Awesome

PixelFolder

This is an official implementation of ECCV 2022 Paper "PixelFolder: An Efficient Progressive Pixel Synthesis Network for Image Generation". The proposed PixelFolder outperforms existing state-of-the-art pixel synthesis methods (e.g. CIPS, INR-GAN), while reducing the number of parameters and computational overhead by more than 50%.

Contents

• Usage
  • Requirements
  • Data Preparation
  • Training
  • Evaluation
• Model Performance
• Checkpoints
• Citation
• Acknowledgement

Usage

Requirements

• Install CUDA==10.1 with cudnn7 following the official installation instructions
• Install Pytorch==1.7.1 and torchvision==0.8.2 with CUDA==10.1:

conda install pytorch==1.7.1 torchvision==0.8.2 torchaudio==0.7.2 -c pytorch

• Install other packages:

pip install -r requirements.txt

Data Preparation

1. Please download the FFHQ or LSUN (Church/Cat/Bedroom) dataset and organize the images in .jpg or .png format to DATASET_PATH. <br> FFHQ: https://github.com/NVlabs/ffhq-dataset<br> LSUN Church/Cat/Bedroom: https://github.com/fyu/lsun

2. Create lmdb datasets.

python prepare_data.py images --out LMDB_PATH --size SIZE DATASET_PATH

where LMDB_PATH is the path of the output lmdb dataset files, SIZE is the target resolution and DATASET_PATH is the source image files.

Training

Run the command sh train.sh to train the model with default hyperparameters. Note that you should first change the hyperparameter path to the path of your dataset.

Args:

• --Generator=PixelFolder to declare the model name. We also support training CIPS and StyleGAN2, you only need to modify the hyperparameter here.
• --batch=int to set the batch size.
• --out_path=str, which is the path of output files including images, checkpoints and tensorboard files.
• --path_fid=str to save the images generated when calculating the FID score.

Evaluation

• FID score
• Precision & Recall
• IS
• PPL
• number of parameters
• GMACS
• Inference Speed

Model Performance

Pretrained Checkpoints

• PixelFolder_ffhq_256 Google Driver
• PixelFolder_ffhq_512 Google Driver
• PixelFolder_ffhq_1024 Google Driver
• PixelFolder_church_256 Google Driver

Citation

If PixelFolder is helpful for your research or you wish to refer the baseline results published here, we'd really appreciate it if you could cite this paper:

@article{he2022pixelfolder,
  title={PixelFolder: An Efficient Progressive Pixel Synthesis Network for Image Generation},
  author={He, Jing and Zhou, Yiyi and Zhang, Qi and Peng, Jun and Shen, Yunhang and Sun, Xiaoshuai and Chen, Chao and Ji, Rongrong},
  journal={arXiv preprint arXiv:2204.00833},
  year={2022}
}

Acknowledgement

Our code is built upon the CIPS implementation and Nvidia-licensed CUDA kernels (fused_bias_act_kernel.cu, upfirdn2d_kernel.cu).

TODO

• Evaluation
  • FID\Prevision\Recall\IS
  • Params\GMACs\Speed