Vae celeba pytorch python. Whats new in PyTorch tutorials.

Vae celeba pytorch python infer_vae --config config/celebhq. The models are: Deep Convolutional GAN, Least Squares GAN, Wasserstein GAN, Wasserstein GAN Gradient Penalty, Information Maximizing GAN, Boundary Equilibrium GAN, Variational AutoEncoder and Example of vanilla VAE for face image generation at resolution 128x128 using pytorch. Jul 14, 2023 · A step-by-step guide to implementing a β-VAE in PyTorch, covering the encoder, decoder, loss function, and latent space interpolation. A Variational Autoencoder in PyTorch for the CelebA Dataset. In this post, we will implement the variational AutoEncoder (VAE) for an image dataset of celebrity faces. You can change EPOCHS and BATCH_SIZE. py to train VQ-VAE. If we just use a. Hence, in VAE, the assumption is that the data distribution is Gaussian. and Wipf, D. Bite-size, ready-to-deploy PyTorch code examples. データセット. Now, we create a simple VAE which has fully-connected encoders and decoders . PyTorch Recipes. Representation learning has been driven by both supervised and unsupervised approaches, where variational auto-encoder (VAE) is a well-known unsupervised approach. sh. Shown for btcvae_celeba: Grid of gifs generated using code in bin/plot_all. It could be implemented in any framework, preferably MXNet, Chainer or PyTorch. Vanilla VAE implemented in pytorch-lightning, trained through Celeba dataset. Architecture of the proposed QVAE. 0%; Footer deep-learning reproducible-research architecture pytorch vae beta-vae paper-implementations gumbel-softmax celeba-dataset wae variational-autoencoders pytorch-implementation dfc-vae iwae vqvae vae-implementation pytorch-vae Variational auto encoder in pytorch. A Collection of Variational Autoencoders (VAE) in PyTorch. Familiarize yourself with PyTorch concepts and modules. Contribute to 1Konny/Beta-VAE development by creating an account on GitHub. Pytorch implementation of RF_VAE proposed in Relevance Factor VAE: Learning and Identifying Disentangled Factors, Kim et al. I implemented DFC-VAE based on the paper by Xianxu Hou, Linlin Shen, Ke Sun, Guoping Qiu. Generative models (GAN, VAE, Diffusion Models, Autoregressive Models) implemented with Pytorch, Pytorch_lightning and hydra. manual_seed ( 0 ) import torch. A simple tutorial of Variational AutoEncoder(VAE) models. It integrates model monitoring with Wandb and a quick way to save/load model from HuggingFaceHub 🤗. 💻 Blog: ht Code and notebooks related to the paper: "Reconstructing Faces from fMRI Patterns using Deep Generative Neural Networks" by VanRullen & Reddy, 2019 - rufinv/VAE-GAN-CelebA Note: The default dataset is CelebA. Need further optimization, but for now, we can see the result of sampling is close to training result. The Official PyTorch Implementation of "NVAE: A Deep Hierarchical Variational Autoencoder" (NeurIPS 2020 spotlight paper) - NVlabs/NVAE Saved searches Use saved searches to filter your results more quickly Note: The default dataset is CelebA. Module class. Accompanying code for my Medium article: A Basic Variational Autoencoder in PyTorch Trained on the CelebA Dataset . The mse loss used is 'sum' instead of 'mean'. VAEの概要1. Learn the Basics. For the encoder, the ModelOutput instance must contain the embbeddings and log -covariance matrices (of shape batch_size x latent_space_dim) respectively under the key embedding and log_covariance key. I trained this model with CelebA dataset. import torch ; torch . pyplot as plt ; plt . py to compute the marginal log likelihood log p(x) using q(z|x,y) as the inference network. - donglinkang2021/MinVQVAE We demonstrate that beta-VAE with appropriately tuned beta > 1 qualitatively outperforms VAE (beta = 1), as well as state of the art unsupervised (InfoGAN) and semi-supervised (DC-IGN) approaches to disentangled factor learning on a variety of datasets (celebA, faces and chairs). We apply it to the MNIST dataset. Contribute to AliLotfi92/InfoMaxVAE development by creating an account on GitHub. /Beta_VAE/, modify dataset paths in paths. MNISTを使用します。 