pix2pixHD

Overview

Project | YouTube | Paper

Pix2PixHD is a Pytorch implementation of a deep learning-based 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 in CVPR 2018.

The pix2pixHD model is available for commercial use via a Berkeley Software Distribution (BSD) License.

Datasets

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

Metadata

Linux or macOS
Python 2 or 3
NVIDIA GPU (11G memory or larger) + CUDA cuDNN
Install PyTorch and dependencies from the NGC Container Registry
Install python libraries dominate

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It can be used for turning semantic label maps into photo-realistic images or synthesizing portraits from face label maps. \n\n\n[High-Resolution Image Synthesis and Semantic Manipulation with Conditional GANs](https://tcwang0509.github.io/pix2pixHD/) in CVPR 2018.\n\nThe pix2pixHD model is available for commercial use via a [Berkeley Software Distribution (BSD)](https://opensource.org/licenses/BSD-3-Clause) License. \n\n[//]: # (Must have H2 heading for overview or introduction - displayed as tab)\n## Datasets\n- We use the Cityscapes dataset. 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