Pix2pix online

Is a conditional GAN that was perhaps the most famous image-to-image translation GAN. However, one major drawback of Pix2Pix is that it requires paired training image datasets.Figure 10: Inputs and outputs of Pix2Pix GANs (image source: Pix2Pix paper).CycleGAN was built upon Pix2Pix and only needs unpaired images, much easier to come by in the real world. It can convert images of apples to oranges, day to night, horses to zebras … ok. These may not be real-world use cases to start with; there are so many other image-to-image GANs developed since then for art and design.Now you can translate your selfie to comics, painting, cartoons, or any other styles you can imagine. For example, I can use White-box CartoonGAN to turn my selfie into a cartoonized version: Figure 12: Input and output of the White-box CartoonGAN (images by the author).Colorization can be applied to not only black and white photos but also artwork or design assets. In the artwork making or UI/UX design process, we start with outlines or contours and then coloring. Automatic colorization could help provide inspiration for artists and designers. Text-to-ImageWe’ve seen a lot of Image-to-Image translation examples by GANs. We could also use words as the condition to generate images, which is much more flexible and intuitive than using class labels as the condition. Combining NLP and computer vision has become a popular research area in recent years. Here are a few examples: StyleCLIP and Taming Transformers for High-Resolution Image Synthesis.Figure 13: A GAN transforms NLP and computer vision (image source: StyleCLIP paper).Beyond imagesGANs can be used for not only images but also music and video. For example, GANSynth from the Magenta project can make music. Here is a fun example of GANs on video motion transfer called “Everybody Dance Now” (YouTube | Paper). I’ve always loved watching this charming video where the dance moves by professional dancers get transferred to the amateurs.Other GAN applicationsHere are a few other GAN applications:Image inpainting: replace the missing portion of the image. Image uncropping or extension: this could be useful in simulating camera parameters in virtual reality. Super-resolution (SRGAN & ESRGAN): enhance an image from lower-resolution to high resolution. This could be very helpful in photo editing or medical image enhancements.Here is an example of how GANs can be used for climate change. Earth Intelligent Engine, an FDL (Frontier Development Lab) 2020 project, uses Pix2PixHD to simulate what an area would look like after flooding. We have seen GAN demos from papers, research labs. and open source projects. These days we are starting to see real commercial applications using GANs. Designers are familiar with using design assets from icons8. Take a look at their website, and you will notice

Architecture with perturbation layers with practical guidance on the methodology and code. Three part seriesSuper Resolution for Satellite Imagery - srcnn repoTensorFlow implementation of "Accurate Image Super-Resolution Using Very Deep Convolutional Networks" adapted for working with geospatial dataRandom Forest Super-Resolution (RFSR repo) including sample dataSuper-Resolution (python) Utilities for managing large satellite imagesEnhancing Sentinel 2 images by combining Deep Image Prior and Decrappify. Repo for deep-image-prior and article on decrappifyThe keras docs have a great tutorial - Image Super-Resolution using an Efficient Sub-Pixel CNNHighRes-net -> Pytorch implementation of HighRes-net, a neural network for multi-frame super-resolution, trained and tested on the European Space Agency’s Kelvin competitionsuper-resolution-using-gan -> Super-Resolution of Sentinel-2 Using Generative Adversarial NetworksSuper-resolution of Multispectral Satellite Images Using Convolutional Neural Networks with paperSmall-Object Detection in Remote Sensing Images with End-to-End Edge-Enhanced GAN and Object Detector Network -> enhanced super-resolution GAN (ESRGAN)pytorch-enhance -> Library of Image Super-Resolution Models, Datasets, and Metrics for Benchmarking or Pretrained Use. Also checkout this implementation in JaxMulti-temporal Super-Resolution on Sentinel-2 Imagery using HighRes-Net, repoimage-super-resolution -> Super-scale your images and run experiments with Residual Dense and Adversarial Networks.SSPSR-Pytorch -> A spatial-spectral prior deep network for single hyperspectral image super-resolutionSentinel-2 Super-Resolution: High Resolution For All (Bands)super-resolution for satellite images using SRCNNCinCGAN -> Unofficial Implementation of Unsupervised Image Super-Resolution using Cycle-in-Cycle Generative Adversarial NetworksSatellite-image-SRGAN using PyTorchSuper Resolution in OpenCVdeepsum -> Deep neural network for Super-resolution of Unregistered Multitemporal images (ESA PROBA-V challenge)3DWDSRNet -> code to reproduce Satellite Image Multi-Frame Super Resolution Using 3D Wide-Activation Neural NetworksImage-to-image translationTranslate images e.g. from SAR to RGB.How to Develop a Pix2Pix GAN for Image-to-Image Translation -> how to develop a Pix2Pix model for translating satellite photographs to Google map images. A good intro to GANSSAR to RGB Translation using CycleGAN -> uses a CycleGAN model in the ArcGIS API for PythonA growing problem of ‘deepfake geography’: How AI falsifies satellite imagesKaggle Pix2Pix Maps -> dataset for pix2pix to take a google map satellite photo and build a street mapguided-deep-decoder -> With guided deep decoder, you can solve different image pair fusion problems, allowing super-resolution, pansharpening or denoisingSARRemoving speckle noise from Sentinel-1 SAR using a CNNA dataset which is specifically made for deep learning on SAR and optical imagery is the SEN1-2 dataset, which contains corresponding patch pairs of Sentinel 1 (VV) and 2 (RGB) data. It is the largest manually curated dataset of S1 and S2 products, with corresponding labels for land use/land cover

