Figure: Real image editing using the proposed In-Domain GAN inversion with a fixed GAN generator.
In the repository, we propose an in-domain GAN inversion method, which not only faithfully reconstructs the input image but also ensures the inverted code to be semantically meaningful for editing. Basically, the in-domain GAN inversion contains two steps:
- Training domain-guided encoder.
- Performing domain-regularized optimization.
[Paper] [Project Page] [Demo]
Please download the pre-trained models from the following links. For each model, it contains the GAN generator and discriminator, as well as the proposed domain-guided encoder.
Path | Description |
---|---|
face_256x256 | In-domain GAN trained with FFHQ dataset. |
tower_256x256 | In-domain GAN trained with LSUN Tower dataset. |
bedroom_256x256 | In-domain GAN trained with LSUN Bedroom dataset. |
MODEL_PATH='styleganinv_face_256.pkl'
IMAGE_LIST='examples/test.list'
python invert.py $MODEL_PATH $IMAGE_LIST
NOTE: We find that 100 iterations are good enough for inverting an image, which takes about 8s (on P40). But users can always use more iterations (much slower) for a more precise reconstruction.
MODEL_PATH='styleganinv_face_256.pkl'
TARGET_LIST='examples/target.list'
CONTEXT_LIST='examples/context.list'
python diffuse.py $MODEL_PATH $TARGET_LIST $CONTEXT_LIST
NOTE: The diffusion process is highly similar to image inversion. The main difference is that only the target patch is used to compute loss for masked optimization.
SRC_DIR='results/inversion/test'
DST_DIR='results/inversion/test'
python interpolate.py $MODEL_PATH $SRC_DIR $DST_DIR
IMAGE_DIR='results/inversion/test'
BOUNDARY='boundaries/expression.npy'
python manipulate.py $MODEL_PATH $IMAGE_DIR $BOUNDARY
NOTE: Boundaries are obtained using InterFaceGAN.
STYLE_DIR='results/inversion/test'
CONTENT_DIR='results/inversion/test'
python mix_style.py $MODEL_PATH $STYLE_DIR $CONTENT_DIR
The GAN model used in this work is StyleGAN. Beyond the original repository, we make following changes:
- Change repleated
$w$ for all layers to different $w$s (Line 428-435 in filetraining/networks_stylegan.py
). - Add the domain-guided encoder in file
training/networks_encoder.py
. - Add losses for training the domain-guided encoder in file
training/loss_encoder.py
. - Add schedule for training the domain-guided encoder in file
training/training_loop_encoder.py
. - Add a perceptual model (VGG16) for computing perceptual loss in file
perceptual_model.py
. - Add training script for the domain-guided encoder in file
train_encoder.py
.
python train.py
Modify train_encoder.py
as follows:
- Add training and test dataset path to
Data_dir
intrain_encoder.py
. - Add generator path to
Decoder_pkl
intrain_encoder.py
.
python train_encoder.py
@article{zhu2020indomain,
title = {In-domain GAN Inversion for Real Image Editing},
author = {Zhu, Jiapeng and Shen, Yujun and Zhao, Deli and Zhou, Bolei},
journal = {arXiv preprint arXiv:2004.00049},
year = {2020}
}