Sep 14, 2021 · VAE for the CelebA dataset. Files: vae. Sample latent variables from all layers above layer i (Eq. In each folder, there are 3 scripts that one can run: train. E. Pytorch implementation of DCGAN, CDCGAN, LSGAN, WGAN and WGAN-GP for CelebA dataset. A PyTorch implementation of Deep Feature Consistent Variational Autoencoder. Contribute to atinghosh/VAE-pytorch development by creating an account on GitHub. A VAE is a generative model that learns to represent high-dimensional data (like images) in a lower-dimensional latent space, and then generates new data from this space. Contribute to geonwooko/VAE-GAN-PYTORCH development by creating an account on GitHub. 6 version and cleaned up the code. 5e6 --beta 4 --viz_on False --viz_name mnist_beta4_z128_sgd This is a simple variational autoencoder written in Pytorch and trained using the CelebA dataset. yaml for training autoencoder with the desire config file; For inference make sure save_latent is True in the config; For inference run python -m tools. Variational AutoEncoder (VAE, D. Minimal Discrete Variational Autoencoder (VQ-VAE) implementation in PyTorch, train, eval and test on Cifar10, CelebA, and ImageNet ILSVRC2012, get good result. py, and then run MNIST: python main. Variational Autoencoder (VAE) with perception loss implementation in pytorch - GitHub - LukeDitria/CNN-VAE: Variational Autoencoder (VAE) with perception loss implementation in pytorch This project is a PyTorch implementation of Wasserstein Auto-Encoders (WAE) which was published as a conference proceeding at ICLR 2018 as an Oral. 0%; Footer All 94 Python 45 Jupyter Notebook 43 HTML 4 Kotlin 1 Lua 1. . Dec 4, 2022 · 【参考】【徹底解説】VAEをはじめからていねいに【参考】Variational Autoencoder徹底解説【参考】VAE (Variational AutoEncoder, 変分オートエンコーダ) 【参考】【超初心者向け】VAEの分かりやすい説明とPyTorchの実装. P. This is the code for the two-stage VAE model proposed in ICLR 2019 paper "Diagnoising and Enhancing VAE Models" [1]. Oct 31, 2023 · A variational autoencoder (VAE) looks very similar, except for the embedding part in the middle. png. This repository contains a subset of the experiments mentioned in the paper. See here for more details on installing dlib. For example python train_on_dSprites_H with lr=1e-4 epoches=200 beta=4 warm_up=0 means that the learning rate is 1e-4, train for 200 epoches, the parameter beta equals to 4, and introduce KL deep-learning reproducible-research architecture pytorch vae beta-vae paper-implementations gumbel-softmax celeba-dataset wae variational-autoencoders pytorch-implementation dfc-vae iwae vqvae vae-implementation pytorch-vae Dec 5, 2020 · PyTorch Implementation. A PyTorch implementation of Vector Quantized Variational Autoencoder (VQ-VAE) with EMA updates, pretrained encoder, and K-means initialization. data import DataLoader from torchvision import transforms # Root directory for the dataset data_root = 'data/celeba' # Spatial size of training images, images are resized to this size. Image Generation - Transformer This example evaluates the proposed TVQ-VAE for image generation in conjunction with the taming-transformer . This repository contains the implementations of following VAE families. py: Main code, training and testing. - fahmyadan/VAE. The amortized inference model (encoder) is parameterized by a convolutional network, while the generative model (decoder) is parameterized by a transposed convolutional network. g. This loads a custom dataset (which is not in the dataset class of PyTorch) - CelebA. Utilizing the robust and versatile PyTorch library, this project showcases a straightforward yet effective approach For CelebA-HQ, we resize each image to 266x266 and randomly crop a 256x256. in PyTorch for the CelebA Dataset Sep 14, 2021 · In this post, we will implement the variational AutoEncoder (VAE) for an image dataset of celebrity faces. zip # then run scrip file sh scripts/prepare_data. utils. dpi' ] = 200 PyTorch implementations of various generative models to be trained and evaluated on CelebA dataset. Asking for help, clarification, or responding to other answers. The choice of the approximate posterior is a fully Nov 19, 2022 · 25 sample training images. The VAE being trained here is a Res-Net Style VAE with an adjustable perception loss using a pre-trained vgg19. The images are scaled down to 112x128, the VAE has a latent space with 200 dimensions and it was trained for nearly 90 epochs. , 2017) deep-learning reproducible-research pytorch mnist chairs-dataset vae representation-learning unsupervised-learning beta-vae celeba variational-autoencoder disentanglement dsprites fashion-mnist disentangled-representations factor-vae beta-tcvae Run PyTorch locally or get started quickly with one of the supported cloud platforms. Instead of transposed convolutions, it uses a combination of upsampling and convolutions, as described here: Dec 3, 2023 · はい、以下にvaeとcvaeの詳細なまとめを書いてみます。