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<span style="font-size: 16px; font-weight: 600;">T</span><span style="font-size: 12px; font-weight: 700;">RELLIS</span> 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 <span style="font-size: 16px; font-weight: 600;">T</span><span style="font-size: 12px; font-weight: 700;">RELLIS</span> is a unified Structured LATent (<span style="font-size: 16px; font-weight: 600;">SL</span><span style="font-size: 12px; font-weight: 700;">AT</span>) representation that allows decoding to different output formats and Rectified Flow Transformers tailored for <span style="font-size: 16px; font-weight: 600;">SL</span><span style="font-size: 12px; font-weight: 700;">AT</span> 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. <span style="font-size: 16px; font-weight: 600;">T</span><span style="font-size: 12px; font-weight: 700;">RELLIS</span> 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

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)

🚧 TODO List

• Release inference code and TRELLIS-image-large model
• Release TRELLIS-text model series
• Release training code and data

📦 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:

   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 different version of CUDA (e.g., if you have CUDA Toolkit 12.2 installed and do not want to install another 11.8 version for submodule compilation), you can remove the --new-env flag and manually install the required dependencies. Refer to PyTorch for the installation command.
   • If you have multiple CUDA Toolkit versions installed, PATH should be set to the correct version before running the command. For example, if you have CUDA Toolkit 11.8 and 12.2 installed, you should run export PATH=/usr/local/cuda-11.8/bin:$PATH before running the command.
   • By default, the code uses the flash-attn backend for attention. For GPUs do not support flash-attn (e.g., NVIDIA V100), you can remove the --flash-attn flag to install xformers only and set the ATTN_BACKEND environment variable to xformers before running the code. See the Minimal Example for more details.
   • The installation may take a while due to the large number of dependencies. Please be patient. If you encounter any issues, you can try to install the dependencies one by one, specifying one flag at a time.
   • If you encounter any issues during the installation, feel free to open an issue or contact us.

   Create a new conda environment named trellis and install the dependencies:

   . ./setup.sh --new-env --basic --xformers --flash-attn --diffoctreerast --spconv --mipgaussian --kaolin --nvdiffrast
   

   The detailed usage of setup.sh can be found by running . ./setup.sh --help.

   Usage: setup.sh [OPTIONS]
   Options:
       -h, --help              Display this help message
       --new-env               Create a new conda environment
       --basic                 Install basic dependencies
       --xformers              Install xformers
       --flash-attn            Install flash-attn
       --diffoctreerast        Install diffoctreerast
       --vox2seq               Install vox2seq
       --spconv                Install spconv
       --mipgaussian           Install mip-splatting
       --kaolin                Install kaolin
       --nvdiffrast            Install nvdiffrast
       --demo                  Install all dependencies for demo
   

🤖 Pretrained Models

We provide the following pretrained models:

The models are hosted on Hugging Face. You can directly load the models with their repository names in the code:

TrellisImageTo3DPipeline.from_pretrained("JeffreyXiang/TRELLIS-image-large")

If you prefer loading the model from local, you can download the model files from the links above and load the model with the folder path (folder structure should be maintained):

TrellisImageTo3DPipeline.from_pretrained("/path/to/TRELLIS-image-large")

💡 Usage

Minimal Example

Here is an example of how to use the pretrained models for 3D asset generation.

import os
# os.environ['ATTN_BACKEND'] = 'xformers'   # Can be 'flash-attn' or 'xformers', default is 'flash-attn'
os.environ['SPCONV_ALGO'] = 'native'        # Can be 'native' or 'auto', default is 'auto'.
                                            # 'auto' is faster but will do benchmarking at the beginning.
                                            # Recommended to set to 'native' if run only once.

import imageio
from PIL import Image
from trellis.pipelines import TrellisImageTo3DPipeline
from trellis.utils import render_utils, postprocessing_utils

# Load a pipeline from a model folder or a Hugging Face model hub.
pipeline = TrellisImageTo3DPipeline.from_pretrained("JeffreyXiang/TRELLIS-image-large")
pipeline.cuda()

# Load an image
image = Image.open("assets/example_image/T.png")

# Run the pipeline
outputs = pipeline.run(
    image,
    seed=1,
    # Optional parameters
    # sparse_structure_sampler_params={
    #     "steps": 12,
    #     "cfg_strength": 7.5,
    # },
    # slat_sampler_params={
    #     "steps": 12,
    #     "cfg_strength": 3,
    # },
)
# outputs is a dictionary containing generated 3D assets in different formats:
# - outputs['gaussian']: a list of 3D Gaussians
# - outputs['radiance_field']: a list of radiance fields
# - outputs['mesh']: a list of meshes

# Render the outputs
video = render_utils.render_video(outputs['gaussian'][0])['color']
imageio.mimsave("sample_gs.mp4", video, fps=30)
video = render_utils.render_video(outputs['radiance_field'][0])['color']
imageio.mimsave("sample_rf.mp4", video, fps=30)
video = render_utils.render_video(outputs['mesh'][0])['normal']
imageio.mimsave("sample_mesh.mp4", video, fps=30)

# GLB files can be extracted from the outputs
glb = postprocessing_utils.to_glb(
    outputs['gaussian'][0],
    outputs['mesh'][0],
    # Optional parameters
    simplify=0.95,          # Ratio of triangles to remove in the simplification process
    texture_size=1024,      # Size of the texture used for the GLB
)
glb.export("sample.glb")

# Save Gaussians as PLY files
outputs['gaussian'][0].save_ply("sample.ply")

After running the code, you will get the following files:

• sample_gs.mp4: a video showing the 3D Gaussian representation
• sample_rf.mp4: a video showing the Radiance Field representation
• sample_mesh.mp4: a video showing the mesh representation
• sample.glb: a GLB file containing the extracted textured mesh
• sample.ply: a PLY file containing the 3D Gaussian representation

Web Demo

app.py provides a simple web demo for 3D asset generation. Since this demo is based on Gradio, additional dependencies are required:

. ./setup.sh --demo

After installing the dependencies, you can run the demo with the following command:

python app.py

Then, you can access the demo at the address shown in the terminal.

The web demo is also available on Hugging Face Spaces!

⚖️ License

TRELLIS models and the majority of the code are licensed under the MIT License. The following submodules may have different licenses:

• diffoctreerast: We developed a CUDA-based real-time differentiable octree renderer for rendering radiance fields as part of this project. This renderer is derived from the diff-gaussian-rasterization project and is available under the LICENSE.

• Modified Flexicubes: In this project, we used a modified version of Flexicubes to support vertex attributes. This modified version is licensed under the LICENSE.

📜 Citation

If you find this work helpful, please consider citing our paper:

@article{xiang2024structured,
    title   = {Structured 3D Latents for Scalable and Versatile 3D Generation},
    author  = {Xiang, Jianfeng and Lv, Zelong and Xu, Sicheng and Deng, Yu and Wang, Ruicheng and Zhang, Bowen and Chen, Dong and Tong, Xin and Yang, Jiaolong},
    journal = {arXiv preprint arXiv:2412.01506},
    year    = {2024}
}