A model to help remove compression artifacts in BC1-BC3/DXT1-DXT5 compressed images (these all have color encoded the same way)

BC1 Smooth 2

BCGone Detailed V2 40-60

Attempts to remove the damages done by BC1 compression.

BCGone Smooth

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

DEDXT

Attempts to remove PVRTC compression artifacts. If 1x_DEPVR is unable to remove some artifacts you should use the other version but it can't remove color artifacts properly so you should overlay color of normal version to it.

DEPVR

DEPVR Artifact

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

[Link to Github Release](https://github.com/Phhofm/models/releases/tag/1xDeH264_realplksr)

# 1xDeH264_realplksr  
Scale: 1  
Architecture: [RealPLKSR](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, H264  
Subject: Photography  
Input Type: Images  
Release Date: 08.08.2024  

Dataset: [nomosv2](https://github.com/muslll/neosr/?tab=readme-ov-file#-datasets)  
Dataset Size: 6000  
OTF (on the fly augmentations): No  
Pretrained Model: [1xDeJPG_realplksr_otf](https://github.com/Phhofm/models/releases/tag/1xDeJPG_realplksr_otf)  
Iterations: 210'000  
Batch Size: 8  
Patch Size: 64  

Description:   
A 1x de h264 model to remove h264 compression.  

Showcase:  
[Slowpics](https://slow.pics/c/71ulMz8P?canvas-mode=fit-height)  
[Imgsli](https://imgsli.com/Mjg1NTQy)

1xDeH264_realplksr

1xDeJPG OmniSR model

Meant to remove jpg artifacts. First compression round on the training dataset was 40-95, second round was 80-100.

Example: https://slow.pics/c/70nGEvPR

PS these are non-otf models. otf experiments have not quite done it for me (probably would need more adjusting), but they can be found in the experiments folder and their outputs in the outputs folder: https://drive.google.com/drive/folders/1RRXZCRVqlqU_iaeF-RGUM9UBWtI0HseY?usp=drive_link

DeJPG_OmniSR

1xDeJPG SRFormer light model

Meant to remove jpg artifacts. First compression round on the training dataset was 40-95, second round was 80-100.

onnx conversions in this folder which contains all files: https://drive.google.com/drive/folders/1RRXZCRVqlqU_iaeF-RGUM9UBWtI0HseY

Example: https://slow.pics/c/70nGEvPR

PS these are non-otf models. otf experiments have not quite done it for me (probably would need more adjusting), but they can be found in the experiments folder and their outputs in the outputs folder: https://drive.google.com/drive/folders/1RRXZCRVqlqU_iaeF-RGUM9UBWtI0HseY?usp=drive_link

DeJPG_SRFormer_light

[Link to Github Release](https://github.com/Phhofm/models/releases/tag/1xDeJPG_realplksr_otf)

1xDeJPG_realplksr_otf   
Scale: 1    
Architecture: [RealPLKSR](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: JPEG, Restoration  
Subject: Photography  
Input Type: Images  
Release Date: 09.07.2024  

Dataset: [nomosv2](https://github.com/muslll/neosr/?tab=readme-ov-file#-datasets)  
Dataset Size: 6000  
OTF (on the fly augmentations): Yes  
Pretrained Model: This one used the 60 model as pretrain, while I used [4xNomos2_realplksr_dysample](https://github.com/Phhofm/models/releases/tag/4xNomos2_realplksr_dysample) as a pretrain for the 60 model.  
Iterations: 196'000  
Batch Size: 24  
GT Size: 64  

Description:   
A 1x dejpg model, trained with otf down to jpg 40 in both degradation processes, see the yml config file for details.  
I also release the 1xDeJPG_realplksr_otf_60 model additionally, which had been trained with less jpg compression in comparison, down to 60 in both cases, where the hope was that it could give a bit higher quality on less compressed inputs.  
The 40 model is released as default since it will be able to handle more compressed inputs in general.  
Since these are trained with otf will slightly deblur. Examples here are compressed, and then recompressed, with jpg 40.  

Showcase:  
[Slowpics](https://slow.pics/c/c0Wft9H4)  
[Imgsli](https://imgsli.com/Mjc3NzMw)

1xDeJPG_realplksr_otf

Pretrained model: 1x_DeJpeg_Fatality_01_200000_G.pth

DeJpeg Fatality PlusULTRA!

[Link to Github Release](https://github.com/Phhofm/models/releases/tag/1xDeNoise_realplksr_otf)

# 1xDeNoise_realplksr_otf  
Scale: 1  
Architecture: [RealPLKSR](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, Denoise    
Subject: Photography  
Input Type: Images  
Release Date: 08.08.2024  

Dataset: [nomosv2](https://github.com/muslll/neosr/?tab=readme-ov-file#-datasets)  
Dataset Size: 6000  
OTF (on the fly augmentations): Yes  
Pretrained Model: [1xDeJPG_realplksr_otf](https://github.com/Phhofm/models/releases/tag/1xDeJPG_realplksr_otf)  
Iterations: 200'000  
Batch Size: 8  
Patch Size: 64  

Description:   
A 1x realplksr model to denoise, trained with the realesrgan-otf pipeline, also handles a bit of jpg compression (if stronger jpg compression handling is needed, [1xDeJPG_realplksr_otf](https://github.com/Phhofm/models/releases/tag/1xDeJPG_realplksr_otf) can be used).

1xDeNoise_realplksr_otf 

Incomplete lite model to remove ROQ compression

DeRoqBeta-lite

Purpose: artifact

This model attempts to make films upscaled to 4K with Topaz Gaia-HQ look more natural and filmic. It sharpens, adds film grain, and smooths out small artefacts from the upscaling process. I recommend adding a tiny amount of grain to the input to seed the model (you can do this in VEAI), otherwise the film grain will remain static across frames that don't move much. Pretrain model used with permission to relicense from Twittman.

Filmify4K v2

A 1x model designed to reduce artifacts and restore detail to images upscaled by 4xFrankendata_FullDegradation_SRFormer. It could possibly work with other upscaling models too.

Frankenfixer_SRFormerLight

Pretrained: Custom JPEG dataset

Model I forgot to release. This doesn't totally remove JPEG artifacts, but it does a decent job at a fast rate. It seemingly does a better job of retaining detail than some other JPEG models. The model is incomplete, I need to train it further on compact rather than ESRGAN. This is just a temporary release

Kim2091 DeJpeg v0

Remove JPEG artifacts from manga without destroying screentone and other details. For 25-5 JPEG Quality

MangaJPEGLQ

NMKD Jaywreck3 (Lite)

Purpose: Texturizing

Tries to reverse heavy h264 compression. Fails. Can be used to texturize images though.

NMKD h264Texturize

This model decompresses images and video compressed using RoQ. Config and presets will be added when Mega decides to let me use their site!

Architecture	RealPLKSR
Scale	1x
Color Mode	RGB
License	CC-BY-4.0 Private use Commercial use Distribution Modifications Credit required State Changes No Liability & Warranty Disclaimer
Date	2024-08-08
Dataset	Nomosv2
Dataset size	6000
Training iterations	210000
Training batch size	8
Training HR size	64
Training OTF	No

1xDeH264_realplksr

1xDeH264_realplksr

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