README.md

[Paper] [Project] [Demo] [BibTeX]

One-2-3-45 rethinks how to leverage 2D diffusion models for 3D AIGC and introduces a novel forward-only paradigm that avoids the time-consuming optimization.

img-2-3d.mp4

text-2-3d.mp4

News

[08/18/2023] Inference code released.

[07/24/2023] Our demo reached the HuggingFace top 4 trending and was featured in 🤗 Spaces of the Week 🔥! Special thanks to HuggingFace 🤗 for sponsoring this demo!!

[07/11/2023] Online interactive demo released! Explore it and create your own 3D models in just 45 seconds!

[06/29/2023] Check out our paper. [X]

Installation

Hardware requirement: an NVIDIA GPU with memory >=18GB (e.g., RTX 3090 or A10). Tested on Ubuntu.

We offer two ways to setup the environment:

Traditional Installation

Step 1: Install Debian packages.

sudo apt update && sudo apt install git-lfs libsparsehash-dev build-essential

Step 2: Create and activate a conda environment.

conda create -n One2345 python=3.10
conda activate One2345

Step 3: Clone the repository to the local machine.

# Make sure you have git-lfs installed.
git lfs install
git clone https://github.com/One-2-3-45/One-2-3-45
cd One-2-3-45

Step 4: Install project dependencies using pip.

# Ensure that the installed CUDA version matches the torch's cuda version.
# Example: CUDA 11.8 installation
wget https://developer.download.nvidia.com/compute/cuda/11.8.0/local_installers/cuda_11.8.0_520.61.05_linux.run
sudo sh cuda_11.8.0_520.61.05_linux.run
export PATH="/usr/local/cuda-11.8/bin:$PATH"
export LD_LIBRARY_PATH="/usr/local/cuda-11.8/lib64:$LD_LIBRARY_PATH"
# Install PyTorch 2.0
pip install --no-cache-dir torch torchvision torchaudio --index-url https://download.pytorch.org/whl/cu118
# Install dependencies
pip install -r requirements.txt
# Install inplace_abn and torchsparse
export TORCH_CUDA_ARCH_LIST="7.0;7.2;8.0;8.6+PTX" # CUDA architectures. Modify according to your hardware.
export IABN_FORCE_CUDA=1
pip install inplace_abn
FORCE_CUDA=1 pip install --no-cache-dir git+https://github.com/mit-han-lab/[email protected]

Step 5: Download model checkpoints.

python download_ckpt.py

Installation by Docker Images

Option 1: Pull and Play (environment and checkpoints). (~22.3G)

# Pull the Docker image that contains the full repository.
docker pull chaoxu98/one2345:demo_1.0
# An interactive demo will be launched automatically upon running the container.
# This will provide a public URL like XXXXXXX.gradio.live
docker run --name One-2-3-45_demo --gpus all -it chaoxu98/one2345:demo_1.0

Option 2: Environment Only. (~7.3G)

# Pull the Docker image that installed all project dependencies.
docker pull chaoxu98/one2345:1.0
# Start a Docker container named One2345.
docker run --name One-2-3-45 --gpus all -it chaoxu98/one2345:1.0
# Get a bash shell in the container.
docker exec -it One-2-3-45 /bin/bash
# Clone the repository to the local machine.
git clone https://github.com/One-2-3-45/One-2-3-45
cd One-2-3-45
# Download model checkpoints. 
python download_ckpt.py
# Refer to getting started for inference.

Getting started (Inference)

First-time running will take longer time to compile the models.

Expected time cost per image: 40s on an NVIDIA A6000.

# 1. Script
python run.py --img_path PATH_TO_INPUT_IMG --half_precision

# 2. Interactive demo (Gradio) with a friendly web interface
#    An URL will be provided in the output 
#    (Local: 127.0.0.1:7860; Public: XXXXXXX.gradio.live)
cd demo/
python app.py

# 3. Jupyter Notebook
example.ipynb

Citation

If you find our code helpful, please cite our paper:

@misc{liu2023one2345,
      title={One-2-3-45: Any Single Image to 3D Mesh in 45 Seconds without Per-Shape Optimization}, 
      author={Minghua Liu and Chao Xu and Haian Jin and Linghao Chen and Mukund Varma T and Zexiang Xu and Hao Su},
      year={2023},
      eprint={2306.16928},
      archivePrefix={arXiv},
      primaryClass={cs.CV}
}