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Neosr's latest update from yesterday included a new adaptation of the multi-scale ssim loss.
This was an experiment to test out the difference between making a SPAN pretrain with pixel loss with L1 criteria (as often used in research) vs mssim loss as its only loss.
Models are provided so they can be used for tests or also used as a pretrain for another SPAN model.


4xpix_span_pretrain

Scale: 4
Architecture: SPAN

Author: Philip Hofmann
License: CC-BY-4.0
Purpose: Pretrain
Subject: Realistic, Anime
Date: 10.04.2024

Dataset: nomos_uni
Dataset Size: 2989
OTF (on the fly augmentations): No
Pretrained Model: None
Iterations: 80'000
Batch Size: 12
GT Size: 128

Description: 4x SPAN pretrain trained on pixel loss with L1 criteria (as often used in research) on downsampled nomos_uni dataset using kim's dataset destroyer with down_up,linear,cubic_mitchell,lanczos,gauss,box (while down_up used the same and with range = 0.15,1.5).
The new augmentations except CutBlur have also been used (since CutBlur is meant to be applied to real-world SR and may cause undesired effects if applied to bicubic-only).
Config and training log provided for more details.


4xmssim_span_pretrain

Scale: 4
Architecture: SPAN

Author: Philip Hofmann
License: CC-BY-4.0
Purpose: Pretrain
Subject: Realistic, Anime
Date: 10.04.2024

Dataset: nomos_uni
Dataset Size: 2989
OTF (on the fly augmentations): No
Pretrained Model: None
Iterations: 80'000
Batch Size: 12
GT Size: 128

Description: 4x SPAN pretrain trained on neosr's new adaptation of the multi-scale ssim loss from yesterdays update on downsampled nomos_uni dataset using kim's dataset destroyer with down_up,linear,cubic_mitchell,lanczos,gauss,box (while down_up used the same and with range = 0.15,1.5).
The new augmentations except CutBlur have also been used (since CutBlur is meant to be applied to real-world SR and may cause undesired effects if applied to bicubic-only).
Config and training log provided for more details.


Showcase:
7 Slowpics Examples

Example1 Example2 Example3 Example4 Example5 Example6 Example7

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