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mmgeneration

MMGeneration is a powerful toolkit for generative models, based on PyTorch and MMCV.

https://github.com/open-mmlab/mmgeneration

Science Score: 44.0%

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    CITATION.cff file
    Found CITATION.cff file
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    Low similarity (10.9%) to scientific vocabulary

Keywords

diffusion-models gan generative generative-adversarial-network mmcv openmmlab pytorch
Last synced: 4 months ago · JSON representation ·

Repository

MMGeneration is a powerful toolkit for generative models, based on PyTorch and MMCV.

Basic Info
Statistics
  • Stars: 1,987
  • Watchers: 25
  • Forks: 235
  • Open Issues: 53
  • Releases: 11
Topics
diffusion-models gan generative generative-adversarial-network mmcv openmmlab pytorch
Created over 4 years ago · Last pushed over 2 years ago
Metadata Files
Readme Contributing License Code of conduct Citation

README.md

 
OpenMMLab website HOT      OpenMMLab platform TRY IT OUT
 

PyPI docs badge codecov license open issues issue resolution

📘Documentation | 🛠️Installation | 👀Model Zoo | 🆕Update News | 🚀Ongoing Projects | 🤔Reporting Issues

English | 简体中文

What's New

MMGeneration has been merged in MMEditing. And we have supported new generation tasks and models. We highlight the following new features:

Introduction

MMGeneration is a powerful toolkit for generative models, especially for GANs now. It is based on PyTorch and MMCV. The master branch works with PyTorch 1.5+.

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)
  • 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)
  • 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)
Training Visualization
GAN Interpolation
GAN Projector
GAN Manipulation

Highlight

  • Positional Encoding as Spatial Inductive Bias in GANs (CVPR2021) has been released in MMGeneration. [Config], [Project Page]
  • Conditional GANs have been supported in our toolkit. More methods and pre-trained weights will come soon.
  • Mixed-precision training (FP16) for StyleGAN2 has been supported. Please check the comparison between different implementations.

Changelog

v0.7.3 was released on 14/04/2023. Please refer to changelog.md for details and release history.

Installation

MMGeneration depends on PyTorch and MMCV. Below are quick steps for installation.

Step 1. Install PyTorch following official instructions, e.g.

```python pip3 install torch torchvision

```

Step 2. Install MMCV with MIM.

pip3 install openmim mim install mmcv-full

Step 3. Install MMGeneration from source.

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

Please refer to get_started.md for more detailed instruction.

Getting Started

Please see get_started.md for the basic usage of MMGeneration. docs/en/quick_run.md can offer full guidance for quick run. For other details and tutorials, please go to our documentation.

ModelZoo

These methods have been carefully studied and supported in our frameworks:

Unconditional GANs (click to collapse) - ✅ [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) (arXiv'2017) - ✅ [PGGAN](configs/pggan/README.md) (ICLR'2018) - ✅ [StyleGANV1](configs/styleganv1/README.md) (CVPR'2019) - ✅ [StyleGANV2](configs/styleganv2/README.md) (CVPR'2020) - ✅ [StyleGANV3](configs/styleganv3/README.md) (NeurIPS'2021) - ✅ [Positional Encoding in GANs](configs/positional_encoding_in_gans/README.md) (CVPR'2021)
Conditional GANs (click to collapse) - ✅ [SNGAN](configs/sngan_proj/README.md) (ICLR'2018) - ✅ [Projection GAN](configs/sngan_proj/README.md) (ICLR'2018) - ✅ [SAGAN](configs/sagan/README.md) (ICML'2019) - ✅ [BIGGAN/BIGGAN-DEEP](configs/biggan/README.md) (ICLR'2019)
Tricks for GANs (click to collapse) - ✅ [ADA](configs/ada/README.md) (NeurIPS'2020)
Image2Image Translation (click to collapse) - ✅ [Pix2Pix](configs/pix2pix/README.md) (CVPR'2017) - ✅ [CycleGAN](configs/cyclegan/README.md) (ICCV'2017)
Internal Learning (click to collapse) - ✅ [SinGAN](configs/singan/README.md) (ICCV'2019)
Denoising Diffusion Probabilistic Models (click to collapse) - ✅ [Improved DDPM](configs/improved_ddpm/README.md) (arXiv'2021)

Related-Applications

Contributing

We appreciate all contributions to improve MMGeneration. Please refer to 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} }

License

This project is released under the Apache 2.0 license. Some operations in MMGeneration are with other licenses instead of Apache2.0. Please refer to LICENSES.md for the careful check, if you are using our code for commercial matters.

