VectorFusion: Text-to-SVG by Abstracting Pixel-Based Diffusion Models

In this work, the authors show that a text-conditioned diffusion model trained on pixel representations of images can be used to generate SVG-exportable vector graphics.

official website: https://vectorfusion.github.io/

VectorFusion rendering process. (64paths, 72videos, 5k)

Updates

[01/2024] 🔥 We released the SVGDreamer. SVGDreamer is a novel text-guided vector graphics synthesis method. This method considers both the editing of vector graphics and the quality of the synthesis.
[12/2023] 🔥 We released the PyTorch-SVGRender. Pytorch-SVGRender is the go-to library for state-of-the-art differentiable rendering methods for image vectorization.
[10/2023] 🔥 We released the DiffSketcher code. A method of synthesizing vector sketches by text prompts.
[10/2023] 🔥 We reproduce the VectorFusion code.

Installation

Step by step

Create a new conda environment:

conda create --name vf python=3.10
conda activate vf

Install pytorch and the following libraries:

conda install pytorch==1.13.1 torchvision==0.14.1 torchaudio==0.13.1 pytorch-cuda=11.6 -c pytorch -c nvidia
pip install omegaconf BeautifulSoup4
pip install shapely
pip install opencv-python scikit-image matplotlib visdom wandb
pip install triton numba
pip install numpy scipy timm scikit-fmm einops
pip install accelerate transformers safetensors datasets

Install CLIP:

pip install ftfy regex tqdm
pip install git+https://github.com/openai/CLIP.git

Install diffusers:

pip install diffusers==0.20.2

Install xformers (require python=3.10):

conda install xformers -c xformers

Install diffvg:

git clone https://github.com/BachiLi/diffvg.git
cd diffvg
git submodule update --init --recursive
conda install -y -c anaconda cmake
conda install -y -c conda-forge ffmpeg
pip install svgwrite svgpathtools cssutils torch-tools
python setup.py install

Docker Usage

docker run --name vectorfusion --gpus all -it --ipc=host ximingxing/svgrender:v1 /bin/bash

Quickstart

Case: Sydney Opera House

Prompt: the Sydney Opera House.
Style: iconography
Preview:


(a) Sample raster image with Stable Diffusion	(b) Convert raster image to a vector via LIVE	(c) VectorFusion: Fine tune by LSDS

LIVE Rendering Process:

iter 0	iter 500	iter 1000	iter 1500	iter 2500	iter 3500

VectorFusion Rendering Process:

iter 0	iter 100	iter 300	iter 400	iter 700	iter 1000

Script:

python run_painterly_render.py \ 
  -c vectorfusion.yaml \
  -pt "the Sydney Opera House. minimal flat 2d vector icon. lineal color. on a white background. trending on artstation" \
  -save_step 50 \ 
  -update "K=6" \ 
  -respath ./workdir/SydneyOperaHouse \
  -d 15486 \
  --download

-c a.k.a --config: configuration file.
-save_step: the step size used to save the result (too frequent calls will result in longer times).
-update: a tool for editing the hyper-params of the configuration file, so you don't need to create a new yaml.
-pt a.k.a --prompt: text prompt.
-respath a.k.a --results_path: the folder to save results.
-d a.k.a --seed: random seed.
--download: download models from huggingface automatically when you first run them.

optional:

-npt, a.k.a --negative_prompt: negative text prompt.
-mv, a.k.a --make_video: make a video of the rendering process (it will take much longer).
-frame_freq, a.k.a --video_frame_freq: the interval of the number of steps to save the image.
-framerate, a.k.a --video_frame_rate: control the playback speed of the output video.

