r/stylegan3
1
0
Fork 0
Code Issues Pull requests Projects Releases Wiki Activity

README.md update

Browse source
This commit is contained in:
Janne Hellsten 2021-10-15 12:38:10 +03:00
parent 1c6608208c
commit e889add106
1 changed files with 2 additions and 2 deletions
Show stats Download patch file Download diff file Expand all files Collapse all files

4
README.md
View file

@ -20,7 +20,7 @@ This repository is an updated version of [stylegan2-ada-pytorch](https://github.
- General improvements: reduced memory usage, slightly faster training, bug fixes. - General improvements: reduced memory usage, slightly faster training, bug fixes.
Compatibility: Compatibility:
- Compatible with old network pickles created using [stylegan2-ada](https://github.com/NVlabs/stylegan2-ada) and [stylegan2-ada-pytorch](https://github.com/NVlabs/stylegan2-ada-pytorch). - Compatible with old network pickles created using [stylegan2-ada](https://github.com/NVlabs/stylegan2-ada) and [stylegan2-ada-pytorch](https://github.com/NVlabs/stylegan2-ada-pytorch). (Note: running old StyleGAN2 models on StyleGAN3 code will produce the same results as running them on stylegan2-ada/stylegan2-ada-pytorch. To benefit from the StyleGAN3 architecture, you need to retrain.)
- Supports old StyleGAN2 training configurations, including ADA and transfer learning. See [Training configurations](./docs/configs.md) for details. - Supports old StyleGAN2 training configurations, including ADA and transfer learning. See [Training configurations](./docs/configs.md) for details.
- Improved compatibility with Ampere GPUs and newer versions of PyTorch, CuDNN, etc. - Improved compatibility with Ampere GPUs and newer versions of PyTorch, CuDNN, etc.
@ -56,7 +56,7 @@ While new generator approaches enable new media synthesis capabilities, they may
* 1–8 high-end NVIDIA GPUs with at least 12 GB of memory. We have done all testing and development using Tesla V100 and A100 GPUs. * 1–8 high-end NVIDIA GPUs with at least 12 GB of memory. We have done all testing and development using Tesla V100 and A100 GPUs.
* 64-bit Python 3.8 and PyTorch 1.9.0 (or later). See https://pytorch.org for PyTorch install instructions. * 64-bit Python 3.8 and PyTorch 1.9.0 (or later). See https://pytorch.org for PyTorch install instructions.
* CUDA toolkit 11.1 or later. (Why is a separate CUDA toolkit installation required? See [Troubleshooting](./docs/troubleshooting.md#why-is-cuda-toolkit-installation-necessary)). * CUDA toolkit 11.1 or later. (Why is a separate CUDA toolkit installation required? See [Troubleshooting](./docs/troubleshooting.md#why-is-cuda-toolkit-installation-necessary)).
- GCC 7 or later (Linux) or Visual Studio (Windows) compilers. Recommended GCC version depends on CUDA version, see for example [CUDA 11.4 system requirements](https://docs.nvidia.com/cuda/archive/11.4.1/cuda-installation-guide-linux/index.html#system-requirements). * GCC 7 or later (Linux) or Visual Studio (Windows) compilers. Recommended GCC version depends on CUDA version, see for example [CUDA 11.4 system requirements](https://docs.nvidia.com/cuda/archive/11.4.1/cuda-installation-guide-linux/index.html#system-requirements).
* Python libraries: see [environment.yml](./environment.yml) for exact library dependencies. You can use the following commands with Miniconda3 to create and activate your StyleGAN3 Python environment: * Python libraries: see [environment.yml](./environment.yml) for exact library dependencies. You can use the following commands with Miniconda3 to create and activate your StyleGAN3 Python environment:
- `conda env create -f environment.yml` - `conda env create -f environment.yml`
- `conda activate stylegan3` - `conda activate stylegan3`