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Comments (2)

woctezuma avatar woctezuma commented on May 21, 2024

For reference, these are the parts of the code mentioned in the post above:

Real-ESRGAN/inference_realesrgan.py

Line 14 in 0573f32

parser.add_argument('--scale', type=int, default=4)

Real-ESRGAN/inference_realesrgan.py

Lines 40 to 45 in 0573f32

if args.scale == 2:
mod_scale = 2
elif args.scale == 1:
mod_scale = 4
else:
mod_scale = None

Real-ESRGAN/inference_realesrgan.py

Lines 46 to 53 in 0573f32

if mod_scale is not None:
h_pad, w_pad = 0, 0
_, _, h, w = img.size()
if (h % mod_scale != 0):
h_pad = (mod_scale - h % mod_scale)
if (w % mod_scale != 0):
w_pad = (mod_scale - w % mod_scale)
img = F.pad(img, (0, w_pad, 0, h_pad), 'reflect')

Real-ESRGAN/inference_realesrgan.py

Lines 59 to 62 in 0573f32

# remove extra pad
if mod_scale is not None:
_, _, h, w = output.size()
output = output[:, :, 0:h - h_pad, 0:w - w_pad]

from real-esrgan.

xinntao avatar xinntao commented on May 21, 2024

@Delviet This is correct.
As in our implementation of RealESRGAN X1 model (have not uploaded yet), the input images are first downsampled with pixel unshuffle. So the spatial size of input images should be divisible of the downsampling scale. We downsample the inputs by a scale of 4. So the mod scale is 4.

from real-esrgan.

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