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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/4xHFA2kLUDVAESwinIR_light%264xHFA2kLUDVAESRFormer_light)
# 4xHFA2kLUDVAESwinIR_light
Name: 4xHFA2kLUDVAESwinIR_light
Author: Philip Hofmann
Release Date: 10.06.2023
License: CC BY 4.0
Network: SwinIR
Arch Option: SwinIR-light
Scale: 4
Purpose: An lightweight anime 4x upscaling model with realistic degradations, based on musl's HFA2k_LUDVAE dataset
Iterations: 350,000
batch_size: 3
HR_size: 256
Epoch: 99 (require iter number per epoch: 3424)
Dataset: HFA2kLUDVAE
Number of train images: 10270
OTF Training: No
Pretrained_Model_G: None
Description: 4x lightweight anime upscaler with realistic degradations (compression, noise, blur). Visual outputs can be found on https://github.com/Phhofm/models/tree/main/4xHFA2kLUDVAE_results, together with timestamps and metrics to compare inference speed on the val set with other trained models/networks on this dataset.
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