Pix2pix face generator download

Author: s | 2025-04-25

Learn how to use Pix2Pix for face generation, and how to evaluate and compare different Pix2Pix models for quality and performance.

Face Generator Pix2Pix GAN - Kaggle

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.

GitHub - ghunkins/Child-Face-Generation: Pix2Pix

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.

[Vinesauce] Vinny - Pix2Pix: Face Generator - YouTube

Create stunning AI-powered content with FakeMe! Whether you want to swap faces in photos and videos, generate professional AI headshots, or bring images to life with animations, FakeMe has all the tools you need. Join our growing community and start transforming your photos with cutting-edge AI technology!TRY THESE FEATURES FOR FREE- Swap faces instantly on photos and videos.- AI Headshot Generator – Create professional business or social media headshots.- AI Avatars – Generate custom avatars in various artistic styles.- AI Photo Generator – Create stunning AI-enhanced photos and videos.- Personalize your content with AI-powered face swaps.- Upload Your Own Content – Use your personal photos and videos for face swaps and animations.- Create fun greeting cards for birthdays, Christmas, Halloween, and more.- Explore daily trending content and generate your own viral creations.PREMIUM FEATURES (requires subscription)- PRO AI Headshot Generator – Create professional business or social media headshots.- PRO AI Avatars – Generate custom avatars in various artistic styles.- PRO AI Photo Generator – Create stunning AI-enhanced photos and videos.- Custom Prompts for AI Headshot / Avatars & Photos- Remove Ads – Enjoy an ad-free experience.- Unlimited Upload Your Own Content – Use your personal photos and videos for face swaps and animations.So why wait? Download FakeMe today and start transforming your photos like a pro! With its easy-to-use interface and endless customization options, FakeMe is the perfect app for anyone looking to add a little extra flair to their photos. Give it a try and see the magic for yourself!In. Learn how to use Pix2Pix for face generation, and how to evaluate and compare different Pix2Pix models for quality and performance.

keras-io/pix2pix-generator - Hugging Face

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

Pix2Pix for Face Generation: Best Practices and Challenges

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 for Face Generation: Evaluation and Comparison - LinkedIn

Generator of fake portraitsPeople tend not to think about the effect that neural networks have on our lives, because usually, we see the result of its work and not the "face" of a neural network. Perhaps that is why the generator of fake photos became the main topic of discussion for several weeks in the media devoted to technology at the end of 2020. Not everyone was able to guess that AI could generate a realistic face of a non-existent person in a couple of seconds. Fake portraits look very realistic and it's frightening. If AI can create faces for itself and can text like real people, then what is going to happen next?Generator of fake faces of non-existent humansWe are talking about the website thispersondoesnotexist.com ("this person does not exist dot com") and are going to tell of the history and areas of application. The way the generator works will be explained further.The AI face generator is powered by StyleGAN, a neural network from Nvidia developed in 2018. GAN consists of 2 competing neural networks, one generates something, and the second tries to find whether results are real or generated by the first. Training ends when the first neural network begins to constantly deceive the second.An interesting point is that the creation of photographs of non-existent people was a by- product: the main goal was to train the AI to recognize fake faces and faces in general. The company needed this to improve the performance of its video cards by automatically recognizing faces and applying other rendering algorithms to them. However, sincethe StyleGAN code is publicly available, an engineer at Uber was able to take it and create a random face generator that rocked the internet.About the generatorFor the user, everything works very simply. As soon as you are on the website random face is generated. You can download the picture if you want. Refresh the page if you don’t like the person that you are seeing. If you see the same face, just wait a couple of seconds, and refresh the page again. The website shows the results of the generator’s work (which are updated every 2-3 seconds) not the generator itself. How to recognize an image of a fake personIt is almost impossible to recognise an image of a fake person. AI is so developed that 90% of fakes are not recognized by an ordinary person and 50% are not recognized by an experienced photographer. There are no services for recognition. Occasionally, a neural network makes mistakes, which is why artifacts appear: an incorrectly bent pattern, a strange hair color, and so on.The only thing you need to do is take a closer look: humans’ visual processing