Pix2pix - Image to Image Translation Using Generative Adversarial NetworksThis repository contains MATLAB code to implement the pix2pix image to image translation method described in the paper by Isola et al. Image-to-Image Translation with Conditional Adversarial Nets.Before you beginGetting startedInstallationTraining a modelGenerating imagesAny problems?FinallyBefore you beginMake sure you have the minimum following requirements:MATLAB R2019b or greaterDeep Learning ToolboxGetting startedInstallationFirst off clone or download the repository to get a copy of the code. Then run the function install.m to ensure that all required files are added to the MATLAB path.Training a modelTo train a model you need many pairs of images of "before" and "after". The classic example is the facades dataset which contains label images of the fronts of buildings, and the corresponding original photo.Use the helper function p2p.util.downloadFacades to download and prepare the dataset for model training. Once that's ready you will have two folders 'A' the input labels, and 'B' the desired output images.To train the model we need to provide the locations of the A and B images, as well as any training options. The model will then try and learn to convert A images into B images![labelFolder, targetFolder] = p2p.util.downloadFacades();We will just use the default options which approximately reproduce the setttings from the original pix2pix paper.options = p2p.trainingOptions();p2pModel = p2p.train(labelFolder, targetFolder, options);Note that with the default options training the model will take several hours on a GPU and requires around 6GB of memory.Generating imagesOnce the model is trained we can use the generator to make generate a new image.exampleInput = imread("docs/labels.png");We can then use the p2p.translate function to convert the input image using trained model. (Note that the generator we have used expects an input image with pixel dimensions divisible by 256)exampleOutput = p2p.translate(p2pModel, exampleInput);imshowpair(exampleInput, exampleOutput, "montage");For an example you can directly run in MATLAB see the Getting Started live script.Any problems?If you have any trouble using this code, report any bugs, or want to request a feature please use the GitHub issues.FinallyThis repository uses some images from the facades dataset used under the CC BY-SA licenceCopyright 2020 The MathWorks, Inc.

Sketch2face: Conditional Generative Adversarial Networks for Transforming Face Sketches into Photorealistic ImagesGeneration of color photorealistic images of human faces from their corresponding grayscale sketches, building off of code from pix2pix.See the paper for this project here.AbstractIn this paper, we present a conditional GAN image translation model for generating realistic human portraits from artist sketches. We modify the existing pix2pix model by introducing four variations of an iterative refinement (IR) model architecture with two generators and one discriminator, as well as a model that incorporates spectral normalization and self-attention into pix2pix. We utilize the CUHK Sketch Database and CUHK ColorFERET Database for training and evaluation. The best-performing model, both qualitatively and quantitatively, uses iterative refinement with L1 and cGAN loss on the first generator and L1 loss on the second generator, likely due to the first-stage sharp image synthesis and second-stage image smoothing. Most failure modes are reasonable and can be attributed to the small dataset size, among other factors. Future steps include masking input images to facial regions, trying other color spaces, jointly training a superresolution model, using a colorization network, learning a weighted average of the generator outputs, and gaining control of the latent space of generated faces.Directory GuideRelevant folders that were significantly modified during the course of this project are:checkpoints contains model logs and training options.data contains the data classes used for handling the data that interface with the models.datasets contains the ColorFERET and CUHK datasets used for training and testing the models.facenet-pytorch contains the cloned GitHub from timesler/facenet-pytorch and the implemented FaceNet evaluation metrics for the model.models contains the model classes for the baseline model, color iterative refinement models, grayscale iterative refinement model, and modified implementations for spectral normalization and self-attention from SAGAN.options contains training and testing options, as well as custom model options for the baseline and the iterative refinement models.results contains the test output images for all 294 samples for each of the models implemented.scripts contains the script to run evaluation metrics for L1, L2 distance and SSIM.

