Metadata-Version: 2.1
Name: mmgen
Version: 0.2.0
Summary: MMGeneration
Home-page: https://github.com/open-mmlab/mmgen
Author: MMGeneration Authors
Author-email: openmmlab@gmail.com
License: Apache License 2.0
Description: <div align="center">
            <img src="https://user-images.githubusercontent.com/12726765/114528756-de55af80-9c7b-11eb-94d7-d3224ada1585.png" width="400"/>
        </div>
        
        Documentation: https://mmgeneration.readthedocs.io/
        
        ## Introduction
        
        English | [简体中文](README_zh-CN.md)
        
        MMGeneration is a powerful toolkit for generative models, especially for GANs now. It is based on PyTorch and [MMCV](https://github.com/open-mmlab/mmcv). The master branch works with **PyTorch 1.5+**.
        
        <div align="center">
            <img src="https://user-images.githubusercontent.com/12726765/114534478-9a65a900-9c81-11eb-8087-de8b6816eed8.png" width="800"/>
        </div>
        
        
        ## Major Features
        
        - **High-quality Training Performance:** We currently support training on Unconditional GANs, Internal GANs, and Image Translation Models. Support for conditional models will come soon.
        - **Powerful Application Toolkit:** A plentiful toolkit containing multiple applications in GANs is provided to users. GAN interpolation, GAN projection, and GAN manipulations are integrated into our framework. It's time to play with your GANs! ([Tutorial for applications](docs/tutorials/applications.md))
        - **Efficient Distributed Training for Generative Models:** For the highly dynamic training in generative models, we adopt a new way to train dynamic models with `MMDDP`. ([Tutorial for DDP](docs/tutorials/ddp_train_gans.md))
        - **New Modular Design for Flexible Combination:** A new design for complex loss modules is proposed for customizing the links between modules, which can achieve flexible combination among different modules. ([Tutorial for new modular design](docs/tutorials/customize_losses.md))
        
        
        <table>
        <thead>
          <tr>
            <td>
        <div align="center">
          <b> Training Visualization</b>
          <br/>
          <img src="https://user-images.githubusercontent.com/12726765/114509105-b6f4e780-9c67-11eb-8644-110b3cb01314.gif" width="200"/>
        </div></td>
            <td>
        <div align="center">
          <b> GAN Interpolation</b>
          <br/>
          <img src="https://user-images.githubusercontent.com/12726765/114679300-9fd4f900-9d3e-11eb-8f37-c36a018c02f7.gif" width="200"/>
        </div></td>
            <td>
        <div align="center">
          <b> GAN Projector</b>
          <br/>
          <img src="https://user-images.githubusercontent.com/12726765/114524392-c11ee200-9c77-11eb-8b6d-37bc637f5626.gif" width="200"/>
        </div></td>
            <td>
        <div align="center">
          <b> GAN Manipulation</b>
          <br/>
          <img src="https://user-images.githubusercontent.com/12726765/114523716-20302700-9c77-11eb-804e-327ae1ca0c5b.gif" width="200"/>
        </div></td>
          </tr>
        </thead>
        </table>
        
        ## Highlight
        
        * **Positional Encoding as Spatial Inductive Bias in GANs (CVPR2021)** has been released in `MMGeneration`.  [\[Config\]](configs/positional_encoding_in_gans/README.md), [\[Project Page\]](https://nbei.github.io/gan-pos-encoding.html)
        * Mixed-precision training (FP16) for StyleGAN2 has been supported. Please check [the comparison](configs/styleganv2/README.md) between different implementations.
        ## Changelog
        
        v0.2.0 was released on 30/05/2021. Please refer to [changelog.md](docs/changelog.md) for details and release history.
        
