biggan

BigGAN

Large Scale GAN Training for High Fidelity Natural Image Synthesis

Abstract

Despite recent progress in generative image modeling, successfully generating high-resolution, diverse samples from complex datasets such as ImageNet remains an elusive goal. To this end, we train Generative Adversarial Networks at the largest scale yet attempted, and study the instabilities specific to such scale. We find that applying orthogonal regularization to the generator renders it amenable to a simple "truncation trick," allowing fine control over the trade-off between sample fidelity and variety by reducing the variance of the Generator's input. Our modifications lead to models which set the new state of the art in class-conditional image synthesis. When trained on ImageNet at 128x128 resolution, our models (BigGANs) achieve an Inception Score (IS) of 166.5 and Frechet Inception Distance (FID) of 7.4, improving over the previous best IS of 52.52 and FID of 18.6.

Introduction

The BigGAN/BigGAN-Deep is a conditional generation model that can generate both high-resolution and high-quality images by scaling up the batch size and the number of model parameters.

We have finished training BigGAN in Cifar10 (32x32) and are aligning training performance in ImageNet1k (128x128). Some sampled results are shown below for your reference.

Results from our BigGAN trained in CIFAR10

Results from our BigGAN trained in ImageNet

Evaluation of our trained BigGAN.

Models	Dataset	FID (Iter)	IS (Iter)	Config	Download
BigGAN 32x32	CIFAR10	9.78(390000)	8.70(390000)	config	model|log
BigGAN 128x128 Best FID	ImageNet1k	8.69(1232000)	101.15(1232000)	config	model|log
BigGAN 128x128 Best IS	ImageNet1k	13.51(1328000)	129.07(1328000)	config	model|log
Note: BigGAN-Deep trained on ImageNet1k will come later.

Converted weights

Since we haven't finished training our models, we provide you with several pre-trained weights which have been evaluated. Here, we refer to BigGAN-PyTorch and pytorch-pretrained-BigGAN.

Evaluation results and download links are provided below.

Models	Dataset	FID	IS	Config	Download	Original Download link
BigGAN 128x128	ImageNet1k	10.1414	96.728	config	model	link
BigGAN-Deep 128x128	ImageNet1k	5.9471	107.161	config	model	link
BigGAN-Deep 256x256	ImageNet1k	11.3151	135.107	config	model	link
BigGAN-Deep 512x512	ImageNet1k	16.8728	124.368	config	model	link

Sampling results are shown below.

Results from our BigGAN-Deep with Pre-trained weights in ImageNet 128x128 with truncation factor 0.4

Results from our BigGAN-Deep with Pre-trained weights in ImageNet 256x256 with truncation factor 0.4

Results from our BigGAN-Deep with Pre-trained weights in ImageNet 512x512 truncation factor 0.4

Sampling with truncation trick above can be performed by command below.

python demo/conditional_demo.py CONFIG_PATH CKPT_PATH --sample-cfg truncation=0.4 # set truncation value as you want

For converted weights, we provide model configs under configs/_base_/models listed as follows:

# biggan_128x128_cvt_BigGAN-PyTorch_rgb.py
# biggan-deep_128x128_cvt_hugging-face_rgb.py
# biggan-deep_256x256_cvt_hugging-face_rgb.py
# biggan-deep_512x512_cvt_hugging-face_rgb.py

Interpolation

To perform image Interpolation on BigGAN(or other conditional models), run

python apps/conditional_interpolate.py CONFIG_PATH  CKPT_PATH  --samples-path SAMPLES_PATH

Image interpolating Results of our BigGAN-Deep

To perform image Interpolation on BigGAN with fixed noise, run

python apps/conditional_interpolate.py CONFIG_PATH  CKPT_PATH  --samples-path SAMPLES_PATH --fix-z

Image interpolating Results of our BigGAN-Deep with fixed noise

To perform image Interpolation on BigGAN with fixed label, run

python apps/conditional_interpolate.py CONFIG_PATH  CKPT_PATH  --samples-path SAMPLES_PATH --fix-y

Image interpolating Results of our BigGAN-Deep with fixed label

Citation

@inproceedings{
    brock2018large,
    title={Large Scale {GAN} Training for High Fidelity Natural Image Synthesis},
    author={Andrew Brock and Jeff Donahue and Karen Simonyan},
    booktitle={International Conference on Learning Representations},
    year={2019},
    url={https://openreview.net/forum?id=B1xsqj09Fm},
}