faceswap_pytorch - Deep fake ready to train on any 2 pair dataset with higher resolution

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Deep fake ready to train on any 2 pair dataset with higher resolution




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faceswap-GAN - A denoising autoencoder + adversarial losses and attention mechanisms for face swapping

  •    Jupyter

Adding Adversarial loss and perceptual loss (VGGface) to deepfakes'(reddit user) auto-encoder architecture. Here is a playground notebook for faceswap-GAN v2.2 on Google Colab. Users can train their own model in the browser without GPU required.

deepfakes - This is the code for "DeepFakes" by Siraj Raval on Youtube

  •    Python

Generate your own faceswap using this algorithm. Post your github link with your code in the youtube comment section of this video. Bonus points if you document your code so that a beginner can do this easily (easier than this code). This is the code for this video on Youtube by Siraj Raval.

faceswap - Non official project based on original /r/Deepfakes thread. Many thanks to him!

  •    Python

Non official project based on original /r/Deepfakes thread. Many thanks to him!

pytorch-pretrained-BigGAN - 🦋A PyTorch implementation of BigGAN with pretrained weights and conversion scripts

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An op-for-op PyTorch reimplementation of DeepMind's BigGAN model with the pre-trained weights from DeepMind. This repository contains an op-for-op PyTorch reimplementation of DeepMind's BigGAN that was released with the paper Large Scale GAN Training for High Fidelity Natural Image Synthesis by Andrew Brock, Jeff Donahue and Karen Simonyan.

generative-models - Collection of generative models, e.g. GAN, VAE in Pytorch and Tensorflow.

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Collection of generative models, e.g. GAN, VAE in Pytorch and Tensorflow. Also present here are RBM and Helmholtz Machine. Generated samples will be stored in GAN/{gan_model}/out (or VAE/{vae_model}/out, etc) directory during training.

deepfakes_faceswap - from deekfakes' faceswap: https://www.reddit.com/user/deepfakes/

  •    Python

As you can see, the code is embarrassingly simple. I don't think it's worth the trouble to keep it secret from everyone. I believe the community are smart enough to finish the rest of the owl. If there is any question, welcome to discuss here.

pytorch-seq2seq - pytorch-seq2seq is a framework for sequence-to-sequence (seq2seq) models in PyTorch

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This is a framework for sequence-to-sequence (seq2seq) models implemented in PyTorch. The framework has modularized and extensible components for seq2seq models, training and inference, checkpoints, etc. This is an alpha release. We appreciate any kind of feedback or contribution. This package requires Python 2.7 or 3.6. We recommend creating a new virtual environment for this project (using virtualenv or conda).

stylegan2-ada-pytorch - StyleGAN2-ADA - Official PyTorch implementation

  •    Python

Abstract: Training generative adversarial networks (GAN) using too little data typically leads to discriminator overfitting, causing training to diverge. We propose an adaptive discriminator augmentation mechanism that significantly stabilizes training in limited data regimes. The approach does not require changes to loss functions or network architectures, and is applicable both when training from scratch and when fine-tuning an existing GAN on another dataset. We demonstrate, on several datasets, that good results are now possible using only a few thousand training images, often matching StyleGAN2 results with an order of magnitude fewer images. We expect this to open up new application domains for GANs. We also find that the widely used CIFAR-10 is, in fact, a limited data benchmark, and improve the record FID from 5.59 to 2.42. This repository is a faithful reimplementation of StyleGAN2-ADA in PyTorch, focusing on correctness, performance, and compatibility.

pytorch-CycleGAN-and-pix2pix - Image-to-image translation in PyTorch (e

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This is our PyTorch implementation for both unpaired and paired image-to-image translation. It is still under active development. The code was written by Jun-Yan Zhu and Taesung Park, and supported by Tongzhou Wang.

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  •    Jupyter

PyTorch implementation of Learning to Discover Cross-Domain Relations with Generative Adversarial Networks. * All samples in README.md are genearted by neural network except the first image for each row. * Network structure is slightly diffferent (here) from the author's code.

