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https://github.com/CompVis/brushstroke-parameterized-style-transfer
TensorFlow implementation of our CVPR 2021 Paper "Rethinking Style Transfer: From Pixels to Parameterized Brushstrokes".
https://github.com/CompVis/brushstroke-parameterized-style-transfer
computer-vision deep-learning differentiable-rendering style-transfer
Last synced: 16 days ago
JSON representation
TensorFlow implementation of our CVPR 2021 Paper "Rethinking Style Transfer: From Pixels to Parameterized Brushstrokes".
- Host: GitHub
- URL: https://github.com/CompVis/brushstroke-parameterized-style-transfer
- Owner: CompVis
- License: mit
- Created: 2021-03-26T17:55:50.000Z (about 3 years ago)
- Default Branch: tensorflow_v1
- Last Pushed: 2021-07-16T16:27:38.000Z (almost 3 years ago)
- Last Synced: 2024-02-23T18:38:36.563Z (4 months ago)
- Topics: computer-vision, deep-learning, differentiable-rendering, style-transfer
- Language: Python
- Homepage: https://compvis.github.io/brushstroke-parameterized-style-transfer/
- Size: 53.3 MB
- Stars: 160
- Watchers: 16
- Forks: 19
- Open Issues: 1
-
Metadata Files:
- Readme: README.md
- License: LICENSE
Lists
- awesome-stars - CompVis/brushstroke-parameterized-style-transfer - TensorFlow implementation of our CVPR 2021 Paper "Rethinking Style Transfer: From Pixels to Parameterized Brushstrokes". (Python)
README
# Rethinking Style Transfer: From Pixels to Parameterized Brushstrokes (CVPR 2021)
### [Project page](https://compvis.github.io/brushstroke-parameterized-style-transfer/) | [Paper](https://arxiv.org/abs/2103.17185) | [Colab](https://colab.research.google.com/drive/1J9B6_G2DSWmaBWw9Ot80W9t7O6pWu8Kw?usp=sharing) | [Colab for Drawing App](https://colab.research.google.com/drive/1ALNRoZgCj35uJ3Xvs24-QDwwtCb2lm3P?usp=sharing)
Rethinking Style Transfer: From Pixels to Parameterized Brushstrokes.
[Dmytro Kotovenko*](https://scholar.google.de/citations?user=T_U8yxwAAAAJ&hl=en), [Matthias Wright*](http://www.matthias-wright.com/), [Arthur Heimbrecht](http://www.aheimbrecht.de/), and [Björn Ommer](https://hci.iwr.uni-heidelberg.de/people/bommer).
* denotes equal contribution
## Implementations
We provide implementations in [Tensorflow 1](https://github.com/CompVis/brushstroke-parameterized-style-transfer/tree/tensorflow_v1) and [Tensorflow 2](https://github.com/CompVis/brushstroke-parameterized-style-transfer/tree/tensorflow_v2). In order to reproduce the results from the paper, we recommend the [Tensorflow 1](https://github.com/CompVis/brushstroke-parameterized-style-transfer/tree/tensorflow_v1) implementation.
## Installation
1. Clone this repository:
```sh
> git clone https://github.com/CompVis/brushstroke-parameterized-style-transfer
> cd brushstroke-parameterized-style-transfer
```
2. Install Tensorflow 1.14 (preferably with GPU support).
If you are using [Conda](https://docs.conda.io/en/latest/index.html), this command will create a new environment and install Tensorflow as well as compatible CUDA and cuDNN versions.
```sh
> conda create --name tf14 tensorflow-gpu==1.14
> conda activate tf14
```
3. Install requirements:
```sh
> pip install -r requirements.txt
```
## Basic Usage
```python
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](https://colab.research.google.com/drive/1J9B6_G2DSWmaBWw9Ot80W9t7O6pWu8Kw?usp=sharing).
## Drawing App
We created a [Streamlit](https://streamlit.io/) app where you can draw curves to control the flow of brushstrokes.
#### Run drawing app on your machine
To run the app on your own machine:
```sh
> 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](https://docs.streamlit.io/en/0.66.0/tutorial/run_streamlit_remotely.html)
#### Run streamlit app from Colab
If you don't have access to GPUs we also created a [Colab](https://colab.research.google.com/drive/1ALNRoZgCj35uJ3Xvs24-QDwwtCb2lm3P?usp=sharing) from which you can start the drawing app.
## Other implementations
[PyTorch implementation](https://github.com/justanhduc/brushstroke-parameterized-style-transfer) by [justanhduc](https://github.com/justanhduc).
## Citation
```
@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}
}
```