metadata

library_name: craftsman-v1-5
license: creativeml-openrail-m
license_name: creativeml-openrail-m
license_link: >-
  https://raw.githubusercontent.com/CompVis/stable-diffusion/refs/heads/main/LICENSE
pipeline_tag: image-to-3d
language:
  - en
  - zh

CraftsMan-v1-5

CraftsMan: High-fidelity Mesh Generation
with 3D Native Generation and Interactive Geometry Refiner

Weiyu Li^1,2, Jiarui Liu^1,2, Hongyu Yan^*1,2, Rui Chen^1,2, Yixun Liang^2,3, Xuelin Chen⁴, Ping Tan^1,2, Xiaoxiao Long^1,2

¹HKUST, ²LightIllusions, ³HKUST(GZ), ⁴Tencent AI Lab

Usage

To use the model, please refer to the official repository for installation and usage instructions.


from craftsman import CraftsManPipeline
import torch

pipeline = CraftsManPipeline.from_pretrained("./ckpts/craftsman-v1-5", device="cuda:0", torch_dtype=torch.float32) # load from local ckpt
mesh = pipeline("https://pub-f9073a756ec645d692ce3d171c2e1232.r2.dev/data/werewolf.png").meshes[0]
mesh.export("werewolf.obj")

🔥🔥🔥 News!!

Nov 16, 2024: 💬 We release the CraftsMan-v1-5

📑 Open-source Plan

Inference
Checkpoints
Training
ComfyUI

🎉 CraftMan-v1-5 Architecture

Get Started

Begin by cloning the repository:

git clone https://github.com/wyysf-98/CraftsMan
cd CraftsMan

Installation Guide for Linux

We provide an env_install.sh script file for setting up environment.

# step 1, create conda env
conda create -n CraftsMan python=3.10
conda activate CraftsMan


# step 2. install torch realated package
conda install -c pytorch pytorch=2.3.0 torchvision=0.18.0 cudatoolkit=11.8

# step 3. install other packages
pip install -r docker/requirements.txt

Using Gradio

We have prepared a gradio demo for you to try out the model. You can run the following command to start the demo.

# std 
python3 gradio.py

Then the demo can be accessed through the output link.

Citation

If you found this repository helpful, please cite our report:

@misc{li2024craftsman,
title         = {CraftsMan: High-fidelity Mesh Generation with 3D Native Generation and Interactive Geometry Refiner}, 
author        = {Weiyu Li and Jiarui Liu and Rui Chen and Yixun Liang and Xuelin Chen and Ping Tan and Xiaoxiao Long},
year          = {2024},
archivePrefix = {arXiv preprint arXiv:2405.14979},
primaryClass  = {cs.CG}
}

License

creativeml-openrail-m