This model corrects jagged lines on animation that has been deinterlaced. It handles simple line doubling, line interpolation, and even Yadif-style artifacts. It can also handle sources that were resized after deinterlacing, for example resizing from ntsc to pal resolutions. If a source has been upscaled after deinterlacing, it will need to be downsized before applying this model.

AnimeUndeint Compact

A fast upscaler that is a replacement for standard methods of cleaning noise from the picture. Ideal for BDRemux 720p and 1080p, but only if the noise is monochrome or close to it. So far, attempts are underway to create a dataset that could eliminate color noise at the same speed.

1x BW Denoise

This model helps repair color bleed and heavy chroma noise that may be present on some older footage, particularly that which was recorded on VHS. It also cleans up rainbows if they are present.

BleedOut Compact

Purpose: Cartoons

This is a simple retrain of SaurusX's 1x_BroadcastToStudioLite_485k model from a couple years ago. This one is trained on compact, actually has less artifacts, and is significantly faster.

__Comparisons:__ <https://slow.pics/c/oGwHyYym>
![1733071729 5752351](https://github.com/user-attachments/assets/f247b870-d49e-4c37-ad39-c4565efe164d)

BroadcastToStudio Compact

This is SaurusX's original description:
Improvement of low-quality cartoons from broadcast sources. Will greatly increase the visual quality of bad broadcast tape sources of '80s and '90s cartoons (e.g. Garfield and Friends, Heathcliff, DuckTales, etc). Directly addresses chroma blur, dot crawl, and rainbowing. You're highly advised to take care of haloing beforehand in your favorite video editor as the model will not fix it and may make existing halos more noticeable.

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Sadly this model has some intense artifacts. Thankfully the SPAN re-train reduced these a bit, but they're still problematic. This was just a quick test of SPAN's capabilities. This was trained only to ~66k iterations, compared to the 480k of the original.

BroadcastToStudio_SPAN

Improvement of low-quality cartoons from broadcast sources.
Will greatly increase the visual quality of bad broadcast tape sources of '80s and '90s cartoons (e.g. Garfield and Friends, Heathcliff, DuckTales, etc). Directly addresses chroma blur, dot crawl, and rainbowing. You're highly advised to take care of haloing beforehand in your favorite video editor as the model will not fix it and may make existing halos more noticeable.

BroadcastToStudio Lite

Create realistic noise and compression artifacts on images. This is meant to synthesize paired dataset for SISR training.

DM600_LUDVAE

DitherDeleter Smooth

Attempts to remove the damages done by dithering. For this model, I downscaled all of the HR images to make their pixel size closer to 1000x1000 using the Box filter and then downscaled them again by 50% using the Point filter. Afterwards, I applied 32-bit Riemersma to every image in the dataset.

https://imgsli.com/NTA5MjU/0/1

DitherDeleterV3 Smooth

Wipes out dot crawl and rainbows in animation.

Dotzilla Compact

Category: DeToon
Purpose: Detooning

An attempt to detoon images/drawings of people

DoubleDetoon

This model is far from perfect, but it does a decent job on removing dot crawl and dehalo\deblock old anime at fast rate without removing many details. chaining it with models like 2x-anifilm-compact or 2xLD-ANIME or 2x_AnimeClassics_UltraLite_510K will give good results i think.

Epsilon-one compact

This is a sharpening/deblurring model for anime video. It was created with three goals in mind:
Be blazing fast
Try to avoid enhancing noise and compression artifacts
Try to avoid over-enhancing intentionally blurred parts of the image

Despite that last point, this is not intended to be used on modern anime which makes heavy of use depth-of-field effects.

HurrDeblur SuperUltraCompact

Upscale Anime while keeping as much of the original detail as possible. Isn't among the sharpest models and if the anime is somewhat blurry it will retain that detail but add more pixels to give a smoother edge. Doesn't change the color very much and may make some scene's very slightly darker.

