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

[Github Release Link](https://github.com/Phhofm/models/releases/Ludvae200)

Name: Ludvae200   
License: CC BY 4.0   
Author: Philip Hofmann   
Network: [LUD-VAE](https://github.com/zhengdharia/LUD-VAE)   
Scale: 1   
Release Date: 25.03.2024   
Purpose: 1x realistic noise degradation model    
Iterations: 190'000   
H_size: 64   
n_channels: 3   
dataloader_batch_size: 16    
H_noise_level: 8   
L_noise_level: 3   
Dataset: [RealLR200](https://drive.google.com/drive/folders/1L2VsQYQRKhWJxe6yWZU9FgBWSgBCk6mz)   
Number of train images: 200   
OTF Training: No   
Pretrained_Model_G: None   

Description:
1x realistic noise degradation model, trained on the [RealLR200](https://drive.google.com/drive/folders/1L2VsQYQRKhWJxe6yWZU9FgBWSgBCk6mz) dataset as found released on the [SeeSR](https://github.com/cswry/SeeSR) github repo.
Next to the ludvae200.pth model file, I provide a ludvae200.zip file which not only contains the code but also an inference script to run this model on the dataset of your choice.
Adapt the ludvae200_inference.py script accordingly by adjusting the file paths at the beginning section, to your input folder, output folder, the folder path holding the ludvae200.pth model, and a folder path where you want the text file to be generated. I made the textfile generation the same way as I did in [Kim's Dataset Destroyer](https://github.com/Kim2091/helpful-scripts/tree/main/Dataset%20Destroyer), which means you will have each image file logged with each of the values used to degrade that specific image file in the resulting text file, which will append only and never overwrite.

You can also adjust the strength settings inside the inference script file to fit to your needs. If you in general want less strong noise for example, you should adjust the temperature upper limit from 0.4 to 0.2 or go even lower.
So in line 96 change "temperature_strength = uniform(0.1,0.4)" to "temperature_strength = uniform(0.1,0.2)" just to give an example.

These values are defaulted to my needs of my last dataset degradation workflow I used, but feel free to adjust these values. You can also do the same as I did, temporarily using deterministic values with multiple runs to determine the min and max values of noise generation you deem suitable for your dataset needs.

See the examples of what this looked like for my last dataset workflow I used my model in.

Ludvae200

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

Purpose: Correct old color photos that are tinted red

Correct old color photos that are tinted red. Model only tested in nature.

RedImage10000

A 1x model designed for skin contrast (although some backgrounds suffer a little modification), also try the other SkinContrast models

1xSkinContrast-High-SuperUltraCompact

A 1x model designed for skin contrast (although some backgrounds suffer a little modification), some images may suffer from the creation of artifacts, High Alternative is the model that generates the most artifacts, also try the other SkinContrast models

1xSkinContrast-HighAlternative-SuperUltraCompact

1xSkinContrast-SuperUltraCompact

A 2x photo upscaling compact model based on microsofts image pairs. This was one of the earliest models I started training and finished it now for release.

Parimg Compact

Nice to release a model again! This one is intended for realistic imagery, and works especially well on faces, hair, and nature shots. It should only be used on somewhat clear shots, without a lot of grain. I trained this model on SPAN, which as of the time of release, you'll need chaiNNer-nightly for. I aimed for a softer, more natural look for this model with as few artifacts as possible.

In addition to the Normal model, I've included a "soft" model. The Soft model is... softer. Basically it was an earlier version of the model with a more limited dataset. It produces more natural output on games or rendered content, but suffers a bit more with realistic stuff.

Note: In shots with DOF (depth of field) or bokeh, unfortunately there will be artifacts. 

Compatibility: You'll have to use the latest chaiNNer-nightly to use this model

ClearRealityV1

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

4xFaceUpSharpDAT

A normal ESRGAN model without degradations and without any pretrain, simply an RRDBNet model trained on paired dataset (4x downsampled) on the full LSDIR dataset (84,991 images / 165 GB)

4xLSDIR

Upscale small good quality photos to 4x their size

My first ever model 😄 Well, it’s not the best, but, it’s something 😉 
I provide some 15 examples from the validation set here for you to visually see the generated output (with chaiNNer), photo dimensions are in the name

LSDIR Compact

Upscale photos to x4 their size.

