Name: 1x Plants
Author: Muf

[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

[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

[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 

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

Category: Foliage/Ground
Purpose: bad upscale

Images of plants, trees or other foliage upscaled with Photoshop Preserve Details 2.0. Sharpens and "subdivides" details and noise so it doesn't look upscaled.

Plants

Good for

Model

Rights

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