Tensorflow implementation of Globally and Locally Consistent Image Completion on celebA dataset.
- smaller image input size (128x128)
- smaller patch sizes
- less number of training iteration (500,000 iterations in the paper)
- Adam optimizer used instead of Adadelta
- Opencv 2.4
- Tensorflow 1.4
-data
-img_align_celeba
-img1.jpg
-img2.jpg
-...
$ python train.py
To continue training
$ python train.py --continue_training=True
$ python test.py --img_path=./data/test/test_img.jpg
Use your mouse to erase pixels in the image.
When you're done, press ENTER.
Result will be shown in few seconds.