Image-to-Image Translation in PyTorch

PyTorch (and PyTorch Lightning) implementation of Neural Style Transfer, Pix2Pix, CycleGAN, and Deep Dream!

RevGAN implementation in PyTorch. We extend the Pix2pix and CycleGAN framework by exploring approximately invertible architectures in 2D and 3D. These architectures are approximately invertible by design and thus partially satisfy cycle-consistency before training even begins. Furthermore, since invertible architectures have constant memory complexity in depth, these models can be built arbitrarily deep without requiring additional memory. In the paper we demonstrate superior quantitative output on the Cityscapes and Maps datasets at near constant memory budget.

https://github.com/junyanz/pytorch-CycleGAN-and-pix2pix.git

In this notebook, we'll be building a generative adversarial network (GAN) trained on the MNIST dataset. From this, we'll be able to generate new handwritten digits! GANs were first reported on in 2014 from Ian Goodfellow and others in Yoshua Bengio's lab. Since then, GANs have exploded in popularity. Here are a few examples to check out: Pix2Pix CycleGAN & Pix2Pix in PyTorch, Jun-Yan Zhu A list of generative models The idea behind GANs is that you have two networks, a generator 𝐺 and a discriminator 𝐷 , competing against each other. The generator makes "fake" data to pass to the discriminator. The discriminator also sees real training data and predicts if the data it's received is real or fake. The generator is trained to fool the discriminator, it wants to output data that looks as close as possible to real, training data. The discriminator is a classifier that is trained to figure out which data is real and which is fake. What ends up happening is that the generator learns to make data that is indistinguishable from real data to the discriminator. The general structure of a GAN is shown in the diagram above, using MNIST images as data. The latent sample is a random vector that the generator uses to construct its fake images. This is often called a latent vector and that vector space is called latent space. As the generator trains, it figures out how to map latent vectors to recognizable images that can fool the discriminator. If you're interested in generating only new images, you can throw out the discriminator after training. In this notebook, I'll show you how to define and train these adversarial networks in PyTorch and generate new images!

An optimized version of CycleGan

forked from junyanz/pytorch-CycleGAN-and-pix2pix

pytorch-CycleGAN-and-pix2pix-master项目代码

Image-to-Image translation using GAN. (Images of humans and turning them into pokémon.) Student project in ICT Seminar 2 (IKT441) at University of Agder. Based on junyanz github repository: https://github.com/junyanz/pytorch-CycleGAN-and-pix2pix.

Image-to-Image Translation in PyTorch

For original repo see https://github.com/junyanz/pytorch-CycleGAN-and-pix2pix

PyTorch implementations of GAN architectures for image generation and translation, including DCGAN, WGAN, CycleGAN, Pix2Pix, SRGAN, ESRGAN, and ProGAN.

Production Pix2Pix & CycleGAN workflows on Cityscapes, IDD, Facades. End-to-end: PyTorch Lightning, DDP, mixed precision, ONNX export, dataset preprocessing and cross-dataset evaluation (mPA/FID). Shows GAN research + production optimization.

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performance optimizing of pytorch-CycleGAN-and-pix2pix onto NVIDIA GPUs, using TensorRT, CV-CUDA, etc.

Fork of https://github.com/junyanz/pytorch-CycleGAN-and-pix2pix

Model created using https://github.com/n1ckfg/pytorch-CycleGAN-and-pix2pix

This repo is imitated from https://github.com/junyanz/pytorch-CycleGAN-and-pix2pix

A clone of CycleGAN (https://github.com/junyanz/pytorch-CycleGAN-and-pix2pix) with a pretrained model about flowers, and fabrics.

This project implements a CycleGAN model, leveraging the [CycleGAN and pix2pix repository](https://github.com/junyanz/pytorch-CycleGAN-and-pix2pix), to perform real-time transformations on input images. The focus is on altering clothing styles in a live webcam feed.

pytorch-CycleGAN-and-pix2pix-master

CycleGAN and pix2pix in PyTorch

Tensorflow Pix2Pix and Pytorch cycleGan

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modify from pytorch-CycleGAN-and-pix2pix

pytorch-CycleGAN-and-pix2pix train log