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 (Natural)

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

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