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github hwalsuklee tensorflow generative model collections collection of generative models in tensorflow github skip to content navigation menu toggle navigation sign in appearance settings platform ai code creation github copilot write better code with ai github copilot app direct agents from issue to merge mcp registry new integrate external tools developer workflows actions automate any workflow codespaces instant dev environments issues plan and track work code review manage code changes code quality enforce quality at merge application security github advanced security find and fix vulnerabilities code security secure your code as you build secret protection stop leaks before they start explore why github documentation blog changelog marketplace view all features solutions by company size enterprises small and medium teams startups nonprofits by use case app modernization devsecops devops ci cd view all use cases by industry healthcare financial services manufacturing government 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contacted cancel submit feedback saved searches use saved searches to filter your results more quickly name query to see all available qualifiers see our documentation cancel create saved search sign in sign up appearance settings resetting focus you signed in with another tab or window reload to refresh your session you signed out in another tab or window reload to refresh your session you switched accounts on another tab or window reload to refresh your session dismiss alert message hwalsuklee tensorflow generative model collections public notifications you must be signed in to change notification settings fork 837 star 3 9k code issues 19 pull requests 5 actions projects security and quality 0 insights additional navigation options code issues pull requests actions projects security and quality insights hwalsuklee tensorflow generative model collections master branches tags go to file code open more actions menu folders and files name name last commit message last commit date latest commit history 76 commits 76 commits assets assets acgan py acgan py began py began py cgan py cgan py cvae py cvae py dragan py dragan py ebgan py ebgan py gan py gan py license license lsgan py lsgan py readme md readme md vae py vae py wgan py wgan py wgan_gp py wgan_gp py infogan py infogan py main py main py ops py ops py prior_factory py prior_factory py utils py utils py view all files repository files navigation readme apache 2 0 license more items tensorflow generative model collections tensorflow implementation of various gans and vaes related repositories pytorch version pytorch version of this repository is availabel at https github com znxlwm pytorch generative model collections are gans created equal a large scale study paper https github com google compare_gan is the code that was used in the paper it provides is fid and rich experimental results for all gan variants generative adversarial networks gans lists name paper link value function gan arxiv lsgan arxiv wgan arxiv wgan_gp arxiv dragan arxiv cgan arxiv infogan arxiv acgan arxiv ebgan arxiv began arxiv variants of gan structure results for mnist network architecture of generator and discriminator is the exaclty sames as in infogan paper for fair comparison of core ideas in all gan variants all implementations for network architecture are kept same except ebgan and began small modification is made for ebgan began since those adopt auto encoder strucutre for discriminator but i tried to keep the capacity of discirminator the following results can be reproduced with command python main py dataset mnist gan_type type epoch 25 batch_size 64 random generation all results are randomly sampled name epoch 2 epoch 10 epoch 25 gan lsgan wgan wgan_gp dragan ebgan began conditional generation each row has the same noise vector and each column has the same label condition name epoch 1 epoch 10 epoch 25 cgan acgan infogan infogan manipulating two continous codes results for fashion mnist comments on network architecture in mnist are also applied to here fashion mnist is a recently proposed dataset consisting of a training set of 60 000 examples and a test set of 10 000 examples each example is a 28x28 grayscale image associated with a label from 10 classes t shirt top trouser pullover dress coat sandal shirt sneaker bag ankle boot the following results can be reproduced with command python main py dataset fashion mnist gan_type type epoch 40 batch_size 64 random generation all results are randomly sampled name epoch 1 epoch 20 epoch 40 gan lsgan wgan wgan_gp dragan ebgan began conditional generation each row has the same noise vector and each column has the same label condition name epoch 1 epoch 20 epoch 40 cgan acgan infogan without hyper parameter tuning from mnist version acgan infogan does not work well as compared with cgan acgan tends to fall into mode collapse infogan tends to ignore noise vector it results in that various style within the same class can not be represented infogan manipulating two continous codes some results for celeba to be added variational auto encoders vaes lists name paper link loss function vae arxiv cvae arxiv dvae arxiv to be added aae arxiv to be added variants of vae structure results for mnist network architecture of decoder generator and encoder discriminator is the exaclty sames as in infogan paper the number of output nodes in encoder is different 2x z_dim for vae 1 for gan the following results can be reproduced with command python main py dataset mnist gan_type type epoch 25 batch_size 64 random generation all results are randomly sampled name epoch 1 epoch 10 epoch 25 vae gan results of gan is also given to compare images generated from vae and gan the main difference vae generates smooth and blurry images otherwise gan generates sharp and artifact images is cleary observed from the results conditional generation each row has the same noise vector and each column has the same label condition name epoch 1 epoch 10 epoch 25 cvae cgan results of cgan is also given to compare images generated from cvae and cgan learned manifold the following results can be reproduced with command python main py dataset mnist gan_type vae epoch 25 batch_size 64 dim_z 2 please notice that dimension of noise vector z is 2 name epoch 1 epoch 10 epoch 25 vae results for fashion mnist comments on network architecture in mnist are also applied to here the following results can be reproduced with command python main py dataset fashion mnist gan_type type epoch 40 batch_size 64 random generation all results are randomly sampled name epoch 1 epoch 20 epoch 40 vae gan results of gan is also given to compare images generated from vae and gan conditional generation each row has the same noise vector and each column has the same label condition name epoch 1 epoch 20 epoch 40 cvae cgan results of cgan is also given to compare images generated from cvae and cgan learned manifold the following results can be reproduced with command python main py dataset fashion mnist gan_type vae epoch 25 batch_size 64 dim_z 2 please notice that dimension of noise vector z is 2 name epoch 1 epoch 10 epoch 25 vae results for celeba to be added folder structure the following shows basic folder structure main py gateway data mnist mnist data not included in this repo t10k images idx3 ubyte gz t10k labels idx1 ubyte gz train images idx3 ubyte gz train labels idx1 ubyte gz fashion mnist fashion mnist data not included in this repo t10k images idx3 ubyte gz t10k labels idx1 ubyte gz train images idx3 ubyte gz train labels idx1 ubyte gz gan py vanilla gan ops py some operations on layer utils py utils logs log files for tensorboard to be saved here checkpoint model files to be saved here acknowledgements this implementation has been based on this repository and tested with tensorflow over ver1 0 on windows 10 and ubuntu 14 04 about collection of generative models in tensorflow topics tensorflow gan mnist infogan generative model vae ebgan generative adversarial networks wgan cvae lsgan variational autoencoder began cgan wgan gp generative models dragan acgan fashion mnist improved wgan resources readme license apache 2 0 license uh oh there was an error while loading please reload this page activity stars 3 9k stars watchers 155 watching forks 837 forks report repository releases no releases published packages 0 uh oh there was an error while loading please reload this page uh oh there was an error while loading please reload this page contributors uh oh there was an error while loading please reload this page languages python 100 0 footer 2026 github inc footer navigation terms privacy security status community docs contact manage cookies do not share my personal information you can t perform that action at this time