset generation capabilities
+- Flexible and adaptable across different use cases
+- Supports multiple platforms (MacOS, Windows, Linux)
+- Open-source with comprehensive documentation and community support
+
+### Performance Highlights
+The system has demonstrated superior performance compared to existing 3D generation methods, consistently outperforming both open-source and closed-source models in key metrics such as condition following, texture quality, and geometric detail.
+
+## Getting Started, Installation, and Setup
+
+### Prerequisites
+
+- Python 3.8 or higher
+- CUDA-compatible GPU recommended
+- Operating Systems: Linux, macOS, Windows
+
+### Installation
+
+You can install the project using pip:
+
+```bash
+pip install git+https://github.com/Tencent/Hunyuan3D-2.git
+```
+
+Alternatively, you can clone the repository and install locally:
+
+```bash
+git clone https://github.com/Tencent/Hunyuan3D-2.git
+cd Hunyuan3D-2
+pip install -e .
+```
+
+### Dependencies
+
+The project requires the following key dependencies:
+- PyTorch
+- Diffusers
+- Transformers
+- OpenCV
+- Gradio (for demos)
+- FastAPI (for server)
+- ONNX Runtime
+- Trimesh
+- Numpy
+
+A full list of dependencies is available in `requirements.txt`.
+
+### Quick Start
+
+#### Using Gradio Web Interface
+
+To launch the web interface:
+
+```bash
+python gradio_app.py
+```
+
+#### Using Python Script
+
+Here's a minimal example for 3D shape generation:
+
+```python
+from hy3dgen import ShapeGenerator
+
+# Initialize the generator
+generator = ShapeGenerator()
+
+# Generate a 3D shape from a text prompt
+shape = generator.generate("A detailed dragon sculpture")
+
+# Save the generated shape
+shape.save("dragon.glb")
+```
+
+### Development Setup
+
+1. Clone the repository:
+```bash
+git clone https://github.com/Tencent/Hunyuan3D-2.git
+cd Hunyuan3D-2
+```
+
+2. Create a virtual environment (recommended):
+```bash
+python -m venv venv
+source venv/bin/activate  # On Windows, use `venv\Scripts\activate`
+```
+
+3. Install dependencies:
+```bash
+pip install -r requirements.txt
+pip install -e .
+```
+
+### GPU Requirements
+
+- CUDA 11.x or newer recommended
+- Minimum 8GB GPU memory for most generation tasks
+- More memory allows for higher resolution and more complex generations
+
+### Troubleshooting
+
+- Ensure you have the latest version of pip
+- If you encounter CUDA-related issues, verify your CUDA and PyTorch versions are compatible
+- For any installation problems, check the project's GitHub issues page
+
+## API Reference
+
+### Text-to-Image Generation
+
+#### `HunyuanDiTPipeline`
+A class for generating high-quality images from text prompts using the Hunyuan DiT model.
+
+##### Constructor
+```python
+HunyuanDiTPipeline(
+    model_path="Tencent-Hunyuan/HunyuanDiT-v1.1-Diffusers-Distilled", 
+    device='cuda'
+)
+```
+- `model_path`: Path to the pre-trained model (default is the official Hunyuan DiT model)
+- `device`: Computing device to use (default is 'cuda')
+
+##### Methods
+- `compile()`: Optimizes the model's transformer for faster inference
+- `__call__(prompt: str, seed: int = 0) -> Image`: Generates an image from a text prompt
+  - `prompt`: Text description of the desired image
+  - `seed`: Random seed for reproducibility (default is 0)
+  - Returns a generated image
+
+###### Example Usage
+```python
+pipeline = HunyuanDiTPipeline()
+pipeline.compile()  # Optional optimization step
+image = pipeline("A beautiful landscape with mountains")
+```
+
+#### Utility Functions
+
+##### `seed_everything(seed: int)`
+Sets a global random seed for reproducibility across NumPy, PyTorch, and Python's random module.
+
+### Rembg Module
+The library includes a rembg module for background removal, which can be imported from `hy3dgen.rembg`.
