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

As the name might suggest, this model aims to upscale text with less distortion that other models. It seems to do a good job generally, but don't expect it to be a state of the art model that can upscale magazines and stuff. It makes things more readable but since it was train on B/W pictures it desaturates them.

BSTexty

Pretrained: 4x_muy4_035_1.pth

This is my first model, so it's not perfect, but I wanted to see if I could train an upscaling model that didn't result in a lot of detail loss and deblurring like the current Compact upscaling models. I believe I accomplished this, but I was unable to reduce contrast shifting. The contrast shifting may cause skin tones to appear incorrect on bright frames, but it's not too bad overall! I'll list a few examples below; more can be found by clicking the Overview link on the Github release page.

Pretrained model: Kim2091_CrappyCompactV2.pth

Bubble AnimeScale Compact v1

2x_Bubble_AnimeScale_SwinIR_Small_v1 was trained to upscale anime frames faithfully without major contrast shifting compared to my compact model. Although much slower compared to my compact model, the results look significantly better! A few example upscales are listed below; more can be found by clicking the Overview link on the Github release page.

Bubble AnimeScale SwinIR Small v1

This is my first 2xcompact model, the main purpose of it is to upscale Dragon ball GT. for upscaling videos that have grain I would recommend denoising and dehaloing it before passing it to the model for temporal stability.

GT-evA

Purpose: Upscale text in very low quality to normal quality.

The upscale model is specifically designed to enhance lower-quality text images, improving their clarity and readability by upscaling them by 2x. It excels at processing moderately sized text, effectively transforming it into high-quality, legible scans. However, the model may encounter challenges when dealing with very small text, as its performance is optimized for text of a certain minimum size. For best results, input images should contain text that is not excessively small.

Text2HD v.1

Interpolation between 4x-UltraSharp and 4x-TextSharp-v0.5. Works amazingly on anime. It also upscales text, but it's far better with anime content. I rebranded this model on 2/10/22 to 4x-AnimeSharp from 4x-TextSharpV1.

Pretrained model: Interpolation between 4x-UltraSharp and 4x-TextSharp-v0.5

AnimeSharp

Low-resolution text/typography and symbols

Architecture	ESRGAN
Scale	2x
Size	64nf23nb
Color Mode	RGB
License	GPL-3.0-only Private use Commercial use Distribution Modifications Credit required Same License State Changes No Liability & Warranty Disclaimer
Date	2020-03-04
Dataset	Randomly generated text, with randomly chosen fonts with varying sizes. Black text on white background
Dataset size	44550
Training iterations	86000
Training epochs	7
Training batch size	4
Training OTF	No

BSTexty

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