of the paper Image Generators with Conditionally-Independent Pixel Synthesis
pip install -r requirements.txt
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.
To play with the models please download checkpoints and check out a notebook.ipynb
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.
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.