Name: 2x AniSD Suite (Multiple Models)
Author: Sirosky

Removes banding, blocking, dithering, aliasing, noise and color tint on DDS Compressed Skin Diffuse Textures.

This should work extremely well on most modern DDS compression types. The training set was compressed with BC3/DXT5, BC3/DXT5 Fast, BC2/DXT3, BC2/DXT3 Fast, and a small number of JPEG compressed images to cover outliers.

This model is trained to remove the slight green color tint that DDS compression tends to add to skin textures, so the model output will not match the original color tone of the input image. This is the desired result though, as DDS compression shifts the colors to a sickly green tint and this model corrects that to more natural color tones.

The training set included faces, body parts, eyes, mouths and hair in a variety of skin types and tones so it should work well on most related diffuse textures.

However it's not just limited to skin, many other images and textures can be cleaned with this model. Designed to be used as a first step cleaning pass before applying additional models after. Check out the other ITF Models.

ITF SkinDiffDDS v1

## Ani4K v2  

 Ani4K v2, as the successor to the original Ani4K, retains its predecessor's fantastic detail retention, depth of field preservation and faithfulness to the original source. As the name suggests, the model is targeted at modern anime, ranging from high-quality Bluray to crappy WEB releases, for upscaling either to 2K or 4K.

An **UltraCompact** version of the model is available on the [Github release](https://github.com/Sirosky/Upscale-Hub/releases/tag/Ani4K-v2). The UltraCompact version is faster, without a perceptual hit to quality in most cases.

📌 [More comparisons](https://slow.pics/c/1HykdyT5)

****

**FAQ**

- *How does v2 differ from v1?* I'm so glad you asked! A shortcoming of the v1 models is that the model really struggled on sources which were poorly mastered. This is unfortunately still very common even with modern anime. v2 is far more capable of dealing with such sources. 
- *How does Ani4K v2 differ from JaNai v3? Which one should I pick?* JaNai v3 is a fantastic model, and shares many of the fundamental training objectives behind Ani4K (DOF preservation, faithfulness, etc.). I'd say that the primary difference is one of training philosophy-- JaNai seeks to render the source as if it was originally mastered in 4K, whereas Ani4K seeks to produce an upscale that is as close as possible to the source (while cleaning up any issues). Long story short, test both models and see which you prefer.
- *What model versions are there?* Ani4K comes in Compact and UltraCompact flavors. Compact is of course a standard option. UltraCompact provides a noticeable performance uplift, without too much impact on quality. I ultimately did not train a SuperUltraCompact variant as I felt the hit to the model quality was far too significant.

Ani4VK v2 Compact

## AniSD Suite (15 models)

**Scale:** 2x (1x for AniSD DB)  
**Architecture:** SPAN / Compact / SwinIR Small / CRAFT / DAT2  / RPLKSR

**Dataset:** Anime frames. Credits to @.kuronoe. and @pwnsweet (EVA dataset) for their contributions to the dataset!  
**Dataset Size:** ~7,000 - ~13,000  

AniSD is a suite of 15 (as of time of writing) specialized SISR models trained to restore and upscale standard definition digital anime from ~2000s and onwards, including both both WEB and DVD releases. Faithfulness to the source and natural-looking output are the guiding principles behind the training of the AniSD models. This means avoiding oversharpened output (which can look especially absurd on standard definition sources), minimizing upscaling artifacts, retaining the natural detail of the source and of course, fixing the standard range of issues found in many DVD/WEB release (chroma issues, compression, haloing/ringing, blur, dotcrawl, banding etc.). Refer to the infographic above for a quick breakdown of the available models, and refer to the [Github release](https://github.com/Sirosky/Upscale-Hub/releases/tag/AniSD-RealPLKSR) for further information.

AniSD Suite (Multiple Models)

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

upscaling old anime. help to denoise and find lines and dehalo

BIGOLDIES

Purpose: Digital Animation

A very focused model meant for upscaling the TMNT 2003 DVDs. Degradations were added via AVISynth in order to match the video on the TMNT 2003 DVDs to correct the source problems. Problems corrected include aliased red chroma, chroma vertical blur, bad deinterlacing, banding, compression "grain", and poor animation line detail. The AVS scripts for the LR's were run through HCEnc to get authentic low bit rate MPEG-2 artifacts for fixing. By design, the final model gives a very digital-looking result and does not do a good job of retaining textures as the style of TMNT 2003 is all flats and gradients.

DigiGradients Lite

Category: Digital Animation


This was trained on the dataset that OptimusPrimal used for his DigitalFilm models. This model cleans up the image removing some noise while upscaling. This model is very fast, running around 200x the speed of a standard ESRGAN model on my system. This is primarily a proof of concept for how fast Real-ESRGAN models can get while still producing nice results.

DigitalFlim SuperUltraCompact

Purpose: Digital Animation

Meant as a versatile model for upscaling high detail digital anime and cartoons. Has debanding, MPEG-2 correction, and halo reduction. Trained to handle both 4:3 and 16:9 DVD material with equal efficacy. Will retain a lot of textures except for the really high freq stuff.