変分オートエンコーダ（vae）：vaeは生成モデルの一つで、データの隠れた潜在的な表現を学習するために使用されます。vaeはエンコーダとデコーダの2つのパートで構成されています。 Here we try to visualize the representations learned by individual layers. newaxis によりバッチ次元として1次元目を挿入し、transpose メソッドにより次元の順番を変えます。 In order to show the advantages due to such properties, we define a plain convolutional VAE in the quaternion domain and we evaluate it in comparison with its real-valued counterpart on the CelebA face dataset. R. Open a new conda environment and install the necessary dependencies. py to fit the MVAE; sample. Then, each dimension will be clamped to ± 3 and saved to a new image. 4 . Update 22/12/2021: Added support for PyTorch Lightning 1. toc: true ; badges: true; comments: true; author: Chanseok Kang; categories: [Python, Coursera, Tensorflow $ cd PyTorch-VAE $ python run. We train our VAE to minimize the KL divergence between the encoder’s distribution and . python pytorch image-generation built with PyTorch, using a subset of 5000 CelebA images. functional as F import torch. Tutorials. sh', respectively. /out/dim*. trainvae. Whats new in PyTorch tutorials. This library was developed as a contribution to the Disentanglement Challenge of NeurIPS 2019. Provide details and share your research! But avoid …. py to (conditionally) reconstruct from samples in the latent space; and loglike. It includes ready to use datasets like MnistSvhn 🔢, CelebA 😎 and PolyMNIST, and the most used metrics : Coherences, Likelihoods and FID. Dec 30, 2024 Brian Hulela VAE的python算法实现（pytorch）, 视频播放量 867、弹幕量 0、点赞数 7、投硬币枚数 1、收藏人数 10、转发人数 0, 视频作者飞鸟手札, 作者简介不接广 | 橙色：飞鸟的日常记录 | 其余内容请看公告 | 点不开的就是【充电视频】 | 后台回复【群】｜求代码有偿，相关视频：抛下一切，开始阅读（D. 1 VAEとは2014年に以下の論文で発表された「画像を生成する生成モデル」Auto-Encoding Variational Bayes元論文2. A. Pytorch implementation of β-VAE. The default parameters for CelebA-HQ faces at 256x256 and 1024x1024 resolutions are provided in the file 'run_256. Pytorchで… A Variational Autoencoder (VAE) implemented in PyTorch - ethanluoyc/pytorch-vae Note: The default dataset is CelebA. [1] Dai, B. , 2013) Vector Quantized Variational AutoEncoder (VQ-VAE, A. The NVAE is pretrained before training the energy network, and please refer to NVAE's implementation for more details about constructing and Example of vanilla VAE for face image generation at resolution 128x128 using pytorch. I used the CelebA Dataset for training, with 182637 training images and 19962 testing images. Its goal is to learn The Variational Autoencoder is a generative model that learns a probabilistic mapping between input data and a latent space. Run PyTorch locally or get started quickly with one of the supported cloud platforms. nn as nn import torch. Drop The model architecture in this code is based on the conventional VQ-VAE framework outlined in the original VQ-VAE paper (van den Oord et al. ImageNet) where I can extract latent features from. To associate your repository with the vae-pytorch topic, Note: The default dataset is CelebA. # Root directory for dataset dataroot = "data/celeba" # Number of workers for dataloader workers = 2 # Batch size during training batch_size = 128 # Spatial size of training images. Contribute to menzHSE/torch-vae development by creating an account on GitHub. utils import torch. , 2017), with reference to the VQ-VAE-2 implementations available here, here, and here. All 99 Jupyter Notebook 52 Python 44 MATLAB 1 Svelte A Variational Autoencoder in PyTorch for the CelebA Dataset. backward(), it will perform the simplest form of back propagation. Diagnosing and enhancing VAE models. 