stylegan2-ada | adaptive discriminator augmentation - Official | Machine Learning library

by NVlabs Python Version: Current License: Non-SPDX

X-Ray Key Features Code Snippets(3)Community Discussions(2)Vulnerabilities Install Support

kandi X-RAY | stylegan2-ada Summary

stylegan2-ada is a Python library typically used in Artificial Intelligence, Machine Learning, Deep Learning, Tensorflow, Generative adversarial networks applications. stylegan2-ada has no bugs, it has no vulnerabilities and it has medium support. However stylegan2-ada build file is not available and it has a Non-SPDX License. You can download it from GitHub.

StyleGAN2 with adaptive discriminator augmentation (ADA) - Official TensorFlow implementation

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Quality

Security

License

Reuse

Support

stylegan2-ada has a medium active ecosystem.

It has 1728 star(s) with 496 fork(s). There are 38 watchers for this library.

It had no major release in the last 6 months.

There are 70 open issues and 38 have been closed. On average issues are closed in 24 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-ada is current.

Quality

stylegan2-ada has 0 bugs and 0 code smells.

Security

stylegan2-ada has no vulnerabilities reported, and its dependent libraries have no vulnerabilities reported.

stylegan2-ada code analysis shows 0 unresolved vulnerabilities.

There are 0 security hotspots that need review.

License

stylegan2-ada has a Non-SPDX License.

Non-SPDX licenses can be open source with a non SPDX compliant license, or non open source licenses, and you need to review them closely before use.

Reuse

stylegan2-ada releases are not available. You will need to build from source code and install.

stylegan2-ada has no build file. You will be need to create the build yourself to build the component from source.

Installation instructions, examples and code snippets are available.

It has 5802 lines of code, 339 functions and 33 files.

It has high code complexity. Code complexity directly impacts maintainability of the code.

Top functions reviewed by kandi - BETA

kandi has reviewed stylegan2-ada and discovered the below as its top functions. This is intended to give you an instant insight into stylegan2-ada implemented functionality, and help decide if they suit your requirements.

Setup training options .
Augment an image .
The training loop .
Main function for D_CV .
Execute command line options .
Synthesize latents .
Evaluate cmethods .
Applies updates to all devices .
Run the model .
Fuse the fused bias .

Get all kandi verified functions for this library.

stylegan2-ada Key Features

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

stylegan2-ada Examples and Code Snippets

StyleGan2-ADA for Google Colab,Install StyleGAN2-ADA on your Google Drive

Jupyter Notebook

Lines of Code : 32

License : No License

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%tensorflow_version 1.x
!nvidia-smi

from google.colab import drive
from pathlib import Path

content_path = Path('/').absolute() / 'content'
drive_path = content_path / 'drive'
drive.mount(str(drive_path))

stylegan2_repo_url  = 'https://github.com/

Getting started,StyleGAN2-ADA Installation,Running Localy

Jupyter Notebook

Lines of Code : 26

License : Permissive (MIT)

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└── root
    ├── gans_training
    │   ├── dataset
    │   │   └── oxford_pets
    │   │       ├── class1
    │   │       │   ├── img1.jpg
    │   │       │   ├── img2.jpg
    │   │       │   └── ...
    │   │       ├── class2
    │   │       │   ├──

StyleGan2-ADA for Google Colab,Train a custom model,Launch training

Jupyter Notebook

Lines of Code : 23

License : No License

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!python "{stylegan2_repo_path / 'train.py'}" --help

training_path = project_path / 'training' / dataset_name
if not training_path.is_dir():
    %mkdir "{training_path}"

#how often should the model generate samples and a .pkl file
snapshot_count = 2

Community Discussions

Trending Discussions on stylegan2-ada

How can I specify a background in Imagemagick mogrify based on the colour of an existing pixel?

StyleGan2 ADA PyTorch Output Image Resolution Assertion Error

QUESTION

How can I specify a background in Imagemagick mogrify based on the colour of an existing pixel?

Asked 2021-Nov-04 at 16:22

I want to use mogrify to set the background colour of a large number of images to be whatever colour is at a specific pixel, to make them square.

The vast majority have a single colour in the image background, or are photos in front of a single colour (so with only slight variations from shadows, etc.).

(The specific purpose in this case is to make the images all the same size and square for StyleGAN2-ADA training, so I want to avoid big "letterbox" rectangles where possible as it would be seen by the discriminator as relevant to the image, where a more faded-in background that approximately matches would be more likely to be ignored. Specifically, I have thousands of pictures of single dolls and action figures from various sources, some of which are "trimmed out" to have a truly solid colour background, others of which are against solid colour tables/walls/etc, for instance, from eBay images and such.)

Since they do not all have the same colour in the image background (the colour in the image, as opposed to the 'background colour' setting as per ImageMagick's jargon), I need to sample a pixel and set the background, but I can't figure out how. I tried things based on methods used to set the whole image to one colour, to no avail.

I have tried:

...

ANSWER

Answered 2021-Nov-04 at 16:22

EDITED TO ADD: While testing various commands I came across a way to modify your original command to make it work on ImageMagick versions as far back as IMv6.8.

Source https://stackoverflow.com/questions/69832948

QUESTION

StyleGan2 ADA PyTorch Output Image Resolution Assertion Error

Asked 2021-Oct-03 at 11:47

I am trying to produce an image using StyleGan2.

Executing this cell:

...

ANSWER

Answered 2021-Oct-03 at 11:47

I have found: The issue is the input dimension of the image must be at least [3, 1024, 1024] mine was [3, 600, 600]

Source https://stackoverflow.com/questions/69421650

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

Vulnerabilities

No vulnerabilities reported

Install stylegan2-ada

Pre-trained networks are stored as *.pkl files that can be referenced using local filenames or URLs:. Outputs from the above commands are placed under out/*.png. You can change the location with --outdir. Temporary cache files, such as CUDA build results and downloaded network pickles, will be saved under $HOME/.cache/dnnlib. This can be overridden using the DNNLIB_CACHE_DIR environment variable.

Support

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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