Projects in OpenMMLab

  • MMCV: OpenMMLab foundational library for computer vision.
  • MIM: MIM installs OpenMMLab packages.
  • MMClassification: OpenMMLab image classification toolbox and benchmark.
  • MMDetection: OpenMMLab detection toolbox and benchmark.
  • MMDetection3D: OpenMMLab's next-generation platform for general 3D object detection.
  • MMRotate: OpenMMLab rotated object detection toolbox and benchmark.
  • MMSegmentation: OpenMMLab semantic segmentation toolbox and benchmark.
  • MMOCR: OpenMMLab text detection, recognition, and understanding toolbox.
  • MMPose: OpenMMLab pose estimation toolbox and benchmark.
  • MMHuman3D: OpenMMLab 3D human parametric model toolbox and benchmark.
  • MMSelfSup: OpenMMLab self-supervised learning toolbox and benchmark.
  • MMRazor: OpenMMLab model compression toolbox and benchmark.
  • MMFewShot: OpenMMLab fewshot learning toolbox and benchmark.
  • MMAction2: OpenMMLab's next-generation action understanding toolbox and benchmark.
  • MMTracking: OpenMMLab video perception toolbox and benchmark.
  • MMFlow: OpenMMLab optical flow toolbox and benchmark.
  • MMEditing: OpenMMLab image and video editing toolbox.
  • MMGeneration: OpenMMLab image and video generative models toolbox.
  • MMDeploy: OpenMMLab model deployment framework.

Owner

  • Name: OpenMMLab
  • Login: open-mmlab
  • Kind: organization
  • Location: China

Citation (CITATION.cff) 

cff-version: 1.2.0
message: "If you use this software, please cite it as below."
authors:
  - name: "MMGeneration Contributors"
title: "OpenMMLab's next-generation toolbox for generative models"
date-released: 2020-07-10
url: "https://github.com/open-mmlab/mmgeneration"
license: Apache-2.0

GitHub Events

Total
  • Issues event: 2
  • Watch event: 109
  • Pull request event: 1
  • Fork event: 12
Last Year
  • Issues event: 2
  • Watch event: 109
  • Pull request event: 1
  • Fork event: 12

Committers

Last synced: 8 months ago

All Time
  • Total Commits: 456
  • Total Committers: 22
  • Avg Commits per committer: 20.727
  • Development Distribution Score (DDS): 0.627
Past Year
  • Commits: 0
  • Committers: 0
  • Avg Commits per committer: 0.0
  • Development Distribution Score (DDS): 0.0
Top Committers
Name Email Commits
plyfager 2****5@q****m 170
LeoXing1996 x****6@h****m 138
nbei n****y@g****m 81
yangyifei P****i@s****g 22
邢哲宁 S****g@c****m 9
taited t****0@g****m 5
Z-Fran 4****n 4
Yosuke Shinya 4****y 3
ckkelvinchan k****n@o****m 3
ys-li 5****i 3
plutoyuxie 3****0@q****m 3
Tommy in Tongji 3****o 2
Kai Zhao 6****1@q****m 2
JiangongWang w****8@i****n 2
zenggyh1900 z****0@g****m 2
Gabriele Valvano g****o@g****m 1
LeeTeng2001 6****1 1
Range King R****Z@g****m 1
jimheo g****1@g****m 1
杨逸飞 y****1@C****l 1
vansin m****e@1****m 1
zeyu z****u@h****m 1
Committer Domains (Top 20 + Academic)
qq.com: 3 163.com: 1 ia.ac.cn: 1 cn0214006957l.domain.sensetime.com: 1 shai14001042l.pjlab.org: 1

