BridgeGAN - Tensorflow implementation of Bridging the Gap between Label- and Reference-based Synthesis in Multi-attribute Image-to-Image Translation.

Last update: Jul 13, 2022

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Overview

Bridging the Gap between Label- and Reference based Synthesis(ICCV 2021)

Tensorflow implementation of Bridging the Gap between Label- and Reference-based Synthesis in Multi-attribute Image-to-Image Translation.

Overview architecture

Experiment Results

CelebA

Preparation

Prerequisites
- Tensorflow 1.15
- Python 2.x with matplotlib, numpy and scipy
Dataset
- CelebA
- Images should be placed in ./data/CelebA/img_align_celeba
- tfrecords file should be placed in ./data/CelebA/celeba_tfrecords

Quick Start

Exemplar commands are listed here for a quick start.

dataset

prepare dataset to product tfrecords file
```
python data_noise_in.py 
```

Training

To train with size of 128 X 128

python train_arch9.py --experiment_name "file_name" --gpu "gpu_num"

Testing

Example of test

python  test_arch9.py --experiment_name "file_name" --gpu "gpu_num"

Citation

If this work is useful for your research, please consider citing:

Owner

huangqiusheng

GitHub Repository

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