This is a 1 Click installer based on the trellis 1 click installer with a embeded Python install , i hade to add and modify the batch files extensivly to make it work based on : Igor Aherne 2025

Usage just Click on the run-gradio.bat file , while installing it will ask you to scroll up and check your cuda version and chose the closest one to you 1-4 avalible cuda cu118,cu121 and cu124)

Youtube Video will be posted RetroUpgrade - Youtube showing the usage of the installer. Video Guide and demo of the 1 click installer : Hi3DGen 1-Click Installer: Turn Any Image into 3D in Seconds!

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Hi3DGen: High-fidelity 3D Geometry Generation from Images via Normal Bridging

Chongjie Ye^1,2*, Yushuang Wu^2*, Ziteng Lu¹, Jiahao Chang¹, Xiaoyang Guo², Jiaqing Zhou², Hao Zhao³, Xiaoguang Han^1#

¹The Chinese University of Hong Kong, Shenzhen,   ²ByteDance,   ³AIR, Tsinghua University

Hi3DGen target at generating high-fidelity 3D geometry from images using normal maps as an intermediate representation. The framework addresses limitations in existing methods that struggle to reproduce fine-grained geometric details from 2D inputs.

Installation

Clone the repo:

git clone --recursive https://github.com/Stable-X/Hi3DGen.git
cd Hi3DGen

Create a conda environment (optional):

conda create -n stablex python=3.10
conda activate stablex

Install dependencies:

# pytorch (select correct CUDA version)
pip install torch==2.4.0 torchvision==0.19.0 --index-url https://download.pytorch.org/whl/{your-cuda-version}
pip install spconv-cu{your-cuda-version}==2.3.6 xformers==0.0.27.post2
# other dependencies
pip install -r requirements.txt

Local Demo 🤗

Run by:

python app.py

License

The model and code of Hi3DGen are adapted from Trellis, which are licensed under the MIT License. While the original Trellis is MIT licensed, we have specifically removed its dependencies on certain NVIDIA libraries (kaolin, nvdiffrast, flexicube) to ensure this adapted version can be used commercially. Hi3DGen itself is distributed under the MIT License.

Citation

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

@article{ye2025hi3dgen,
  title={Hi3DGen: High-fidelity 3D Geometry Generation from Images via Normal Bridging},
  author={Ye, Chongjie and Wu, Yushuang and Lu, Ziteng and Chang, Jiahao and Guo, Xiaoyang and Zhou, Jiaqing and Zhao, Hao and Han, Xiaoguang},
  journal={arXiv preprint arXiv:2503.22236}, 
  year={2025}
}