Issues and Pull Requests

Last synced: 4 months ago

All Time
  • Total issues: 84
  • Total pull requests: 117
  • Average time to close issues: 2 months
  • Average time to close pull requests: 7 days
  • Total issue authors: 74
  • Total pull request authors: 23
  • Average comments per issue: 2.39
  • Average comments per pull request: 1.11
  • Merged pull requests: 86
  • Bot issues: 0
  • Bot pull requests: 0
Past Year
  • Issues: 2
  • Pull requests: 1
  • Average time to close issues: about 2 hours
  • Average time to close pull requests: N/A
  • Issue authors: 2
  • Pull request authors: 1
  • Average comments per issue: 0.0
  • Average comments per pull request: 0.0
  • Merged pull requests: 0
  • Bot issues: 0
  • Bot pull requests: 0
View more stats
Top Authors
Issue Authors
  • ronghui19 (4)
  • bingpo1314 (4)
  • zeng-hello-world (2)
  • monkeycc (2)
  • gvalvano (2)
  • zxxxxxxh (2)
  • MrPeterJin (1)
  • yuninn (1)
  • penincillin (1)
  • wj-data (1)
  • Adamdad (1)
  • pjh4993 (1)
  • KeranLi (1)
  • greatbaozi001 (1)
  • nbei (1)
Pull Request Authors
  • LeoXing1996 (35)
  • nbei (29)
  • plyfager (22)
  • Z-Fran (5)
  • shinya7y (3)
  • ckkelvinchan (3)
  • Taited (2)
  • zeng-hello-world (2)
  • zeakey (2)
  • MichaelMonashev (1)
  • zengyh1900 (1)
  • TommyZihao (1)
  • makecent (1)
  • LeeTeng2001 (1)
  • Nourollah (1)
Top Labels
Issue Labels
kind/bug (11) priority/P0 (10) community/good first issue (4) status/WIP (3) kind/feature (2) status/need more info (1) info/1.x (1)
Pull Request Labels
priority/P0 (16) kind/bug (7) kind/feature (6) status/WIP (3) kind/enhancement (3) kind/doc (1) priority/P1 (1)

Packages

  • Total packages: 1
  • Total downloads: unknown
  • Total dependent packages: 0
  • Total dependent repositories: 0
  • Total versions: 10
proxy.golang.org: github.com/open-mmlab/mmgeneration
  • Versions: 10
  • Dependent Packages: 0
  • Dependent Repositories: 0
Rankings
Dependent packages count: 6.5%
Average: 6.7%
Dependent repos count: 6.9%
Last synced: 4 months ago

Dependencies

requirements/docs.txt pypi
  • click *
  • docutils ==0.16.0
  • m2r *
  • mmcls ==0.18.0
  • myst-parser *
  • opencv-python *
  • prettytable *
  • scipy *
  • sphinx ==4.0.2
  • sphinx-copybutton *
  • sphinx_markdown_tables *
requirements/mminstall.txt pypi
  • mmcls >=0.18.0
  • mmcv-full >=1.3.0,<=1.6.0
requirements/readthedocs.txt pypi
  • mmcv *
  • torch *
  • torchvision *
requirements/runtime.txt pypi
  • mmcls *
  • ninja *
  • numpy *
  • prettytable *
  • scikit-image *
  • scipy *
  • tqdm *
  • yapf *
requirements/tests.txt pypi
  • codecov *
  • flake8 *
  • interrogate *
  • isort ==4.3.21
  • pytest *
  • pytest-runner *
.github/workflows/lint.yml actions
  • actions/checkout v2 composite
  • actions/setup-python v2 composite
  • mxschmitt/action-tmate v3 composite
.github/workflows/merge_stage_test.yml actions
  • actions/checkout v2 composite
  • actions/setup-python v2 composite
  • codecov/codecov-action v1.0.14 composite
  • mxschmitt/action-tmate v3 composite
.github/workflows/pr_stage_test.yml actions
  • actions/checkout v2 composite
  • actions/setup-python v2 composite
  • codecov/codecov-action v1.0.14 composite
  • mxschmitt/action-tmate v3 composite
.github/workflows/publish-to-pypi.yml actions
  • actions/checkout v2 composite
  • actions/setup-python v1 composite
.github/workflows/test_mim.yml actions
  • actions/checkout v2 composite
  • actions/setup-python v2 composite
.circleci/docker/Dockerfile docker
  • pytorch/pytorch ${PYTORCH}-cuda${CUDA}-cudnn${CUDNN}-devel build
docker/Dockerfile docker
  • pytorch/pytorch ${PYTORCH}-cuda${CUDA}-cudnn${CUDNN}-devel build
requirements.txt pypi
setup.py pypi