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https://github.com/taki0112/SPADE-Tensorflow
Simple Tensorflow implementation of "Semantic Image Synthesis with Spatially-Adaptive Normalization" a.k.a. GauGAN, SPADE (CVPR 2019 Oral)
https://github.com/taki0112/SPADE-Tensorflow
Last synced: 3 months ago
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Simple Tensorflow implementation of "Semantic Image Synthesis with Spatially-Adaptive Normalization" a.k.a. GauGAN, SPADE (CVPR 2019 Oral)
- Host: GitHub
- URL: https://github.com/taki0112/SPADE-Tensorflow
- Owner: taki0112
- License: mit
- Created: 2019-04-26T02:29:43.000Z (over 5 years ago)
- Default Branch: master
- Last Pushed: 2022-06-06T22:28:29.000Z (over 2 years ago)
- Last Synced: 2024-07-22T20:09:56.006Z (3 months ago)
- Language: Python
- Homepage:
- Size: 35.1 MB
- Stars: 365
- Watchers: 17
- Forks: 67
- Open Issues: 18
-
Metadata Files:
- Readme: README.md
- License: LICENSE
Awesome Lists containing this project
- awesome-normalization-techniques - [Python Reference
README
# Semantic Image Synthesis with SPADE (GauGAN) - Tensorflow
Simple Tensorflow implementation of "Semantic Image Synthesis with Spatially-Adaptive Normalization" (CVPR 2019 Oral)
### [Project page](https://nvlabs.github.io/SPADE/) | [Paper](https://arxiv.org/abs/1903.07291) | [Pytorch code](https://github.com/NVlabs/SPADE)
## Requirements
* scipy == 1.2.0
* The latest version is not available. `imsave` is deprecated.
* tqdm
* numpy
* pillow
* opencv-python
* tensorflow-gpu
* keras
## Preparation
* **YOUR DATASET**
* Image
* Segmentation map
* Don't worry. I do one-hot encoding of segmentation map automatically (whether color or gray)
* **CelebAMask-HQ**
* Download from [here](https://github.com/switchablenorms/CelebAMask-HQ)
### Pretrained model
* Download [**checkpoint**](https://drive.google.com/file/d/1UIj7eRJeNWrDS-3odyaoLhcqk0tNcEez/view?usp=sharing)
* CelebAMask-HQ, hinge loss
* It is a **better performance** than the results in the `READEME`
* `segmap_label.txt` for CelebAMask-HQ
* {(0, 0, 0): 0, (0, 0, 255): 1, (255, 0, 0): 2, (150, 30, 150): 3, (255, 65, 255): 4, (150, 80, 0): 5, (170, 120, 65): 6, (125, 125, 125): 7, (255, 255, 0): 8, (0, 255, 255): 9, (255, 150, 0): 10, (255, 225, 120): 11, (255, 125, 125): 12, (200, 100, 100): 13, (0, 255, 0): 14, (0, 150, 80): 15, (215, 175, 125): 16, (220, 180, 210): 17, (125, 125, 255): 18}
## Usage
```
├── dataset
└── YOUR_DATASET_NAME
├── image
├── 000001.jpg
├── 000002.png
└── ...
├── segmap
├── 000001.jpg
├── 000002.png
└── ...
├── segmap_test
├── a.jpg
├── b.png
└── ...
├── segmap_label.txt (Automatically created)
├── guide.jpg (example for guided image translation task)
```
### Train
```
> python main.py --dataset spade_celebA --img_ch 3 --segmap_ch 3 --phase train
```
### Random test
```
> python main.py --dataset spade_celebA --segmap_ch 3 --phase random
```
### Guide test
```
> python main.py --dataset spade_celebA --img_ch 3 --segmap_ch 3 --phase guide --guide_img ./guide_img.png
```
## Our Results
### Loss grpah
### CityScape
### CelebA-HQ (Style Manipulation)
---
### CelebA-HQ (Random Manipulation)
---
## How about the Least-Square loss ?
### CelebA-HQ (Style Manipulation)
---
### CelebA-HQ (Random Manipulation)
---
## Architecture
*Generator* | *Image Encoder* | *Discriminator* | *All-in-one* |
:---: | :---: | :---: | :---: |
| 
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| 
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### SPADE architecture
*SPADE* | *SPADE Residual Block* |
:---: | :---: |
| 
|
## Author
[Junho Kim](http://bit.ly/jhkim_ai)