Fallin

Ani4K is a 2x model for creating natural-looking 2K and 4K upscales of modern anime. The model places emphasis on depth of field and blur effect retention, which are extremely common in modern anime and something that many anime models struggle with. In addition, Ani4K is trained for strong detail retention, WEB/BD compression clean-up, and producing pleasing lineart. 

More info on the Github page: https://github.com/Sirosky/Sirosky-Upscaling-Models.

Blur Retention Comparisons (including vs. xinntao's 2x paper model): https://imgsli.com/MTg2NTg5/4/5

General Comparisons: https://imgsli.com/MTg2ODI2

Ani4K

2x general purpose anime upscaler, with a focus on cleaning up scuffed sources while enhancing texture detail. While the model was trained on DVDs, it still works well on modern anime. The model is trained to deal with noise, compression artifacts, blur, bleeding, haloing and scuffed line art. Apparently, it also learned to deal with some dotcrawl.

AniScale

AniScale 2 is a versatile and faithful anime model trained for use on a variety of post ~2000 sources. As the name suggests, this is the successor to the original AniScale, and a substantial upgrade in nearly every respect. Superb blur and depth of field handling, thorough WEB and DVD compression repair, and pleasing line art refinement are the hallmarks of AniScale 2. A few notes:

- While AniScale 2 is trained first and foremost as an OmniSR model, AniScale 2 is also intended to be a platform to explore multiple SISR archs. For starters, I've trained OmniSR and Compact versions, but more will come.
- AniScale 2 also comes with a "refiner" model, creatively named AniScale 2 Refiner (AS2R). AS2R is a 1x Compact model (for maximum speed) trained to supplement and increase the versatility of AniScale 2, without making the base model excessively aggressive. The Refiner is focused on providing light sharpening, fixing line art and line thinning, depending on whether you run the model before or after upscaling with AniScale 2. 
- For more information, please visit the [Github wiki page](https://github.com/Sirosky/Upscale-Hub/wiki/%F0%9F%8C%9F-AniScale-2-&--AniScale-2-Refiner).

**Comparisons**

- [General Comparisons](https://imgsli.com/MjEyMjQw/0/1)
- [Depth of Field/Blur Comparisons](https://imgsli.com/MjEyMjQ1/5/4) (including vs. my favorite model punching bag)

AniScale-2-Compact

AniScale-2-DITN

AniScale-2-ESRGAN

AniScale-2-ESRGAN-Lite

AniScale-2-OmniSR

Purpose: Anime/Pretrained

A 2x Ultra Lite model coming in under 8MB. Trained with over 15 sets of LRs ranging in a wide amount of issues. Handles Rainbows, Dot Crawl, MPEG/H.264 Compression, and may even assist in removing halos, and fixing blurriness in certain cases. This is my first public model for everyone. Best when used on old anime that is grainy. I can't say what anime it's best suited for as I have tried multiple series, and have found it does a good job on most all the tests. I wouldn't say use this for Western Animation, but it may work. I have done a few tests that I have shown in the upscale results, but that was chained with other models to achieve such a result. This model is meant to retain the more natural look of a series. There is a color shift on the end result, not drastic, but still noticable. I figure you should fix any color issues in post that way to give a more polished upscale. Big thanks to @SaurusX for the model name, and just helping out in general with anything.

AnimeClassics UltraLite

Real-time 2x Real-ESRGAN Compact/UltraCompact/SuperUltraCompact models designed for upscaling 1080p anime to 4K. 

The aim of these models is to address scaling, blur, oversharpening, and compression artifacts while upscaling to deliver a result that appears as if the anime was originally mastered in 4K resolution. Can be set up to run in real-time with mpv on Windows using https://github.com/the-database/mpv-upscale-2x_animejanai

The development of the V3 models spanned over seven months, during which over 100 release candidate models were trained and meticulously refined. The V3 models introduce several improvements compared to V2, including:

More faithful appearance to original source
Improved handling of oversharpening artifacts, ringing, aliasing
Better at preserving intentional blur in scenes using depth of field
More accurate line colors, darkness, and thickness
Better preservation of soft shadow edges

Overall, the V3 models yield significantly more natural and faithful results compared to the V2 models.test

AnimeJaNai_HD_V3_Compact

Real-time 2x Real-ESRGAN Compact/UltraCompact/SuperUltraCompact models designed for upscaling 1080p anime to 4K.

The aim of these models is to address scaling, blur, oversharpening, and compression artifacts while upscaling to deliver a result that appears as if the anime was originally mastered in 4K resolution. Can be set up to run in real-time with mpv on Windows using https://github.com/the-database/mpv-upscale-2x_animejanai

The development of the V3 models spanned over seven months, during which over 100 release candidate models were trained and meticulously refined. The V3 models introduce several improvements compared to V2, including:

More faithful appearance to original source Improved handling of oversharpening artifacts, ringing, aliasing Better at preserving intentional blur in scenes using depth of field More accurate line colors, darkness, and thickness Better preservation of soft shadow edges

Overall, the V3 models yield significantly more natural and faithful results compared to the V2 models.

