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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/4xNature_realplksr_dysample)
# 4xNature_realplksr_dysample
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: Restoration
Subject: Realistic
Input Type: Images
Release Date: 13.08.2024
Dataset: [Nature](https://github.com/Phhofm/models/releases/tag/nature_dataset)
Dataset Size: 7'000
OTF (on the fly augmentations): No
Pretrained Model: [4xNomos2_realplksr_dysample](https://github.com/Phhofm/models/releases/tag/4xNomos2_realplksr_dysample)
Iterations: 265'000
Batch Size: 8
Patch Size: 64
Description:
A Dysample RealPLKSR 4x upscaling model for photographs nature (animals, plants).
LR prepared with down_up, linear, cubic_mitchell, lanczos, gauss and box scaling with some gaussian blur and jpg compression down to 75 (as released with my dataset, the LRx4 folder).
Trained with dysample, ea2fpn, ema, eco, adan_sf, mssim, perceptual, color, luma, dists, ldl and ff (see config toml file).
Based on my [Nature Dataset](https://github.com/Phhofm/models/releases/tag/nature_dataset) which is a curated version of the [iNaturalist 2017 Dataset](https://github.com/visipedia/inat_comp/blob/master/2017/README.md) for the purpose of training single image super resolution models.
Use the [4xNature_realplksr_dysample.pth](https://github.com/Phhofm/models/releases/download/4xNature_realplksr_dysample/4xNature_realplksr_dysample.pth) file for inference. Also provided is a static onnx conversion with 3 256 256. Config, state, and net_d files are additionally provided for trainers, to maybe create an improved version 2 of this model or to train a similiar model from this state.
Showcase:
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