        ## ModelZoo
        
        These methods have been carefully studied and supported in our frameworks:
        
        
        <details open>
        <summary>Unconditional GANs (click to collapse)</summary>
        
        - ✅ [DCGAN](configs/dcgan/README.md) (ICLR'2016)
        - ✅ [WGAN-GP](configs/wgan-gp/README.md) (NIPS'2017)
        - ✅ [LSGAN](configs/lsgan/README.md) (ICCV'2017)
        - ✅ [GGAN](configs/ggan/README.md) (Axiv'2017)
        - ✅ [PGGAN](configs/pggan/README.md) (ICLR'2018)
        - ✅ [StyleGANV1](configs/styleganv1/README.md) (CVPR'2019)
        - ✅ [StyleGANV2](configs/styleganv2/README.md) (CVPR'2020)
        - ✅ [Positional Encoding in GANs](configs/positional_encoding_in_gans/README.md) (CVPR'2021)
        
        </details>
        
        <details open>
        <summary>Image2Image Translation (click to collapse)</summary>
        
        - ✅ [Pix2Pix](configs/pix2pix/README.md) (CVPR'2017)
        - ✅ [CycleGAN](configs/cyclegan/README.md) (ICCV'2017)
        
        </details>
        
        <details open>
        <summary>Internal Learning (click to collapse)</summary>
        
        - ✅ [SinGAN](configs/singan/README.md) (ICCV'2019)
        
        </details>
        
        
        ## License
        
        This project is released under the [Apache 2.0 license](LICENSE). Some operations in `MMGeneration` are with other licenses instead of Apache2.0. Please refer to [LICENSES.md](LICENSES.md) for the careful check, if you are using our code for commercial matters.
        
        ## Installation
        
        Please refer to [get_started.md](docs/get_started.md) for installation.
        
        ## Getting Started
        
        Please see [get_started.md](docs/get_started.md) for the basic usage of MMGeneration. [docs/quick_run.md](docs/quick_run.md) can offer full guidance for quick run. For other details and tutorials, please go to our [documentation](https://mmgeneration.readthedocs.io/).
        
        ## Contributing
        
        We appreciate all contributions to improve MMGeneration. Please refer to [CONTRIBUTING.md](https://github.com/open-mmlab/mmcv/blob/master/CONTRIBUTING.md) in MMCV for more details about the contributing guideline.
        
        ## Citation
        
        If you find this project useful in your research, please consider cite:
        
        ```BibTeX
        @misc{2021mmgeneration,
            title={{MMGeneration}: OpenMMLab Generative Model Toolbox and Benchmark},
            author={MMGeneration Contributors},
            howpublished = {\url{https://github.com/open-mmlab/mmgeneration}},
            year={2021}
        }
        ```
        
        ## Projects in OpenMMLab
        
        - [MMCV](https://github.com/open-mmlab/mmcv): OpenMMLab foundational library for computer vision.
        - [MMClassification](https://github.com/open-mmlab/mmclassification): OpenMMLab image classification toolbox and benchmark.
        - [MMDetection](https://github.com/open-mmlab/mmdetection): OpenMMLab detection toolbox and benchmark.
        - [MMDetection3D](https://github.com/open-mmlab/mmdetection3d): OpenMMLab's next-generation platform for general 3D object detection.
        - [MMSegmentation](https://github.com/open-mmlab/mmsegmentation): OpenMMLab semantic segmentation toolbox and benchmark.
        - [MMAction2](https://github.com/open-mmlab/mmaction2): OpenMMLab's next-generation action understanding toolbox and benchmark.
        - [MMTracking](https://github.com/open-mmlab/mmtracking): OpenMMLab video perception toolbox and benchmark.
        - [MMPose](https://github.com/open-mmlab/mmpose): OpenMMLab pose estimation toolbox and benchmark.
        - [MMEditing](https://github.com/open-mmlab/mmediting): OpenMMLab image and video editing toolbox.
        - [MMOCR](https://github.com/open-mmlab/mmocr): A Comprehensive Toolbox for Text Detection, Recognition and Understanding.
        - [MMGeneration](https://github.com/open-mmlab/mmgeneration): OpenMMLab's next-generation toolbox for generative models.
        
Platform: UNKNOWN
Classifier: Development Status :: 4 - Beta
Classifier: License :: OSI Approved :: Apache Software License
Classifier: Operating System :: OS Independent
Classifier: Programming Language :: Python :: 3
Classifier: Programming Language :: Python :: 3.6
Classifier: Programming Language :: Python :: 3.7
Classifier: Programming Language :: Python :: 3.8
Description-Content-Type: text/markdown
