Difficulty utilizing pretrained "pix2Pix" GAN implementation using Deep Network Designer

Hi there

I am attempting to implement a pretrained "Pix2Pix" GAN that was trained using tensorflow/Keras example:

Pix2Pix tensorflow

after (tensorflow) training I saved the model for the Generator to the required model.h5 file.

In Matlab:

import the layers and weights

lgraph=importKerasLayers(modelfile,'ImportWeights',true,"OutputLayerType","regression")

have also attempted the above without the regression layer but the following still occurs:

Using the Deep Network Designer I import lgraph from the workspace and then export with pretrained parameters - unless I am mistaken the weights and biases should be as was trained in Tensorflow?

Exectute the live scripts that Deep Network Designer generated to create the Lgraph with Weights,Biases etc

however once I create the generator from the lgraph:

Generator=assembleNetwork(lgraph)

and then pass an input image (correctly sized, normalized etc) through the generator

Y=predict(Generator,dlPic)

the output appears to return an unchanged input image?

Am I missing any important aspects?

Is it necessary to define the discriminator, loss functions etc if I only wish to utilize the pretrained Generator architecture?

Any assistance would be GREATLY appreciated!