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

Category: Skin
Purpose: Skin Upscaling

Adds plausible high frequency detail and removes subtle blur. This is an early unfinished attempt at a x1 Lite model designed specifically for enhancing detail on skin diffuse textures of 3d characters. Even in its current state it works quite well.

Best suited for uncompressed or cleaned textures - otherwise it may just enhance any existing compression artefacts too. The training set included faces, body parts, eyes 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 enhanced with this model. The results are subtle, so run multiple times if desired.

Pretrained model: 50/50 Interpolation of DIV2K-Lite and SpongeBC1-Lite

ITF SkinDiffDetail Lite v1

[Designing a Practical Degradation Model for Deep Blind Image Super-Resolution (ICCV, 2021)](https://github.com/cszn/BSRGAN)

BSRGANx2

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

# 4xArtFaces_realplksr_dysample  
Scale: 4  
Architecture: [RealPLKSR with Dysample](https://github.com/muslll/neosr/?tab=readme-ov-file#supported-archs)  
Architecture Option: [realplksr](https://github.com/muslll/neosr/blob/master/neosr/archs/realplksr_arch.py)  

Author: Philip Hofmann  
License: CC-BY-0.4  
Purpose: Restoration  
Subject: Art  
Input Type: Images  
Release Date: 08.08.2024  

Dataset: [ArtFaces](https://github.com/Phhofm/models/releases/tag/dataset_artfaces)  
Dataset Size: 5'630  
OTF (on the fly augmentations): No  
Pretrained Model: [4xNomos2_realplksr_dysample](https://github.com/Phhofm/models/releases/tag/4xNomos2_realplksr_dysample)  
Iterations: 139'000  
Batch Size: 6  
Patch Size: 64  

Description:  
A Dysample RealPLKSR 4x upscaling model for art / painted faces.   
Based on my [ArtFaces Dataset](https://github.com/Phhofm/models/releases/tag/dataset_artfaces) which is a curated version of the [metfaces dataset](https://github.com/NVlabs/metfaces-dataset) for the purpose of training single image super resolution models.

 4xArtFaces_realplksr_dysample

Upscale Steve Harvey, but maybe other things, somehow??

BSSbeveHarvey

BSRGAN

BigFace

BigFace V3 Blend

BigFace V3 Clear

BigFace v3

FArtFace

4x photo upscaler for faces with otf jpg compression, blur and resize, trained on FFHQ dataset. This has been trained on and for faces, but i guess can also be used for other photos, might be able to retain skin detail. This is not face restoration, but simply a 4x upscaler trained on faces, therefore input images need to be of good quality if good output quality is desired.

4xFFHQDAT

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

Face-Ality V1 (Fatality Faces)

Face De-blur - slightly out of focus / blurred images of faces. It is aimed at faces / hair

Face Focus

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

4xFaceUpDAT

4xFaceUpLDAT

4xFaceUpSharpDAT

4xFaceUpSharpLDAT

Upscale faces both pixelized and real

Pretrained model: 4x_Faces_N_250000.pth

Faces_04_N

Nomos8kHAT-L_otf

Purpose: Art/People

A sharp upscaler trained specifically for small sprite faces. Does not blend, so avoid using on painted/photo portraits unless you were trying to retain more of the outlines somehow.

SmolFace

SmolFace Clean

Purpose: Faces / Game Textures

character textures for star wars games, including the heroes, rebels, sith and imperial. Plus a few main aliens…Why called trixie? Because jar jars big adventure would be too long of a name… This also provides good upscale for face textures for general purpose as well as basic star wars

Trixie

Upscales blurry 128px faces, usefull for enhancing that someone in a small picture.

Architecture	ESRGAN
Scale	4x
Size	64nf23nb
Color Mode	RGB
License	CC-BY-NC-SA-4.0 Private use Distribution Modifications Credit required Same License State Changes No Liability & Warranty Disclaimer
Date	2019-07-23
Dataset size	7510
Training iterations	180000
Training epochs	237
Training batch size	10
Training HR size	128

Faces_04_N

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