4xHFA2kLUDVAESwinIR_light & 4xHFA2kLUDVAESRFormer_light
I am releasing two lightweight models for anime upscaling with realistic degradations (compression, noise, blur) trained on HFA2kLUDVAE with two different networks:
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.
Name: 4xHFA2kLUDVAESRFormer_light
Author: Philip Hofmann
Release Date: 10.06.2023
License: CC BY 4.0
Network: SRFormer
Arch Option: SRFormer-light
Scale: 4
Purpose: A 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: 97 (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.
