PixColor Pytorch implementation

paper link: here

PixColor is a state-of-the-art colorization method. It is able to produce multiple versions of colored images when given a single black and white image input. The two main networks require separate training. As you can already infer from the image below, a slight drawback can be that the model is a bit heavy and is trained with the aid of 8(!) GPUs.

***Note This is not a complete implementation. The coloring network needs to be added.

• There are four main networks included in the architecture

pix_network_1.py

1. Conditioning Network: Pretrain conditioning network on COCO image segmentation

2. Adaptation Network: Conditioning and adaptation network turn brightness channel Y into a set of features that are used for conditioning the PixelCNN.

3. Coloring Network(pixelCNN): pixelCNN is optimized alongside conditioning and adaptation network. It predicts a low resolution chrominance of the image

pix_network_2.py

4. Refinement Network: The low resolution color image made from the previous network is fed into the refinement network, which then produces a full resolution colorization