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📘使用文档 | 🛠️安装教程 | 👀模型库 | 🆕更新记录 | 🚀进行中的项目 | 🤔提出问题

English | 简体中文

最新进展

我们将MMGeneration合入了MMEditing,并支持了新的生成任务和算法。请关注以下新特性:

简介

MMGeneration 是一个基于 PyTorch 和MMCV的强有力的生成模型工具箱,尤其专注于 GAN 模型。 主分支目前支持 PyTorch 1.5 以上的版本。

主要特性

  • 高质量高性能的训练: 我们目前支持 Unconditional GANs, Internal GANs, 以及 Image Translation Models 的训练。很快将会支持 conditional models 的训练。
  • 强有力的应用工具箱: 为用户提供了丰富的工具箱,包含 GANs 中的多种应用。我们的框架集成了 GANs 的插值,投影和编辑。请用你的 GANs 尽情尝试!(应用教程)
  • 生成模型的高效分布式训练: 对于生成模型中的高度动态训练,我们采用 MMDDP 的新方法来训练动态模型。(DDP教程)
  • 灵活组合的新型模块化设计: 针对复杂的损失模块,我们提出了一种新的设计,可以自定义模块之间的链接,实现不同模块之间的灵活组合。 (新模块化设计教程)
训练可视化
GAN 插值
GAN 投影
GAN 编辑

亮点

  • Positional Encoding as Spatial Inductive Bias in GANs (CVPR2021) 已在 MMGeneration 中发布. [配置文件], [项目主页]
  • 我们已经支持训练目前主流的 Conditional GANs 模型,更多的方法和预训练权重马上就会发布,敬请期待。
  • 混合精度训练已经在 StyleGAN2 中进行了初步支持,请到这里查看各种实现方式的详细比较。

更新日志

v0.7.3 在 14/04/2023 发布。 关于细节和发布历史,请参考 changelog.md

安装

MMGeneration 依赖 PyTorchMMCV,以下是安装的简要步骤。

步骤 1. 依照官方教程安装PyTorch,例如

pip3 install torch torchvision

步骤 2. 使用 MIM 安装 MMCV

pip3 install openmim
mim install mmcv-full

步骤 3. 从源码安装 MMGeneration

git clone https://github.com/open-mmlab/mmgeneration.git
cd mmgeneration
pip3 install -e .

更详细的安装指南请参考 get_started.md .

快速入门

对于 MMGeneration 的基本使用请参考 快速入门。其他细节和教程,请参考我们的文档

模型库

这些算法在我们的框架中得到了认真研究和支持。

Unconditional GANs (点击折叠)
Conditional GANs (点击折叠)
Tricks for GANs (点击折叠)
  • ADA (NeurIPS'2020)
Image2Image Translation (点击折叠)
Internal Learning (点击折叠)
Denoising Diffusion Probabilistic Models (点击折叠)

相关应用

贡献指南

我们感谢所有的贡献者为改进和提升 MMGeneration 所作出的努力。请参考贡献指南来了解参与项目贡献的相关指引。

引用

如果您发现此项目对您的研究有用,请考虑引用:

@misc{2021mmgeneration,
    title={{MMGeneration}: OpenMMLab Generative Model Toolbox and Benchmark},
    author={MMGeneration Contributors},
    howpublished = {\url{https://github.com/open-mmlab/mmgeneration}},
    year={2020}
}

开源许可证

该项目采用 Apache 2.0 license 开源许可证。MMGeneration 中的一些操作使用了其他许可证。如果您使用我们的代码进行商业事务,请参考 许可证 并仔细检查。

OpenMMLab 的其他项目

  • MMCV: OpenMMLab 计算机视觉基础库
  • MIM: MIM 是 OpenMMlab 项目、算法、模型的统一入口
  • MMClassification: OpenMMLab 图像分类工具箱
  • MMDetection: OpenMMLab 目标检测工具箱
  • MMDetection3D: OpenMMLab 新一代通用 3D 目标检测平台
  • MMRotate: OpenMMLab 旋转框检测工具箱与测试基准
  • MMSegmentation: OpenMMLab 语义分割工具箱
  • MMOCR: OpenMMLab 全流程文字检测识别理解工具箱
  • MMPose: OpenMMLab 姿态估计工具箱
  • MMHuman3D: OpenMMLab 人体参数化模型工具箱与测试基准
  • MMSelfSup: OpenMMLab 自监督学习工具箱与测试基准
  • MMRazor: OpenMMLab 模型压缩工具箱与测试基准
  • MMFewShot: OpenMMLab 少样本学习工具箱与测试基准
  • MMAction2: OpenMMLab 新一代视频理解工具箱
  • MMTracking: OpenMMLab 一体化视频目标感知平台
  • MMFlow: OpenMMLab 光流估计工具箱与测试基准
  • MMEditing: OpenMMLab 图像视频编辑工具箱
  • MMGeneration: OpenMMLab 图片视频生成模型工具箱
  • MMDeploy: OpenMMLab 模型部署框架

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我们会在 OpenMMLab 社区为大家

  • 📢 分享 AI 框架的前沿核心技术
  • 💻 解读 PyTorch 常用模块源码
  • 📰 发布 OpenMMLab 的相关新闻
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简介

基于 PyTorch 和 MMCV 的图像和视频生成工具箱,支持多种生成模型如图像生成,条件生成,图像转换的训练和测试。它同时提供了丰富的应用,包含了 GANs 的插值,投影和图像编辑。 展开 收起
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