A model trained with the goal of preserving texture while removing halftones. I do plan on making improved versions down the line, as this one didn't come out as well as I'd like.

Bendel_Halftone

model was created as a compact analog of popular models that delete a screenton in the process of overlaying a new screenton.

Speed(image:1441x2048, video card: 3060 12gb)

  1x_wtp_descreentone_compact: 1.2 sec.

wtp_descreentone_compact

Purpose: remove paper texture and screentone from independent manga scans

Well, I don’t even know how to describe it, a small breakthrough in my war with self-scans, I’m still not very happy with it, but maybe the lump will come in handy one day https://discord.com/channels/547949405949657098/547949806761410560/1204387665798103050

DWTP DP dat2 beta

Purpose: remove paper texture and screentone from independent manga scans

This is my first model for this, it can be said to remove poorly https://discord.com/channels/547949405949657098/1159598245417332796/1185562840304721921

DWTP DS dat2 v1

DAT descreenton model, designed to reduce discrepancies on tiles due to too much loss of the first version, while getting rid of the removal of paper texture https://discord.com/channels/547949405949657098/944307589284495410/1192058535257854042

DWTP DS dat2 v3

I've been experimenting a lot since the last descripton was released to save even more detail, and to get rid of some of the beginner's soreness. All three models are not yet perfect and I will develop them, but what is the essence of the three models, the models on omni is just a division of VL eats less large screenton, VH is just a re-trained model on a modified dataset and with VL as a prevoritolnoy shadowing that it would demolish more screenton, while not going to demolish all the textures and very large screenton. Esrgan model was trained on a dataset VH for use in the colab, it works in some places worse than Omni, so could not fight with overlapping screenton.

DWTP_descreenton_H_esrgan

DWTP_descreenton_VH4

DWTP_descreenton_VL4

Purpose: manga screentone removal

I haven’t shared models for a long time, I think one of these days I’ll upload a dat version and a less accurate esrgan. This version of the model was meant to be an attempt to get as much detail out of the scans as possible while removing the screentone, so some things had to be sacrificed, but overall it is better than all my previous models combined in terms of accuracy and tone.

[**examples**](https://slow.pics/c/zbOGXoDv)
[colab which contains the rest of my models](https://colab.research.google.com/drive/1w8fgsmh9fcHDtpQnYy77aKgpHEWlKpj-?usp=sharing#scrollTo=aYbqM1pWkPTT)
||In general, this is a collab for convenient scaling of manga in the ru segment of translators and there are other people’s models there, if I suddenly added someone else’s model that has a license, please let me know, I’ll correct it||

DWTP ds esrgan 5

Cartoons/pixel art/other non-realistic stuff with dithering

FSDedither Manga

Manga-style images with or without dithering - cartoons, maybe pixel art, etc

FSMangaV2

Category: Art/Pixel Art
Purpose: Pixel Art/Sprites

Upscales medium resolution Sprites, dithered or undithered, can also upscale manga/anime and gameboy camera images.

Fatality 1

Fatality Comix

Fatality Comix MK3

Purpose: Illustrations, digital art, manga covers

Model for color images including manga covers and color illustrations, digital art, visual novel art, artbooks, and more. The DAT2 version is the highest quality version but also the slowest. See the ESRGAN version for faster performance. 

https://slow.pics/c/GfArurPG

IllustrationJaNai_V1_DAT2

Purpose: Illustrations, digital art, manga covers

Model for color images including manga covers and color illustrations, digital art, visual novel art, artbooks, and more. The ESRGAN version is high quality with balanced performance. See the DAT2 version for maximum quality. 

https://slow.pics/c/GfArurPG

IllustrationJaNai_V1_ESRGAN

Manga109Attempt

4x photo upscaler on the SSDIR_Sharp dataset, trained with otf, using the same settings as Real_HAT_GAN.  
These otf values are very high in my opinion. This was an experiment if I used the same settings as Real_HAT_GAN (therefore the name).   
This model will denoise very strongly and smooth out a lot (so a lot of details will be lost. But maybe this effect might be beneficial to someone)

4xReal_SSDIR_DAT_GAN

The model was trained for some tests, but maybe someone will need to remove a screentone with a color image; in addition to the screentone, it can handle small halftones quite well

WTP ColorDS

I just decided to finish off the previous one, made the manga design more clear and corrected the deviation in color temperature by adding a more diverse set of colors

WTP UDS Esrgan

Purpose: Grayscale pencil/sketch style art with paper textures (Made in Abyss specific)

Old model trained for pencil sketch style art, good at restoring the paper and pencil and other textures , specifically the Made in Abyss manga, but should work for art with a similar style.

Comparison:
https://slow.pics/c/D0MuvwLY

eula digimanga MiA 65k

Black and white digital manga with halftones.

eula digimanga bw v1

Vast improvement over v1 in low frequency detail; moiré and artifacting reduced significantly and less random noise from JPEG artefacts in the input. Also now only works on 1 channel images, so it runs slightly faster on average and resulting images are much smaller but it might not work on some ESRGAN implementations, I personally recommend using chaiNNer. v1 may still be better in some edge cases.

There's also a supplementary 1x model that denoises very low quality LRs and smooths halftones so the image works better with the 4x model. Only trained it to help build the dataset and it's useless for already decent-ish LRs but may help you in some situations.

eula digimanga bw v2 nc1

In general, what was the point? The project was conceived for a manga scale with a screentone using the method of superimposing a new screentone, but for this the old screentone must be removed, but this is not all the same important contour. With the screentone overlay method, the contour should be as close to 1 bit as possible, otherwise we will see full contours, wavy contours, etc. This is the whole idealogy of this model.

wtp_descreentone

Purpose: pre-workout to speed up your workouts

I see that many people are trying manga scale models on the Omni, and to speed up the process I decided to do preliminary training, the only loss is the resize: (“lanczos”, “bicubic”, “bilenear”, “HAMMING”)

Original link: https://drive.google.com/drive/folders/1ln1ZneTvqPbvi8nsJwzjMoYSDQ_DcHBS?usp=sharing

Architecture	OmniSR
Scale	4x
Color Mode	RGB
License	None
Date	2023-09-14
Dataset size	122000
Training iterations	105000
Training batch size	4
Training HR size	256

wtp_descreentone

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