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mmgeneration - MMGeneration is a powerful toolkit for generative models, based on PyTorch and MMCV.

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MMGeneration is a powerful toolkit for generative models, especially for GANs now. It is based on PyTorch and MMCV. The master branch works with PyTorch 1.5+. v0.2.0 was released on 30/05/2021. Please refer to changelog.md for details and release history.

pytorch-fid - Compute FID scores with PyTorch.

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This is a port of the official implementation of Fréchet Inception Distance to PyTorch. See https://github.com/bioinf-jku/TTUR for the original implementation using Tensorflow. FID is a measure of similarity between two datasets of images. It was shown to correlate well with human judgement of visual quality and is most often used to evaluate the quality of samples of Generative Adversarial Networks. FID is calculated by computing the Fréchet distance between two Gaussians fitted to feature representations of the Inception network.

T2F - T2F: text to face generation using Deep Learning

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Text-to-Face generation using Deep Learning. This project combines two of the recent architectures StackGAN and ProGAN for synthesizing faces from textual descriptions. The project uses Face2Text dataset which contains 400 facial images and textual captions for each of them. The data can be obtained by contacting either the RIVAL group or the authors of the aforementioned paper. The code is present in the implementation/ subdirectory. The implementation is done using the PyTorch framework. So, for running this code, please install PyTorch version 0.4.0 before continuing.

PyTorch-GAN - PyTorch implementations of Generative Adversarial Networks.

  •    Python

Collection of PyTorch implementations of Generative Adversarial Network varieties presented in research papers. Model architectures will not always mirror the ones proposed in the papers, but I have chosen to focus on getting the core ideas covered instead of getting every layer configuration right. Contributions and suggestions of GANs to implement are very welcomed. Synthesizing high resolution photorealistic images has been a long-standing challenge in machine learning. In this paper we introduce new methods for the improved training of generative adversarial networks (GANs) for image synthesis. We construct a variant of GANs employing label conditioning that results in 128x128 resolution image samples exhibiting global coherence. We expand on previous work for image quality assessment to provide two new analyses for assessing the discriminability and diversity of samples from class-conditional image synthesis models. These analyses demonstrate that high resolution samples provide class information not present in low resolution samples. Across 1000 ImageNet classes, 128x128 samples are more than twice as discriminable as artificially resized 32x32 samples. In addition, 84.7% of the classes have samples exhibiting diversity comparable to real ImageNet data.

PyTorchZeroToAll - Simple PyTorch Tutorials Zero to ALL!

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Quick 3~4 day lecture materials for HKUST students. If you cannot access the GoogleDoc for somehow, please check out pdf files in slides. However, slides in GoogleDrive are always latest. We really appreciate your comments.

neuropod - A uniform interface to run deep learning models from multiple frameworks

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Neuropod is a library that provides a uniform interface to run deep learning models from multiple frameworks in C++ and Python. Neuropod makes it easy for researchers to build models in a framework of their choosing while also simplifying productionization of these models. It currently supports TensorFlow, PyTorch, TorchScript, Keras and Ludwig.

StarGAN - Unified Generative Adversarial Networks for Multi-Domain Image-to-Image Translation

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PyTorch implementation of StarGAN: Unified Generative Adversarial Networks for Multi-Domain Image-to-Image Translation. StarGAN can flexibly translate an input image to any desired target domain using only a single generator and a discriminator.

pix2pixHD - Synthesizing and manipulating 2048x1024 images with conditional GANs

  •    Python

Pytorch implementation of our method for high-resolution (e.g. 2048x1024) photorealistic image-to-image translation. It can be used for turning semantic label maps into photo-realistic images or synthesizing portraits from face label maps. High-Resolution Image Synthesis and Semantic Manipulation with Conditional GANs Ting-Chun Wang1, Ming-Yu Liu1, Jun-Yan Zhu2, Andrew Tao1, Jan Kautz1, Bryan Catanzaro1 1NVIDIA Corporation, 2UC Berkeley In arxiv, 2017.

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