Vinny's Pix2Pix AI Face Generator - Toolify

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. Learn how to use Pix2Pix for face generation, and how to evaluate and compare different Pix2Pix models for quality and performance. Vinesauce Vinny Pix2Pix Face Generator; YOUR FAV CHARACTERS IRL FOTOGENERATOR; pix2; CARTOON TO HORRIFIC REALITY Pix2Pix; This Website is a Nightmare Generator;

Messing Around With Pix2Pix: Face Generator. - YouTube

If you want to see the potential face of your child with your partner or assess how closely your existing child matches an AI prediction, then you are at the right place because we will list a free AI baby face generator that combines the genetic attributes of two individuals, usually prospective parents to generate a visual representation of what their potential offspring might look like.These baby face generators only require two photos, one of you and one of your lover, then a full-color baby photo will be generated consisting of the facial features of you two. If you want to use one picture of yours to foresee the look of your kid, the generator would also work. Best AI Baby Face Generator FreeNow get the list of Best AI Baby Face Generators free.1. Futurebaby AIIf you want to discover how your baby might look, then futurebaby AI is the best tool because it allows users to use an advanced AI baby face generator to see the future baby’s faces. You need to follow the three steps to create a baby face generator.First step, Simply upload clear, bright photos of you and your partner for the best results. After uploading the image, its AI analyzes the photos and generates a realistic baby photo. At last, You will receive the generated baby photo to view, download, and share with friends and family.Features:Meet your future baby with a free AI baby generator.It’s completely free with no hidden fees or charges.You can use it to see what your baby would look like with your celebrity crush.It analyzes in-depth the facial details of you and your partner, such as eye color, nose shape, and smile. 2. Aibabygenerator.ioSee Your Future Baby In One Click. It is an online tool that creates ultra-realistic images of future babies by analyzing and combining up to 70 unique facial features from both parents. Aibabygenerator.io uses unique technology, fine-tuned with a dataset of 4 million internal corporate images to provide a close mirror for both your features. Anyone can utilize this tool with two images. Simply upload your and your partner’s photos to its baby generator and AI brings your future baby to life. It’s designed to be user-friendly, secure, and accessible to everyone, offering both free and premium options for users curious about their future families.Features:An AI-powered tool that helps couples visualize what their future children might look like.It has served over 3,000 families with more than 12,000 generated baby photos.Create realistic predictions of what a future child might look like by analyzing up to 70 unique facial features.Allows users to select gender, age, and other attributes to customize the generated baby images. 3. Vidnoz AI Baby Face Generator

