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: 9 days ago
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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 (over 3 years ago)
• Default Branch: tensorflow_v1
• Last Pushed: 2021-07-16T16:27:38.000Z (over 3 years ago)
• Last Synced: 2024-08-01T13:29:10.694Z (3 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: 163
• Watchers: 17
• Forks: 20
• Open Issues: 1
• Metadata Files:
  • Readme: README.md
  • License: LICENSE

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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}

}

```