Hi ptrblck I actually posted this question to stackoverflow and some one helped me solve it. I neglected an important concept in progressive networks and that is fading in the channels. Eg toRGB and FromRGB in the progan paper. Here is the answer that helped me build more intuitions.

Second, this isn’t really about implementation but theory. You cannot magically convert a convolution layer from accepting 1 channels to 8 channels. There are numbers of ways to expand your convolution filter like appending random weights but I think it is not what you wanted.
From the second paper (It is a GAN, but the idea is the same), it does not expand any filters. Instead, the filters maintain their shape in the entire training process. Meaning that, you would have a
Conv2D(8, 16, 3, 1, 1)
from the very beginning (assuming you only have those two layers). An obvious problem pops out – your grayscale image is a 1-channel input, but your convolution requires a 8-channel input in the first stage of training. In the second paper, it uses an extra 1x1 convolution layer to map RGB <-> feature maps. In your case, that would be
Conv2D(1, 8, 1)
which maps 1-channel input to 8-channel output. This can be thrown away after you are done with first stage.
There are other techniques like gradually fading in using a weight term stated in the paper. I suggest you read them, especially the second one.

So in the end I don’t have to manually switch channels I can keep a predefine conv layer and convert my image into a multi channel object then discard the 1x1 conv block. I am going to read the pro gan paper again since a lot of things are clearer now. Thanks for the help.