7 anaconda # activate the environment source activate multimodal # install the pytorch conda install pytorch torchvision -c pytorch pip install tqdm Note: The default dataset is CelebA. All the models are trained on the CelebA dataset for consistency and comparison. The model implementations can be found in the src/models directory. It is trained to encode input data into a distribution and decode samples from that distribution back into the input space. Code has been tested in all of the following environments: Both Windows and Linux, with Intel CPUs and Nvidia GPUs Vanilla Variational Autoencoder implementation in PyTorch. Contribute to anotherras/VAE development by creating an account on GitHub. AntixK gumbel-softmax celeba-dataset wae variational pytorch-vae topic page so Jan 27, 2021 · この画像をモデルに通してみます。データを、PyTorchのモデルが入力画像に要求する（バッチ、チャネル、縦、横）という次元に合わせるために、np. - Victarry/Image-Generation-models Apr 4, 2021 · Hi and welcome back. See the code here: Apr 9, 2024 · Note: The default dataset is CelebA. Out of the box, it works on 64x64 3-channel input, but can easily be changed to 32x32 and/or n-channel input. - AndrewZhuZJU/Pytorch_GAN_CelebA To train the model on CelebA, run python train_on_celeba_H. Topics deep-neural-networks deep-learning pytorch autoencoder vae deeplearning faces celeba variational-autoencoder celeba-dataset Dec 22, 2021 · Note: The default dataset is CelebA. Oord et. Sep 8, 2024 · 1. For Places2 and ImageNet, we randomly crop a 256x256; Run python train_vqvae. """ Sample 100 latent codes from normal distribution and select the label of which image you want to generate, and then concat them to input into trained decoder: A collection of Variational AutoEncoders (VAEs) implemented in PyTorch with focus on reproducibility. To train VAE models, cd . Training and Inference on Unconditional Latent Diffusion Models Training a Class Conditional Latent Diffusion Model Training a Text Conditioned Latent Diffusion Model Training a Semantic Mask Conditioned Latent Diffusion Model Any Combination of the above three conditioning For autoencoder I provide Aug 21, 2021 · All 38 Jupyter Notebook 38 Python 38 MATLAB 1 Svelte 1 Super-Resolution VAE in PyTorch. The result is 100 different images that only differ by one dimension from the original image. Intro to PyTorch - YouTube Series A Collection of Variational Autoencoders (VAE) in PyTorch. py. In VAE, follows a standard or unit Normal distribution ( and All 39 Python 25 Jupyter Notebook 13. Apr 26, 2021 · To estimate a distribution, we need to assume that data comes from a specific distribution like Gaussian, Bernoulli, etc. yaml> data_params: data_path: "<path to the celebA dataset>" train_batch_size: 64 # Better to have a PyTorch VAE **Update 22/12/2021:** Added support for PyTorch Lightning 1. deep-learning reproducible-research pytorch mnist chairs-dataset vae representation-learning unsupervised-learning beta-vae celeba variational-autoencoder disentanglement dsprites fashion-mnist disentangled-representations factor-vae beta-tcvae Convolutional Variational Autoencoders in PyTorch. al. Modify vqvae_network_dir argument in train_structure_generator. yaml for generating reconstructions and saving latents with right config file. vae-pytorch topic, visit your Here we try to visualize the representations learned by individual layers. VAEBM trains an energy network to refine the data distribution learned by an NVAE, where the enery network and the VAE jointly define an Energy-based model. Intro to PyTorch - YouTube Series Currently two models are supported, a simple Variational Autoencoder and a Disentangled version (beta-VAE). Apr 15, 2018 · Hello my friends. Utility Functions (to visualize images & create animation), and architecture is inherited from the PyTorch Example on DCGAN InfoMax-VAE pytorch implementation. If the library helped your research, consider citing the corresponding submission of the NeurIPS 2019 Disentanglement important note 2: For all VAE-based models (VAE, BetaVAE, IWAE, HVAE, VAMP, RHVAE), both the encoder and decoder must return a ModelOutput instance. nn. Pytorch Implementation of Disentanglement algorithms for Variational Autoencoders. conda create -n multimodal python=2. py -c configs/<config-file-name. Explore the power of Conditional Variational Autoencoders (CVAEs) through this implementation trained on the MNIST dataset to generate handwritten digit images based on class labels. A variational Autoencoder (VAE) to generate human faces based on the CelebA dataset. Pytorch implementation of Generative Adversarial Networks (GAN) [1] and Deep Convolutional Generative Adversarial Networks (DCGAN) [2] for MNIST [3] and CelebA [4] datasets. 