Awesome

CIPS -- Official Pytorch Implementation

of the paper Image Generators with Conditionally-Independent Pixel Synthesis

Teaser

Requirements

pip install -r requirements.txt

Usage

First create lmdb datasets:

python prepare_data.py images --out LMDB_PATH --n_worker N_WORKER --size SIZE1,SIZE2,SIZE3,... DATASET_PATH

This will convert images to jpeg and pre-resizes it.

To train on FFHQ-256 or churches please run:

python3 -m torch.distributed.launch --nproc_per_node=8 --master_port=1234 train.py --n_sample=8 --batch=4 --fid_batch=8 --Generator=CIPSskip --output_dir=skip-[ffhq/churches] --img2dis --num_workers=16 DATASET_PATH

To train on patches add --crop=PATCH_SIZE. PATCH_SIZE has to be a power of 2.

Pretrained Checkpoints

Generate samples

To play with the models please download checkpoints and check out a notebook.ipynb

Progressive training

We also tried to train progressively on FFHQ starting from 256×256 initialization and got FID 10.07. We will update the paper with the training details soon. Checkpoint name is ffhq1024.pt. Samples are below.

Sample from FFHQ trained progressively

Citation

If you found our work useful, please don't forget to cite

@article{anokhin2020image,
  title={Image Generators with Conditionally-Independent Pixel Synthesis},
  author={Anokhin, Ivan and Demochkin, Kirill and Khakhulin, Taras and Sterkin, Gleb and Lempitsky, Victor and Korzhenkov, Denis},
  journal={arXiv preprint arXiv:2011.13775},
  year={2020}
}

The code is heavely based on the styleganv2 pytorch implementation

Nvidia-licensed CUDA kernels (fused_bias_act_kernel.cu, upfirdn2d_kernel.cu) is for non-commercial use only.