Awesome-GANs with Tensorflow

Tensorflow implementation of GANs (Generative Adversarial Networks)

WIP : This repo is about to be refactored & supporting `tf 2.x`.

Maybe some of the codes wouldn't work on master branch, because i'm just working on the branch.

Environments

Because of the image and model size, (especially BEGAN, SRGAN, StarGAN, ... using high resolution images as input), if you want to train them comfortably, you need a GPU which has more than 8GB.

But, of course, the most of the implementations use MNIST or CIFAR-10, 100 DataSets. Meaning that we can handle it with EVEN lower spec GPU than 'The Preferred' :).

Usage

Now on refactoring... All GAN training script can be run module-wisely like below. (WIP)

Install dependencies

You can also use conda, virtualenv environments.

$ python3 -m pip install -r requirements.txt

Train GANs

Before running the model, make sure that

downloading the dataset like CelebA, MNIST, etc what you want
In awesome_gans/config.py, there are several configurations, customize with your flavor!
running the model like below

$ python3 -m awesome_gans.acgan

DataSets

Supporting datasets are ... (code is in /awesome_gans/datasets.py)

MNIST / ~~Fashion MNIST~~
CIFAR10 / 100
CelebA/CelebA-HQ
Pix2Pix
DIV2K
(more DataSets will be added soon!)

Repo Tree

│
├── awesome_gans (source codes & eplainations & results & models) 
│        │
│        ├── acgan
│        │    ├──gen_img (generated images)
│        │    │     ├── train_xxx.png
│        │    │     └── train_xxx.png
│        │    ├── model  (pre-trained model file)
│        │    │     └── model.txt (google-drive link)
│        │    ├── __init__.py
│        │    ├── __main__.py
│        │    ├── model.py (gan model)
│        │    ├── train.py (gan trainer)
│        │    ├── gan_tb.png   (tensorboard loss plot)
│        │    └── readme.md    (results & explainations)
│        ├── config.py         (configurations)
│        ├── modules.py        (networks & operations)
│        ├── utils.py          (auxiliary utils)
│        ├── image_utils.py    (image processing)
│        └── datasets.py       (dataset loader)
├── CONTRIBUTING.md
├── Makefile   (for linting the codes)
├── LICENSE
├── README.md  (Usage & GAN paper list-up)
└── requirements.txt

Pre-Trained Models

Here's a google drive link. You can download pre-trained models from here

Papers & Codes

Here's the list-up for tons of GAN papers. all papers are sorted by alphabetic order.

Start

Here's the beginning of the GAN.

Name	Summary	Paper	Code
GAN	Generative Adversarial Networks	[arXiv]	[code]

Theory & Concept

Here for the theories & concepts of the GAN.

