BCGone Smooth

To retain details while removing artifacts caused by dxt compression on textures

DEDXT

Removing compression artifacts from DXT1 compressed textures. This model was created to remove DXT1 compression artifacts from textures imported into the Source Engine. Compressed textures in the engine sometimes have a green-tint which this model also corrects. The data for this model contained a good mix of diffuse textures and normal maps which means this model is pretty good at removing compression from normals as well. Creating this model was a real learning experience for me and I hope someone finds a good use for it.

DXTDecompressor Source V3

This model is made for Source Engine textures. It tries to remove compression artifacts such as blockyness, discoloration, green tint. It does pretty well on a lot of things, realistic stuff as well, but it was mostly made to work on TF2 textures. It's made to keep as much detail as possible, without any unnecessary denoising/sharpening. Huge thanks to Twittman for assisting me along this journey.

DXTless SourceEngine

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

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

[Link to Github Release with more infos to the process](https://github.com/Phhofm/models/releases/tag/2xAoMR_mosr)

2xAoMR_mosr  
Scale: 4  
Architecture: [MoSR](https://github.com/umzi2/MoSR)  
Architecture Option: [mosr](https://github.com/umzi2/MoSR/blob/95c5bf73cca014493fe952c2fbc0bdbe593da08f/neosr/archs/mosr_arch.py#L117)  

Author: Philip Hofmann  
License: CC-BY-0.4  
Purpose: A 2x mosr upscaling model for game textures  
Subject: Game Textures  
Input Type: Images  
Release Date: 21.09.2024 (dd/mm/yy)  

Dataset: Game Textures from Age of Mythology: Retold  
Dataset Size: 13'847  
OTF (on the fly augmentations): No  
Pretrained Model: [4xNomos2_hq_mosr](https://github.com/Phhofm/models/releases/tag/4xNomos2_hq_mosr)  
Iterations: 510'000  
Batch Size: 4  
Patch Size: 64  

## Description:   
In short: A 2x game texture mosr upscaling model, trained on and for (but not limited to) Age of Mythology: Retold textures.

Since I have been playing Age of Mythology: Retold (casual player), I thought it would be interesting to train an single image super resolution model on (and for) game textures of AoMR, but this model should be usable for other game textures aswell.
This is a 2x model, since the biggest texture images are already 4096x4096, I thought going 4x on those would be overkill (also there are already 4x game texture upscaling models, so this model can be used for similiar cases where 4x is not needed).

Model Showcase:
[Slowpics](https://slow.pics/s/IlkmsToH)

2xAoMR_mosr

Gamecube and Wii textures Dumps.

It is designed as a faster alternative to the full size model, it requires much less Vram and is significantly faster.

HDCube Compact

Category: Fabric/Cloth
Purpose: Fabric

This model set upscales fabric or cloth textures (works on cats too!). The Alt model is just an earlier iteration version. It may work better on some images.The images need to be minimally compressed or passed through a decompression model first. It works with DDS compression though.

Fabric

Fabric-Alt

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: Gamecube and Wii textures.

Gamecube and Wii textures (mainly DXT and 8bit color compression). Is good for preserving fine details without affecting the original style too much, it is not suitable for pixel art, small icons and text under 16 pixel.

Pretrained model: 4x_NMKD Siax

HDCube

Gamecube and Wii textures Dumps.

https://github.com/Venomalia/HDcube/tree/main/v2

HDCube2

Purpose: Gamecube and Wii textures.
Pretrained: 4x_HDcube2

It can be used for all image formats supported by Gamecube and Wii hardware and can remove its typical artifacts like CMPR Block Compression (DXT1 algorithm, also known as BC1), color palette errors, color reduction up to 8bit color depth and 1bit alpha depth.

HDCube3

A 4x upscaler for BC1-compressed normal maps with a zeroed-out B channel. The output normals will also have a constant-zero B channel. Use external software or image editing plugins to properly normalize the generated normals and generate the Z component (if necessary). E.g. chaiNNer can do this with Normalize Normal Map node. Do not rely on this network producing unit vectors. 

**Dataset transformation:** \
The LR have been compressed with various BC1 compression settings (dithering, weighting) using Texconv version 2021.11.8.1. Since the contents of the B channel influence the R and G channels during compression, the LR were given a B channel that was either the Z component of the normal, a random constant color, or a random texture (usually the G channel of the associated albedo) before compression. The LR + B channel was then compressed and the resulting BC1-compressed DDS was converted back into a PNG and had its B channel zeroed out.

