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 https://github.com/lucidrains/rectified-flow-pytorch/blob/1f59e7e9122439931968126d4ac662c1f0ce24ac/rectified_flow_pytorch/rectified_flow.py#L69

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, https://github.com/richzhang/PerceptualSimilarity/issues/72#issue-941220573

Or nothing in specific?

Thankyou