HFGI: High-Fidelity GAN Inversion for Image Attribute Editing

High-Fidelity GAN Inversion for Image Attribute Editing

Update: We released the inference code and the pre-trained model on Oct. 31. The training code is coming soon.

paper | project website | demo video

Introduction

We present a novel high-fidelity GAN inversion framework that enables attribute editing with image-specific details well-preserved (e.g., background, appearance and illumination).

To Do

• Release the inference code
• Release the pretrained model
• Release the training code (upon approval)

Set up

Installation

git clone https://github.com/Tengfei-Wang/HFGI.git
cd HFGI

Environment

The environment can be simply set up by Anaconda (only tested for inference):

conda create -n HFGI python=3.7
conda activate HFGI
pip install torch==1.6.0+cu101 torchvision==0.7.0+cu101 -f https://download.pytorch.org/whl/torch_stable.html
pip install matplotlib
conda install ninja
conda install -c 3dhubs gcc-5

Or, you can also set up the environment from the provided environment.yml:

conda env create -f environment.yml

Quick Start

Pretrained Models

Please download our pre-trained model and put it in ./checkpoint.

Prepare Images

We put some images from CelebA-HQ in ./test_imgs, and you can quickly try them (and other images from CelebA-HQ or FFHQ).
For customized images, it is encouraged to first pre-process (align & crop) them, and then edit with our model. See FFHQ for alignment details.

Inference

Modify inference.sh according to the follwing instructions, and run:
(It is possibly slow for the first-time running.)

bash inference.sh

Training

Coming soon

Video Editing

The source videos and edited results in our paper can be found in this link.
For video editing, we first pre-process (align & crop) each frame, and then perform editing with the pre-trained model.

More Results

Citation

If you find this work useful for your research, please cite:

@article{wang2021HFGI,
      author = {Tengfei Wang and Yong Zhang and Yanbo Fan and Jue Wang and Qifeng Chen},
      title = {High-Fidelity GAN Inversion for Image Attribute Editing}, 
      journal = {arxiv:2109.06590},  
      year = {2021}
}