All Projects → open-mmlab → Mmediting

open-mmlab / Mmediting

Licence: apache-2.0
OpenMMLab Image and Video Editing Toolbox

Programming Languages

python
139335 projects - #7 most used programming language

Projects that are alternatives of or similar to Mmediting

Pix2pix
Image-to-image translation with conditional adversarial nets
Stars: ✭ 8,765 (+234.8%)
Mutual labels:  generative-adversarial-network, image-generation
Natsr
Natural and Realistic Single Image Super-Resolution with Explicit Natural Manifold Discrimination (CVPR, 2019)
Stars: ✭ 105 (-95.99%)
Mutual labels:  generative-adversarial-network, super-resolution
Dcgan Tensorflow
A Tensorflow implementation of Deep Convolutional Generative Adversarial Networks trained on Fashion-MNIST, CIFAR-10, etc.
Stars: ✭ 70 (-97.33%)
Mutual labels:  generative-adversarial-network, image-generation
Multi Viewpoint Image Generation
Given an image and a target viewpoint, generate synthetic image in the target viewpoint
Stars: ✭ 23 (-99.12%)
Mutual labels:  generative-adversarial-network, image-generation
Gesturegan
[ACM MM 2018 Oral] GestureGAN for Hand Gesture-to-Gesture Translation in the Wild
Stars: ✭ 136 (-94.81%)
Mutual labels:  generative-adversarial-network, image-generation
Tensorflow Srgan
Tensorflow implementation of "Photo-Realistic Single Image Super-Resolution Using a Generative Adversarial Network" (Ledig et al. 2017)
Stars: ✭ 33 (-98.74%)
Mutual labels:  generative-adversarial-network, super-resolution
Lggan
[CVPR 2020] Local Class-Specific and Global Image-Level Generative Adversarial Networks for Semantic-Guided Scene Generation
Stars: ✭ 97 (-96.29%)
Mutual labels:  generative-adversarial-network, image-generation
Pytorch Cyclegan
A clean and readable Pytorch implementation of CycleGAN
Stars: ✭ 558 (-78.69%)
Mutual labels:  generative-adversarial-network, image-generation
Awesome Gan For Medical Imaging
Awesome GAN for Medical Imaging
Stars: ✭ 1,814 (-30.71%)
Mutual labels:  generative-adversarial-network, super-resolution
Cyclegan
Software that can generate photos from paintings, turn horses into zebras, perform style transfer, and more.
Stars: ✭ 10,933 (+317.61%)
Mutual labels:  generative-adversarial-network, image-generation
Contrastive Unpaired Translation
Contrastive unpaired image-to-image translation, faster and lighter training than cyclegan (ECCV 2020, in PyTorch)
Stars: ✭ 822 (-68.6%)
Mutual labels:  generative-adversarial-network, image-generation
Focal Frequency Loss
Focal Frequency Loss for Generative Models
Stars: ✭ 141 (-94.61%)
Mutual labels:  generative-adversarial-network, image-generation
Srgan Tensorflow
Tensorflow implementation of the SRGAN algorithm for single image super-resolution
Stars: ✭ 754 (-71.2%)
Mutual labels:  generative-adversarial-network, super-resolution
Jsi Gan
Official repository of JSI-GAN (Accepted at AAAI 2020).
Stars: ✭ 42 (-98.4%)
Mutual labels:  generative-adversarial-network, super-resolution
Data Efficient Gans
[NeurIPS 2020] Differentiable Augmentation for Data-Efficient GAN Training
Stars: ✭ 682 (-73.95%)
Mutual labels:  generative-adversarial-network, image-generation
Ganspace
Discovering Interpretable GAN Controls [NeurIPS 2020]
Stars: ✭ 1,224 (-53.25%)
Mutual labels:  generative-adversarial-network, image-generation
Fast Srgan
A Fast Deep Learning Model to Upsample Low Resolution Videos to High Resolution at 30fps
Stars: ✭ 417 (-84.07%)
Mutual labels:  generative-adversarial-network, super-resolution
Hidt
Official repository for the paper "High-Resolution Daytime Translation Without Domain Labels" (CVPR2020, Oral)
Stars: ✭ 513 (-80.4%)
Mutual labels:  generative-adversarial-network, image-generation
Mlds2018spring
Machine Learning and having it Deep and Structured (MLDS) in 2018 spring
Stars: ✭ 124 (-95.26%)
Mutual labels:  generative-adversarial-network, image-generation
Unetgan
Official Implementation of the paper "A U-Net Based Discriminator for Generative Adversarial Networks" (CVPR 2020)
Stars: ✭ 139 (-94.69%)
Mutual labels:  generative-adversarial-network, image-generation

Introduction

English | 简体中文

Documentation actions codecov PyPI LICENSE Average time to resolve an issue Percentage of issues still open

MMEditing is an open source image and video editing toolbox based on PyTorch. It is a part of the OpenMMLab project.

The master branch works with PyTorch 1.3+. Please kindly note that MMEditing will switch to PyTorch 1.5+ from Oct. 2021. The compatibility to earlier versions of PyTorch will no longer be guaranteed.

Documentation: https://mmediting.readthedocs.io/en/latest/.

Major features

  • Modular design

    We decompose the editing framework into different components and one can easily construct a customized editor framework by combining different modules.

  • Support of multiple tasks in editing

    The toolbox directly supports popular and contemporary inpainting, matting, super-resolution and generation tasks.

  • State of the art

    The toolbox provides state-of-the-art methods in inpainting/matting/super-resolution/generation.

Model Zoo

Supported algorithms:

Inpainting
Matting
Super-Resolution
Generation

Please refer to model_zoo for more details.

License

This project is released under the Apache 2.0 license.

Changelog

v0.10.0 was released in 2021-8-12.

Note that MMSR has been merged into this repo, as a part of MMEditing. With elaborate designs of the new framework and careful implementations, hope MMEditing could provide better experience.

Installation

Please refer to install.md for installation.

Get Started

Please see getting_started.md for the basic usage of MMEditing.

Citation

If you find this project useful in your research, please consider cite:

@misc{mmediting2020,
    title={OpenMMLab Editing Estimation Toolbox and Benchmark},
    author={MMEditing Contributors},
    howpublished = {\url{https://github.com/open-mmlab/mmediting}},
    year={2020}
}

Contributing

We appreciate all contributions to improve MMEditing. Please refer to CONTRIBUTING.md in MMCV for the contributing guideline.

Acknowledgement

MMEditing is an open source project that is contributed by researchers and engineers from various colleges and companies. We appreciate all the contributors who implement their methods or add new features, as well as users who give valuable feedbacks. 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.

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.
  • MMSegmentation: OpenMMLab semantic segmentation toolbox and benchmark.
  • MMAction2: OpenMMLab's next-generation action understanding toolbox and benchmark.
  • MMTracking: OpenMMLab video perception toolbox and benchmark.
  • MMPose: OpenMMLab pose estimation toolbox and benchmark.
  • MMEditing: OpenMMLab image and video editing toolbox.
  • MMOCR: A Comprehensive Toolbox for Text Detection, Recognition and Understanding.
  • MMGeneration: A powerful toolkit for generative models.
  • MMFlow: OpenMMLab optical flow toolbox and benchmark.
  • MMFewShot: OpenMMLab FewShot Learning Toolbox and Benchmark.
  • MMHuman3D: OpenMMLab Human Pose and Shape Estimation Toolbox and Benchmark.
Note that the project description data, including the texts, logos, images, and/or trademarks, for each open source project belongs to its rightful owner. If you wish to add or remove any projects, please contact us at [email protected].