Rethinking Style Transfer: From Pixels to Parameterized Brushstrokes.
Dmytro Kotovenko*, Matthias Wright*, Arthur Heimbrecht, and Björn Ommer.
* denotes equal contribution
We provide implementations in Tensorflow 1 and Tensorflow 2. In order to reproduce the results from the paper, we recommend the Tensorflow 1 implementation.
- Clone this repository:
> git clone https://github.com/CompVis/brushstroke-parameterized-style-transfer > cd brushstroke-parameterized-style-transfer
- Install Tensorflow 1.14 (preferably with GPU support).
If you are using Conda, this command will create a new environment and install Tensorflow as well as compatible CUDA and cuDNN versions.> conda create --name tf14 tensorflow-gpu==1.14 > conda activate tf14
- Install requirements:
> pip install -r requirements.txt
from PIL import Image
import model
content_img = Image.open('images/content/golden_gate.jpg')
style_img = Image.open('images/style/van_gogh_starry_night.jpg')
stylized_img = model.stylize(content_img,
style_img,
num_strokes=5000,
num_steps=100,
content_weight=1.0,
style_weight=3.0,
num_steps_pixel=1000)
stylized_img.save('images/stylized.jpg')
or open Colab.
We created a Streamlit app where you can draw curves to control the flow of brushstrokes.
To run the app on your own machine:
> CUDA_VISIBLE_DEVICES=0 streamlit run app.py
You can also run the app on a remote server and forward the port to your local machine: https://docs.streamlit.io/en/0.66.0/tutorial/run_streamlit_remotely.html
If you don't have access to GPUs we also created a Colab from which you can start the drawing app.
PyTorch implementation by justanhduc.
@article{kotovenko_cvpr_2021,
title={Rethinking Style Transfer: From Pixels to Parameterized Brushstrokes},
author={Dmytro Kotovenko and Matthias Wright and Arthur Heimbrecht and Bj{\"o}rn Ommer},
journal={CVPR},
year={2021}
}