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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},
}