CONDITIONAL GAN: pix2pixHD

Project

Pytorch implementation of our method for high-resolution (e.g. 2048x1024) photorealistic image-to-image translation. It can be used for turning semantic label maps into photo-realistic images or synthesizing portraits from face label maps.

Image-to-image translation at 2k/1k resolution

• Our src images and preprocess image

- Image-to-image translation results

Prerequisites

• Linux or macOS
• Python 2 or 3
• NVIDIA GPU (11G memory or larger) + CUDA cuDNN

Getting Started

Installation

• Install PyTorch and dependencies from http://pytorch.org
• Install python libraries dominate.

pip install dominate

## pip install ...
pip install dominate -i http://pypi.douban.com/simple --trusted-host pypi.douban.com --user

• Clone this repo:

git clone https://github.com/NVIDIA/pix2pixHD
cd pix2pixHD

Dataset

• We use the BONC dataset.

Training

• Train a model
• python train.py --label_nc 0 --no_instance --resize_or_crop 1088 --gpu_ids 0,1 --no_flip --tf_log
• python test.py --label_nc 0 --no_instance --resize_or_crop none --name bp_ab --resize_or_crop none --gpu_ids 0,1 --no_flip