GFPGAN VS tensorflow

Mainly as the base model. https://insightface.ai/ There was some post processing done to further improve quality. https://github.com/TencentARC/GFPGAN

It's not animating really, just lip sync and face restoration, here I used: https://github.com/Rudrabha/Wav2Lip and https://github.com/TencentARC/GFPGAN respectively.

Real-ESRGAN/GFPGAN https://github.com/xinntao/Real-ESRGAN (Real-ESRAN - Upscale images, facial restoration with GFPGAN setting) https://github.com/TencentARC/GFPGAN (GFPGAN - Facial restoration and Upscale) -------MATTE AND COMPOSITE--------

Also I suggest implementing GFPGAN face fixing asap

Download https://github.com/TencentARC/GFPGAN/releases/download/v1.3.0/GFPGANv1.4.pth to Data/gfpgan/gfpgan.pth

Go to https://github.com/TencentARC/GFPGAN for instructions.

Cloning https://github.com/TencentARC/GFPGAN.git (to revision 8d2447a2d918f8eba5a4a01463fd48e45126a379) to c:\users\k\appdata\local\temp\pip-req-build-m7_2xzvf

Not really just run the 6 installation instructions here: https://github.com/AUTOMATIC1111/stable-diffusion-webui. You will have the model already in the stable-diffusion sub-directory it needs copying and renaming to model.ckpt to where it tells you. I found the models database confusing and just searched google for GFPGANv1.4.pth and placed this in the automatic1111 install directory used this one: https://github.com/TencentARC/GFPGAN/releases/download/v1.3.4/GFPGANv1.4.pth

To get up to speed with TensorFlow, check their quickstart Support TensorFlow on GitHub ⭐

Explore here.

Part of the major userbase pull in GitHub revolves around hosting a considerable number of popular projects including Angular, React, Kubernetes, cpython, Ruby, tensorflow, and well even the software that powers this site Forem.

Right but that's not an inherent GPU determinism issue. It's a software issue.

https://github.com/tensorflow/tensorflow/issues/3103#issueco... is correct that it's not necessary, it's a choice.

Your line of reasoning appears to be "GPUs are inherently non-deterministic don't be quick to judge someone's code" which as far as I can tell is dead wrong.

Admittedly there are some cases and instructions that may result in non-determinism but they are inherently necessary. The author should thinking carefully before introducing non-determinism. There are many scenarios where it is irrelevant, but ultimately the issue we are discussing here isn't the GPU's fault.

and things like y = layers.ELU()(y) work as expected. I wanted to see a list of the available layers so I went to the Tensorflow GitHub repository and to the keras directory. There's a warning in that directory that says:

You can use Tensorflow's deep learning API for this.

This is indeed the bread-and-butter, but there is use of all sorts of standard linear algebra algorithms. You can check various xla-related (accelerated linear algebra) folders in tensorflow or torch folders in pytorch to see the list of what is used [1],[2]

[1] https://github.com/tensorflow/tensorflow/tree/8d9b35f442045b...

[2] https://github.com/pytorch/pytorch/blob/6e3e3dd477e0fb9768ee...

2. TensorFlow Developed by the Google Brain team, TensorFlow is a powerful open-source machine learning framework that’s perfect for deep learning and neural network projects. With TensorFlow, you can build and train complex models using an intuitive and flexible API, making it an essential tool for any data scientist looking to delve into deep learning.

I found the official Tensorflow V1 code from a Github branch here (https://github.com/tensorflow/tensorflow/blob/r0.7/tensorflow/models/rnn/ptb/ptb_word_lm.py). All code necessary to run that file is in the /ptb folder (except data).