alenaliu/awesome-gans

Awesome Generative Adversarial Networks with tensorflow

Awesome-GANs with Tensorflow

Tensorflow implementation of GANs(Generative Adversarial Networks)

Environments

Preferred Environment

• OS : Windows 10 / Linux Ubuntu x86-64 ~
• CPU : any (quad core ~)
• GPU : GTX 1060 6GB ~
• RAM : 16GB ~
• Library : TF 1.x with CUDA 9.0~ + cuDNN 7.0~
• Python 3.x

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' :).

Prerequisites

• python 3.x
• tensorflow 1.x
• numpy
• scipy (some features are about to deprecated, it'll be replaced to OpenCV SOON!)
• scikit-image
• opencv-python
• pillow
• h5py
• tqdm
• Internet :)

Usage

Dependency Install

$ sudo python3 -m pip install -r requirements.txt

Training GAN

(Before running train.py, MAKE SURE run after downloading DataSet & changing DataSet's directory in xxx_train.py)
just after it, RUN train.py
$ python3 xxx_train.py

DataSets

Now supporting(?) DataSets are... (code is in /datasets.py)

• MNIST / Fashion MNIST
• CiFar-10 / 100
• CelebA/CelebA-HQ
• pix2pix DataSets
• DIV2K DataSets
• ImageNet DataSets
• UrbanSound8K
• 3DShapeNet DataSet
• (more DataSets will be added soon!)

Repo Tree

│
├── xxGAN
│    ├──gan_img (generated images)
│    │     ├── train_xxx.png
│    │     └── train_xxx.png
│    ├── model  (model)
│    │     └── model.txt (google-drive link for pre-trained model)
│    ├── gan_model.py (gan model)
│    ├── gan_train.py (gan trainer)
│    ├── gan_tb.png   (Tensor-Board result)
│    └── readme.md    (results & explains)
├── tfutil.py         (useful TF util)
├── image_utils.py    (image processing)
└── datasets.py       (DataSet loader)

Pre-Trained Models

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

Papers & Codes

Name	Summary	Paper	Code
3D GAN	3D Generative Adversarial Networks	[MIT]
ACGAN	Auxiliary Classifier Generative Adversarial Networks	[arXiv]	[code]
AdaGAN	Boosting Generative Models	[arXiv]
AnoGAN	Unsupervised Anomaly Detection with Generative Adversarial Networks	[arXiv]	[code]
BeatGAN	Generating Drum Loops via GANs	[arXiv]
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]
CGAN	Conditional Generative Adversarial Networks	[arXiv]	[code]
CipherGAN	Unsupervised Cipher Cracking Using Discrete GANs	[github]
CoGAN	Coupled Generative Adversarial Networks	[arXiv]	[code]
CycleGAN	Unpaired img2img translation using Cycle-consistent Adversarial Networks	[arXiv]	[code]
DAGAN	Instance-level Image Translation by Deep Attention Generative Adversarial Networks	[arXiv]
DCGAN	Deep Convolutional Generative Adversarial Networks	[arXiv]	[code]
DeblurGAN	Blind Motion Deblurring Using Conditional Adversarial Networks	[arXiv]
DiscoGAN	Discover Cross-Domain Generative Adversarial Networks	[arXiv]
DRAGAN	On Convergence and Stability of Generative Adversarial Networks	[arXiv]	[code]
DualGAN	Unsupervised Dual Learning for Image-to-Image Translation	[arXiv]
eCommerceGAN	A Generative Adversarial Network for E-commerce	[arXiv]
EBGAN	Energy-based Generative Adversarial Networks	[arXiv]	[code]
f-GAN	Training Generative Neural Samplers using Variational Divergence Minimization	[arXiv]	[code]
GAN	Generative Adversarial Networks	[arXiv]	[code]
GP-GAN	Towards Realistic High-Resolution Image Blending	[arXiv]
Softmax GAN	Generative Adversarial Networks with Softmax	[arXiv]	[code]
GAP	Generative Adversarial Parallelization	[arXiv]
GEGAN	Generalization and Equilibrium in Generative Adversarial Networks	[arXiv]
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]
PassGAN	A Deep Learning Approach for Password Guessing	[arXiv]
PGGAN	Progressive Growing of GANs for Improved Quality, Stability, and Variation	[arXiv]
SeAtGAN	Self-Attention Generative Adversarial Networks	[arXiv]	[code]
SpAtGAN	Generative Adversarial Network with Spatial Attention for Face Attribute Editing	[ECCV2018]
SalGAN	Visual Saliency Prediction Generative Adversarial Networks	[arXiv]
SEGAN	Speech Enhancement Generative Adversarial Network	[arXiv]
SeqGAN	Sequence Generative Adversarial Networks with Policy Gradient	[arXiv]
SGAN	Stacked Generative Adversarial Networks	[arXiv]	[code]
SGAN++	Realistic Image Synthesis with Stacked Generative Adversarial Networks	[arXiv]	[code]
SRGAN	Photo-Realistic Single Image Super-Resolution Using a Generative Adversarial Network	[arXiv]	[code]
StableGAN	Stabilizing Adversarial Nets With Prediction Methods	[arXiv]
StarGAN	Unified Generative Adversarial Networks for Multi-Domain Image-to-Image Translation	[arXiv]	[code]
TAC-GAN	Text Conditioned Auxiliary Classifier Generative Adversarial Network	[arXiv]
TempoGAN	A Temporally Coherent, Volumetric GAN for Super-resolution Fluid Flow	[arXiv]
TextureGAN	Controlling Deep Image Synthesis with Texture Patches	[arXiv]
TripleGAN	Triple Generative Adversarial Networks	[arXiv]
TwinGAN	Cross-Domain Translation fo Human Portraits	[github]
UGAN	Unrolled Generative Adversarial Networks	[arXiv]
WaveGAN	Synthesizing Audio with Generative Adversarial Networks	[arXiv]
WGAN	Wasserstein Generative Adversarial Networks	[arXiv]	[code]
ImprovedWGAN	Improved Training of Wasserstein Generative Adversarial Networks	[arXiv]	[code]
XGAN	Unsupervised Image-to-Image Translation for Many-to-Many Mappings	[arXiv]

To-Do

1. Implement DeblurGAN
2. Implement 3DGAN - later when DataSet is ready.
3. Implement BeatGAN - later when DataSet is ready.
4. Implement TempoGAN - later when DataSet is ready.
5. Fix PGGAN, SGAN, SGAN++

ETC

Any suggestions and PRs and issues are WELCONE :)

Author

HyeongChan Kim / @kozistr