Digitoon Lite

Purpose: 2x anime upscaler which tries to preserve dof

2x anime upscaler, a non-otf version of the HFA2kCompact model which tries to preserve dof by not deblurring.

Original Link: https://drive.google.com/drive/folders/1RZbALUTCf4DYDTVMBR74b8Nt-7m6TEyk?usp=drive_link

HFA2k compact multijpg

Upscaling frames from Irodori anime (namely kemono friends) from 540p (the source render resolution) to 1080p, low resolution flat shaded art, de-JPEG of the aforementioned

KemonoScale v2

I trained Skr's great LD-Anime model on compact architecture. It upscales while fixing numerous video problems, including: noise/grain, compression artifacts, rainbows, dot crawl, halos and color bleed. This compact version may look slightly worse than Skr's original model, but runs significantly faster and also retains the correct colors better than the original model did.

LD-Anime Compact

Purpose: Denoise/Dehalo

Denoise, dehalo, derainbow old anime

LD-Anime_Skr v1.0

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 Giroro

This is a model for the restoration of My Little Pony: Friendship is Magic, however it also works decently well on all similar art. 

It was trained in 2x on ground truth 3840x2160 HRs and 1920x1080 LRs of varying compression, so it is able to upscale from 1080p to 2160p, where its detail retention is great, however it may create noticeable artifacting if looked at closely, like areas of randomly coloured pixels along edges. In 1x or 1.5x (2x upscaled and then downscaled back down) it performs extremely well, almost perfectly in fact, in correcting colours, removing compression, and crisping up lines - and this is the way the model is intended to be used (hence the acronym of its name being "SS", or "supersampling").

**[Github Release](https://github.com/Derpiesaurus/models/releases/tag/v1.0)**   
**Showcase:** https://slow.pics/s/1ixqCSjy

MLP StarSample V1.0

2x compact fast universal upscaler pair trained with jpg degradation (down to 40) and multiscale (down_up, bicubic, bilinear, box, nearest, lanczos).

2x-NomosUni_compact_multijpg

2x span fast universal upscaler pair trained with jpg degradation (down to 40) and multiscale (down_up, bicubic, bilinear, box, nearest, lanczos).

2x-NomosUni_span_multijpg

Purpose: Compression Removal, Noise Reduction, Line Correction, MPEG2 / LD Artifact Removal

This is my first model release. The model was inspired from a personal project which I have been pursuing for some time now, which this model aims to solve. This model will upscale low resolution hand drawn animation from 1970s to 2000. The colors are retained with effective noise control. Details and Textures are maintained to a good degree considering animations. Color Spills are also corrected depending on the colors. Shades of white and yellows have been difficult. It also makes the lines slightly sharper and thinner. This could be a plus depending on your source. The model is also temporally stable across my tests with little observable issues. 

**Showcase:** 
Images - https://imgsli.com/MjYwNzY1/12/13
Video Sample -  https://t.ly/Jp7-w vs Upscale - https://t.ly/PdsKs

2x Pooh V4

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

Purpose: VHS Restoration

An advanced VHS recording model designed to enhance video quality by reducing artifacts such as haloing, ghosting, and noise patterns. Optimized primarily for PAL resolution (NTSC might work good as well).

VHS2HD

Purpose: 

Emulating Waifu2x at Noise Level 3 NOTE: You can't use this with regular esrgan forks or the bot, it has to be run through basicsr

Waifaux NL3 SRResNet

Purpose: 

Trained this model to see how it would work trying to essentially get the same results as Waifu2x but with ESRGAN

Waifaux NL3 SuperLite

Since the above 4xFFHQDAT model is not able to handle the noise present in low quality input images, i made a small variant/finetune of this, the 4xFFHQLDAT model. This model might come in handy if your input image is of bad quality/not suited for the previous model. I basically made this model in a response to an input image posted in upscaling-results channel as a request to this upscale model (since 4xFFHQDAT would not be able to handle noise), see Imgsli1 example below for result.

4xFFHQLDAT

Description: 4x photo upscaler for faces, trained on the FaceUp dataset. These models are an improvement over the previously released 4xFFHQDAT and are its successors. These models are released together with the FaceUp dataset, plus the accompanying [youtube video](https://www.youtube.com/watch?v=TBiVIzQkptI)

This model comes in 4 different versions:  
4xFaceUpDAT (for good quality input)  
4xFaceUpLDAT (for lower quality input, can additionally denoise)  
4xFaceUpSharpDAT (for good quality input, produces sharper output, trained without USM but sharpened input images, good quality input)  
4xFaceUpSharpLDAT (for lower quality input, produces sharper output, trained without USM but sharpened input images, can additionally denoise)  

I recommend trying out 4xFaceUpDAT

4xFaceUpLDAT

4xFaceUpSharpLDAT

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)

Architecture	SPAN
Scale	2x
Size	feature channels: 48
Color Mode	RGB
License	CC-BY-NC-4.0 Private use Distribution Modifications Credit required State Changes No Liability & Warranty Disclaimer
Date	2024-06-09
Dataset	Anime frames
Dataset size	13000
Training OTF	No

AniSD Suite (Multiple Models)

AniSD Suite (15 models)

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