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Hierarchical Semantic Regularization of Latent Spaces in StyleGANs

Project Page

Requirements

  • 64-bit Python 3.8 and PyTorch 1.8.0 (or later). See https://pytorch.org/ for PyTorch install instructions.
  • CUDA toolkit 11.0 or later.
  • python libraries: see scripts/requirements.txt
  • StyleGAN2 code relies heavily on custom PyTorch extensions. For detail please refer to the repo stylegan2-ada-pytorch

To setup conda env with all requirements and pretrained networks run the following command:

conda create -n hsr python=3.8
conda activate hsr
git clone https://github.com/val-iisc/hsr.git
cd hsr
bash docs/setup.sh

Inference

To generate images:

# random image generation from LSUN Church model

python generate.py --outdir=out --trunc=1 --seeds=85,265,297,849 --network=<path to network>

The above command generates 4 images using the provided seed values and saves it in out directory controlled by --outdir. Our generator architecture is same as styleGAN2 and can be similarly used in the Python code as described in stylegan2-ada-pytorch.

Evaluation:

python calc_metrics.py --network <path to network> --metrics ppl2_wend,fid50k_full,pr50k3_full --data ./datasets/ffhq256x256.zip

Pretrained Models: We provide pretrained models for the folowing datasets, trained at 256x256 resolution. FFHQ, LSUN-Church

Datasets

Dataset preparation is same as given in stylegan2-ada-pytorch. Example setup for 100-shot AnimalFace Dog and LSUN Church

AnimalFace Dog

mkdir datasets
wget https://data-efficient-gans.mit.edu/datasets/AnimalFace-dog.zip -P datasets

LSUN Church

cd ..
git clone https://github.com/fyu/lsun.git
cd lsun
python3 download.py -c church_outdoor
unzip church_outdoor_train_lmdb.zip
cd ../hsr
mkdir datasets
python dataset_tool.py --source ../lsun/church_outdoor_train_lmdb/ --dest datasets/church.zip --transform=center-crop --width=256 --height=256

All other datasets can be downloaded from their repsective websites:

FFHQ, LSUN Categories, AFHQ, AnimalFace Dog, AnimalFace Cat

Training

python train_baseline.py --outdir ./models --data ../datasets/ffhq256x256.zip --cfg paper256 --mirror 1 --aug noaug --batch 16 --gpus 2 --kimg 500
python train_hsr.py --outdir ./models --data ../datasets/ffhq256x256.zip --cfg paper256 --mirror 1 --aug noaug --batch 16 --gpus 2 --resume ./models/<path to baseline model directory>/network-snapshot-000500.pkl --model-name DINO

Citation

@InProceedings{karmali2022hierarchical,
author="Karmali, Tejan
and Parihar, Rishubh
and Agrawal, Susmit
and Rangwani, Harsh
and Jampani, Varun
and Singh, Maneesh
and Babu, R. Venkatesh",
title="Hierarchical Semantic Regularization of Latent Spaces in StyleGANs",
booktitle="Computer Vision -- ECCV 2022",
year="2022",
pages="443--459",
}

Acknowledgments

Our codebase is built on vision-aided-gan.