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Apache-2.0

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PaddleGAN

飞桨生成对抗网络开发套件--PaddleGAN,为开发者提供经典及前沿的生成对抗网络高性能实现,并支撑开发者快速构建、训练及部署生成对抗网络,以供学术、娱乐及产业应用。

GAN--生成对抗网络,被“卷积网络之父”Yann LeCun(杨立昆)誉为「过去十年计算机科学领域最有趣的想法之一」,是近年来火遍全网,AI研究者最为关注的深度学习技术方向之一。

Licensepython version

近期活动🔥🔥🔥

产品动态

文档教程

安装

入门教程

模型库

产业级应用

在线教程

您可以通过人工智能学习与实训社区AI Studio 的示例工程在线体验PaddleGAN的部分能力:

在线教程 链接
人脸融合-PaddleGAN七夕特辑 点击体验
表情动作迁移-一键实现多人版「蚂蚁呀嘿」 点击体验
老北京视频修复 点击体验
表情动作迁移-当苏大强唱起unravel 点击体验

效果展示

人脸融合

风格迁移

老视频修复

动作迁移

超分辨率

妆容迁移

人脸动漫化

写实人像卡通化

照片动漫化

唇形同步

版本更新

  • v2.1.0 (2021.12.8)

    • 发布视频超分辨率模型PP-MSVSR以及多个预训练权重
    • 发布BasicVSR,IconVSR与Basicvsr++等多个效果领先的视频超分辨率模型及其预训练模型
    • 发布轻量级动作驱动模型(体积压缩:229M->10.1M),并优化融合效果
    • 发布高分辨率的FOMM和Wav2Lip预训练模型
    • 发布人脸反演,人脸融合和人脸编辑等多个基于StyleGANv2的有趣应用
    • 发布百度自研且效果领先的风格迁移模型LapStyle及其有趣应用,并上线官网体验页面
    • 发布轻量的图像超分辨模型PAN
  • v2.0.0 (2021.6.2)

  • v2.0.0-beta (2021.3.1)

    • 完全切换Paddle 2.0.0版本的API。
    • 发布超分辨模型:ESRGAN,RealSR,LESRCNN,DRN等
    • 发布唇形迁移模型:Wav2Lip
    • 发布街景动漫化模型:AnimeGANv2
    • 发布人脸动漫化模型:U-GAT-IT ,Photo2Cartoon
    • 发布高清人脸生成模型:StyleGAN2
  • v0.1.0 (2020.11.02)

    • 初版发布,支持Pixel2Pixel、CycleGAN、PSGAN模型,支持视频插针、超分、老照片/视频上色、视频动作生成等应用。
    • 模块化设计,接口简单易用。

欢迎加入PaddleGAN技术交流群

扫描二维码加入PaddleGAN QQ群[群号:1058398620],获得更高效的问题答疑,与各行业开发者交流讨论,我们期待您的加入!

扫描二维码回复关键字"GAN"即可加入官方微信交流群!

PaddleGAN 特别兴趣小组(Special Interest Group)

最早于1961年被ACM(Association for Computing Machinery)首次提出并使用,国际顶尖开源组织包括Kubernates都采用SIGs的形式,使拥有同样特定兴趣的成员可以共同分享、学习知识并进行项目开发。这些成员不需要在同一国家/地区、同一个组织,只要大家志同道合,都可以奔着相同的目标一同学习、工作、玩耍~

PaddleGAN SIG就是这样一个汇集对GAN感兴趣小伙伴们的开发者组织,在这里,有百度飞桨的一线开发人员、有来自世界500强的资深工程师、有国内外顶尖高校的学生。

我们正在持续招募有兴趣、有能力的开发者加入我们一起共同建设本项目,并一起探索更多有用、有趣的应用。欢迎大家在加入群后联系我们讨论加入SIG并参与共建事宜。

SIG贡献:

  • zhen8838: 贡献AnimeGANv2.
  • Jay9z: 贡献DCGAN的示例、修改安装文档等。
  • HighCWu: 贡献c-DCGAN和WGAN,以及对paddle.vision.datasets数据集的支持;贡献inversion部分代码复现。
  • hao-qiang & minivision-ai : 贡献人像卡通化photo2cartoon项目。
  • lyl120117:贡献去模糊MPRNet推理代码。

贡献代码

我们非常欢迎您可以为PaddleGAN提供任何贡献和建议。大多数贡献都需要同意参与者许可协议(CLA)。当提交拉取请求时,CLA机器人会自动检查您是否需要提供CLA。 只需要按照机器人提供的说明进行操作即可。CLA只需要同意一次,就能应用到所有的代码仓库上。关于更多的流程请参考贡献指南

许可证书

本项目的发布受Apache 2.0 license许可认证。

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"You" (or "Your") shall mean an individual or Legal Entity exercising permissions granted by this License. "Source" form shall mean the preferred form for making modifications, including but not limited to software source code, documentation source, and configuration files. "Object" form shall mean any form resulting from mechanical transformation or translation of a Source form, including but not limited to compiled object code, generated documentation, and conversions to other media types. "Work" shall mean the work of authorship, whether in Source or Object form, made available under the License, as indicated by a copyright notice that is included in or attached to the work (an example is provided in the Appendix below). "Derivative Works" shall mean any work, whether in Source or Object form, that is based on (or derived from) the Work and for which the editorial revisions, annotations, elaborations, or other modifications represent, as a whole, an original work of authorship. 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