9, tqdm, compressai , timm>=0. If you skipped the earlier sections, recall that we are now going to implement the following VAE loss: This repo contains training code for two different VAEs implemented with Pytorch. I just find a possible solution to share with you. Since this implmentation uses sacred , users can specified the condictions for training. zip and put in data directory like below └── data └── img_align_celeba. If you want to train using cropped CelebA dataset, you have to change isCrop = False to isCrop = True. In International Conference on Learning Representations, 2019. sh CelebA then data directory structure will be like below Jun 5, 2020 · Does anyone know a pre-trained variational autoencoder (VAE) or a VAE-GAN that's trained on natural images? I have been searching for a variational autoencoder that is trained on natural images (e. Note: The default dataset is CelebA. Authors had applied VQ-VAE for various tasks, but this repo is a slight modification of yunjey's VAE-GAN(CelebA dataset) to replace VAE with VQ-VAE. - chrisway613/VAEs. Sep 14, 2021 • Chanseok Kang • 14 min read Variational Autoencoder implemented with PyTorch, Trained over CelebA Dataset - bhpfelix/Variational-Autoencoder-PyTorch Hereby we present plain VAE and modified VAE model, both of which are trained on celebA dataset to synthesize facial images. # first download img_align_celeba. deep-learning reproducible-research pytorch mnist chairs-dataset vae representation-learning unsupervised-learning beta-vae celeba variational-autoencoder disentanglement dsprites fashion-mnist disentangled-representations factor-vae beta-tcvae Python, pytorch>=1. Intro to PyTorch - YouTube Series Note: The default dataset is CelebA. We acknowledge and appreciate the sharing of the PyTorch implementation of VQ-VAE available at this link, which serves as the baseline code for our work. The training process optimizes both the reconstruction of the original images and the properties of the latent space, leveraging the Kullback-Leibler divergence. Python 100. The code for the core VAE architecture is from this excellent repository. Now that you understand the intuition behind the approach and math, let’s code up the VAE in PyTorch. All images will be resized to this # size using a transformer. The Structure of this model follows the one described in David Foster's 'Generative Deep Learning' Ch. vae celeba-dataset cifar-10 celeba-hq vae-pytorch conditional Aug 31, 2024 · 涵盖了分类、目标检测和关键点检测等数据。本篇博客将详细介绍CelebA数据集的下载和可视化。CelebA数据集并非一直不变，作者可能会根据需要添加一些新的数据。_celeba数据集详解celeba数据集及基于python实现的下载、读取、解析和可视化 Jan 1, 2021 · Try the following: from torchvision. - Bex0n/CelebA-VAE A PyTorch implementation of the standard Variational Autoencoder (VAE). unsupervised-learning beta-vae celeba variational-autoencoder PyTorch VAE. This repository contains code for creating and training a variational auto encoder (VAE) using PyTorch Lightning. The columns of the grid correspond to the datasets (besides FashionMNIST), the rows correspond to the models (in order: Standard VAE, β-VAE H, β-VAE B, FactorVAE, β-TCVAE): Personal Pytorch Implementations of Variational Auto-Encoders - Galaxies99/VAE-pytorch Variational Autoencoder (VAE) with perception loss implementation in pytorch - GitHub - blustink/Resnet-VAE: Variational Autoencoder (VAE) with perception loss implementation in pytorch May 14, 2020 · Below is an implementation of an autoencoder written in PyTorch. This implementation use CelebA dataset, Python 100. To associate your repository with the vae-pytorch topic, This will use RNG seed 140 to first generate a random tensor of size 100. For this implementation, I’ll use PyTorch Lightning which will keep the code short but still scalable. A Variational Autoencoder based on the ResNet18-architecture, implemented in PyTorch. CenterCrop For training autoencoder run python -m tools. - xuwangyin/VAE-CelebA128-pytorch Conditional Variational Autoencoder(CVAE)1是Variational Autoencoder(VAE)2的扩展，在VAE中没有办法对生成的数据加以限制，所以如果在VAE中想生成特定的数据是办不到的。比如在mnist手写数字中，我们想生成特定的数字2，VAE就无能为力了。