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---
license: cc-by-4.0
pipeline_tag: image-to-image
tags:
- pytorch
- super-resolution
- pretrain
---
[Link to Github Release](https://github.com/Phhofm/models/releases/tag/4xmssim_realplksr_dysample_pretrain)
# 4xmssim_realplksr_dysample_pretrain
Scale: 4
Architecture: [RealPLKSR with Dysample](https://github.com/muslll/neosr/?tab=readme-ov-file#supported-archs)
Architecture Option: [realplksr](https://github.com/muslll/neosr/blob/master/neosr/archs/realplksr_arch.py)
Author: Philip Hofmann
License: CC-BY-0.4
Purpose: Pretrained
Subject: Photography
Input Type: Images
Release Date: 27.06.2024
Dataset: [nomosv2](https://github.com/muslll/neosr/?tab=readme-ov-file#-datasets)
Dataset Size: 6000
OTF (on the fly augmentations): No
Pretrained Model: None (=From Scratch)
Iterations: 200'000
Batch Size: 8
GT Size: 192, 512
Description: [Dysample](https://arxiv.org/pdf/2308.15085) had been recently added to RealPLKSR, which from what I had seen can resolve or help avoid the checkerboard / grid pattern on inference outputs. So with the [commits from three days ago, the 24.06.24, on neosr](https://github.com/muslll/neosr/commits/master/?since=2024-06-24&until=2024-06-24), I wanted to create a 4x photo pretrain I can then use to train more realplksr models with dysample specifically to stabilize training at the beginning.
Showcase:
[Imgsli](https://imgsli.com/Mjc0OTA1)
[Slowpics](https://slow.pics/c/I9grkcqM)
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