pix2pixHD

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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.

High-Resolution Image Synthesis and Semantic Manipulation with Conditional GANs
Ting-Chun Wang¹, Ming-Yu Liu¹, Jun-Yan Zhu², Andrew Tao¹, Jan Kautz¹, Bryan Catanzaro^1
1NVIDIA Corporation, ²UC Berkeley
In arxiv, 2017.

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

• Our label-to-streetview results

- Interactive editing results

- Additional streetview results

• Label-to-face and interactive editing results

• Our editing interface

Prerequisites

• Linux or macOS
• Python 2 or 3
• NVIDIA GPU (12G or 24G memory) + CUDA cuDNN

Getting Started

Installation

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

pip install dominate

• Clone this repo:

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

Testing

• A few example Cityscapes test images are included in the datasets folder.
• Please download the pre-trained Cityscapes model from here (google drive link), and put it under ./checkpoints/label2city_1024p/
• Test the model (bash ./scripts/test_1024p.sh):

#!./scripts/test_1024p.sh
python test.py --name label2city_1024p --netG local --ngf 32 --resize_or_crop none

The test results will be saved to a html file here: ./results/label2city_1024p/test_latest/index.html.

More example scripts can be found in the scripts directory.

Dataset

• We use the Cityscapes dataset. To train a model on the full dataset, please download it from the official website (registration required). After downloading, please put it under the datasets folder in the same way the example images are provided.

Training

• Train a model at 1024 x 512 resolution (bash ./scripts/train_512p.sh):

#!./scripts/train_512p.sh
python train.py --name label2city_512p

• To view training results, please checkout intermediate results in ./checkpoints/label2city_512p/web/index.html. If you have tensorflow installed, you can see tensorboard logs in ./checkpoints/label2city_512p/logs by adding --tf_log to the training scripts.

Multi-GPU training

• Train a model using multiple GPUs (bash ./scripts/train_512p_multigpu.sh):

#!./scripts/train_512p_multigpu.sh
python train.py --name label2city_512p --batchSize 8 --gpu_ids 0,1,2,3,4,5,6,7

Note: this is not tested and we trained our model using single GPU only. Please use at your own discretion.

Training at full resolution

• To train the images at full resolution (2048 x 1024) requires a GPU with 24G memory (bash ./scripts/train_1024p_24G.sh). If only GPUs with 12G memory are available, please use the 12G script (bash ./scripts/train_1024p_12G.sh), which will crop the images during training. Performance is not guaranteed using this script.

More Training/test Details

• Flags: see options/train_options.py and options/base_options.py for all the training flags; see options/test_options.py and options/base_options.py for all the test flags.
• Instance map: we take in both label maps and instance maps as input. If you don't want to use instance maps, please specify the flag --no_instance.

Citation

If you find this useful for your research, please use the following.

@article{wang2017highres,
  title={High-Resolution Image Synthesis and Semantic Manipulation with Conditional GANs},
  author={Ting-Chun Wang and Ming-Yu Liu and Jun-Yan Zhu and Andrew Tao and Jan Kautz and Bryan Catanzaro},
  journal={arXiv preprint arXiv:1711.11585},
  year={2017}
}

Acknowledgments

This code borrows heavily from pytorch-CycleGAN-and-pix2pix.