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DeepFaceLab | DeepFaceLab is the leading software for creating deepfakes. | Machine Learning library

 by   iperov Python Version: DF.wf.288res.384.92.72.22 License: GPL-3.0

 by   iperov Python Version: DF.wf.288res.384.92.72.22 License: GPL-3.0

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kandi X-RAY | DeepFaceLab Summary

DeepFaceLab is a Python library typically used in Artificial Intelligence, Machine Learning, Deep Learning, Tensorflow, OpenCV applications. DeepFaceLab has no vulnerabilities, it has a Strong Copyleft License and it has medium support. However DeepFaceLab has 10 bugs and it build file is not available. You can download it from GitHub.
DeepFaceLab is the leading software for creating deepfakes.
Support
Support
Quality
Quality
Security
Security
License
License
Reuse
Reuse

kandi-support Support

  • DeepFaceLab has a medium active ecosystem.
  • It has 28629 star(s) with 6398 fork(s). There are 950 watchers for this library.
  • It had no major release in the last 12 months.
  • There are 329 open issues and 704 have been closed. On average issues are closed in 45 days. There are no pull requests.
  • It has a neutral sentiment in the developer community.
  • The latest version of DeepFaceLab is DF.wf.288res.384.92.72.22
DeepFaceLab Support
Best in #Machine Learning
Average in #Machine Learning
DeepFaceLab Support
Best in #Machine Learning
Average in #Machine Learning

quality kandi Quality

  • DeepFaceLab has 10 bugs (9 blocker, 0 critical, 1 major, 0 minor) and 530 code smells.
DeepFaceLab Quality
Best in #Machine Learning
Average in #Machine Learning
DeepFaceLab Quality
Best in #Machine Learning
Average in #Machine Learning

securitySecurity

  • DeepFaceLab has no vulnerabilities reported, and its dependent libraries have no vulnerabilities reported.
  • DeepFaceLab code analysis shows 0 unresolved vulnerabilities.
  • There are 0 security hotspots that need review.
DeepFaceLab Security
Best in #Machine Learning
Average in #Machine Learning
DeepFaceLab Security
Best in #Machine Learning
Average in #Machine Learning

license License

  • DeepFaceLab is licensed under the GPL-3.0 License. This license is Strong Copyleft.
  • Strong Copyleft licenses enforce sharing, and you can use them when creating open source projects.
DeepFaceLab License
Best in #Machine Learning
Average in #Machine Learning
DeepFaceLab License
Best in #Machine Learning
Average in #Machine Learning

buildReuse

  • DeepFaceLab releases are available to install and integrate.
  • DeepFaceLab has no build file. You will be need to create the build yourself to build the component from source.
  • DeepFaceLab saves you 6841 person hours of effort in developing the same functionality from scratch.
  • It has 14185 lines of code, 1032 functions and 125 files.
  • It has high code complexity. Code complexity directly impacts maintainability of the code.
DeepFaceLab Reuse
Best in #Machine Learning
Average in #Machine Learning
DeepFaceLab Reuse
Best in #Machine Learning
Average in #Machine Learning
Top functions reviewed by kandi - BETA

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

  • Initialize the options .
  • Start the process .
  • Main function .
  • Initializes the QPainter .
  • Generate batch function .
  • Sort by best faces .
  • Test for the LaPaas test .
  • Compute the Bezier curve .
  • Create a video from a sequence of sequences .
  • Load data from file .

DeepFaceLab Key Features

DeepFaceLab is the leading software for creating deepfakes.

Community Discussions

Trending Discussions on DeepFaceLab
  • DeepFaceLab_NVIDIA outputs an error during the 'data_src faceset extract' - TensorFlow
Trending Discussions on DeepFaceLab

QUESTION

DeepFaceLab_NVIDIA outputs an error during the 'data_src faceset extract' - TensorFlow

Asked 2021-Mar-09 at 22:15

In the DeepFaceLab the 4th and/or 5th process which is extracting the faceset from the data_src and/or data_dst outputs this error:

[wf] Face type ( f/wf/head ?:help ) :
wf
[0] Max number of faces from image ( ?:help ) :
0
[512] Image size ( 256-2048 ?:help ) :
512
[90] Jpeg quality ( 1-100 ?:help ) :
90
[n] Write debug images to aligned_debug? ( y/n ) :
n
Extracting faces...

Error while subprocess initialization: Traceback (most recent call last):
  File "C:\DeepFaceLab\DeepFaceLab_NVIDIA\_internal\python-3.6.8\lib\site-packages\tensorflow\python\pywrap_tensorflow.py", line 64, in <module>
    from tensorflow.python._pywrap_tensorflow_internal import *
ImportError: DLL load failed: A dynamic link library (DLL) initialization routine failed.

During handling of the above exception, another exception occurred:

On line 64 inside pywrap_tensorflow.py the code and the comments are written like this:

  # pylint: disable=wildcard-import,g-import-not-at-top,line-too-long,undefined-variable
  try:
    from tensorflow.python._pywrap_tensorflow_internal import *
  # This try catch logic is because there is no bazel equivalent for py_extension.
  # Externally in opensource we must enable exceptions to load the shared object
  # by exposing the PyInit symbols with pybind. This error will only be
  # caught internally or if someone changes the name of the target _pywrap_tensorflow_internal.

Similar issues have been raised in GitHub but no concrete answer can be found. Help is very much appreciated.

ANSWER

Answered 2021-Feb-16 at 06:19

So after a long time, I finally found the fix. Since I know the training will take a very long time I decided to do this on a laptop that had no GPU. For this very reason, the fix was to do the 10th step first or the cmd file named 10)Make CPU only. Doing so will uninstall the preinstalled TensorFlow and download an older version so an Internet Connection will be required. Additionally, executing it cannot be undone, this means DeepFaceLab will now run through CPU only 4ever unless you reinstall it again.

This should hopefully solve the problem.

Source https://stackoverflow.com/questions/65529897

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

Vulnerabilities

No vulnerabilities reported

Install DeepFaceLab

You can download it from GitHub.
You can use DeepFaceLab like any standard Python library. You will need to make sure that you have a development environment consisting of a Python distribution including header files, a compiler, pip, and git installed. Make sure that your pip, setuptools, and wheel are up to date. When using pip it is generally recommended to install packages in a virtual environment to avoid changes to the system.

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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