---
title: OpenLRM
emoji: ⚡
colorFrom: indigo
colorTo: gray
sdk: gradio
sdk_version: 4.19.2
app_file: app.py
pinned: false
license: apache-2.0
---
# OpenLRM: Open-Source Large Reconstruction Models
[](LICENSE)
[](LICENSE_WEIGHT)
[](https://arxiv.org/abs/2311.04400)
[](https://huggingface.co/zxhezexin)
[](https://huggingface.co/spaces/zxhezexin/OpenLRM)
## News
- [2024.03.04] Version update v1.1. Release model weights trained on both Objaverse and MVImgNet. Codebase is majorly refactored for better usability and extensibility. Please refer to [v1.1.0](https://github.com/3DTopia/OpenLRM/releases/tag/v1.1.0) for details.
- [2024.01.09] Updated all v1.0 models trained on Objaverse. Please refer to [HF Models](https://huggingface.co/zxhezexin) and overwrite previous model weights.
- [2023.12.21] [Hugging Face Demo](https://huggingface.co/spaces/zxhezexin/OpenLRM) is online. Have a try!
- [2023.12.20] Release weights of the base and large models trained on Objaverse.
- [2023.12.20] We release this project OpenLRM, which is an open-source implementation of the paper [LRM](https://arxiv.org/abs/2311.04400).
## Setup
### Installation
```
git clone https://github.com/3DTopia/OpenLRM.git
cd OpenLRM
```
### Environment
- Install requirements for OpenLRM first.
```
pip install -r requirements.txt
```
- Please then follow the [xFormers installation guide](https://github.com/facebookresearch/xformers?tab=readme-ov-file#installing-xformers) to enable memory efficient attention inside [DINOv2 encoder](openlrm/models/encoders/dinov2/layers/attention.py).
## Quick Start
### Pretrained Models
- Model weights are released on [Hugging Face](https://huggingface.co/zxhezexin).
- Weights will be downloaded automatically when you run the inference script for the first time.
- Please be aware of the [license](LICENSE_WEIGHT) before using the weights.
| Model | Training Data | Layers | Feat. Dim | Trip. Dim. | In. Res. | Link |
| :--- | :--- | :--- | :--- | :--- | :--- | :--- |
| openlrm-obj-small-1.1 | Objaverse | 12 | 512 | 32 | 224 | [HF](https://huggingface.co/zxhezexin/openlrm-obj-small-1.1) |
| openlrm-obj-base-1.1 | Objaverse | 12 | 768 | 48 | 336 | [HF](https://huggingface.co/zxhezexin/openlrm-obj-base-1.1) |
| openlrm-obj-large-1.1 | Objaverse | 16 | 1024 | 80 | 448 | [HF](https://huggingface.co/zxhezexin/openlrm-obj-large-1.1) |
| openlrm-mix-small-1.1 | Objaverse + MVImgNet | 12 | 512 | 32 | 224 | [HF](https://huggingface.co/zxhezexin/openlrm-mix-small-1.1) |
| openlrm-mix-base-1.1 | Objaverse + MVImgNet | 12 | 768 | 48 | 336 | [HF](https://huggingface.co/zxhezexin/openlrm-mix-base-1.1) |
| openlrm-mix-large-1.1 | Objaverse + MVImgNet | 16 | 1024 | 80 | 448 | [HF](https://huggingface.co/zxhezexin/openlrm-mix-large-1.1) |
Model cards with additional details can be found in [model_card.md](model_card.md).
### Prepare Images
- We put some sample inputs under `assets/sample_input`, and you can quickly try them.
- Prepare RGBA images or RGB images with white background (with some background removal tools, e.g., [Rembg](https://github.com/danielgatis/rembg), [Clipdrop](https://clipdrop.co)).
### Inference
- Run the inference script to get 3D assets.
- You may specify which form of output to generate by setting the flags `EXPORT_VIDEO=true` and `EXPORT_MESH=true`.
- Please set default `INFER_CONFIG` according to the model you want to use. E.g., `infer-b.yaml` for base models and `infer-s.yaml` for small models.
- An example usage is as follows:
```
# Example usage
EXPORT_VIDEO=true
EXPORT_MESH=true
INFER_CONFIG="./configs/infer-b.yaml"
MODEL_NAME="zxhezexin/openlrm-mix-base-1.1"
IMAGE_INPUT="./assets/sample_input/owl.png"
python -m openlrm.launch infer.lrm --infer $INFER_CONFIG model_name=$MODEL_NAME image_input=$IMAGE_INPUT export_video=$EXPORT_VIDEO export_mesh=$EXPORT_MESH
```
## Training
To be released soon.
## Acknowledgement
- We thank the authors of the [original paper](https://arxiv.org/abs/2311.04400) for their great work! Special thanks to Kai Zhang and Yicong Hong for assistance during the reproduction.
- This project is supported by Shanghai AI Lab by providing the computing resources.
- This project is advised by Ziwei Liu and Jiaya Jia.
## Citation
If you find this work useful for your research, please consider citing:
```
@article{hong2023lrm,
title={Lrm: Large reconstruction model for single image to 3d},
author={Hong, Yicong and Zhang, Kai and Gu, Jiuxiang and Bi, Sai and Zhou, Yang and Liu, Difan and Liu, Feng and Sunkavalli, Kalyan and Bui, Trung and Tan, Hao},
journal={arXiv preprint arXiv:2311.04400},
year={2023}
}
```
```
@misc{openlrm,
title = {OpenLRM: Open-Source Large Reconstruction Models},
author = {Zexin He and Tengfei Wang},
year = {2023},
howpublished = {\url{https://github.com/3DTopia/OpenLRM}},
}
```
## License
- OpenLRM as a whole is licensed under the [Apache License, Version 2.0](LICENSE), while certain components are covered by [NVIDIA's proprietary license](LICENSE_NVIDIA). Users are responsible for complying with the respective licensing terms of each component.
- Model weights are licensed under the [Creative Commons Attribution-NonCommercial 4.0 International License](LICENSE_WEIGHT). They are provided for research purposes only, and CANNOT be used commercially.