Name	Summary	Paper	Code
ACGAN	Auxiliary Classifier Generative Adversarial Networks	[arXiv]	[code]
AdaGAN	Boosting Generative Models	[arXiv]	[~~code~~]
bCR	Improved Consistency Regularization for GANs	[arXiv]	[~~code~~]
BEGAN	Boundary Equilibrium Generative Adversarial Networks	[arXiv]	[code]
BGAN	Boundary-Seeking Generative Adversarial Networks	[arXiv]	[code]
BigGAN	Large Scale GAN Training for High Fidelity Natural Image Synthesis	[arXiv]	[~~code~~]
CGAN	Conditional Generative Adversarial Networks	[arXiv]	[code]
CoGAN	Coupled Generative Adversarial Networks	[arXiv]	[code]
DCGAN	Deep Convolutional Generative Adversarial Networks	[arXiv]	[code]
DRAGAN	On Convergence and Stability of Generative Adversarial Networks	[arXiv]	[code]
EBGAN	Energy-based Generative Adversarial Networks	[arXiv]	[code]
f-GAN	Training Generative Neural Samplers using Variational Divergence Minimization	[arXiv]	[code]
GP-GAN	Towards Realistic High-Resolution Image Blending	[arXiv]	[~~code~~]
Softmax GAN	Generative Adversarial Networks with Softmax	[arXiv]	[code]
GAP	Generative Adversarial Parallelization	[arXiv]	[~~code~~]
GEGAN	Generalization and Equilibrium in Generative Adversarial Networks	[arXiv]	[~~code~~]
G-GAN	Geometric GAN	[arXiv]	[~~code~~]
InfoGAN	Interpretable Representation Learning by Information Maximizing Generative Adversarial Networks	[arXiv]	[code]
LAPGAN	Laplacian Pyramid Generative Adversarial Networks	[arXiv]	[code]
LSGAN	Loss-Sensitive Generative Adversarial Networks	[arXiv]	[code]
MAGAN	Margin Adaptation for Generative Adversarial Networks	[arXiv]	[code]
MRGAN	Mode Regularized Generative Adversarial Networks	[arXiv]	[code]
MSGGAN	Multi-Scale Gradients for Generative Adversarial Networks	[arXiv]	[~~code~~]
PGGAN	Progressive Growing of GANs for Improved Quality, Stability, and Variation	[arXiv]	[~~code~~]
RaGAN	The relativistic discriminator: a key element missing from standard GAN	[arXiv]	[~~code~~]
SeAtGAN	Self-Attention Generative Adversarial Networks	[arXiv]	[code]
SphereGAN	Sphere Generative Adversarial Network Based on Geometric Moment Matching	[CVPR2019]	[~~code~~]
SGAN	Stacked Generative Adversarial Networks	[arXiv]	[~~code~~]
SGAN++	Realistic Image Synthesis with Stacked Generative Adversarial Networks	[arXiv]	[~~code~~]
SinGAN	Learning a Generative Model from a Single Natural Image	[arXiv]	[~~code~~]
StableGAN	Stabilizing Adversarial Nets With Prediction Methods	[arXiv]	[~~code~~]
StyleGAN	A Style-Based Generator Architecture for Generative Adversarial Networks	[arXiv]	[~~code~~]
StyleGAN2	Analyzing and Improving the Image Quality of StyleGAN	[arXiv]	[~~code~~]
StyleGAN2 ADA	StyleGAN2 with adaptive discriminator augmentation	[arXiv]	[~~code~~]
TripleGAN	Triple Generative Adversarial Networks	[arXiv]	[~~code~~]
UGAN	Unrolled Generative Adversarial Networks	[arXiv]	[~~code~~]
WGAN	Wasserstein Generative Adversarial Networks	[arXiv]	[code]
WGAN-GP	Improved Training of Wasserstein Generative Adversarial Networks	[arXiv]	[code]

Applied Vision

Here for the GAN applications on Vision domain, like image-to-image translation, image in-painting, single image super resolution , etc.

