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New release MMEditing v1.0.0rc7 [07/04/2023]:
- Support DiffuserWrapper
- Support ControlNet (training and inference).
- Support PyTorch 2.0 (successfully compile 33+ models on 'inductor' backend).
MMEditing has supported all the tasks, models, metrics, and losses in MMGeneration and unifies interfaces of all components based on MMEngine 😍.
Please refer to changelog.md for details and release history.
Please refer to migration documents to migrate from old version MMEditing 0.x to our brand new 1.x version.
- 📖 Introduction
- 🙌 Contributing
- 🛠️ Installation
- 📊 Model Zoo
- 🤝 Acknowledgement
- 🖊️ Citation
- 🎫 License
- 🏗️ ️OpenMMLab Family
MMEditing is an open-source image and video editing&generating toolbox based on PyTorch. It is a part of the OpenMMLab project.
Currently, MMEditing support multiple image and video generation/editing tasks.
mmediting_supported_tasks.mp4
The best practice on our main branch works with Python 3.8+ and PyTorch 1.9+.
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State of the Art
MMEditing provides state-of-the-art generative models to process, edit and synthesize images and videos.
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Powerful and Popular Applications
MMEditing supports popular and contemporary image restoration, text-to-image, 3D-aware generation, inpainting, matting, super-resolution and generation applications. Specifically, MMEditing supports GAN interpolation, GAN projection, GAN manipulations and many other popular GAN’s applications. It’s time to play with your GANs!
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New Modular Design for Flexible Combination
We decompose the editing framework into different modules and one can easily construct a customized editor framework by combining different modules. Specifically, a new design for complex loss modules is proposed for customizing the links between modules, which can achieve flexible combinations among different modules.(Tutorial for losses)
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Efficient Distributed Training
With the support of MMSeparateDistributedDataParallel, distributed training for dynamic architectures can be easily implemented.
More and more community contributors are joining us to make our repo better. Some recent projects are contributed by the community including:
- GLIDE is contributed by @Taited.
- Restormer is contributed by @AlexZou14.
- SwinIR is contributed by @Zdafeng.
Projects is opened to make it easier for everyone to add projects to MMEditing.
We appreciate all contributions to improve MMEditing. Please refer to CONTRIBUTING.md in MMCV and CONTRIBUTING.md in MMEngine for more details about the contributing guideline.
MMEditing depends on PyTorch, MMEngine and MMCV. Below are quick steps for installation.
Step 1. Install PyTorch following official instructions.
Step 2. Install MMCV with MIM.
pip3 install openmim
# wait for more pre-compiled pkgs to release
mim install 'mmcv>=2.0.0'Step 3. Install MMEditing from source.
git clone https://github.com/open-mmlab/mmediting.git
cd mmediting
pip3 install -e .Please refer to installation for more detailed instruction.
Getting Started
Please see quick run and inference for the basic usage of MMEditing.
| Conditional GANs | Unconditional GANs | Image Restoration | Image Super-Resolution |
| Video Super-Resolution | Video Interpolation | Image Colorization | Image Translation |
| Inpainting | Matting | Text-to-Image | 3D-aware Generation |
Please refer to model_zoo for more details.
MMEditing is an open source project that is contributed by researchers and engineers from various colleges and companies. We wish that the toolbox and benchmark could serve the growing research community by providing a flexible toolkit to reimplement existing methods and develop their own new methods.
We appreciate all the contributors who implement their methods or add new features, as well as users who give valuable feedbacks. Thank you all!
If MMEditing is helpful to your research, please cite it as below.
@misc{mmediting2022,
title = {{MMEditing}: {OpenMMLab} Image and Video Editing Toolbox},
author = {{MMEditing Contributors}},
howpublished = {\url{https://github.com/open-mmlab/mmediting}},
year = {2022}
}This project is released under the Apache 2.0 license. Please refer to LICENSES for the careful check, if you are using our code for commercial matters.
- MMEngine: OpenMMLab foundational library for training deep learning models.
- 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.