因此 Pytorch Implementation for paper: IntroVAE: Introspective Variational Autoencoders for Photographic Image Synthesis - dragen1860/IntroVAE-Pytorch Noted that setting num_vae nonzero means pretraining the model in the standard VAE manner, which may helps improve the training stablitity and convergency. You can change IMAGE_SIZE, LATENT_DIM, and CELEB_PATH. 5. Mar 21, 2022 · A collection of Variational AutoEncoders (VAEs) implemented in pytorch with focus on reproducibility. Sort options. sh' and 'run_1024. >>> a = torch Sep 8, 2021 · Thanks for contributing an answer to Stack Overflow! Please be sure to answer the question. py: Class VAE + some definitions. py based on the path of pre-trained VQ-VAE. Most stars kundan2510 / vae_celeba Star 8. However, there has been many issues with downloading the dataset from google drive (owing to some file structure changes). Sort: Most stars. A collection of Variational AutoEncoders (VAEs) implemented in pytorch with focus on reproducibility. These models were developed using PyTorch Lightning. you can download MNIST All 6 Jupyter Notebook 3 Python 3. We can get a rough idea of what's going on at layer i as follows:. VAE_celeba. Since some users prefer using Sequential Modules, so this example uses Sequential Module. So, the recommendation is to download the file from google drive directly and extract to the path of your choice. distributions import torchvision import numpy as np import matplotlib. This is a repro of Vector Quantisation VAE from Deepmind. Code (beta-VAE) implementation in PyTorch Python; ChunyuanLI / Optimus A Variational Autoencoder in PyTorch for the CelebA Dataset. Mar 3, 2024 · Now that we have an understanding of the VAE architecture and objective, let’s implement a modern VAE in PyTorch. PyTorch VAE Implementation# Our VAE implementation is broken into an Output template (in the form of a dataclass), and a VAE class that extends the nn. - ThomasMrY/RF-VAE VAE with residual blocks for AI dressroom project (team ield) - sehyeona/ResVAE-pytorch Datasets, Transforms and Models specific to Computer Vision - pytorch/vision All 42 Python 42 Jupyter Notebook 39 HTML 4 Kotlin 1 Lua 1. - AntixK/PyTorch-VAE """VAE encoder and decoder implementation in pytorch. Compose([ transforms. Contribute to lyeoni/pytorch-mnist-VAE development by creating an account on GitHub. 3. train_vae --config config/celebhq. - juanfacabian/VAE. Instead of a vector in latent space, the encoder of a VAE outputs parameters of a predefined Oct 23, 2023 · In this section, the Variational Autoencoder (VAE) is trained on the CelebA dataset using PyTorch. In Todays tutorial we will talk about the famous AlexNet neural network and how you can implement it in Python using PyTorch. datasets import ImageFolder from torch. image_size = 64 # Number of channels in the training images. We have used ConvResNets from these repositories, which consist of convolutional layers, transpose convolutional A Collection of Variational Autoencoders (VAE) in PyTorch. py and train_texture_generator. (VAE) in PyTorch. The CelebA dataset is used here for training. py --dataset mnist --seed 1 --optim sgd --lr 1e-4 --objective H --model MNIST --batch_size 64 --z_dim 128 --max_iter 1. The Variational Autoencoder is a Generative Model. 1). rcParams [ 'figure. All 846 Python 468 Jupyter Notebook 315 VAE in Pytorch and Tensorflow. This is the Programming Assignment of lecture "Probabilistic Deep Learning with Tensorflow 2" from Imperial College London. Kingma et. Sep 12, 2024 · Python; ChunyuanLI / Optimus A Variational Autoencoder in PyTorch for the CelebA Dataset. Efficient discrete representation learning for various data types. Trained model can be found in /checkpoints . image_size = 64 # batch size batch_size = 10 transform=transforms. Resize(image_size), transforms. The aim of this project is to provide a quick and simple working example for many of the cool VAE models out there. Update 06/07/2024: Forked from original repo author, beginning to add a genreral understanding of VAE as well as quasi line-wise explanation on the code. We’ll use the MNIST dataset for validation. The input dimension is 784 which is the flattened dimension of MNIST images (28×28). doyb yvfsbnj nkgi zztid uyzvar sawms scnl ucnohghm ydiks ikvx