stylegan2-pytorch | Simplest working implementation Stylegan2 , state | Machine Learning library
kandi X-RAY | stylegan2-pytorch Summary
kandi X-RAY | stylegan2-pytorch Summary
Simplest working implementation of Stylegan2, state of the art generative adversarial network, in Pytorch. Enabling everyone to experience disentanglement
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Top functions reviewed by kandi - BETA
- 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
stylegan2-pytorch Key Features
stylegan2-pytorch Examples and Code Snippets
Lines of Code : 112
License : No License
Copy@inproceedings{choi2020starganv2,
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 python misc_imdb_preprocessing.py
python all_in_one.py --attribute [gender/age] --phase train --db_root [imdb_dataset_path]
python all_in_one.py --attribute [gender/age] --phase test --db_root [imdb_dataset_path]
python generate_distillation.py - python prepare_data.py --out ./dataset/ffhq/lmdb_256.mdb --size 256 --path ./dataset/ffhq
python -m torch.distributed.launch --nproc_per_node=4 --master_port=15694 train_ffhq.py --root ./dataset/ffhq/lmdb_256.mdb --batch 4 --dataset ffhq --exp_name src/connection.h:27:20: fatal error: Python.h: No such file or directory
sudo apt-get install python3.7-dev
Community Discussions
Trending Discussions on stylegan2-pytorch
QUESTION
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:
...ANSWER
Answered 2021-Dec-12 at 16:12Just 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.
QUESTION
I try to install a package (https://github.com/lucidrains/stylegan2-pytorch) on my AWS EC2 instance. I have installed Python 3.7 and is trying to install the package through that by running:
...ANSWER
Answered 2021-Sep-18 at 21:19By using the --verbose-flag I saw:
QUESTION
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:
ANSWER
Answered 2020-Oct-31 at 21:26PyTorch 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.
You installed the CPU only version of PyTorch. In this case PyTorch wasn't compiled with CUDA support so it didn't support CUDA.
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 pytorch.org. 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
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No vulnerabilities reported
Install stylegan2-pytorch
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.
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