DiagonalGAN

Official Pytorch Implementation of "Diagonal Attention and Style-based GAN for Content-Style Disentanglement in Image Generation and Translation" (ICCV 2021)

Gihyun Kwon, Jong Chul Ye

Arxiv : link CVF : link

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Abstract

One of the important research topics in image generative models is to disentangle the spatial contents and styles for their separate control. Although StyleGAN can generate content feature vectors from random noises, the resulting spatial content control is primarily intended for minor spatial variations, and the disentanglement of global content and styles is by no means complete. Inspired by a mathematical understanding of normalization and attention, here we present a novel hierarchical adaptive Diagonal spatial ATtention (DAT) layers to separately manipulate the spatial contents from styles in a hierarchical manner. Using DAT and AdaIN, our method enables coarse-to-fine level disentanglement of spatial contents and styles. In addition, our generator can be easily integrated into the GAN inversion framework so that the content and style of translated images from multi-domain image translation tasks can be flexibly controlled. By using various datasets, we confirm that the proposed method not only outperforms the existing models in disentanglement scores, but also provides more flexible control over spatial features in the generated images.

Citation

@inproceedings{kwon2021diagonal,
  title={Diagonal attention and style-based GAN for content-style disentanglement in image generation and translation},
  author={Kwon, Gihyun and Ye, Jong Chul},
  booktitle={Proceedings of the IEEE/CVF International Conference on Computer Vision},
  pages={13980--13989},
  year={2021}
}

Environment Settings

Python 3.6.7 +

Pytorch 1.5.0 +

Dataset

For faster training, we recommend .jpg file format.

Download Link: CelebA-HQ / AFHQ

Unzip the files and put the folder into the data directory (./data/Celeb/data1024 , ./data/afhq)

To process the data for multidomain Diagonal GAN, run

./data/Celeb/Celeb_proc.py

After download the CelebA-HQ dataset to save males / females images in different folders.

We randomly selected 1000 images as validation set for each domain (1000 males / 1000 females).

Save validation files into ./data/Celeb/val/males , ./data/Celeb/val/females

Train

Train Basic Diagonal GAN

For full-resolution CelebA-HQ training,

python train.py --datapath ./data/Celeb/data1024 --sched --max_size 1024 --loss r1

For full-resolution AFHQ training,

python train.py --datapath ./data/afhq --sched --max_size 512 --loss r1

Train Multidomain Diagonal GAN

For training multidomain (Males/ Females) models, run

python train_multidomain.py --datapath ./data/Celeb/mult --sched --max_size 256

Train IDInvert Encoders on pre-trained Multidomain Diagonal GAN

For training IDInvert on pre-trained model,

python train_idinvert.py --ckpt $MODEL_PATH$

or you can download the pre-trained Multidomain model.

Save the model in ./checkpoint/train_mult/CelebAHQ_mult.model

and set $MODEL_PATH$ as above.

Additional latent code optimization ( for inference )

To further optimize the latent codes,

python train_idinvert_opt.py --ckpt $MODEL_PATH$ --enc_ckpt $ENC_MODEL_PATH$

MODEL_PATH is pre-trained multidomain model directory, and

ENC_MODEL_PATH is IDInvert encoder model directory.

You can download the pre-trained IDInvert encoder models.

We also provide optimized latent codes.

Pre-trained model Download

Pre-trained Diagonal GAN on 1024x1024 CelebA-HQ : Link save to ./checkpoint/train_basic

Pre-trained Diagonal GAN on 512x512 AFHQ : Link save to ./checkpoint/train_basic

Pre-trained Multidomain Diagonal GAN on 256x256 CelebA-HQ : Link save to ./checkpoint/train_mult

Pre-trained IDInvert Encoders on 256x256 CelebA-HQ : Link save to ./checkpoint/train_idinvert

Optimized latent codes : Link save to ./codes

Generate Images

To generate the images from the pre-trained model,

python generate.py --mode $MODE$ --domain $DOM$ --target_layer $TARGET$

for $MODE$ , there is three choices (sample , mixing, interpolation).

using 'sample' just sample random samples,

for 'mixing', generate images with random code on target layer $TARGET$

for 'interpolate', generate with random interpolation on target layer $TARGET$

also, we can choose style or content with setting $DOM$ with 'style' or 'content'

Generate Images on Inverted model

To generate the images from the pre-trained IDInvert,

python generate_idinvert.py --mode $MODE$ --domain $DOM$ --target_layer $TARGET$

for $MODE$ , there is three choices (sample , mixing, encode).