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Top AI Repos tracks AI repositories on GitHub and answers two different questions about each one: is it moving right now, and would you bet a product on it.
Synthesizing and manipulating 2048x1024 images with conditional GANs
| Date | Stars |
|---|---|
| 2026-07-24 | 6927 |
| 2026-07-25 | 6928 |
| 2026-07-28 | 6928 |
| 2026-07-30 | 6928 |
| 2026-08-06 | 6929 |
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<img src='imgs/teaser_720.gif' align="right" width=360> <br><br><br><br> # pix2pixHD ### [Project](https://tcwang0509.github.io/pix2pixHD/) | [Youtube](https://youtu.be/3AIpPlzM_qs) | [Paper](https://arxiv.org/pdf/1711.11585.pdf) <br> 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. <br><br> [High-Resolution Image Synthesis and Semantic Manipulation with Conditional GANs](https://tcwang0509.github.io/pix2pixHD/) [Ting-Chun Wang](https://tcwang0509.github.io/)<sup>1</sup>, [Ming-Yu Liu](http://mingyuliu.net/)<sup>1</sup>, [Jun-Yan Zhu](http://people.eecs.berkeley.edu/~junyanz/)<sup>2</sup>, Andrew Tao<sup>1</sup>, [Jan Kautz](http://jankautz.com/)<sup>1</sup>, [Bryan Catanzaro](http://catanzaro.name/)<sup>1</sup> <sup>1</sup>NVIDIA Corporation, <sup>2</sup>UC Berkeley In CVPR 2018. ## Image-to-image translation at 2k/1k resolution - Our label-to-streetview results <p align='center'> <img src='imgs/teaser_label.png' width='400'/> <img src='imgs/teaser_ours.jpg' width='400'/> </p> - Interactive editing results <p align='center'> <img src='imgs/teaser_style.gif' width='400'/> <img src='imgs/teaser_label.gif' width='400'/> </p> - Additional streetview results <p align='center'> <img src='imgs/cityscapes_1.jpg' width='400'/> <img src='imgs/cityscapes_2.jpg' width='400'/> </p> <p align='center'> <img src='imgs/cityscapes_3.jpg' width='400'/> <img src='imgs/cityscapes_4.jpg' width='400'/> </p> - Label-to-face and interactive editing results <p align='center'> <img src='imgs/face1_1.jpg' width='250'/> <img src='imgs/face1_2.jpg' width='250'/> <img src='imgs/face1_3.jpg' width='250'/> </p> <p align='center'> <img src='imgs/face2_1.jpg' width='250'/> <img src='imgs/face2_2.jpg' width='250'/> <img src='imgs/face2_3.jpg' width='250'/> </p> - Our editing interface <p align='center'> <img src='imgs/city_short.gif' width='330'/> <img src='imgs/face_short.gif' width='450'/> </p> ## 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](https://github.com/Knio/dominate). ```bash pip install dominate ``` - Clone this repo: ```bash 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](https://drive.google.com/file/d/1OR-2aEPHOxZKuoOV34DvQxreqGCSLcW9/view?usp=drive_link) (google drive link), and put it under `./checkpoints/label2city_1024p/` - Test the model (`bash ./scripts/test_1024p.sh`): ```bash #!./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](https://www.cityscapes-dataset.com/) (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`): ```bash #!./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 tra
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27
Boris Fomitchev · NVIDIA
2
Jun-Yan Zhu · Carnegie Mellon University: · United States
2
Ming-Yu Liu 劉洺堉 · NVIDIA · United States
2
2
Ming · CVTE Research
2
1
Would you bet a product on this? Bounded 0–100 and slow moving.
matched fp:432f942c36468c98, topic:deep-learning, topic:pytorch
matched fp:432f942c36468c98, topic:computer-vision
matched fp:432f942c36468c98, topic:gan