Name: 1x NMKD Jaywreck3-Soft (Lite)
Author: Nmkd

1xDeJPG OmniSR model

Meant to remove jpg artifacts. First compression round on the training dataset was 40-95, second round was 80-100.

Example: https://slow.pics/c/70nGEvPR

PS these are non-otf models. otf experiments have not quite done it for me (probably would need more adjusting), but they can be found in the experiments folder and their outputs in the outputs folder: https://drive.google.com/drive/folders/1RRXZCRVqlqU_iaeF-RGUM9UBWtI0HseY?usp=drive_link

DeJPG_OmniSR

1xDeJPG SRFormer light model

Meant to remove jpg artifacts. First compression round on the training dataset was 40-95, second round was 80-100.

onnx conversions in this folder which contains all files: https://drive.google.com/drive/folders/1RRXZCRVqlqU_iaeF-RGUM9UBWtI0HseY

Example: https://slow.pics/c/70nGEvPR

PS these are non-otf models. otf experiments have not quite done it for me (probably would need more adjusting), but they can be found in the experiments folder and their outputs in the outputs folder: https://drive.google.com/drive/folders/1RRXZCRVqlqU_iaeF-RGUM9UBWtI0HseY?usp=drive_link

DeJPG_SRFormer_light

Pretrained model: 1x_DeJpeg_Fatality_01_200000_G.pth

DeJpeg Fatality PlusULTRA!

This model is meant to reduce or eliminate JPEG Compression without making the original images too smooth or killing detail. It manages to do a fairly good job but don't expect overly compressed images to work with this.

Pretrained model: 1xESRGAN + previous attempts

JPEGDestroyer

Pretrained: Trained on Custom (CC0 Textures)

Gallery with sample images: https://drive.google.com/open?id=1HWokMsUwsR_Mw-NOJgc8OdS8mkdIje_C

JPG (00-20%)

Pretrained: Trained on Custom (Photos / Manga)

JPG (20-40%)

JPG (40-60%)

JPG (60-80%)

JPG (80-100%)

Pretrained: Custom JPEG dataset

Model I forgot to release. This doesn't totally remove JPEG artifacts, but it does a decent job at a fast rate. It seemingly does a better job of retaining detail than some other JPEG models. The model is incomplete, I need to train it further on compact rather than ESRGAN. This is just a temporary release

Kim2091 DeJpeg v0

Remove JPEG artifacts from manga without destroying screentone and other details. For 80-50 JPEG Quality

MangaJPEGHQ

Remove JPEG artifacts from manga without destroying screentone and other details. For 95-80 JPEG Quality

MangaJPEGHQPlus

Remove JPEG artifacts from manga without destroying screentone and other details. For 25-5 JPEG Quality

MangaJPEGLQ

Remove JPEG artifacts from manga without destroying screentone and other details. For 60-30 JPEG Quality

MangaJPEGMQ

Model designed exclusively to remove compression artifacts while preserving noise in Manhwa, Manhua, Webtoons etc.

1x Manhwa Null

NMKD Jaywreck3 (Lite)

NMKD Jaywreck3-Soft (Lite)

Purpose: Texturizing

Tries to reverse heavy h264 compression. Fails. Can be used to texturize images though.

Architecture	ESRGAN
Scale	1x
Size	32nf12nb
Color Mode	RGB
License	WTFPL Private use Commercial use Distribution Modifications Disclaimer
Date	2020-10-13
Dataset	DIV2K Train + Valid
Training iterations	320000
Training batch size	4
Training HR size	112

NMKD Jaywreck3-Soft (Lite)

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