Awesome

VQ-I2I

PyTorch implementaton of our ECCV 2022 paper "Vector Quantized Image-to-Image Translation". You can visit our project website here.

In this paper, we propose a novel unified framework which is able to tackle image-to-image translation, unconditional generation of input domains and diverse extension based on an existing image. <img src='fig/teaser.png' width="800px">

Paper

Vector Quantized Image-to-Image Translation
Yu-Jie Chen*, Shin-I Cheng*, Wei-Chen Chiu, Hung-Yu Tseng, Hsin-Ying Lee
European Conference on Computer Vision (ECCV), 2022 (* equal contribution)

Please cite our paper if you find it useful for your research.

@inproceedings{chen2022eccv,
 title = {Vector Quantized Image-to-Image Translation},
 author = {Yu-Jie Chen and Shin-I Cheng and Wei-Chen Chiu and Hung-Yu Tseng and Hsin-Ying Lee},
 booktitle = {European Conference on Computer Vision (ECCV)},
 year = {2022}
}

Installation and Environment

• Prerequisities: Python 3.6 & Pytorch (at least 1.4.0)
• Clone this repo

git clone https://github.com/cyj407/VQ-I2I.git
cd VQ-I2I

• We provide a conda environment script, please run the following command after cloning our repo.

conda env create -f vqi2i_env.yml

Datasets

• Yosemite (winter, summer) dataset: You can follow the instructions in CycleGAN website to download the Yosemite (winter, summer) dataset.
• AFHQ (cat, dog, wildlife) You can follow the instructions in StarGAN v2 website to download the AFHQ (cat, dog, wildlife) dataset.
• Portrait (portrait, photography): 6452 photography images from CelebA dataset, 1811 painting images downloaded and cropped from Wikiart.
• Cityscapes (street scene, semantic labeling): 3475 street scenes and the corresponding semantic labelings from the cityscapes dataset.

Please save the dataset images separately, e.g. Yosemite dataset:

• trainA directory for training summer images.
• trainB directory for training winter images.
• testA directory for testing summer images.
• testB directory for testing winter images.

First Stage

Train

Unpaired I2I task

python unpair_train.py --device <gpu_num> --root_dir <dataset_path> \
--dataset <dataset_name>\
--epoch_start <epoch_start> --epoch_end <epoch_end>

• You can also append arguments for hyperparameters, e.g.: --ne <ne> --ed <ed> --z_channel <z_channel>.

Paired I2I task

python pair_train.py --device <gpu_num> --root_dir <dataset_path> \
--dataset <dataset_name>\
--epoch_start <epoch_start> --epoch_end <epoch_end>

• Used on Cityscapes dataset only.
• You can also append arguments for hyperparameters, e.g.: --ne <ne> --ed <ed> --z_channel <z_channel>.

Test (unpaired I2I translation.)

• Save the translation results.

python save_transfer.py --device <gpu_num> --root_dir <dataset_path> --dataset <dataset_name> \
--checkpoint_dir <checkpoint_dir> --checkpoint_epoch <checkpoint_epoch> \
--save_name <save_dir_name>

• --atob True: transfer domain A to domain b; otherwise, B to A.
• --intra_transfer True: enable intra-domain translation.
• You can also modify arguments for hyperparameters, e.g.: --ne <ne> --ed <ed> --z_channel <z_channel>.

Using the pre-trained models

• Download the pre-trained models, here we provide the pre-trained models for the four datasets.
  • Yosemite(summer, winter)256X256: --ed 512, --ne 512, --z_channel 256
  • AFHQ(cat, dog)256X256: --ed 256, --ne 256, --z_channel 256
  • Portrait(portrait, photography)256X256: --ed 256, --ne 256, --z_channel 256
  • Cityscapes(street scene, semantic labeling)256X256: --ed 256, --ne 64, --z_channel 128

Second stage

Train

python autoregressive_train.py --device <gpu_num> --root_dir <dataset_path> \
--dataset <dataset_name> --first_stage_model <first_stage_model_path> \
--epoch_start <epoch_start> --epoch_end <epoch_end>

• You can also append arguments for hyperparameters, e.g.: --ne <ne> --ed <ed> --z_channel <z_channel>.

Test

Using the pre-trained models

• Download the pre-trained transformer models, here we provide the pre-trained transformer model for the Yosemite dataset.
  • Yosemite(summer, winter)256X256: --ed 512, --ne 512, --z_channel 256

Unconditional Generation

python save_uncondtional.py --device <gpu_num> \
--root_dir <dataset_path> --dataset <dataset_name> \
--first_stage_model <first_stage_model_path> \
--transformer_model <second_stage_model_path> \
--save_name <save_dir_name>

• --sty_domain 'B': specify to generate domain B style images

Image Extension/Completion

Image extension

python save_extension.py --device <gpu_num> \
--root_dir <dataset_path> --dataset <dataset_name> \
--first_stage_model <first_stage_model_path> \
--transformer_model <second_stage_model_path> \
--save_name <save_dir_name>

• --input_domain B: select domain B images from the testing set as input.
• --sty_domain A: select domain A as the referenced styles to achieve translation.
• --double_extension True: enable the double-sided extension; default False.
• --pure_extension True: only extend the input images without translation; default False.
• --extend_w <extend_pixels>: extends for 128/192 pixels on the width; default 128.

Image completion

python save_completion.py -device <gpu_num> \
--root_dir <dataset_path> --dataset <dataset_name> \
--first_stage_model <first_stage_model_path> \
--transformer_model <second_stage_model_path> \
--save_name <save_dir_name>

• --input_domain B: select domain B images from the testing set as input.
• --sty_domain A: select domain A as the referenced styles to achieve translation.
• --pure_completion True: only extend the input images without translation; default True.
• --partial_input top-left: given top-left corner image as the input. There are two more options, left-half (given the left-half image as input), and top-half (given the top-half image as input).

Transitional Stylization

• The demonstration of all applications (includes transitional stylization) are in VQ-I2I-Applications.ipynb

Acknowledgments

Our code is based on VQGAN. The implementation of the disentanglement architecture is borrowed from MUNIT.