Pytorch implementation of few-shot semantic image synthesis

Last update: Sep 26, 2022

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Overview

Few-shot Semantic Image Synthesis Using StyleGAN Prior

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

Prerequisites

Python3
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

Download and decompress the file containing our pretrained encoders and put the "results" directory in the parent directory.
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

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.

Manually annotate the generated image in [result_dir]/data/images and save the annotated mask in [result_dir]/data/labels.
Edit ./config/data_configs.py and ./config/paths_config.py appropriately to use the annotated pairs as a training set.
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.

Pytorch implementation of few-shot semantic image synthesis

Related tags

Overview

Few-shot Semantic Image Synthesis Using StyleGAN Prior

Prerequisites

Preparation

Inference with our pre-trained models

Training

Using StyleGAN samples as few-shot training data

Citation

Acknowledgements

Owner

MOT-Tracking-by-Detection-Pipeline - For Tracking-by-Detection format MOT (Multi Object Tracking), is it a framework that separates Detection and Tracking processes?

Pytorch Implementation of paper "Noisy Natural Gradient as Variational Inference"

Official tensorflow implementation for CVPR2020 paper “Learning to Cartoonize Using White-box Cartoon Representations”

Turning pixels into virtual points for multimodal 3D object detection.

Code and hyperparameters for the paper "Generative Adversarial Networks"

Multi-Horizon-Forecasting-for-Limit-Order-Books

Ensemble Visual-Inertial Odometry (EnVIO)

Source code for "UniRE: A Unified Label Space for Entity Relation Extraction.", ACL2021.

Python Blood Vessel Topology Analysis

Code for "Adversarial attack by dropping information." (ICCV 2021)

Unofficial TensorFlow implementation of Protein Interface Prediction using Graph Convolutional Networks.

Microsoft Cognitive Toolkit (CNTK), an open source deep-learning toolkit

LeetCode Solutions https://t.me/tenvlad

Optical machine for senses sensing using speckle and deep learning

PyGCL: A PyTorch Library for Graph Contrastive Learning

Implementation of TabTransformer, attention network for tabular data, in Pytorch

A Gura parser implementation for Python

QuanTaichi evaluation suite

Supporting code for the Neograd algorithm

Implementation for paper "Towards the Generalization of Contrastive Self-Supervised Learning"