Anti Aliasing

Purpose: Anti aliasing / Deblur

To eliminate aliasing and general artifacts caused by not enough resolution while bringing out details Im stopping its training here because it's getting worse, i think of some aligment issues by game's rendering pipeline + downscaling...

GainRESV3 (Aggro)

GainRESV3 (Natural)

GainRESV3 (Passive)

Eliminate aliasing and general artifacts caused by not enough resolution. Model knows to add clarity and soften oversharpened areas. Gives image a supersampled look. Had a better version thats more faithful to details but i lost it.

GainRESV4

Takes composite/RF/VHS NES footage and attempts to restore it to RGB quality. Assumes footage has been properly deinterlaced via field duplication from 240p to 480p/720p/etc. Note that:

* All footage was captured in 240p/480p/720p NTSC.
* RGB footage was captured via an AV Famicom with the RGB Blaster via the Retrotink 2x or GBS Control.
* The model was trained exclusively on individual frames, so it can't fix things like dropouts.
* The even and odd fields of NES composite tend to be a bit...different from each other, so there will be some jitter at 60fps.
* I don't have access to an NES Toploader, so I wouldn't expect it to fix the jailbars very well.

Revision History:

* 1.5.0 (06/29/2025): Applied additional augmentations to reduce overfitting. Also added a small amount of 720p training data.
* 1.0.0 (11/03/2024): Initial release.

NES Composite to RGB

Takes composite/RF/VHS NES footage and attempts to restore it to RGB quality. Assumes footage has been properly deinterlaced via field duplication from 240p to 480p/720p/etc. Note that:

* All footage was captured in 240p/480p/720p NTSC.
* RGB footage was captured via an AV Famicom with the RGB Blaster via the Retrotink 2x or GBS Control.
* The model was trained exclusively on individual frames, so it can't fix things like dropouts.
* The even and odd fields of NES composite tend to be a bit...different from each other, so there will be some jitter at 60fps.
* I don't have access to an NES Toploader, so I wouldn't expect it to fix the jailbars very well.

Compared to the OmniSR variant of this model, this version runs more quickly (despite the larger model file size), and should do a good job on footage captured directly from the console using composite. If your footage is from a VHS tape or was captured over RF, the slower OmniSR model is probably a better choice.

Revision History:

* 1.5.0 (09/17/2025): Added "Small" version of model

NES-Composite-2-RGB-Small

Takes composite/RF/RGB/VHS Genesis footage and attempts to restore it to RGB quality. Assumes footage has been properly deinterlaced via field duplication from 240p to 480p/720p/etc. Note that:

* All footage was captured in 240p/480p/720p NTSC.
* Ground truth RGB footage was captured via a triple-bypassed Model 2 Genesis with new capacitors via the Retrotink 2x or GBS Control. Other footage was captured from various Model 1 Genesis consoles (with and without the "High Definition Graphics" mark).
* The model was trained exclusively on individual frames, so it can't fix things like dropouts.
* Should help fix color, blur, and jailbars.
* Cannot un-dither fully blended pixels.

Revision History:

1.0.0 (09/02/2025): Initial release.

SEGA Genesis Cleanup

SS Anti Alias 9x

Takes composite/RF/RGB/VHS Genesis footage and attempts to restore it to RGB quality. Assumes footage has been properly deinterlaced via field duplication from 240p to 480p/720p/etc. Note that:

* All footage was captured in 240p/480p/720p NTSC.
* Ground truth RGB footage was captured via a triple-bypassed Model 2 Genesis with new capacitors via the Retrotink 2x or GBS Control. Other footage was captured from various Model 1 Genesis consoles (with and without the "High Definition Graphics" mark).
* The model was trained exclusively on individual frames, so it can't fix things like dropouts.
* Should help fix color, blur, and jailbars.
* Cannot un-dither fully blended pixels.

Compared to the OmniSR variant of this model, this version runs more quickly (despite the larger model file size), and should do a good job on footage captured directly from the console using composite or RGB. If your footage is from a VHS tape or was captured over RF, the slower OmniSR model is probably a better choice.

Revision History:

1.0.0 (09/16/2025): Initial release.

