How do you align output and GT for reference metrics when sizes differ?

Thanks for the great work and for releasing the code.

I have a question about the evaluation protocol for the full-reference metrics, specifically how output/GT resolution mismatch is handled.

The inference pipeline requires the network input to be a multiple of 128 in each spatial dimension. As far as I can tell,
examples/WanVSR/infer_flashvsr_v1.1_tiny.py
tW = (sW // multiple) * multiple # multiple = 128
tH = (sH // multiple) * multiple
upscale_then_center_crop(...)

Because of this floor-crop, the SR output is not the same size as the original ground truth. For example, on REDS (GT 1280×720):

LR input: 320×180
×4 upscale: 1280×720
floor to multiple of 128: 1280×640 (width 1280 is already 10×128, but height 720 → 640)
So the output loses 80 px in height relative to the GT.

Question
The paper reports PSNR/SSIM/LPIPS on YouHQ40, REDS, and SPMCS, but I couldn't find how the output and GT are spatially aligned before computing these pixel-aligned metrics. When the output size (e.g. 1280×640) differs from the GT size (e.g. 1280×720):

How do you align them for the reference metrics — do you center-crop the GT to the output size, pad the output back, or something else?
Are the reported PSNR/SSIM/LPIPS computed on the cropped region only, or on the full GT frame?
If baselines produce full-resolution outputs while FlashVSR is evaluated on a cropped region, how is a fair comparison ensured?
Could you share the exact evaluation alignment step (ideally the script) so the numbers can be reproduced and compared fairly? Thanks!

Start with examples/WanVSR/infer_flashvsr_v1.1_tiny.py and trace how the output dimensions are produced after the multiple-of-128 crop. Search the repository for the PSNR, SSIM, and LPIPS evaluation entry points; done means the spatial alignment step and evaluation region are documented well enough to reproduce and compare the reported metrics.