Pretrained model: Pony Models

Loyaldk SharpKeroro

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

1x Manhwa Null

A fast upscaler that is a replacement for standard methods of cleaning noise from the picture. Ideal for BDRemux or WEB-DL 720p or 1080p it. It gets rid of floral noise well, the smaller its grain size, the more uniform the resolution for all videos will be. If you need a more stable video, then you can also add 2x-DigitalFlim-SuperUltraCompact after this upscaler. My upscaler can slightly simplify the geometry of very small objects.

1x RGB max Denoise

Restoring frames from the show "Sheeep"

This model was trained to restore "Sheeep" while retaining and enhancing the noise present in the show. The model amazingly finished training in only 8.8k iterations with no pretrain, with a dataset of only 4 image pairs. This model should work well for animes and cartoons with a lot of grain present. There are some slight haloing issues in dark colors unfortunately, but I was unable to fix it.

SheeepIt!

First ever lite BC1/DXT1 model. Probably only useful for cartoony textures like those in spongebob games or other cartoon licensed games.

Pretrained model: 1x_DIV2K-Lite_80k

SpongeBC1 Lite

Purpose: Animation - Quantized

Removed color quantization/indexing and dithering from cartoon style images and textures

Pretrained model: 1x_1xDEDITHER_32_512_126900_G

Spongebob De-Quantize

Purpose: Upscaling older cartoons

This is yet another retrain of SaurusX's SwatKats_Lite model. The dataset was reprocessed with my [Find Misaligned Images](<https://github.com/Kim2091/helpful-scripts/tree/main/Find%20Misaligned%20Images>) script, along with the new [ImgAlign](<https://github.com/sonic41592/ImgAlign>) update, which drastically reduced artifacts and increased the model's capabilities. This particular model is roughly on par with or slightly behind the original, doing better in some spots and worse in others. Refer to the attached examples to see this.

The advantage of this over the original is the speed improvement of Compact over ESRGAN-lite. In a 480p test on an RTX 4090, the original ESRGAN-lite model took 0.28 seconds to process a frame vs Compact's 0.13 seconds.

https://slow.pics/s/dF3Icjpv

OR

<https://imgsli.com/MjQxMzc1/0/1>

SwatKats Compact

Fix vertical blur / split lines / shadowing. A 1x lite model of my 2xSwatKats. Resolves the same video problems as before, but 1x and faster and meant for chaining to other 2x models (or whatever). Input MUST be 540 vertical as the blur problem is very resolution sensitive.

SwatKats Lite

Category: VHS Tapes
Purpose: VHS

Best when used on cartoons, it can work on anime. Due to the dataset it does struggle a bit with orange colors and grainy dark spots. This model is meant to be used to clean up the image before using it on a 2x or 4x model. Example with 2x-BIGOLDIES https://imgsli.com/OTQ0NjM/1/0.

ToonVHS

The "Complete Series" release of "Where on Earth is Carmen Sandiego?" by Mill Creek has a number of episodes that suffer from some rather harsh deinterlacing. This model attempts to restore the footage to something closer to a more advanced deinterlacing job. It may also be useful on episodes (primarily found in seasons 3 & 4) that were produced with advanced compositing where certain layers used similar forced deinterlacing techniques, and for cleaning up artifacts left by Yadif, QTGMC, etc.

Revision History:
* v1.1.0: Added data to improve handing of certain situations, particularly in season 3.
* v1.0.0: Initial release.

Where On Earth-Deinterlace Fix-Large

Artifacts Dithering alsa

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

Architecture	ESRGAN
Scale	1x
Size	64nf23nb
Color Mode	RGB
License	CC-BY-NC-4.0 Private use Distribution Modifications Credit required State Changes No Liability & Warranty Disclaimer
Date	2019-11-02
Dataset	1 frame from every scene of every episode of season 11 of Spongebob, downscaled 50% with nearest neighbor, then downscaled by 25% with box for the HR, then color quantized with 256 colors for the LR
Dataset size	4803
Training iterations	130000
Training epochs	26
Training batch size	1
Training HR size	128

Spongebob De-Quantize

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