TRELLIS

Structured 3D Latentsfor Scalable and Versatile 3D Generation

TRELLIS is a large 3D asset generation model. It takes in text or image prompts and generates high-quality 3D assets in various formats, such as Radiance Fields, 3D Gaussians, and meshes. The cornerstone of TRELLIS is a unified Structured LATent (SLAT) representation that allows decoding to different output formats and Rectified Flow Transformers tailored for SLAT as the powerful backbones. We provide large-scale pre-trained models with up to 2 billion parameters on a large 3D asset dataset of 500K diverse objects. TRELLIS significantly surpasses existing methods, including recent ones at similar scales, and showcases flexible output format selection and local 3D editing capabilities which were not offered by previous models.

*Check out our Project Page for more videos and interactive demos!*

🌟 Features
- High Quality: It produces diverse 3D assets at high quality with intricate shape and texture details.
- Versatility: It takes text or image prompts and can generate various final 3D representations including but not limited to *Radiance Fields*, *3D Gaussians*, and *meshes*, accommodating diverse downstream requirements.
- Flexible Editing: It allows for easy editings of generated 3D assets, such as generating variants of the same object or local editing of the 3D asset.

⏩ Updates

03/25/2025
- Release training code.
- Release TRELLIS-text models and asset variants generation.
  - Examples are provided as example_text.py and example_variant.py.
  - Gradio demo is provided as app_text.py.
  - *Note: It is always recommended to do text to 3D generation by first generating images using text-to-image models and then using TRELLIS-image models for 3D generation. Text-conditioned models are less creative and detailed due to data limitations.*

12/26/2024
- Release TRELLIS-500K dataset and toolkits for data preparation.

12/18/2024
- Implementation of multi-image conditioning for TRELLIS-image model. (#7). This is based on tuning-free algorithm without training a specialized model, so it may not give the best results for all input images.
- Add Gaussian export in app.py and example.py. (#40)

📦 Installation

Prerequisites
- System: The code is currently tested only on Linux.  For windows setup, you may refer to #3 (not fully tested).
- Hardware: An NVIDIA GPU with at least 16GB of memory is necessary. The code has been verified on NVIDIA A100 and A6000 GPUs.  
- Software:   
  - The CUDA Toolkit is needed to compile certain submodules. The code has been tested with CUDA versions 11.8 and 12.2.  
  - Conda is recommended for managing dependencies.  
  - Python version 3.8 or higher is required. 

Installation Steps
1. Clone the repo:
    sh
    git clone --recurse-submodules https://github.com/microsoft/TRELLIS.git
    cd TRELLIS

2. Install the dependencies:
    Before running the following command there are somethings to note:
    - By adding --new-env, a new conda environment named trellis will be created. If you want to use an existing conda environment, please remove this flag.
    - By default the trellis environment will use pytorch 2.4.0 with CUDA 11.8. If you want to use a

……（后略，见原文）