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

Video game textures, mostly metal rusty, clean or painted

Fallout Weapons (Fallout 4 Weapons?)

Fallout Weapons V2

Images from the Fate/Extra games. Works better on the newer ones.

Just a model I trained quickly to test traiNNer-redux and see what I could do with OTF. Works well enough

FatePlus Compact

Forest

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

Category: Foliage/Ground
Purpose: Upscales ground textures

Ground

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

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

Morrowind 2.0

A 4x upscaler for uncompressed normal maps with a zeroed-out B channel. The input is required to have no alpha and constant-zero blue channel. The model can handle some light compression artifacts but has trouble with quantization artifacts. 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.


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

Normal RG0

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

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 my [PBRify_Remix](<https://github.com/Kim2091/PBRify_Remix>) project. This is a much more capable model based on SwinIR Medium, which should strike a balance between capacity for learning + inference speed. It appears to have done so :)

### Replaced by 4x-PBRify_UpscalerDAT2_V1

4x-PBRify_UpscalerSIR-M_V2

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

[Link to Github Release](https://github.com/Phhofm/models/releases/4xTextureDAT2_otf)

Name: 4xTextureDAT2_otf   
Author: Philip Hofmann   
Release: 13.12.2023   
License: CC BY 4.0   
Network: DAT   
Arch Option: DAT2   
Scale: 4   
Purpose: 4x texture image upscaler, handling jpg compression, some noise and slight blur   
Iterations: 125000   
batch_size: 6   
HR_size: 128   
Dataset: GTAV_512_Dataset   
Number of train images: 30122   
OTF Training: Yes   
Pretrained_Model_G: DAT_2_x4   

Description: 4x upscale texture images, trained with the Real-ESRGAN otf pipeline so handles jpg compression, some noise and slight blur

4xTextureDAT2_otf

[Github Release Link](https://github.com/Phhofm/models/releases/tag/4xTextures_GTAV_rgt-s)

## 4xTextures_GTAV_rgt-s

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

**License:** CC-BY-0.4  
**Purpose:** Restoration   
**Subject:** Game Textures   
**Input Type:** Images   
**Release Date:** 04.05.2024   

**Dataset:** [GTAV_512_Textures](https://discord.com/channels/547949405949657098/905446120333930566/1132367991808466975)   
**Dataset Size:** 8492   
**OTF (on the fly augmentations):** No   
**Pretrained Model:** RGT_S_x4   
**Iterations:** 165'000   
**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.

**Showcase:**
[Slow Pics](https://slow.pics/s/GqXdJ6Pd)

4xTextures_GTAV_rgt-s

Various game textures. Primary wood, metal, stone

ThiefGold

Version of the previous model but based on Manga109 pretrained model and with slightly different dataset. Sometimes gives better results especially for wood and metal, sometimes worse. Sometime generates the same dotted artifacts on very bright/white images.

ThiefGoldMod

Purpose: Game textures/Art

This model upscales artwork for the game Volfoss (2001). The model peaked in quality very quickly. The NR model removes most noise, but has the downside of removing transparent portions. Use the main model in most cases.

VolArt

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	ESRGAN
Scale	4x
Size	64nf23nb
Color Mode	RGB
License	None
Date	2020-05-15
Dataset	Thief Gold & Thief II community made textures
Training iterations	110000
Training batch size	8
Training HR size	128

ThiefGold

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