es)
fake_images = fake_images.permute(0, 3, 1, 2)
print(fake_images.shape)
# torch.Size([10, 3, 256, 256]) Now, we can compute the FID using torchmetrics.   Copied from torchmetrics.image.fid import FrechetInceptionDistance

fid = FrechetInceptionDistance(normalize=True)
fid.update(real_images, real=True)
fid.update(fake_images, real=False)

print(f"FID: {float(fid.compute())}")
# FID: 177.7147216796875 The lower the FID, the better it is. Seve