Yes @ptrblck, it seems that my generator still does output a 64x64 image, even if I set ngf (the number of filters in the generator to 32.
This is the current generator:
nz = 100
ngf = 64 #originally, need to change to 32?
nc = 3
self.main = nn.Sequential(
            # input is Z, going into a convolution
            nn.ConvTranspose2d(     nz, ngf * 8, 4, 1, 0, bias=False),
            nn.BatchNorm2d(ngf * 8),
            nn.ReLU(True),
            # state size. (ngf*8) x 4 x 4
            nn.ConvTranspose2d(ngf * 8, ngf * 4, 4, 2, 1, bias=False),
            nn.BatchNorm2d(ngf * 4),
            nn.ReLU(True),
            # state size. (ngf*4) x 8 x 8
            nn.ConvTranspose2d(ngf * 4, ngf * 2, 4, 2, 1, bias=False),
            nn.BatchNorm2d(ngf * 2),
            nn.ReLU(True),
            # state size. (ngf*2) x 16 x 16
            nn.ConvTranspose2d(ngf * 2,     ngf, 4, 2, 1, bias=False),
            nn.BatchNorm2d(ngf),
            nn.ReLU(True),
            # state size. (ngf) x 32 x 32
            # filter_size was originally 4, changed to 1 to avoid
            # RuntimeError: sizes must be non-negative
            nn.ConvTranspose2d(    ngf,      nc, 1, 2, 1, bias=False),
            nn.Tanh()
            # state size. (nc) x 64 x 64
        )

Is there a simple/standard change I can make to the above? Or just trial-and-error 'til the tensor is the right shape? Also, any idea as to what caused the weird behavior of the native (unchanged DCGAN) experiment?
Thanks as always for the help!