Fréchet inception distance (FID) Score for same vectors is not zero

Question

I was trying to use the implementation of FID score from this resource: https://machinelearningmastery.com/how-to-implement-the-frechet-inception-distance-fid-from-scratch/ but when I run the FID score for the same vectors (act1) I expected the FID score to be 0, instead I am getting a large number. Where am I going wrong?

# example of calculating the frechet inception distance
import numpy
from numpy import cov
from numpy import trace
from numpy import iscomplexobj
from numpy.random import random
from scipy.linalg import sqrtm

# calculate frechet inception distance
def calculate_fid(act1, act2):
    # calculate mean and covariance statistics
    mu1, sigma1 = act1.mean(axis=0), cov(act1, rowvar=False)
    mu2, sigma2 = act2.mean(axis=0), cov(act2, rowvar=False)
    # calculate sum squared difference between means
    ssdiff = numpy.sum((mu1 - mu2)**2.0)
    # calculate sqrt of product between cov
    covmean = sqrtm(sigma1.dot(sigma2))
    # check and correct imaginary numbers from sqrt
    if iscomplexobj(covmean):
        covmean = covmean.real
    # calculate score
    fid = ssdiff + trace(sigma1 + sigma2 - 2.0 * covmean)
    return fid

# define two collections of activations
act1 = random(10*2048)
act1 = act1.reshape((10,2048))
act2 = random(10*2048)
act2 = act2.reshape((10,2048))
# fid between act1 and act1
fid = calculate_fid(act1, act1)
print('FID (same): %.3f' % fid)
# fid between act1 and act2
fid = calculate_fid(act1, act2)
print('FID (different): %.3f' % fid)

Results:

FID (same): -66113130760175032991744.000
FID (different): -55213970774324510299478046898216203619608871777363092441300193790394368.000

I was expecting the fid score for the same vectors to be zero but it is coming up as large number which was contrary to what I understood of FID scores and what is demonstrated in the source linked above.