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NVIDIA/FastPhotoStyle · source

Snapshot 0 · 2026-11-04T12:47:25Z · Python ·

Content fingerprint11fba3cddf574264afe1e5d70b57885e8be47d799ae276632917de64791a2b9d

GitHub description at capture

Style transfer, deep learning, feature transform

README at capture

[![License CC BY-NC-SA 4.0](https://img.shields.io/badge/license-CC4.0-blue.svg)](https://raw.githubusercontent.com/NVIDIA/FastPhotoStyle/master/LICENSE.md)
![Python 2.7](https://img.shields.io/badge/python-2.7-green.svg)
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## FastPhotoStyle

### License
Copyright (C) 2018 NVIDIA Corporation.  All rights reserved.
Licensed under the CC BY-NC-SA 4.0 license (https://creativecommons.org/licenses/by-nc-sa/4.0/legalcode).

<img src="https://raw.githubusercontent.com/NVIDIA/FastPhotoStyle/master/teaser.png" width="800" title="Teaser results"> 


### What's new
 
 | Date     | News |
 |----------|--------------|
 |2018-07-25| Migrate to pytorch 0.4.0. For pytorch 0.3.0 user, check out [FastPhotoStyle for pytorch 0.3.0](https://github.com/NVIDIA/FastPhotoStyle/releases/tag/f33e07f). |
 |          | Add a [tutorial](TUTORIAL.md) showing 3 ways of using the FastPhotoStyle algorithm.|
 |2018-07-10| Our paper is accepted by the ECCV 2018 conference!!! | 


### About

Given a content photo and a style photo, the code can transfer the style of the style photo to the content photo. The details of the algorithm behind the code is documented in our arxiv paper. Please cite the paper if this code repository is used in your publications.

[A Closed-form Solution to Photorealistic Image Stylization](https://arxiv.org/abs/1802.06474) <br> 
[Yijun Li (UC Merced)](https://sites.google.com/site/yijunlimaverick/), [Ming-Yu Liu (NVIDIA)](http://mingyuliu.net/), [Xueting Li (UC Merced)](https://sunshineatnoon.github.io/), [Ming-Hsuan Yang (NVIDIA, UC Merced)](http://faculty.ucmerced.edu/mhyang/), [Jan Kautz (NVIDIA)](http://jankautz.com/) <br>
European Conference on Computer Vision (ECCV), 2018 <br>


### Tutorial

Please check out the [tutorial](TUTORIAL.md).