license: cc-by-4.0
pipeline_tag: image-to-image
tags:
- pytorch
- super-resolution
4xNomos2_realplksr_dysample
Scale: 4
Architecture: RealPLKSR with Dysample
Architecture Option: realplksr
Author: Philip Hofmann
License: CC-BY-0.4
Purpose: Pretrained
Subject: Photography
Input Type: Images
Release Date: 30.06.2024
Dataset: nomosv2
Dataset Size: 6000
OTF (on the fly augmentations): No
Pretrained Model: 4xmssim_realplksr_dysample_pretrain
Iterations: 185'000
Batch Size: 8
GT Size: 256, 512
Description:
A Dysample RealPLKSR 4x upscaling model that was trained with / handles jpg compression down to 70 on the Nomosv2 dataset, preserved DoF.
Based on the 4xmssim_realplksr_dysample_pretrain I released 3 days ago.
This model affects / saturate colors, which can be counteracted a bit by using wavelet color fix, as used in these examples.
Added a static (3 256 256) onnx conversion, with fp32 and fully optimized. This can be used with chaiNNer, since the dysample pth file would be unsupported. (Removed other conversions like statis with 128 because they would produce different results, but static 256 gives the same result as using the pth file with neosr testscript.
Showcase:
Slowpics
(Click on image for better view)