Why LPIPS loss uses classification layers of VGG and not original LPIPS repo which are calibrated for perception

[MohitLamba94]

Hello,
Thankyou for detailed and yet easy to follow implementation of Rectified Flow paper.

I was going through the original implementation by authors and the authors use the original LPIPS library as noted below,

https://github.com/gnobitab/RectifiedFlow/blob/5a1fd4dd3ea7db764ce370a84ce35f9c8b15fde6/ImageGeneration/sde_lib.py#L28

But in your implementation LPIPS is just the vanilla VGG loss at the classification layer

rectified-flow-pytorch/rectified_flow_pytorch/rectified_flow.py

Line 69 in 1f59e7e

vgg.classifier = nn.Sequential(*vgg.classifier[:-2])

Any reason for not using the actual repository of LPIPS and instead using the original VGG?
I believe in LPIPS VGG is modified wherein some calibration is done to align with human perception.

Have you done so because of the below issue,
richzhang/PerceptualSimilarity#72 (comment)

Or nothing in specific?

Thankyou