F2DGAN-pytorch

The simplified version of our paper: Exact Fusion via Feature Distribution Matching for Few-shot Image Generation, which is accepted in CVPR 2024.

Exact Fusion via Feature Distribution Matching for Few-shot Image Generation

Yingbo Zhou, Yutong Ye, Pengyu Zhang, Xian Wei, and Mingsong Chen

Prerequisites

• Python 3.8
• Pytorch 1.8
• Nvidia GPU + CUDA

Preparing Dataset

Download the datasets and unzip them in datasets folder.

Training

python train.py --conf configs/flower_f2dgan.yaml \
--output_dir results/flower_f2dgan \
--gpu 0

• You may also customize the parameters in configs.

Testing

python test.py --name results/flower_f2dgan --gpu 0

The generated images will be saved in results/flower_f2dgan/test.

FID and LPIPS Evaluation

python main_metric.py --gpu 0 --dataset flower \
--name results/flower_f2dgan \
--real_dir datasets/for_fid/flower --ckpt gen_00100000.pt \
--fake_dir test_for_fid

Acknowledgement

Our code is designed based on LoFGAN.

The code for calculating FID is based on pytorch-fid