MMGeneration has been merged in [MMEditing](https://github.com/open-mmlab/mmediting/tree/1.x). And we have supported new generation tasks and models. We highlight the following new features:
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+**.
-**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/en/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/en/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/en/tutorials/customize_losses.md))
-**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)
- 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](configs/styleganv2/README.md) between different implementations.
## Changelog
v0.7.3 was released on 14/04/2023. Please refer to [changelog.md](docs/en/changelog.md) for details and release history.
## Installation
MMGeneration depends on [PyTorch](https://pytorch.org/) and [MMCV](https://github.com/open-mmlab/mmcv).
Below are quick steps for installation.
**Step 1.**
Install PyTorch following [official instructions](https://pytorch.org/get-started/locally/), e.g.
Please refer to [get_started.md](docs/en/get_started.md) for more detailed instruction.
## Getting Started
2. 编译whl包并安装
pip install -e .
python3 setup.py -v bdist_wheel
pip install dist/mmgeneration*
Please see [get_started.md](docs/en/get_started.md) for the basic usage of MMGeneration. [docs/en/quick_run.md](docs/en/quick_run.md) can offer full guidance for quick run. For other details and tutorials, please go to our [documentation](https://mmgeneration.readthedocs.io/).
## ModelZoo
These methods have been carefully studied and supported in our frameworks:
<detailsopen>
<summary>Unconditional GANs (click to collapse)</summary>
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},
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.
MMGeneration has been merged in [MMEditing](https://github.com/open-mmlab/mmediting/tree/1.x). And we have supported new generation tasks and models. We highlight the following new features:
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+**.
-**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/en/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/en/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/en/tutorials/customize_losses.md))
-**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)
- 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](configs/styleganv2/README.md) between different implementations.
## Changelog
v0.7.3 was released on 14/04/2023. Please refer to [changelog.md](docs/en/changelog.md) for details and release history.
## Installation
MMGeneration depends on [PyTorch](https://pytorch.org/) and [MMCV](https://github.com/open-mmlab/mmcv).
Below are quick steps for installation.
**Step 1.**
Install PyTorch following [official instructions](https://pytorch.org/get-started/locally/), e.g.
```python
pip3installtorchtorchvision
```
**Step 2.**
Install MMCV with [MIM](https://github.com/open-mmlab/mim).
Please refer to [get_started.md](docs/en/get_started.md) for more detailed instruction.
## Getting Started
Please see [get_started.md](docs/en/get_started.md) for the basic usage of MMGeneration. [docs/en/quick_run.md](docs/en/quick_run.md) can offer full guidance for quick run. For other details and tutorials, please go to our [documentation](https://mmgeneration.readthedocs.io/).
## ModelZoo
These methods have been carefully studied and supported in our frameworks:
<detailsopen>
<summary>Unconditional GANs (click to collapse)</summary>
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},
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.
## Projects in OpenMMLab
-[MMCV](https://github.com/open-mmlab/mmcv): OpenMMLab foundational library for computer vision.
