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Fix the Noise: Disentangling Source Feature for Controllable Domain Translation</sub>

Teaser image Fix the Noise: Disentangling Source Feature for Controllable Domain Translation<br> Dongyeun Lee, Jae Young Lee, Doyeon Kim, Jaehyun Choi, Jaejun Yoo, Junmo Kim<br> https://arxiv.org/abs/2303.11545

Abstract: Recent studies show strong generative performance in domain translation especially by using transfer learning techniques on the unconditional generator. However, the control between different domain features using a single model is still challenging. Existing methods often require additional models, which is computationally demanding and leads to unsatisfactory visual quality. In addition, they have restricted control steps, which prevents a smooth transition. In this paper, we propose a new approach for high-quality domain translation with better controllability. The key idea is to preserve source features within a disentangled subspace of a target feature space. This allows our method to smoothly control the degree to which it preserves source features while generating images from an entirely new domain using only a single model. Our extensive experiments show that the proposed method can produce more consistent and realistic images than previous works and maintain precise controllability over different levels of transformation.

Recent Updates

2023-05-09 Add several useful code for inference. For detailed usage, refer to Inference.
2023-05-15 Add

Requirements

Our code is highly based on the official implementation of stylegan2-ada-pytorch. Please refer to requirements for detailed requirements.

Python libraries:

pip install click requests tqdm pyspng ninja imageio-ffmpeg==0.4.3 lpips

Docker users:

docker build --tag sg2ada:latest .
docker run --gpus all --shm-size 64g -it -v /etc/localtime:/etc/localtime:ro -v /mnt:/mnt -v /data:/data --name sg2ada sg2ada /bin/bash

Pretrained Checkpoints

You can download the pre-trained checkpoints used in our paper:

Setting	Resolution	Config	Description
FFHQ → MetFaces	256x256	paper256	Trained initialized with official pre-trained model on FFHQ 256 from Pytorch implementation of stylegan2-ada-pytorch.
FFHQ → AAHQ	256x256	paper256	Trained initialized with official pre-trained model on FFHQ 256 from Pytorch implementation of stylegan2-ada-pytorch.
Church → Cityscape	256x256	stylegan2	Trained initialized with official pre-trained model on LSUN Church config-f from Tensorflow implementation of stylegan2.

Datasets

We provide official dataset download pages and our processing code for reproducibility. You could alse use official processing code in stylegan2-ada-pytorch. However, doing so does not guarantee reported performance.

MetFaces: Download the MetFaces dataset and unzip it.

# Resize MetFaces
python dataset_resize.py --source data/metfaces/images --dest data/metfaces/images256x256

AAHQ: Download the AAHQ dataset and process it following original instruction.

# Resize AAHQ
python dataset_resize.py --source data/aahq-dataset/aligned --dest data/aahq-dataset/images256x256

Wikiart Cityscape: Download cityscape from Wikiart and unzip it.

# Resize Wikiart Cityscape
python dataset_resize.py --source data/wikiart_cityscape/images --dest data/wikiart_cityscape/images256x256

Train new networks using FixNoise

Using FixNoise, base command for training stylegan2-ada network as follows:

FFHQ → MetFaces

python train.py --outdir=${OUTDIR} --data=${DATADIR} --cfg=paper256 --resume=ffhq256 --fm=0.05

FFHQ → AAHQ

python train.py --outdir=${OUTDIR} --data=${DATADIR} --cfg=paper256 --resume=ffhq256 --fm=0.05

Church → Cityscape

python train.py --outdir=${OUTDIR} --data=${DATADIR} --cfg=stylegan2 --resume=church256 --fm=0.05

Additionally, we provide detailed training scripts used in our experiments.

Inference

Generate interpolated images

To generate interpolated images according to different noise, run:

# Generate MetFaces images without truncation
python generate.py --cfg=paper256 --outdir=out --trunc_psi=1 --seeds=865-1000 \\
    --network=pretrained/metfaces-fm0.05-001612.pkl

# Generate MetFaces images with truncation
python generate.py --cfg=paper256 --outdir=out --trunc_psi=0.7 --trunc_cutoff=8 --seeds=865-1000 \\
    --network=pretrained/metfaces-fm0.05-001612.pkl

# Generate AAHQ images with truncation
python generate.py --cfg=paper256 --outdir=out --trunc_psi=0.7 --trunc_cutoff=8 --seeds=865-1000 \\
    --network=pretrained/aahq-fm0.05-010886.pkl

# Generate Wikiart images with truncation
python generate.py --cfg=stylegan2 --outdir=out --trunc_psi=0.7 --trunc_cutoff=8 --seeds=865-1000 \\
    --network=pretrained/wikiart-fm0.05-004032.pkl

You can change interpolation steps by modifying --interp-step.

Projecting images to latent space

To find the matching latent code for a given image file, run:

python projector_z.py --outdir=${OUTDIR} --target_dir=${DATADIR} \
    --https://nvlabs-fi-cdn.nvidia.com/stylegan2-ada-pytorch/pretrained/transfer-learning-source-nets/ffhq-res256-mirror-paper256-noaug.pkl

We modify projector.py to project image to z space of StyleGAN2. To use multiple gpus, add --gpus arguments. You can render the resulting latent vector by specifying --projected-z-dir for generate.py.

# Render an image from projected Z
python generate.py --cfg=paper256 --outdir=out --trunc_psi=0.7 --trunc_cutoff=8 \\
    --projected-z-dir=./projected --network=pretrained/aahq-fm0.05-010886.pkl

Demo

We provide noise interpolation example code in jupyter notebook.

FFHQ → MetFaces

FFHQ → AAHQ

Church → Cityscape

Citation

@InProceedings{Lee_2023_CVPR,
    author    = {Lee, Dongyeun and Lee, Jae Young and Kim, Doyeon and Choi, Jaehyun and Yoo, Jaejun and Kim, Junmo},
    title     = {Fix the Noise: Disentangling Source Feature for Controllable Domain Translation},
    booktitle = {Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR)},
    month     = {June},
    year      = {2023},
    pages     = {14224-14234}
}

License

The majority of FixNoise is licensed under CC-BY-NC, however, portions of this project are available under a separate license terms: all codes used or modified from stylegan2-ada-pytorch is under the Nvidia Source Code License.