StyleFlow | StyleFlow: Attribute-conditioned Exploration of StyleGAN-generated Images using Conditional Continu | Machine Learning library

by RameenAbdal Python Version: Current License: No License

X-Ray Key Features Code Snippets(3)Community Discussions(1)Vulnerabilities Install Support

kandi X-RAY | StyleFlow Summary

StyleFlow is a Python library typically used in Artificial Intelligence, Machine Learning, Deep Learning, Generative adversarial networks applications. StyleFlow has no bugs, it has no vulnerabilities, it has build file available and it has medium support. You can download it from GitHub.

StyleFlow: Attribute-conditioned Exploration of StyleGAN-generated Images using Conditional Continuous Normalizing Flows (ACM TOG 2021)

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StyleFlow has a medium active ecosystem.

It has 2323 star(s) with 336 fork(s). There are 87 watchers for this library.

It had no major release in the last 6 months.

There are 58 open issues and 54 have been closed. On average issues are closed in 17 days. There are 20 open pull requests and 0 closed requests.

It has a neutral sentiment in the developer community.

The latest version of StyleFlow is current.

Quality

StyleFlow has 0 bugs and 0 code smells.

Security

StyleFlow has no vulnerabilities reported, and its dependent libraries have no vulnerabilities reported.

StyleFlow code analysis shows 0 unresolved vulnerabilities.

There are 0 security hotspots that need review.

License

StyleFlow does not have a standard license declared.

Check the repository for any license declaration and review the terms closely.

Without a license, all rights are reserved, and you cannot use the library in your applications.

Reuse

StyleFlow releases are not available. You will need to build from source code and install.

Build file is available. You can build the component from source.

Installation instructions, examples and code snippets are available.

It has 4960 lines of code, 330 functions and 43 files.

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

Top functions reviewed by kandi - BETA

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

Applies updates .
Add parameters widgets to the widget .
Aligns a face image .
Run the model .
Fuse a CUDA kernel .
Return a TensorFlow plugin .
Open the given URL .
Upsample a convolution layer .
Updates the scene image
Finalize the autosummaries .

Get all kandi verified functions for this library.

StyleFlow Key Features

No Key Features are available at this moment for StyleFlow.

StyleFlow Examples and Code Snippets

StyleFlow For Content-Fixed Image to Image Translation,Citation

Python

Lines of Code : 7

License : No License

Copy

@article{fan2022styleflow,
  title={StyleFlow For Content-Fixed Image to Image Translation},
  author={Fan, Weichen and Chen, Jinghuan and Ma, Jiabin and Hou, Jun and Yi, Shuai},
  journal={arXiv e-prints},
  pages={arXiv--2207},
  year={2022}
}

StyleFlow For Content-Fixed Image to Image Translation,b. How to Train,1. MS-COCO to WikiArt

Python

Lines of Code : 6

License : No License

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#example
/path/COCO_train2014_000000188902.jpg
/path/COCO_train2014_000000139276.jpg

rootA: /path/src/train.txt

rootB: /path/target/train.txt

StyleFlow For Content-Fixed Image to Image Translation,b. How to Train,2. GTA to Cityscapes

Python

Lines of Code : 6

License : No License

Copy

#example
/path/xxx.jpg
/path/xxx.jpg

rootA: /path/src/train.txt

rootB: /path/target/train.txt

Community Discussions

Trending Discussions on StyleFlow

I constantly get ResolvePackageNotFound

QUESTION

I constantly get ResolvePackageNotFound

Asked 2021-Jan-17 at 05:16

When I type conda env create -f environment.yml

I constantly get

...

ANSWER

Answered 2021-Jan-15 at 14:57

Conda does not work well with large environments in which everything pinned to specific versions (in contrast to other ecosystems in which pinning everything is the standard). The result of conda env export, which is what this probably is, here also includes the build numbers, which are almost always too specific (and often platform-specific) for the purpose of installing the right version of the software. It's great for things like reproducibility of scientific work (specific versions and builds of everything need to be known), but not great for installing software (there is plenty of flexibility in versions that should work with any package).

I'd start by removing the build pins (dropping everything after the second = in each line) so that only the versions are pinned. After that, I'd start removing version pins.

Source https://stackoverflow.com/questions/65735532

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

Vulnerabilities

No vulnerabilities reported

Install StyleFlow

This code requires PyTorch, TensorFlow, Torchdiffeq, Python 3+ and Pyqt5. Please install dependencies by. StyleGAN2 relies on custom TensorFlow ops that are compiled on the fly using NVCC. To correctly setup the StyleGAN2 generator follow the Requirements in this repo.
You must have CUDA (>=10.0 && <11.0) and nvidia-docker2 installed first !.

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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