vector_fusion 论文复现

这是一篇小众的自动化矢量图生成,但是很有意思!

Setup

1. Clone the repo:

git clone https://github.com/ydove0324/vector_fusion.git
cd vector_fusion

2. Create a new conda environment and install the libraries:

conda create --name vector_fusion python=3.8.15
conda activate vector_fusion
pip install torch==1.12.1+cu113 torchvision==0.13.1+cu113 --extra-index-url https://download.pytorch.org/whl/cu113
conda install -y numpy scikit-image
conda install -y -c anaconda cmake
conda install -y -c conda-forge ffmpeg
pip install svgwrite svgpathtools cssutils numba torch-tools scikit-fmm easydict visdom freetype-py shapely
pip install opencv-python==4.5.4.60  
pip install kornia==0.6.8
pip install wandb
pip install shapely
pip install open_clip_torch

3. Install diffusers:

pip install diffusers==0.8
pip install transformers scipy ftfy accelerate

4. Install diffvg:

git clone https://github.com/BachiLi/diffvg.git
cd diffvg
git submodule update --init --recursive
python setup.py install

5. Paste your HuggingFace access token for StableDiffusion in the TOKEN file.

Run Experiments

conda activate vector_fusion
cd vector_fusion

# Please modify the parameters accordingly in the file and run:
bash run_vector_fusion.sh

# Or run :
python code/main.py --experiment <experiment> --semantic_concept <concept> --seed <seed> 
如:
python code/main.py --experiment reinit --seed 147 --semantic_concept "bicycle" --optim_path 128 

• --semantic_concept : 你希望根据该语义信息生成图片,如"bicycle"
• --optim_path : 多少个矢量图贝塞尔曲线, 越多矢量图越精细, 但速度也越慢, 默认128, 若配置不行, 可以64或32

Optional arguments:

• --prompt_suffix : Default: "minimal flat 2d vector. lineal color. trending on artstation"

Acknowledgement

这些代码基于以下工作

1. Diffusers
2. Diffvg.
3. Live.
4. Word_As_Image
5. open-clip

Licence

This work is licensed under a Creative Commons Attribution-ShareAlike 4.0 International License.