Once you have your tensor in CPU, another possibility is to apply Sigmoid to your output and estimate a threshold (the mid point for example) in order to save it as an binary image.
from torchvision.utils import save_image

img1 = torch.sigmoid(output) # output is the output tensor of your UNet, the sigmoid will center the range around 0.
# Binarize the image
threshold = (img1.min() + img1.max()) * 0.5
ima = torch.where(img1 > threshold, 0.9, 0.1)
save_image(ima, 'BIN_ima.png')

Or  you could try to “greyscale” the image…
img1 = torch.sigmoid(output) 
min = img1.min()
max = img1.max()
img2 = 1./(max-min) * img1 + 1.*min / (min-max)
save_image(img2, 'GREY_img.png')