4xLSDIRCompact Series 3

I am releasing the Series 3 from my 4xLSDIRCompact models. In general my suggestion is, if you have good quality input images use 4xLSDIRCompactN3, otherwise try 4xLSDIRCompactC3 which will be able to handle jpg compression and a bit of blur, or then 4xLSDIRCompactCR3, which is an interpolation between C3 and R3 to be able to handle a bit of noise additionally.

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Name: 4xLSDIRCompactN3
Author: Philip Hofmann
Release Date: 11.04.2023
License: CC BY 4.0
Model Architecture: SRVGGNetCompact
Scale: 4
Purpose: Upscale good quality input photos to x4 their size
Iterations: 185'000
batch_size: Variable(1-10)
HR_size: 256
Dataset: LSDIR
Dataset_size: 84991 hr + 84991 lr
OTF Training No
Pretrained_Model_G: 4x_Compact_Pretrain.pth
Description: The original 4xLSDIRCompact a bit more trained, cannot handle degradation
Total Training Time: 32+ hours

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Name: 4xLSDIRCompactC3
Author: Philip Hofmann
Release Date: 11.04.2023
License: CC BY 4.0
Model Architecture: SRVGGNetCompact
Scale: 4
Purpose: Upscale compressed photos to x4 their size
Iterations: 230’000
batch_size: Variable(1-20)
HR_size: 256
Dataset: LSDIR
Dataset_size: 8000-84991 hr& lr
OTF Training No
Pretrained_Model_G: 4xLSDIRCompact
Description: Able to handle JPG compression (30-100).
Total Training Time: 33+ hours

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Name: 4xLSDIRCompactR3
Author: Philip Hofmann
Release Date: 11.04.2023
License: CC BY 4.0
Model Architecture: SRVGGNetCompact
Scale: 4
Purpose: Upscale (degraded) photos to x4 their size
Iterations: 192’500
batch_size: Variable(1-45)
HR_size: 256
Dataset: LSDIR
Dataset_size: 10000-84991 hr& lr
OTF Training No
Pretrained_Model_G: 4xLSDIRCompact.pth
Description: Trained on synthetic data, meant to handle more degradations
Total Training Time: 61+ hours

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