GAN.pth

a pytorch gan framework

Features

• As GAN develop so quickly, keeping up to date matters

• Providing GAN specific features as possible i.e modular,but keep Flexible as possible

  • AdaIn, Spectral normalization, skip connections, resnets
  • Out-of-box Models
  • Trainer:train_generator, train_discriminator
  • GAN Loss
  • Metrics
  • Demo

• Documentation

• Production oriented, Research Friendly

  • distributed training(multiple gpus and multiple machines)
  • easy to deploy on production(performance optimized for mobile platform and server side)

• Custom Datasets & Pretrained Models provided in GANHub

• Easy to extension For Example

  • do config through args file for each model
  • train_gene
  • well structured code(object oriented programming)

• well tested, as close as possible to official sota effect

Inspired by following frameworks

torchgan

PytorchGANZoo

PyTorch-GAN

StarGAN_v2-Tensorflow

Classical Models

StyleGAN/StyleGAN2

StarGAN/StarGAN2

FUNIT

Components

Discriminator

progressive growing (pggan) self-attention

Generator

style-based generator(stylegan)

GAN Loss

Non-Saturating Loss + R1/R2 WGAN+GP

Metrics

IS(Inception Score) FID(Inception Distance) LPIPS PPL

Datasets

face aligner

Utils

video interpolation reporter: tensorboard reporter

Demos

use GAN.pth framework to develop following apps as demos

Demo GANS

• Vanilla GAN
• DCGAN

Faceswap

AI Stylist

AI Portraits

Please access [GANHub]https://github.com/habout632/GANHub) for more demos, datasets, pretrained networks.

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