Case: Ming Dynasty Vase

Prompt: A photo of a Ming Dynasty vase on a leather topped table.
Style: iconography
Preview:


(a) Sample raster image with Stable Diffusion	(b) Convert raster image to a vector via LIVE	(c) VectorFusion: Fine tune by LSDS

Script:

python run_painterly_render.py -c vectorfusion.yaml -pt "A photo of a Ming Dynasty vase on a leather topped table. minimal flat 2d vector icon. lineal color. on a white background. trending on artstation" -save_step 50 -respath ./workdir/vase -d 683692

Case: Astronaut

Prompt: An astronaut figure.
Style: iconography
Preview:


(a) Sample raster image with Stable Diffusion	(b) Convert raster image to a vector via LIVE	(c) VectorFusion: Fine tune by LSDS

Script:

python run_painterly_render.py -c vectorfusion.yaml -pt "An astronaut figure. minimal flat 2d vector icon. lineal color. on a white background. trending on artstation" -save_step 50 -respath ./workdir/astronaut -d 522178

Case: Guitar

Prompt: Electric guitar.
Style: Pixel-Art
Preview:


(a) Sample raster image with Stable Diffusion	(b) Convert raster image to a vector via LIVE	(c) VectorFusion: Fine tune by LSDS

Script:

python run_painterly_render.py -c vectorfusion.yaml -pt "Electric guitar. pixel art. trending on artstation" -save_step 50 -respath ./workdir/guitar -update "style=pixelart" -d 445997

Case: Dragon

Prompt: watercolor painting of a firebreathing dragon.
Style: Sketch
Preview:


SVG initialization	VectorFusion fine-tune 500 step	VectorFusion fine-tune 1500 step

Script:

python run_painterly_render.py -c vectorfusion.yaml -pt "watercolor painting of a firebreathing dragon. minimal 2d line drawing. trending on artstation" -save_step 50 -respath ./workdir/dragon-sketch -update "style=sketch num_segments=5 radius=0.5 sds.num_iter=1500" -d 106764

Other Cases

# Sketch style
CUDA_VISIBLE_DEVICES=0 python run_painterly_render.py -c vectorfusion.yaml -pt "watercolor painting of a firebreathing dragon. minimal 2d line drawing. trending on artstation" -save_step 50 -respath ./workdir/dragon-sketch -update "style=sketch skip_live=True num_paths=32 num_segments=5 radius=0.5 sds.num_iter=1500" -rdbz
CUDA_VISIBLE_DEVICES=0 python run_painterly_render.py -c vectorfusion.yaml -pt "A cat. minimal 2d line drawing. trending on artstation" -save_step 50 -respath ./workdir/cat-sketch -update "style=sketch skip_live=True num_paths=32 num_segments=5 radius=0.5 sds.num_iter=1500" -rdbz

More Examples:

check the Examples.md for more cases.

More Scripts:

check the Run.md for more scripts.

Acknowledgement

The project is built based on the following repository:

We gratefully thank the authors for their wonderful works.

Citation

If you use this code for your research, please cite the following work:

@inproceedings{jain2023vectorfusion,
  title={Vectorfusion: Text-to-svg by abstracting pixel-based diffusion models},
  author={Jain, Ajay and Xie, Amber and Abbeel, Pieter},
  booktitle={Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition},
  pages={1911--1920},
  year={2023}
}
@inproceedings{xing2023diffsketcher,
  title={DiffSketcher: Text Guided Vector Sketch Synthesis through Latent Diffusion Models},
  author={XiMing Xing and Chuang Wang and Haitao Zhou and Jing Zhang and Qian Yu and Dong Xu},
  booktitle={Thirty-seventh Conference on Neural Information Processing Systems},
  year={2023},
  url={https://openreview.net/forum?id=CY1xatvEQj}
}

Licence

This repo is licensed under a MIT License.

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vectorfusion-pytorch's Introduction

VectorFusion: Text-to-SVG by Abstracting Pixel-Based Diffusion Models

Updates

Installation

Step by step

Docker Usage

Quickstart

Case: Sydney Opera House

Case: Ming Dynasty Vase

Case: Astronaut

Case: Guitar

Case: Dragon

Other Cases

Acknowledgement

Citation

Licence

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