Simple Tensorflow implementation of "Semantic Image Synthesis with Spatially-Adaptive Normalization" (CVPR 2019 Oral)
- scipy == 1.2.0
- The latest version is not available.
imsave
is deprecated.
- The latest version is not available.
- tqdm
- numpy
- pillow
- opencv-python
- tensorflow-gpu
- keras
- 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
- Download checkpoint
- 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}
├── 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)
> python main.py --dataset spade_celebA --img_ch 3 --segmap_ch 3 --phase train
> python main.py --dataset spade_celebA --segmap_ch 3 --phase random
> python main.py --dataset spade_celebA --img_ch 3 --segmap_ch 3 --phase guide --guide_img ./guide_img.png
Generator | Image Encoder | Discriminator | All-in-one |
---|---|---|---|
SPADE | SPADE Residual Block |
---|---|