+
+### Shape Generation
+The `hy3dgen.shapegen` module provides functionality for 3D shape generation with several submodules:
+- `models`: Neural network architectures for shape generation
+- `pipelines`: Processing pipelines
+- `preprocessors`: Input preprocessing utilities
+- `postprocessors`: Output post-processing utilities
+- `schedulers`: Noise scheduling for generation
+- `utils`: Utility functions for shape generation
+
+### Texture Generation
+The `hy3dgen.texgen` module offers texture generation capabilities with components:
+- `custom_rasterizer`: Custom rendering utilities
+- `differentiable_renderer`: Differentiable mesh rendering
+- `hunyuanpaint`: Texture painting pipeline
+- `utils`: Various texture-related utility functions
+
+### Additional Utilities
+- `text2image`: Text-to-image generation module
+- Configuration and model utilities are available throughout the package
+
+## Project Structure
+
+The project is a Python-based 3D generation library with a structured, modular architecture. The key directories and their purposes are as follows:
+
+### Main Package
+The `hy3dgen` directory serves as the core package, containing the primary implementation of the 3D generation library:
+
+#### Submodules
+- `shapegen`: Handles shape generation capabilities
+  - `models`: Contains machine learning models for shape generation
+    - `autoencoders`: Implementations of autoencoder architectures
+    - `denoisers`: Denoising models and techniques
+  - `pipelines.py`: Defines processing pipelines for shape generation
+  - `preprocessors.py` and `postprocessors.py`: Handle input preprocessing and output postprocessing
+  - `schedulers.py`: Manages generation scheduling
+
+- `texgen`: Responsible for texture generation and rendering
+  - `custom_rasterizer`: Custom rendering implementation with C++ and CUDA kernel extensions
+  - `differentiable_renderer`: Provides differentiable rendering utilities
+  - `hunyuanpaint`: Texture painting pipeline
+  - `pipelines.py`: Texture generation processing pipelines
+  - `utils`: Utility functions for texture-related operations
+
+### Examples and Demonstrations
+The `examples` directory contains script demonstrations of various generation capabilities:
+- Shape generation scripts
+- Multiview generation examples
+- Texture generation scripts
+
+### Documentation
+The `docs` directory includes documentation resources:
+- Sphinx documentation configuration
+- Markdown documentation files
+- Configuration for documentation generation
+
+### Additional Resources
+- `assets`: Contains various supporting resources like images, environment maps, and example data
+- `blender_addon.py`: A Blender plugin integration
+- `gradio_app.py`: A Gradio web interface for the library
+- `api_server.py`: An API server implementation
+
+### Project Configuration
+- `setup.py`: Package setup and installation configuration
+- `requirements.txt`: Python package dependencies
+- `LICENSE` and `NOTICE`: Legal and licensing information
+
+The project follows a clean, modular design that separates concerns between shape generation, texture generation, and utility functions, making it extensible and easy to understand.