Sega Genesis Cleanup-Small

A 1x model designed for skin contrast (although some backgrounds suffer a little modification), also try the other SkinContrast models

1xSkinContrast-High-SuperUltraCompact

A 1x model designed for skin contrast (although some backgrounds suffer a little modification), some images may suffer from the creation of artifacts, High Alternative is the model that generates the most artifacts, also try the other SkinContrast models

1xSkinContrast-HighAlternative-SuperUltraCompact

1xSkinContrast-SuperUltraCompact

Purpose: Anti-aliasing, Restoration

I was bored, so I did this. This model uses DPID as the scaling algorithm for the HRs. The original images were 8k or 12k. It's significantly sharper than Box/Area scaling, yet does a great job with aliasing. This allows for a very sharp model with minimal artifacts, even on the SPAN version.

The main model is trained on 12k images captured with Nvidia Ansel. It took about 2 days capturing manual 4k and 12k pairs for this model. The 4k captures were used as the LR, the 12k captures were resized to 4k with DPID with randomized lambda values, then trained on as HRs. 

The Alt model is trained exclusively on 8k images from my 8k dataset, resized to 4k with dpid. This provides a clearer result with less noise, but it doesn't handle long edges well at all.

Thanks to CF2lter for advice on preparing the dataset, and umzi2 for creating the [rust version of DPID](<https://github.com/umzi2/pepedpid>). 

**Showcase:** https://slow.pics/c/TCyqje9K
![Animation (2)](https://github.com/user-attachments/assets/fb9f010a-a4e1-4537-8b23-9a69974011c6)

SuperScale

SuperScale_Alt_RPLKSR_S

SuperScale_RPLKSR_S

Purpose: Artifact Removal

A version of UnResize trained on RCAN, which is faster and provides better quality than ESRGAN

This model does **not remove compression or perform deblurring**, unlike the original UnResize models. __It **only** removes scaling artifacts.__

I've attached the script I used to create the dataset (it utilizes imagemagick) and the config for [traiNNer-redux](https://github.com/the-database/traiNNer-redux)

UnResizeOnly_RCAN

This model is designed to upscale game screenshots (3D Games) by 4 times. The SPSR version is an improvement over the ESRGAN based V2.

BS_ScreenBooster_SPSR

Fine-tuned 4xFSDedither to upscale images from the game Riven, but should be better in general, particularly on ordered dithering. I adjusted the dataset to have a better variety of dithering parameters, and turned up the HFEN and pixel loss to get better details and color restoration with less noise.

FSDedither Riven

This model is intended to mainly handle PS2 compression and a mixture of Realistic and cartoonish textures, it's not meant to be used for very low resolution textures such as item icons.

GameAI 1.0

GameAI 2.0

Purpose: Chainmail game textures. Alternatively, it can be used to turn plain images into chainmail.

A highly generative model for chainmail game textures. Works fairly well on items without visible chainmail texture (ie just a grey area) or items with clear chainmail texture, less well on items with present but poorly defined chainmail texture. I'm not 100% happy with it but it is still an improvement over existing models in enough situations to be worth releasing.

Link

Purpose: Upscaling pack.png

Designed to upscale one specific minecraft screenshot. Results on dissimilar screenshots may be poor.

MinecraftAlphaUpscaler with Good data

Purpose: Upscaling pack.png

Upscales Minecraft screenshots by 4x. May suffer from haloing, weird patterns on blocks and JPEG-like artifacts.

Minepack

Purpose: Upscaling pack.png

Designed to upscale one specific minecraft screenshot. Results on dissimilar screenshots may be poor.

Pretrained model: 4xESRGAN originally, but I used each previous version as a pretrained until I got to v4.

Architecture	ESRGAN
Scale	1x
Size	64nf23nb
Color Mode	RGB
License	WTFPL Private use Commercial use Distribution Modifications Disclaimer
Date	2022-02-26
Dataset	5K resolution shots from paladins rendered by 200% for hr and 37.5%(1080p) for lr then downscaled to 1080
Dataset size	150
Training iterations	9000
Training epochs	1000
Training batch size	8
Training HR size	32

GainRESV3 (Natural)

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