How to change architecture of conditional GAN to generate 224x224x3 images?

I am following matlab example on conditional GAN at https://www.mathworks.com/help/deeplearning/ug/train-conditional-generative-adversarial-network.html

This example is for image size 64x64x3. I am wondering what changes should be done in layersGenerator and layersDiscriminator to generate 224x224x3 images.

This is my code:

inputSize = [224 224 3] or [256 256 3];

Note if Factor=2 (below) then I get image size 128x128x3. If Factor=4 then generated image size if 256x256x3. However, during the loop, it gives an error that trainedVariance is negative.

inputSize = [64 64 3];
Factor = 4; %if Factor =2 then 128x128x3 image size is generated; 
inputSize = Factor*inputSize(1:2);
numClasses = 2;
augimds = augmentedImageDatastore(inputSize(1:2),XTrain,YTrain);
augimdsValidation = augmentedImageDatastore(inputSize(1:2),XValidation,YValidation);

numLatentInputs = 100;%100
embeddingDimension = 50;
numFilters = Factor*64;%224;

filterSize = 5;
projectionSize = Factor*[4 4 1024];

layersGenerator = [
    featureInputLayer(numLatentInputs)
    fullyConnectedLayer(prod(projectionSize))
    functionLayer(@(X) feature2image(X,projectionSize),Formattable=true)
    concatenationLayer(3,2,Name="cat");
    transposedConv2dLayer(filterSize,4*numFilters,Stride=2,Cropping="same")
    batchNormalizationLayer
    reluLayer
    transposedConv2dLayer(filterSize,2*numFilters,Stride=2,Cropping="same")
    batchNormalizationLayer
    reluLayer
    transposedConv2dLayer(filterSize,numFilters,Stride=2,Cropping="same")
    batchNormalizationLayer
    reluLayer
    transposedConv2dLayer(filterSize,3,Stride=2,Cropping="same")
    tanhLayer];

lgraphGenerator = layerGraph(layersGenerator);

layers = [
    featureInputLayer(1)
    embeddingLayer(embeddingDimension,numClasses)
    fullyConnectedLayer(prod(projectionSize(1:2)))
    functionLayer(@(X) feature2image(X,[projectionSize(1:2) 1]),Formattable=true,Name="emb_reshape")];

lgraphGenerator = addLayers(lgraphGenerator,layers);
lgraphGenerator = connectLayers(lgraphGenerator,"emb_reshape","cat/in2");

netG = dlnetwork(lgraphGenerator);

dropoutProb = 0.75;
%numFilters = 64;
scale = 0.2;
filterSize = 5;

layersDiscriminator = [
    imageInputLayer(inputSize,Normalization="none")
    dropoutLayer(dropoutProb)
    concatenationLayer(3,2,Name="cat")
    convolution2dLayer(filterSize,numFilters,Stride=2,Padding="same")
    leakyReluLayer(scale)
    convolution2dLayer(filterSize,2*numFilters,Stride=2,Padding="same")
    batchNormalizationLayer
    leakyReluLayer(scale)
    convolution2dLayer(filterSize,4*numFilters,Stride=2,Padding="same")
    batchNormalizationLayer
    leakyReluLayer(scale)
    convolution2dLayer(filterSize,8*numFilters,Stride=2,Padding="same")
    batchNormalizationLayer
    leakyReluLayer(scale)
    convolution2dLayer(Factor*4,1)];

lgraphDiscriminator = layerGraph(layersDiscriminator);

layers = [
    featureInputLayer(1)
    embeddingLayer(embeddingDimension,numClasses)
    fullyConnectedLayer(prod(inputSize(1:2)))
    functionLayer(@(X) feature2image(X,[inputSize(1:2) 1]),Formattable=true,Name="emb_reshape")];

lgraphDiscriminator = addLayers(lgraphDiscriminator,layers);
lgraphDiscriminator = connectLayers(lgraphDiscriminator,"emb_reshape","cat/in2");

netD = dlnetwork(lgraphDiscriminator);

However, the above code gives an error at

[~,~,gradientsG,gradientsD,stateG,scoreG,scoreD] = ...
            dlfeval(@modelLoss2,netG,netD,X,T,Z,flipFactor);

The size of generated image at

[XGenerated,stageG] = forward(netG,Z,T);

is 256x256x3. However, an error comes stating that trainedVariance is not positive

Could you assist me which transposedConv2dLayer to change to adjust the size to 224x224x3 or 256x256x3?

Thanks for your help