+
+## Technologies Used
+
+### Programming Languages
+- Python
+
+### Machine Learning and Deep Learning
+- PyTorch: Core deep learning framework
+- Diffusers: Generative AI model pipeline
+- Transformers: Advanced natural language and machine learning models
+- Accelerate: Distributed training and inference library
+
+### Computer Vision and Image Processing
+- OpenCV: Image and video processing
+- Torchvision: Computer vision utilities
+- Rembg: Background removal library
+- ONNX Runtime: Machine learning model inference
+
+### 3D Graphics and Mesh Processing
+- Trimesh: 3D mesh manipulation
+- PyMeshLab: Mesh processing and editing
+- PyGLTFlib: GLTF file handling
+- XAtlas: UV mapping and texture coordinate generation
+
+### Web and API Frameworks
+- Gradio: Machine learning demo and interface creation
+- FastAPI: Web API development
+- Uvicorn: ASGI server for web applications
+
+### Utility Libraries
+- NumPy: Numerical computing
+- tqdm: Progress bar and iteration tracking
+- Einops: Tensor manipulation and reshaping
+- OmegaConf: Configuration management
+
+### Development and Build Tools
+- Ninja: Build system
+- PyBind11: C++ and Python interoperability
+
+## Additional Notes
+
+### Compatibility and System Requirements
+
+Hunyuan3D 2.0 is designed to be cross-platform, supporting macOS, Windows, and Linux. The project requires:
+- Minimum 6 GB VRAM for shape generation
+- 16 GB VRAM for combined shape and texture generation
+- PyTorch (installed from official site)
+- Python environment with dependencies specified in `requirements.txt`
+
+### Performance Considerations
+
+The framework offers multiple model variants optimized for different use cases:
+- Mini models (0.6B parameters) for lower computational requirements
+- Multiview models for enhanced 3D generation
+- Turbo and Fast variants with reduced inference time
+
+### Troubleshooting and Support
+
+For technical support and community discussions, users can:
+- Join the Discord community: [Hunyuan3D Discord](https://discord.gg/dNBrdrGGMa)
+- Explore community-contributed extensions and tools
+- Refer to example scripts in the `examples/` directory for advanced usage
+
+### Upcoming Features
+
+The project roadmap includes:
+- TensorRT optimization
+- Finetuning capabilities
+- Ongoing model and performance improvements
+
+### Research and Citations
+
+If you use Hunyuan3D in academic or commercial projects, consider citing the project's technical reports available on arXiv.
+
+## Contributing
+
+We welcome contributions from the community! By participating in this project, you can help improve Hunyuan3D and make it even more powerful.
+
+### Ways to Contribute
+
+1. **Reporting Issues**
+   - Open GitHub issues for bug reports, feature requests, or documentation improvements
+   - Provide clear, detailed descriptions and, when possible, reproducible examples
+
+2. **Proposing Extensions**
+   - Submit pull requests for bug fixes
+   - Develop new features or improvements to existing functionality
+   - Enhance documentation or add examples
+
+### Contribution Guidelines
+
+#### Code Contributions
+- Ensure your code follows the project's existing code style and conventions
+- Write clear, concise, and descriptive commit messages
+- Include appropriate tests for new features or bug fixes
+- Update documentation to reflect your changes
+
+#### Pull Request Process
+- Fork the repository and create your branch from `main`
+- Ensure all tests pass and there are no merge conflicts
+- Include a clear description of your changes in the pull request
+- Your pull request will be reviewed by the maintainers
+
+#### Development Setup
+```bash
+# Clone the repository
+git clone https://github.com/Tencent/Hunyuan3D-2.git
+cd Hunyuan3D-2
+
+# Install development dependencies
+pip install -r requirements.txt
+pip install -e .
+
+# Install additional rendering dependencies 
+cd hy3dgen/texgen/custom_rasterizer
+python3 setup.py install
+cd ../../..
+cd hy3dgen/texgen/differentiable_renderer
+python3 setup.py install
+```
+
+### Community
+- Join our [Discord community](https://discord.gg/dNBrdrGGMa) for discussions and support
+- Check out [community resources and extensions](https://github.com/Tencent/Hunyuan3D-2#community-resources)
+
+### Code of Conduct
+- Be respectful and considerate of others
+- Collaborate constructively
+- Focus on improving the project for everyone
+
+### Licensing
+By contributing, you agree that your contributions will be licensed under the [Tencent Hunyuan Non-Commercial License Agreement](LICENSE).
+
+## License
+
+The project is licensed under the **Tencent Hunyuan 3D 2.0 Community License Agreement**.
+
+#### Key License Terms
+- The license is valid only in territories outside the European Union, United Kingdom, and South Korea
+- Non-exclusive, non-transferable, and royalty-free limited license
+- Must comply with the Acceptable Use Policy
+- Restrictions apply for commercial use, especially for products with over 1 million monthly active users
+
+#### License Compatibility
+- You may distribute the work within the specified territory
+- Must include the original license text with distributions
+- Modifications must be clearly marked
+
+#### Full License Details
+For complete license terms, please refer to the [LICENSE](LICENSE) file in the repository.
+
+#### Important Notes
+- No warranties are provided with the software
+- Users are responsible for compliance with the license terms
+- Trademark usage is restricted
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