[Vinesauce] Vinny Creates Realistic Faces with Pix2Pix Face Generator

Vidnoz AI is the best tool to create any type of video and photo. You can utilize this tool for both video editors and photo editors. It also offers one of the best tools AI baby face generator that allows users to get AI-predicted baby face photos of your future child.Predict your baby face for free without annoying signup or subscription. Simply upload parents’ photos with clear faces, set baby appearance settings like Gender, Age, and Expression, and click Generate Now to start the creation. In a few seconds, it creates the baby face picture with a download option.Features:Get AI-predicted baby face photos of your future child.Allows you to see your future baby in different genders, ages, and expressions.Its baby face generator stands out for its brilliant features and industry-leading technology.It is a free online AI tool made especially for you. No ads or watermarks will be added to your results. 4. Seeyourbaby AISeeyourbaby AI utilizes AI to predict the appearance of your future baby based on photos of both parents. It is a cutting-edge AI tool designed to satisfy that curiosity by predicting what your future baby might look like.It uses sophisticated AI algorithms to analyze uploaded photos of both parents. Its AI Baby Generator analyzes your unique features and combines parents’ photos to predict your future son and daughter’s appearance.Features:It uses AI to take your photos and analyze your unique features to predict your future child’s appearance. It generates lifelike images of your potential child based on photos of the parents. Its AI baby face generator creates your future baby photos with 90% resemblance. You will receive four images of potential baby boys and four of baby girls delivered straight to your inboxes.5. Aibabygenerator.comIt is a tool that reveals what your baby will look like. Aibabygenerator allows you to get a sneak peek into what your future child might look like just by submitting a photo of yourself and your partner. By simply uploading photos of yourself and your partner, it generates ultra-realistic baby photos.It is super flexible and can work with just one photo or even a picture of your favorite celebrity. It uses advanced AI technology to provide hyper-realistic baby photos that have an average of 93% facial match rate with you.Features:See Your Future Baby in Different Settings. This tool is loved by 9,025+ happy customers. See what a baby with your Celebrity Crush would look like.Upload your photo and its AI predicts what your future baby will look like.ConclusionI have shared the 5 Best Free AI Baby Face Generator that helps you to discover your future baby. I have tasted all the tools, but Futurebaby AI stands out from the list and is also available. Learn how to use Pix2Pix for face generation, and how to evaluate and compare different Pix2Pix models for quality and performance. Vinesauce Vinny Pix2Pix Face Generator; YOUR FAV CHARACTERS IRL FOTOGENERATOR; pix2; CARTOON TO HORRIFIC REALITY Pix2Pix; This Website is a Nightmare Generator;

cgan-face-generator: Face generator from sketches using cGAN (pix2pix

AI Baby Generator: Maker Face 更新日期 2025-01-13 当前版本 1.1.14 提供者 AI Baby Generator: Maker Face电脑版简介 想入坑AI Baby Generator: Maker Face，可是手机屏幕太小，一跑游戏就变烫，怎么办？使用逍遥模拟器，在电脑的大屏幕上畅快体验！在电脑上下载安装AI Baby Generator: Maker Face，不用担心电池当掉，想玩多久玩多久，顺畅跑一天~全新的逍遥模拟器8，绝对是您体验AI Baby Generator: Maker Face电脑版的好选择。完美的按键映射系统让AI Baby Generator: Maker Face如端游般运行； AI Baby Generator: Maker Face电脑版截图&视频 透过逍遥模拟器，在电脑上下载AI Baby Generator: Maker Face，享受大荧屏的畅快体验。 AI Baby Generator will predict your future baby face. 游戏信息 AI Baby Generator will predict your future baby face.Have you ever thought How will my baby look like? or What will my baby look like? If yes, then this application “Future Baby Generator” is perfect for you. Future Baby Generator uses advanced artificial intelligence (AI) algorithm. We use the newest facial recognition technologies to analyze faces of Mother and Father and generate an image of your cute baby. You can see the baby face with this Baby Maker app.App consists of 2 features:-1- Future Baby Generator (Make a baby)You need to follow below steps for baby face generator.◆ Choose photos of Father.◆ Choose photos of Mother.◆ Press on the “Check Future Baby” button and wait for a second. Future Baby Generator will use the artificial intelligence and make a baby for you.2- Like Parent (Baby Maker percentage)You need to follow below steps to predict your child match percentage with Mom and Dad.◆ Choose photos of Father.◆ Choose photos of Mother.◆ Choose photos of your cute baby.◆ Press on the “Check Analyse Child” button and wait for a second. Baby Maker will use the artificial intelligence and analyse the percentage of Mom and Dad match with your baby.3- Baby Name (Name of your cute baby)◆ Press on the “Baby Name” button.◆ You can check list of Baby Names. Origin of the Name and description so It will be easy for you to conclude your baby name.Future Baby Generator is now simple and you need to keep below points in mind to achieve the best results:◆ The images are of high quality, good lighting condition.◆ Face looking directly at the camera.◆ Face without beard.◆ Choose Future Baby’s skin tone.Find out how you and your partner's baby face can look like with this baby maker app!!! Try it with your boyfriend's/girlfriend's