AnimeJaNai_HD_V3_SuperUltraCompact

AnimeJaNai_HD_V3_UltraCompact

Realtime 2x model intended for high or medium quality 1080p anime with an emphasis on correcting the inherit blurriness of anime while preserving details and colors. Not suitable for artifact-heavy or highly compressed content as it will just sharpen artifacts. Also works with SD anime by running the model twice. Can be set up to run with mpv on Windows using https://github.com/the-database/mpv-upscale-2x_animejanai

Minimum of RTX 3080 is recommended for running UltraCompact model on 1080p in realtime; RTX 4090 is required to run Compact on 1080p in realtime. SuperUltraCompact should run in realtime on 1080p on some lower cards. The compact model is recommended on SD content. 

Samples: https://imgsli.com/MTUxMDYx \
Comparisons to Anime4K + other compact models and upscalers: https://imgsli.com/MTUxMjY4

AnimeJaNai Standard v1 Compact

AnimeJaNai Standard v1 SuperUltraCompact

AnimeJaNai Standard v1 UltraCompact

AnimeJaNai Strong v1 Compact

AnimeJaNai Strong v1 UltraCompact

Real-time 2x Real-ESRGAN Compact/UltraCompact/SuperUltraCompact models designed for upscaling 1080p anime to 4K. The models correct the inherent blurriness found in anime while preserving details and colors. Optimized for upscaling from 1080p to 4K, but can still produce worthwhile results when upscaling some lower-resolution anime. Can be set up to run in real-time with mpv on Windows using https://github.com/the-database/mpv-upscale-2x_animejanai

Most HD anime are not produced in native 1080p resolution but rather have a production resolution between 720p and 1080p. When the anime is distributed to consumers, the video is scaled up to 1080p, causing scaling artifacts and blur in the video. The aim is to address these issues while upscaling to deliver a result that appears as if the anime was originally mastered in 4K resolution.

Development of the V2 models spanned over four months, during which over 200 release candidate models were trained and meticulously refined. The V2 models introduce several improvements compared to V1:

More accurate "native-res aware" sharpening, so the model works just as well on blurry native 720p sources, sharper native 1080p sources, and everything in between
More accurate colors including line colors
Improved artifact handling
Better preservation and enhancement of background details and grain

Overall, the V2 models yield much more natural and faithful results compared to V1.

AnimeJaNai v2 Compact

AnimeJaNai v2 SuperUltraCompact

AnimeJaNai v2 UltraCompact

GitHub Link: https://github.com/Kim2091/Kim2091-Models/releases/tag/2x-AnimeSharpV2_Set

This is my first anime model in years. Hopefully you guys can find a good use-case for it. Included are 4 models:

1. RealPLKSR (Higher quality, slower)
2. MoSR (Lower quality, faster)

There are Sharp and Soft versions of both
When to use each:
- __Sharp:__ For heavily degraded sources. Sharp models have issues depth of field but are best at removing artifacts 
- __Soft:__ For cleaner sources. Soft models preserve depth of field but may not remove other artifacts as well

__Notes:__
- MoSR doesn't work in chaiNNer currently
- To use MoSR:
  1. Use the ONNX version in tools like [VideoJaNai](<https://github.com/the-database/VideoJaNai>)
  2. Update spandrel in the latest version of ComfyUI

The ONNX version may produce slightly different results than the .pth version. If you have issues, try the .pth model.

2x-AnimeSharpV2_MoSR_Sharp

2x-AnimeSharpV2_MoSR_Soft

2x-AnimeSharpV2_RPLKSR_Sharp

2x-AnimeSharpV2_RPLKSR_Soft

Purpose: Real time upscaler for 1080p anime

 Fallin has two versions: “Soft” and "Strong" 

 For most users, “Soft” is a more safe choice, it has no oversharpening artifacts, and it is better at removing compression artifacts and denoising. If you want a safe choice, you should choose “Soft”.

 “Strong” is the oversharpened version of Soft. Oversharpening artifacts may appear on lines, and lines may not always look good due to oversharpening. Unlike “Soft”, it has a problem with whitening faces. If you like sharpness, choose “Strong”. And I think “Strong” is better in DOF

 Note: Keep in mind that Strong produces oversharpening artifacts.

Thanks to @styler00dollar  for helping me fix the color issue.

Architecture	CUGAN
Scale	2x
Color Mode	RGB
License	CC-BY-4.0 Private use Commercial use Distribution Modifications Credit required State Changes No Liability & Warranty Disclaimer
Date	2025-09-14
Dataset	Anime BD
Dataset size	3000
Training iterations	150000
Training batch size	8
Training HR size	256
Training OTF	No

Fallin Strong

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