Falsos generados por algoritmo suponen un nuevo capítulo en los peligros de los deepfakes. DeepNude podría utilizarse para 'revenge porn' incluso aunque en realidad nunca hubieran existido esas imágenes sin ropa. El resultado es tan realista que el impacto que podría tener es similar. Una aplicación creada "por diversión y curiosidad" como explica el autor pero que genera toda una serie de dudas sobre la privacidad y enlaza de nuevo el machismo con la tecnología. ¿Por qué el algoritmo únicamente funciona con mujeres? "Porque las imágenes de mujeres desnudas son más fáciles de encontrar online", explica el autor a Vice. A lo que añade que espera en el futuro crear una versión con hombres también. Basada en un algoritmo open source DeepNude no detecta correctamente las imágenes que no pertenecen a mujeres de carne y hueso. Los datos de DeepNude se guardan en local y no se suben a la nube indica la página web. El autor de la aplicación no ha querido revelar su identidad real, aunque dice llamarse 'Alberto' y procedente de Estonia. Sí explica más detalles sobre cómo funciona el algoritmo de DeepNude. El software estaría basado en pix2pix, un algoritmo open source desarrollado por la Universidad de California, Berkeley en 2017. Se trata de un algoritmo que utiliza redes neuronales para tratar con una gran base de datos de imágenes. En este caso, más de 10.000 imágenes de mujeres desnudas que el programador habría utilizado para "entrenar" a la inteligencia artificial. Mientras que los vídeos de

Online Multi-Granularity Distillation for GAN Compression (ICCV2021)This repository contains the pytorch codes and trained models described in the ICCV2021 paper "Online Multi-Granularity Distillation for GAN Compression". This algorithm is proposed by ByteDance, Intelligent Creation, AutoML Team (字节跳动-智能创作-AutoML团队).Authors: Yuxi Ren*, Jie Wu*, Xuefeng Xiao, Jianchao Yang.OverviewPerformancePrerequisitesLinuxPython 3CPU or NVIDIA GPU + CUDA CuDNNGetting StartedInstallationClone this repo:git clone OMGDInstall dependencies.conda create -n OMGD python=3.7conda activate OMGDpip install torch==1.7.0 torchvision==0.8.0 torchaudio==0.7.0 pip install -r requirements.txt Data preparationedges2shoesDownload the datasetbash datasets/download_pix2pix_dataset.sh edges2shoes-rGet the statistical information for the ground-truth images for your dataset to compute FID.bash datasets/download_real_stat.sh edges2shoes-r BcityscapesDownload the datasetDownload the dataset (gtFine_trainvaltest.zip and leftImg8bit_trainvaltest.zip) from here, and preprocess it.python datasets/get_trainIds.py database/cityscapes-origin/gtFine/python datasets/prepare_cityscapes_dataset.py \--gtFine_dir database/cityscapes-origin/gtFine \--leftImg8bit_dir database/cityscapes-origin/leftImg8bit \--output_dir database/cityscapes \--train_table_path datasets/train_table.txt \--val_table_path datasets/val_table.txtGet the statistical information for the ground-truth images for your dataset to compute FID.bash datasets/download_real_stat.sh cityscapes Ahorse2zebraDownload the datasetbash datasets/download_cyclegan_dataset.sh horse2zebraGet the statistical information for the ground-truth images for your dataset to compute FID.bash datasets/download_real_stat.sh horse2zebra Abash datasets/download_real_stat.sh horse2zebra Bsummer2winterDownload the datasetbash datasets/download_cyclegan_dataset.sh summer2winter_yosemiteGet the statistical information for the ground-truth images for your dataset to compute FID from herePretrained ModelWe provide a list of pre-trained models in link. DRN model can used to compute mIoU link.Trainingpretrained vgg16we should prepare weights of a vgg16 to calculate the style losstrain student model using OMGDRun the following script to train a unet-style student on cityscapes dataset,all scripts for cyclegan and pix2pix on horse2zebra,summer2winter,edges2shoes and cityscapes can be found in ./scriptsbash scripts/unet_pix2pix/cityscapes/distill.shTestingtest student models, FID or mIoU will be calculated, take unet-style generator on cityscapes dataset as an examplebash scripts/unet_pix2pix/cityscapes/test.shCitationIf you use this code for your research, please cite our paper.@article{ren2021online,title={Online Multi-Granularity Distillation for GAN Compression},author={Ren, Yuxi and Wu, Jie and Xiao, Xuefeng and Yang, Jianchao},journal={arXiv preprint arXiv:2108.06908},year={2021}}AcknowledgementsOur code is developed based on GAN Compression