Package: mmgen
OpenMMLab websiteHOTOpenMMLab platformTRY IT OUT📘Documentation|🛠️Installation|👀Model Zoo|🆕Update News|🚀Ongoing Projects|🤔Reporting IssuesEnglish |简体中文What's NewMMGeneration has been merged inMMEditing. And we have supported new generation tasks and models. We highlight the following new features:🌟 Text2Image✅GLIDE✅Disco-Diffusion✅Stable-Diffusion🌟 3D-aware Generation✅EG3DIntroductionMMGeneration is a powerful toolkit for generative models, especially for GANs now. It is based on PyTorch andMMCV. The master branch works withPyTorch 1.5+.Major FeaturesHigh-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 withMMDDP. (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 VisualizationGAN InterpolationGAN ProjectorGAN ManipulationHighlightPositional Encoding as Spatial Inductive Bias in GANs (CVPR2021)has been released inMMGeneration.[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 checkthe comparisonbetween different implementations.Changelogv0.7.3 was released on 14/04/2023. Please refer tochangelog.mdfor details and release history.InstallationMMGeneration depends onPyTorchandMMCV.
Below are quick steps for installation.Step 1.Install PyTorch followingofficial instructions, e.g.pip3installtorchtorchvisionStep 2.Install MMCV withMIM.pip3 install openmim
mim install mmcv-fullStep 3.Install MMGeneration from source.git clone https://github.com/open-mmlab/mmgeneration.git
cd mmgeneration
pip3 install -e .Please refer toget_started.mdfor more detailed instruction.Getting StartedPlease seeget_started.mdfor the basic usage of MMGeneration.docs/en/quick_run.mdcan offer full guidance for quick run. For other details and tutorials, please go to ourdocumentation.ModelZooThese methods have been carefully studied and supported in our frameworks:Unconditional GANs (click to collapse)✅DCGAN(ICLR'2016)✅WGAN-GP(NIPS'2017)✅LSGAN(ICCV'2017)✅GGAN(arXiv'2017)✅PGGAN(ICLR'2018)✅StyleGANV1(CVPR'2019)✅StyleGANV2(CVPR'2020)✅StyleGANV3(NeurIPS'2021)✅Positional Encoding in GANs(CVPR'2021)Conditional GANs (click to collapse)✅SNGAN(ICLR'2018)✅Projection GAN(ICLR'2018)✅SAGAN(ICML'2019)✅BIGGAN/BIGGAN-DEEP(ICLR'2019)Tricks for GANs (click to collapse)✅ADA(NeurIPS'2020)Image2Image Translation (click to collapse)✅Pix2Pix(CVPR'2017)✅CycleGAN(ICCV'2017)Internal Learning (click to collapse)✅SinGAN(ICCV'2019)Denoising Diffusion Probabilistic Models (click to collapse)✅Improved DDPM(arXiv'2021)Related-Applications✅MMGEN-FaceStylorContributingWe appreciate all contributions to improve MMGeneration. Please refer toCONTRIBUTING.mdin MMCV for more details about the contributing guideline.CitationIf you find this project useful in your research, please consider cite:@misc{2021mmgeneration,title={{MMGeneration}: OpenMMLab Generative Model Toolbox and Benchmark},author={MMGeneration Contributors},howpublished={\url{https://github.com/open-mmlab/mmgeneration}},year={2021}}LicenseThis project is released under theApache 2.0 license. Some operations inMMGenerationare with other licenses instead of Apache2.0. Please refer toLICENSES.mdfor the careful check, if you are using our code for commercial matters.Projects in OpenMMLabMMCV: 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.