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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/4xHFA2k_ludvae_realplksr_dysample)
# 4xLSDIRCompactR
Name: 4xLSDIRCompactR
Author: Philip Hofmann
Release Date: 17.03.2023
License: CC BY 4.0
Network: SRVGGNetCompact
Scale: 4
Purpose: 4x photo uspcaler that handles jpg compression, noise and slight
Iterations: 130000
batch_size: Variable(1-5)
HR_size: 256
Dataset: LSDIR
Dataset_size: 84991
OTF Training No
Pretrained_Model_G: 4xLSDIRCompact.pth
Description: Extending my last 4xLSDIRCompact model to Real-ESRGAN, meaning trained on synthetic data instead to handle more kinds of degradations, it should be able to handle compression, noise, and slight blur.
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Here is a comparison to show that 4xLSDIRCompact cannot handle compression artifacts, and that these two models will produce better output for that specific scenario. These models are not ‘better’ than the previous one, they are just meant to handle a different use case: https://imgsli.com/MTYyODY3
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