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---
license: cc-by-4.0
pipeline_tag: image-to-image
tags:
- pytorch
- super-resolution
---
[Link to Github Release](https://github.com/Phhofm/models/releases/tag/4xNomos2_hq_mosr)
# 4xNomos2_hq_mosr
Scale: 4
Architecture: [MoSR](https://github.com/umzi2/MoSR)
Architecture Option: [mosr](https://github.com/umzi2/MoSR/blob/95c5bf73cca014493fe952c2fbc0bdbe593da08f/neosr/archs/mosr_arch.py#L117)
Author: Philip Hofmann
License: CC-BY-0.4
Purpose: Upscaler
Subject: Photography
Input Type: Images
Release Date: 25.08.2024
Dataset: [nomosv2](https://github.com/muslll/neosr/?tab=readme-ov-file#-datasets)
Dataset Size: 6000
OTF (on the fly augmentations): No
Pretrained Model: [4xmssim_mosr_pretrain](https://github.com/Phhofm/models/releases/tag/4xmssim_mosr_pretrain)
Iterations: 190'000
Batch Size: 6
Patch Size: 64
Description:
A 4x [MoSR](https://github.com/umzi2/MoSR) upscaling model, meant for non-degraded input, since this model was trained on non-degraded input to give good quality output.
If your input is degraded, use a 1x degrade model first. So for example if your input is a .jpg file, you could use a 1x dejpg model first.
PS I also provide an onnx conversion in the Attachements, I verified correct output with chainner:
<img src="https://github.com/user-attachments/assets/7f2be678-9d63-43e3-bfb6-59c94827a828" width=25% height=25%>
## Model Showcase:
[Slowpics](https://slow.pics/c/cqGJb0gT)
(Click on image for better view)
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