4xLSDIRCompactv2 supersedes the previously released models, it combines all my progress on my compact model. Both CompactC and CompactR had received around 8 hours more training since release with batch size 10 (CompactR had only been up to 5 on release), and these two were then interpolated together. This allows v2 to handle some degradations, while preserving the details of the CompactC model. Examples: https://imgsli.com/MTY0Njgz/0/2

LSDIR Compact v2

Upscale small photos with compression to 4x their size.

Trying to extend my previous model to be able to handle compression (JPG 100-30) by manually altering the training dataset, since 4xLSDIRCompact can't handle compression. Use this instead of 4xLSDIRCompact if your photo has compression (like an image from the web).

LSDIR Compact C

Upscale compressed photos to x4 their size.

Able to handle JPG compression (30-100).

LSDIR Compact C3

Upscale good quality input photos to x4 their size

The original 4xLSDIRCompact a bit more trained, cannot handle degradation

LSDIRCompactN

Upscale small photos with compression, noise and slight blur to 4x their size.

Extending my last 4xLSDIRCompact model to Real-ESRGAN, meaning trained on synthetic data instead to handle more kinds of degradations, it should be able to handle compression, noise, and slight blur.

Here is a comparison to show that 4xLSDIRCompact cannot handle compression artifacts, and that these two models will produce better output for that specific scenario. These models are not ‘better’ than the previous one, they are just meant to handle a different use case.

LSDIR Compact R

Upscale (degraded) photos to x4 their size. 

Trained on synthetic data, meant to handle more degradations

LSDIR Compact R3

The RealESRGAN_x4plus finetuned with the big LSDIR dataset (84,991 images / 165 GB), with manually added jpg compression.

4xLSDIRplusC

The RealESRGAN_x4plus finetuned with the big LSDIR dataset (84,991 images / 165 GB), no degradation.

4xLSDIRplusN

[Link to Github Release](https://github.com/Phhofm/models/releases/tag/4xDRCT-mssim-pretrains)

Since no [DRCT](https://github.com/ming053l/DRCT/tree/main) model releases exist yet, I am releasing my drct-s, drct and drct-l pretrains. 
These can be used to speed up and stabilize early training stages when training new drct models.
Trained with mssim (and augs, and color and lumaloss) on downscaled [nomosuni](https://github.com/muslll/neosr/tree/0ecbeb98dcefd97801da0ea39ca2cd03a87fce21?tab=readme-ov-file#datasets).
Training files are also provided additionally.

## 4xmssim_drct-l_pretrain

**Scale:** 4  
**Architecture:** [DRCT](https://github.com/ming053l/DRCT)  
**Architecture Option:** [DRCT-L](https://github.com/ming053l/DRCT/blob/8c13e63135f494913cd504c073e41ef52250d1d4/options/train/train_DRCT-L_SRx4_ImageNet_from_scratch.yml#L55)  

**Author:** Philip Hofmann  
**License:** CC-BY-0.4  
**Purpose:** Pretrained  
**Subject:** Anime, Photography  
**Input Type:** Images  
**Release Date:** 28.04.2024  

**Dataset:** [nomos_uni](https://github.com/muslll/neosr?tab=readme-ov-file#datasets)  
**Dataset Size:** 2989  
**OTF (on the fly augmentations):** No  
**Pretrained Model:** None (=From Scratch)  
**Iterations:** 108'000  
**Batch Size:** 2-6  
**GT Size:** 128-320  

**Description:** A pretrain to start drct-l model training, since there are no official released drct pretrains yet I trained these myself and release them here.

Architecture	ESRGAN
Scale	1x
Size	64nf23nb
Color Mode	RGB
License	CC0-1.0 Private use Commercial use Distribution Modifications No Liability & Warranty Disclaimer
Date	2021-06-23
Dataset	plant.zip from OutdoorSceneTrain_v2 upscaled 200% with Preserve Details 2.0
Dataset size	1036
Training iterations	400000
Training epochs	3099
Training batch size	8
Training HR size	128

Plants

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