[{"data":1,"prerenderedAt":-1},["ShallowReactive",2],{"project-70784":3},{"id":4,"name":5,"fullName":6,"owner":7,"repo":5,"description":8,"homepage":9,"htmlUrl":10,"language":11,"languages":10,"totalLinesOfCode":10,"stars":12,"forks":13,"watchers":14,"openIssues":15,"contributorsCount":16,"subscribersCount":16,"size":16,"stars1d":16,"stars7d":17,"stars30d":18,"stars90d":16,"forks30d":16,"starsTrendScore":16,"compositeScore":19,"rankGlobal":10,"rankLanguage":10,"license":20,"archived":21,"fork":21,"defaultBranch":22,"hasWiki":23,"hasPages":21,"topics":24,"createdAt":10,"pushedAt":10,"updatedAt":25,"readmeContent":26,"aiSummary":27,"trendingCount":16,"starSnapshotCount":16,"syncStatus":17,"lastSyncTime":28,"discoverSource":29},70784,"FastPhotoStyle","NVIDIA\u002FFastPhotoStyle","NVIDIA","Style transfer, deep learning, feature transform","",null,"Python",11180,1190,268,48,0,2,5,70.73,"Other",false,"master",true,[],"2026-06-12 04:00:57","[![License CC BY-NC-SA 4.0](https:\u002F\u002Fimg.shields.io\u002Fbadge\u002Flicense-CC4.0-blue.svg)](https:\u002F\u002Fraw.githubusercontent.com\u002FNVIDIA\u002FFastPhotoStyle\u002Fmaster\u002FLICENSE.md)\n![Python 2.7](https:\u002F\u002Fimg.shields.io\u002Fbadge\u002Fpython-2.7-green.svg)\n![Python 3.5](https:\u002F\u002Fimg.shields.io\u002Fbadge\u002Fpython-3.5-green.svg)\n\n## FastPhotoStyle\n\n### License\nCopyright (C) 2018 NVIDIA Corporation.  All rights reserved.\nLicensed under the CC BY-NC-SA 4.0 license (https:\u002F\u002Fcreativecommons.org\u002Flicenses\u002Fby-nc-sa\u002F4.0\u002Flegalcode).\n\n\u003Cimg src=\"https:\u002F\u002Fraw.githubusercontent.com\u002FNVIDIA\u002FFastPhotoStyle\u002Fmaster\u002Fteaser.png\" width=\"800\" title=\"Teaser results\"> \n\n\n### What's new\n \n | Date     | News |\n |----------|--------------|\n |2018-07-25| Migrate to pytorch 0.4.0. For pytorch 0.3.0 user, check out [FastPhotoStyle for pytorch 0.3.0](https:\u002F\u002Fgithub.com\u002FNVIDIA\u002FFastPhotoStyle\u002Freleases\u002Ftag\u002Ff33e07f). |\n |          | Add a [tutorial](TUTORIAL.md) showing 3 ways of using the FastPhotoStyle algorithm.|\n |2018-07-10| Our paper is accepted by the ECCV 2018 conference!!! | \n\n\n### About\n\nGiven 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.\n\n[A Closed-form Solution to Photorealistic Image Stylization](https:\u002F\u002Farxiv.org\u002Fabs\u002F1802.06474) \u003Cbr> \n[Yijun Li (UC Merced)](https:\u002F\u002Fsites.google.com\u002Fsite\u002Fyijunlimaverick\u002F), [Ming-Yu Liu (NVIDIA)](http:\u002F\u002Fmingyuliu.net\u002F), [Xueting Li (UC Merced)](https:\u002F\u002Fsunshineatnoon.github.io\u002F), [Ming-Hsuan Yang (NVIDIA, UC Merced)](http:\u002F\u002Ffaculty.ucmerced.edu\u002Fmhyang\u002F), [Jan Kautz (NVIDIA)](http:\u002F\u002Fjankautz.com\u002F) \u003Cbr>\nEuropean Conference on Computer Vision (ECCV), 2018 \u003Cbr>\n\n\n### Tutorial\n\nPlease check out the [tutorial](TUTORIAL.md).\n\n\n","FastPhotoStyle 是一个基于深度学习的图片风格迁移项目。它能够将一张图片的风格迁移到另一张图片上，同时保持内容的真实感。该项目利用了特征变换技术来实现快速且高质量的风格转换效果，并支持Python 2.7和3.5版本。FastPhotoStyle适合用于艺术创作、图像编辑以及需要对照片进行个性化处理的应用场景中，如社交媒体滤镜开发等。此外，项目提供了详细的教程文档，方便用户理解和使用该算法。","2026-06-11 03:34:10","high_star"]