endo-yuki-t / fewshot-smis

Pytorch implementation of few-shot semantic image synthesis

Few-shot Semantic Image Synthesis Using StyleGAN Prior

The extended version is available here.

Our method can synthesize photorealistic images from dense or sparse semantic annotations using a few training pairs and a pre-trained StyleGAN.

Prerequisites

1. Python3
2. PyTorch

Preparation

Download and decompress the file containing StyleGAN pre-trained models and put the "pretrained_models" directory in the parent directory.

Inference with our pre-trained models

1. Download and decompress the file containing our pretrained encoders and put the "results" directory in the parent directory.
2. For example, our results for celebaMaskHQ in a one-shot setting can be generated as follows:

python scripts/inference.py --exp_dir=results/celebaMaskHQ_oneshot --checkpoint_path=results/celebaMaskHQ_oneshot/checkpoints/iteration_100000.pt --data_path=./data/CelebAMask-HQ/test/labels/ --couple_outputs --latent_mask=8,9,10,11,12,13,14,15,16,17

Inference results are generated in results/celebaMaskHQ_oneshot. If you use other datasets, please specify --exp_dir, --checkpoint_path, and --data_path appropriately.

Training

For each dataset, you can train an encoder as follows:

• CelebAMask

python scripts/train.py --exp_dir=[result_dir] --dataset_type=celebs_seg_to_face --stylegan_weights pretrained_models/stylegan2-ffhq-config-f.pt --start_from_latent_avg --label_nc 19 --input_nc 19

• CelebALandmark

python scripts/train.py --exp_dir=[result_dir] --dataset_type=celebs_landmark_to_face --stylegan_weights pretrained_models/stylegan2-ffhq-config-f.pt --start_from_latent_avg --label_nc 71 --input_nc 71 --sparse_labeling

Intermediate training outputs with the StyleGAN pre-trained with the CelebA-HQ dataset. It can be seen that the layouts of the bottom-row images reconstructed from the middle-row pseudo semantic masks gradually become close to those of the top-row StyleGAN samples as the training iterations increase.

• LSUN church

python scripts/train.py --exp_dir=[result_dir] --dataset_type=lsunchurch_seg_to_img --stylegan_weights pretrained_models/stylegan2-church-config-f.pt --style_num 14 --start_from_latent_avg --label_nc 151 --input_nc 151

• LSUN car

python scripts/train.py --exp_dir=[result_dir] --dataset_type=lsuncar_seg_to_img --stylegan_weights pretrained_models/stylegan2-car-config-f.pt --style_num 16 --start_from_latent_avg --label_nc 5 --input_nc 5

• LSUN cat

python scripts/train.py --exp_dir=[result_dir] --dataset_type=lsuncat_scribble_to_img --stylegan_weights pretrained_models/stylegan2-cat-config-f.pt --style_num 14 --start_from_latent_avg --label_nc 9 --input_nc 9 --sparse_labeling

• Ukiyo-e

python scripts/train.py --exp_dir=[result_dir] --dataset_type=ukiyo-e_scribble_to_img --stylegan_weights pretrained_models/ukiyoe-256-slim-diffAug-002789.pt --style_num 14 --channel_multiplier 1 --start_from_latent_avg --label_nc 8 --input_nc 8 --sparse_labeling

• Anime

python scripts/train.py --exp_dir=[result_dir] --dataset_type=anime_cross_to_img --stylegan_weights pretrained_models/2020-01-11-skylion-stylegan2-animeportraits-networksnapshot-024664.pt --style_num 16 --start_from_latent_avg --label_nc 2 --input_nc 2 --sparse_labeling

Using StyleGAN samples as few-shot training data

1. Run the following script:

python scripts/generate_stylegan_samples.py --exp_dir=[result_dir] --stylegan_weights ./pretrained_models/stylegan2-ffhq-config-f.pt --style_num 18 --channel_multiplier 2

Then a StyleGAN image (*.png) and a corresponding latent code (*.pt) are obtained in [result_dir]/data/images and [result_dir]/checkpoints.

2. Manually annotate the generated image in [result_dir]/data/images and save the annotated mask in [result_dir]/data/labels.

3. Edit ./config/data_configs.py and ./config/paths_config.py appropriately to use the annotated pairs as a training set.

4. Run a training command above with appropriate options.

Citation

Please cite our paper if you find the code useful:

@article{endo2021fewshotsmis,
  title = {Few-shot Semantic Image Synthesis Using StyleGAN Prior},
  author = {Yuki Endo and Yoshihiro Kanamori},
  journal   = {CoRR},
  volume    = {abs/2103.14877},
  year      = {2021}
}

Acknowledgements

This code heavily borrows from the pixel2style2pixel repository.

fewshot-smis's Issues

About lsun cars segmentation

hi, where to download the segmentation masks for lsun cars? Thanks!

links to download more data

Hi, could you please provide the urls to download more data with their masks?