Name: 1x BroadcastToStudio_SPAN
Author: Kim2091

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

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

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

This model was trained on lossless video frames of Metal Arms: Glitch in the System, compressed with Bink V1 for the LR frames. It's a lot more efficient than my first DeBink model, and also has less artifacts. It's not quite as robust, but the compression is barely noticeable in videos after processing with this.

Sample video: https://cdn.discordapp.com/attachments/903415274521374750/904129653374078976/XB_Demo_Mov-85600_G.mp4

DeBink Lite

This model removes early 2000s Bink and other compression artifacts. Works well on almost any image or video compression type.

DeBink v4

DeBink v5

Incomplete lite model to remove ROQ compression

DeRoqBeta-lite

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

These models deblur most images. It was trained mostly on aniso2 and iso blurring (BSRGAN augmentation) with some gaussian mixed in. It performs well on most blurry images, but I'd recommend using something like Fatality_Deblur for very strong gaussian blur.

Focus

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

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

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

This model decompresses images and video compressed using RoQ. Config and presets will be added when Mega decides to let me use their site!

RoQ_nRoll

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

This is SaurusX's original description:
Fix vertical blur / split lines / shadowing. A 1x 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.

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The goal for this retrain was to get the "magic" of SwatKatsLite into SPAN for much faster video processing. It's about 80% of the way there. There's some artifacts here and there that aren't present in the original, and it has small amounts of color halos in certain areas. I've retrained this model so many times that I've just decided to release it. This particular release was trained to ~450k.

The included Family Guy image shows the color ringing issue. Sorry. 

Update: I've added a second alt model that has less color ringing, but doesn't do quite as good with line repair. I believe I've reached architecture limits

SwatKats_SPAN

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.

Architecture	SPAN
Scale	1x
Color Mode	RGB
License	CC-BY-NC-SA-4.0 Private use Distribution Modifications Credit required Same License State Changes No Liability & Warranty Disclaimer
Date	2023-12-14
Dataset	SaurusX's BroadcastToStudio dataset
Dataset size	10000
Training iterations	66000
Training OTF	No

BroadcastToStudio_SPAN

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