stylegan2-pytorch | Simplest working implementation Stylegan2 , state | Machine Learning library

 by   lucidrains Python Version: 1.8.9 License: MIT

kandi X-RAY | stylegan2-pytorch Summary

kandi X-RAY | stylegan2-pytorch Summary

stylegan2-pytorch is a Python library typically used in Institutions, Learning, Education, Artificial Intelligence, Machine Learning, Deep Learning, Pytorch, Tensorflow, Generative adversarial networks applications. stylegan2-pytorch has no bugs, it has no vulnerabilities, it has build file available, it has a Permissive License and it has medium support. You can install using 'pip install stylegan2-pytorch' or download it from GitHub, PyPI.

Simplest working implementation of Stylegan2, state of the art generative adversarial network, in Pytorch. Enabling everyone to experience disentanglement

            kandi-support Support

              stylegan2-pytorch has a medium active ecosystem.
              It has 3495 star(s) with 580 fork(s). There are 69 watchers for this library.
              It had no major release in the last 12 months.
              There are 123 open issues and 134 have been closed. On average issues are closed in 29 days. There are 3 open pull requests and 0 closed requests.
              It has a neutral sentiment in the developer community.
              The latest version of stylegan2-pytorch is 1.8.9

            kandi-Quality Quality

              stylegan2-pytorch has 0 bugs and 0 code smells.

            kandi-Security Security

              stylegan2-pytorch has no vulnerabilities reported, and its dependent libraries have no vulnerabilities reported.
              stylegan2-pytorch code analysis shows 0 unresolved vulnerabilities.
              There are 0 security hotspots that need review.

            kandi-License License

              stylegan2-pytorch is licensed under the MIT License. This license is Permissive.
              Permissive licenses have the least restrictions, and you can use them in most projects.

            kandi-Reuse Reuse

              stylegan2-pytorch releases are available to install and integrate.
              Deployable package is available in PyPI.
              Build file is available. You can build the component from source.
              Installation instructions, examples and code snippets are available.
              It has 1352 lines of code, 122 functions and 6 files.
              It has medium code complexity. Code complexity directly impacts maintainability of the code.

            Top functions reviewed by kandi - BETA

            kandi has reviewed stylegan2-pytorch and discovered the below as its top functions. This is intended to give you an instant insight into stylegan2-pytorch implemented functionality, and help decide if they suit your requirements.
            • Train a model
            • Set random seed
            • Remove all files and folders
            • Run training
            • Perform DiffAugment
            • Flip tensor onto a given probability
            • Difference x
            • Convert styles to images
            • Forward the convolution
            • Forward the given styles
            • Convert x to pixel
            • Apply the convolutional transformer
            • Compute the logit transform
            • Forward the convolution layer
            • Convert noise to styles
            Get all kandi verified functions for this library.

            stylegan2-pytorch Key Features

            No Key Features are available at this moment for stylegan2-pytorch.

            stylegan2-pytorch Examples and Code Snippets

            Jupyter Notebookdot img1Lines of Code : 112dot img1no licencesLicense : No License
            copy iconCopy
              title={StarGAN v2: Diverse Image Synthesis for Multiple Domains},
              author={Yunjey Choi and Youngjung Uh and Jaejun Yoo and Jung-Woo Ha},
              booktitle={Proceedings of the IEEE Conference on Computer Vision and Patte  
            Simple Pytorch StyleGAN2-Distillation Implementation,Usage
            Pythondot img2Lines of Code : 8dot img2no licencesLicense : No License
            copy iconCopy
            python --attribute [gender/age] --phase train --db_root [imdb_dataset_path] 
            python --attribute [gender/age] --phase test --db_root [imdb_dataset_path]
            python -  
            Self-Diagnosing GAN,StyleGAN2
            Pythondot img3Lines of Code : 4dot img3License : Permissive (Apache-2.0)
            copy iconCopy
            python --out ./dataset/ffhq/lmdb_256.mdb --size 256 --path ./dataset/ffhq
            python -m torch.distributed.launch --nproc_per_node=4 --master_port=15694 --root ./dataset/ffhq/lmdb_256.mdb --batch 4 --dataset ffhq --exp_name   
            copy iconCopy
            src/connection.h:27:20: fatal error: Python.h: No such file or directory
            sudo apt-get install python3.7-dev

            Community Discussions


            How to make conda use its own gcc version?
            Asked 2021-Dec-12 at 16:12

            I am trying to run the training of stylegan2-pytorch on a remote system. The remote system has gcc (9.3.0) installed on it. I'm using conda env that has the following installed (cudatoolkit=10.2, torch=1.5.0+, and ninja=1.8.2, gcc_linux-64=7.5.0). I encounter the following error:



            Answered 2021-Dec-12 at 16:12

            Just to share, not sure it will help you. However it shows that in standard conditions it is possible to use the conda gcc as described in the documentation instead of the system gcc.



            WARNING: Legacy build of wheel for 'pysqlite3' created no files. - How do I build a non-legacy build of wheels?
            Asked 2021-Sep-18 at 21:19

            I try to install a package ( on my AWS EC2 instance. I have installed Python 3.7 and is trying to install the package through that by running:



            Answered 2021-Sep-18 at 21:19

            By using the --verbose-flag I saw:



            Pytorch says that CUDA is not available
            Asked 2020-Oct-31 at 21:26

            I'm trying to run Pytorch on a laptop that I have. It's an older model but it does have an Nvidia graphics card. I realize it is probably not going to be sufficient for real machine learning but I am trying to do it so I can learn the process of getting CUDA installed.

            I have followed the steps on the installation guide for Ubuntu 18.04 (my specific distribution is Xubuntu).

            My graphics card is a GeForce 845M, verified by lspci | grep nvidia:



            Answered 2020-Oct-31 at 21:26

            PyTorch doesn't use the system's CUDA library. When you install PyTorch using the precompiled binaries using either pip or conda it is shipped with a copy of the specified version of the CUDA library which is installed locally. In fact, you don't even need to install CUDA on your system to use PyTorch with CUDA support.

            There are two scenarios which could have caused your issue.

            1. You installed the CPU only version of PyTorch. In this case PyTorch wasn't compiled with CUDA support so it didn't support CUDA.

            2. You installed the CUDA 10.2 version of PyTorch. In this case the problem is that your graphics card currently uses the 418.87 drivers, which only support up to CUDA 10.1. The two potential fixes in this case would be to either install updated drivers (version >= 440.33 according to Table 2) or to install a version of PyTorch compiled against CUDA 10.1.

            To determine the appropriate command to use when installing PyTorch you can use the handy widget in the "Quick start locally" section at Just select the appropriate operating system, package manager, and CUDA version then run the recommended command.

            In your case one solution was to use


            Community Discussions, Code Snippets contain sources that include Stack Exchange Network


            No vulnerabilities reported

            Install stylegan2-pytorch

            You will need a machine with a GPU and CUDA installed. Then pip install the package like this. If you are using a windows machine, the following commands reportedly works.
            You will then have to enter your AWS access keys, which you can retrieve from the management console under AWS Management Console > Profile > My Security Credentials > Access Keys.
            Archive your training data and upload it to an S3 bucket
            Provision your EC2 instance (I used an Ubuntu AMI)
            Log into your EC2 instance via SSH
            Install the aws CLI client and configure it:
            If you have a lot of training data, you may need to provision extra block storage via EBS.
            Also, you may need to spread your data across multiple archives.
            You should run this on a screen window so it won't terminate once you log out of the SSH session.


            For any new features, suggestions and bugs create an issue on GitHub. If you have any questions check and ask questions on community page Stack Overflow .
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