https://github.com/CQFIO/PhotographicImageSynthesis

Photographic Image Synthesis with Cascaded Refinement Networks
https://github.com/CQFIO/PhotographicImageSynthesis

cascaded-refinement-networks image-synthesis tensorflow

Last synced: about 1 month ago
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Photographic Image Synthesis with Cascaded Refinement Networks

• Host: GitHub
• URL: https://github.com/CQFIO/PhotographicImageSynthesis
• Owner: CQFIO
• Created: 2017-07-28T17:05:53.000Z (about 7 years ago)
• Default Branch: master
• Last Pushed: 2022-02-07T16:54:09.000Z (over 2 years ago)
• Last Synced: 2024-02-15T01:32:27.600Z (7 months ago)
• Topics: cascaded-refinement-networks, image-synthesis, tensorflow
• Language: Python
• Homepage: https://cqf.io/ImageSynthesis/
• Size: 1.72 MB
• Stars: 1,249
• Watchers: 70
• Forks: 230
• Open Issues: 14
• Metadata Files:
  • Readme: README.md

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README

        
# Photographic Image Synthesis with Cascaded Refinement Networks

This is a Tensorflow implementation of cascaded refinement networks to synthesize photographic images from semantic layouts.



## Setup

### Requirement

Required python libraries: Tensorflow (>=1.0) + Scipy + Numpy + Pillow.

Tested in Ubuntu + Intel i7 CPU + Nvidia Titan X (Pascal) with Cuda (>=8.0) and CuDNN (>=5.0). CPU mode should also work with minor changes.

### Quick Start (Testing)

1. Clone this repository.

2. Download the pretrained models from Google Drive by running "python download_models.py". It takes several minutes to download all the models.

3. Run "python demo_512p.py" or "python demo_1024p.py" (requires large GPU memory) to synthesize images.

4. The synthesized images are saved in "result_512p/final" or "result_1024p/final".

### Training

To train a model at 256p resolution, please set "is_training=True" and change the file paths for training and test sets accordingly in "demo_256p.py". Then run "demo_256p.py".

To train a model at 512p resolution, we fine-tune the pretrained model at 256p using "demo_512p.py". Also change "is_training=True" and file paths accordingly.

To train a model at 1024p resolution, we fine-tune the pretrained model at 512p using "demo_1024p.py". Also change "is_training=True" and file paths accordingly.

## Video

https://youtu.be/0fhUJT21-bs

## Citation

If you use our code for research, please cite our paper:

Qifeng Chen and Vladlen Koltun. Photographic Image Synthesis with Cascaded Refinement Networks. In ICCV 2017.

## Amazon Turk Scripts

The scripts are put in the folder "mturk_scripts".

## Todo List

1. Add the code and models for the GTA dataset.

## Question

If you have any question or request about the code and data, please email me at [email protected]. If you need the pretrained model on NYU, please send an email to me.

## License

MIT License