More information: https://github.com/RunDevelopment/ESRGAN-models/blob/main/normals/README.md

Normal RG0 BC1

A 4x upscaler for BC7-compressed normal maps with a zeroed-out B channel and no alpha. The input is required to have no alpha and constant-zero blue channel. BC7-compressed images with an alpha channel should be handled by the BC1 model instead. This is because BC7 achieves very different quality for images with and without an alpha channel. The output normals will also have a constant-zero B channel. The output normals will also have a constant-zero B channel. Use external software or image editing plugins to properly normalize the generated normals and generate the Z component (if necessary). E.g. chaiNNer can do this with Normalize Normal Map node. Do not rely on this network producing unit vectors. 

**Dataset transformation:** \
The LR have been compressed using Texconv version 2021.11.8.1. Since the contents of the B channel influence the R and G channels during compression, the LR were given a B channel that was either the Z component of the normal, a random constant color, or a random texture (usually the G channel of the associated albedo) before compression. The LR + B channel was then compressed and the resulting BC7-compressed DDS was converted back into a PNG and had its B channel zeroed out.

More information: https://github.com/RunDevelopment/ESRGAN-models/blob/main/normals/README.md

Normal RG0 BC7

PBRify Github: https://github.com/Kim2091/PBRify_Remix

Release Link: https://github.com/Kim2091/Kim2091-Models/releases/tag/4x-PBRify_RPLKSRd_V3

This update brings a new upscaling model, 4x-PBRify_RPLKSRd_V3. This model is roughly 8x faster than the current DAT2 model, while being *higher quality*. It produces far more natural detail, resolves lines and edges more smoothly, and cleans up compression artifacts better.

As a result of those improvements, PBR is also much improved. It tends to be clearer with less defined artifacts. 

However, this model is currently **only compatible with ComfyUI**. chaiNNer has not yet been updated to support this architecture.

[More Comparisons](https://imgsli.com/Mjk5NjQ5)

4x-PBRify_RPLKSRd_V3

Project page: https://github.com/Kim2091/PBRify_Remix

Yet another model in the PBRify_Remix series. This is a new upscaler to replace the previous 4x-PBRify_UpscalerSIR-M_V2 model.

This model far exceeds the quality of the previous, with far more natural detail generation and better reconstruction of lines and edges.

Comparison: https://slow.pics/c/DCjlXPGb

4x-PBRify_UpscalerDAT2_V1

This is part of a larger model set, PBRify_Remix. PBRify_Remix is an easy way to upscale and generate PBR textures using existing low quality textures. The dataset consists of ethically sourced textures from websites like ambientCG and Polyhaven, which sets it apart from most other models.

It's intended for use with RTX Remix (hence the name) but it'll work for other things as well.

4x-PBRify_UpscalerSPANV4

This model was made to upscale realistic low-res textures that are compressed by either JPEG or BC1. From my testing, this works rather well on realistic GameCube textures such as the ones from Shrek Extra Large and the board textures from Mario Party 4. This model could also work on some real life images, especially the ones that are taken outdoors.

RealisticRescaler

## 4xTextures_GTAV_rgt-s_dither

**Scale:** 4  
**Architecture:** [RGT](https://github.com/zhengchen1999/RGT)  
**Architecture Option:** RGT-S  

**Author:** Philip Hofmann  
**License:** CC-BY-0.4  
**Purpose:** Restoration  
**Subject:** Game Textures  
**Input Type:** Images  
**Release Date:** 08.05.2024  

**Dataset:** [GTAV_512_Textures](https://discord.com/channels/547949405949657098/905446120333930566/1132367991808466975)  
**Dataset Size:** 7061  
**OTF (on the fly augmentations):** No  
**Pretrained Model:** 4xTextures_GTAV_rgt-s  
**Iterations:** 128'000  
**Batch Size:** 6,4  
**GT Size:** 128,256  

**Description:**
A model to upscale game textures, trained on GTAV Textures, handles jpg compression down to 80 and was trained with dithering. Basically the previous 4xTextures_GTAV_rgt-s model but extended to handle dithering.

**Showcase:**  
[Slow Pics 25 Examples](https://slow.pics/s/EW7Ifiuw)

4xTextures_GTAV_rgt-s_dither

A 4x upscaler for BC1-compressed wood textures. The model was trained on wood planks, wood stems, and a bit of tree bark. It performs reasonably well on woody textures and produces sharp outputs. However, it also tends to slightly shift the hue of the image, so results might appear slightly more red.

More information: https://github.com/RunDevelopment/ESRGAN-models/blob/main/wood/README.md

Architecture	Compact
Scale	2x
Size	64nf16nc
Color Mode	RGB
License	CC-BY-4.0 Private use Commercial use Distribution Modifications Credit required State Changes No Liability & Warranty Disclaimer
Date	2023-02-09
Dataset	Artifact free textures and photos (stone, wood, metal, clothes, plants, characters, FX)
Dataset size	20000
Training iterations	500000
Training epochs	459
Training batch size	8
Training HR size	128

HDCube Compact

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