This is the Windows app named Simple StyleGan2 for Pytorch whose latest release can be downloaded as v1.8.9.zip. It can be run online in the free hosting provider OnWorks for workstations.
Download and run online this app named Simple StyleGan2 for Pytorch with OnWorks for free.
Follow these instructions in order to run this app:
- 1. Downloaded this application in your PC.
- 2. Enter in our file manager https://www.onworks.net/myfiles.php?username=XXXXX with the username that you want.
- 3. Upload this application in such filemanager.
- 4. Start any OS OnWorks online emulator from this website, but better Windows online emulator.
- 5. From the OnWorks Windows OS you have just started, goto our file manager https://www.onworks.net/myfiles.php?username=XXXXX with the username that you want.
- 6. Download the application and install it.
- 7. Download Wine from your Linux distributions software repositories. Once installed, you can then double-click the app to run them with Wine. You can also try PlayOnLinux, a fancy interface over Wine that will help you install popular Windows programs and games.
Wine is a way to run Windows software on Linux, but with no Windows required. Wine is an open-source Windows compatibility layer that can run Windows programs directly on any Linux desktop. Essentially, Wine is trying to re-implement enough of Windows from scratch so that it can run all those Windows applications without actually needing Windows.
SCREENSHOTS:
Simple StyleGan2 for Pytorch
DESCRIPTION:
Simple Pytorch implementation of Stylegan2 that can be completely trained from the command-line, no coding needed. You will need a machine with a GPU and CUDA installed. You can also specify the location where intermediate results and model checkpoints should be stored. You can increase the network capacity (which defaults to 16) to improve generation results, at the cost of more memory. By default, if the training gets cut off, it will automatically resume from the last checkpointed file. Once you have finished training, you can generate images from your latest checkpoint. If a previous checkpoint contained a better generator, (which often happens as generators start degrading towards the end of training), you can load from a previous checkpoint with another flag. A technique used in both StyleGAN and BigGAN is truncating the latent values so that their values fall close to the mean. The small the truncation value, the better the samples will appear at the cost of sample variety.
Features
- Multi-GPU training
- Low amounts of Training Data
- This framework also allows for you to add an efficient form of self-attention to the designated layers of the discriminator
- The more GPU memory you have, the bigger and better the image generation will be
- Nvidia recommended having up to 16GB for training 1024x1024 images
- Deployment on AWS
Programming Language
Python
Categories
This is an application that can also be fetched from https://sourceforge.net/projects/simple-stylegan2-pyt.mirror/. It has been hosted in OnWorks in order to be run online in an easiest way from one of our free Operative Systems.