Name	Summary	Paper	Code
3D GAN	3D Generative Adversarial Networks	[MIT]	[~~code~~]
CycleGAN	Unpaired img2img translation using Cycle-consistent Adversarial Networks	[arXiv]	[code]
DAGAN	Instance-level Image Translation by Deep Attention Generative Adversarial Networks	[arXiv]	[~~code~~]
DeblurGAN	Blind Motion Deblurring Using Conditional Adversarial Networks	[arXiv]	[~~code~~]
DualGAN	Unsupervised Dual Learning for Image-to-Image Translation	[arXiv]	[~~code~~]
EdgeConnect	Generative Image Inpainting with Adversarial Edge Learning	[arXiv]	[~~code~~]
ESRGAN	Enhanced Super-Resolution Generative Adversarial Networks	[arXiv]	[~~code~~]
FUNIT	Few-Shot Unsupervised Image-to-Image Translation	[arXiv]	[~~code~~]
CA & GA	Generative Image Inpainting w/ Contextual Attention & Gated Convolution	[CVPR2018], [ICCV2019]	[~~code~~]
HiFaceGAN	Face Renovation via Collaborative Suppression and Replenishment	[arXiv]	[~~code~~]
MUNIT	Multimodal Unsupervised Image-to-Image Translation	[arXiv]	[~~code~~]
SpAtGAN	Generative Adversarial Network with Spatial Attention for Face Attribute Editing	[ECCV2018]	[~~code~~]
SalGAN	Visual Saliency Prediction Generative Adversarial Networks	[arXiv]	[~~code~~]
SRGAN	Photo-Realistic Single Image Super-Resolution Using a Generative Adversarial Network	[arXiv]	[code]
StarGAN	Unified Generative Adversarial Networks for Multi-Domain Image-to-Image Translation	[arXiv]	[code]
StarGAN V2	Diverse Image Synthesis for Multiple Domains	[arXiv]	[~~code~~]
TecoGAN	Learning Temporal Coherence via Self-Supervision for GAN-based Video Generation	[arXiv]	[~~code~~]
TextureGAN	Controlling Deep Image Synthesis with Texture Patches	[arXiv]	[~~code~~]
TUNIT	Rethinking the Truly Unsupervised Image-to-Image Translation	[arXiv]	[~~code~~]
TwinGAN	Cross-Domain Translation fo Human Portraits	[github]	[~~code~~]
UNIT	Unsupervised Image-to-Image Translation Networks	[arXiv]	[~~code~~]
XGAN	Unsupervised Image-to-Image Translation for Many-to-Many Mappings	[arXiv]	[~~code~~]

Applied Audio

Here for the GAN applications on Audio domain, like wave generation, wave to wave translation, etc.

Name	Summary	Paper	Code
AAS	Adversarial Audio Synthesis	[arXiv]	[~~code~~]
BeatGAN	Generating Drum Loops via GANs	[arXiv]	[~~code~~]
GANSynth	Adversarial Neural Audio Synthesis	[arXiv]	[~~code~~]
MuseGAN	Multi-track Sequential GANs for Symbolic Music Generation and Accompaniment	[arXiv]	[~~code~~]
SEGAN	Speech Enhancement Generative Adversarial Network	[arXiv]	[~~code~~]
StarGAN-VC	Non-parallel many-to-many voice conversion with star generative adversarial networks	[arXiv]	[~~code~~]
TempoGAN	A Temporally Coherent, Volumetric GAN for Super-resolution Fluid Flow	[arXiv]	[~~code~~]
Parallel WaveGAN	A fast waveform generation model based on GAN with multi-resolution spectrogram	[arXiv]	[~~code~~]
WaveGAN	Synthesizing Audio with Generative Adversarial Networks	[arXiv]	[~~code~~]

Applied Others

Here for the GAN applications on other domains, like nlp, tabular, etc.

Name	Summary	Paper	Code
AnoGAN	Unsupervised Anomaly Detection with Generative Adversarial Networks	[arXiv]	[~~code~~]
CipherGAN	Unsupervised Cipher Cracking Using Discrete GANs	[github]	[~~code~~]
DiscoGAN	Discover Cross-Domain Generative Adversarial Networks	[arXiv]	[~~code~~]
eCommerceGAN	A Generative Adversarial Network for E-commerce	[arXiv]	[~~code~~]
PassGAN	A Deep Learning Approach for Password Guessing	[arXiv]	[~~code~~]
SeqGAN	Sequence Generative Adversarial Networks with Policy Gradient	[arXiv]	[~~code~~]
TAC-GAN	Text Conditioned Auxiliary Classifier Generative Adversarial Network	[arXiv]	[~~code~~]

Useful Resources

Here for the useful resources when you try to train and stable a gan model.

Name	Summary	Link
GAN Hacks	a bunch of tips & tricks to train GAN stable	github

To-Do

updating worth a try GAN papers
refactoring the whole codes
supporting tensorflow 2.x
linking to the official implementations, if not, unofficial implementations

Note

Any suggestions and PRs and issues are WELCOME :)

Author

HyeongChan Kim / @kozistr

mo666666/Awesome-GANs