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

[Link to Github Release](https://github.com/Phhofm/models/releases/tag/4xNomos2_hq_atd)

4xNomos2_hq_atd  
Scale: 4  
Architecture: [ATD](https://github.com/LabShuHangGU/Adaptive-Token-Dictionary)  
Architecture Option: [atd](https://github.com/muslll/neosr/blob/dc4e3742132bae2c2aa8e8d16de3a9fcec6b1a74/neosr/archs/atd_arch.py#L891)  

Author: Philip Hofmann  
License: CC-BY-0.4  
Purpose: Upscaler  
Subject: Photography  
Input Type: Images  
Release Date: 05.09.2024  

Dataset: [nomosv2](https://github.com/muslll/neosr/?tab=readme-ov-file#-datasets)  
Dataset Size: 6000  
OTF (on the fly augmentations): No  
Pretrained Model: 003_ATD_SRx4_finetune  
Iterations: 180'000  
Batch Size: 2  
Patch Size: 48  
Norm: true  

Description:   
An atd 4x upscaling model, similiar to the [4xNomos2_hq_dat2](https://github.com/Phhofm/models/releases/tag/4xNomos2_hq_dat2) or [4xNomos2_hq_mosr](https://github.com/Phhofm/models/releases/tag/4xNomos2_hq_mosr) models, trained and for usage on non-degraded input to give good quality output.

4xNomos2_hq_atd

[Link to Github Release](https://github.com/Phhofm/models/releases/tag/4xNomos2_hq_dat2)

# 4xNomos2_hq_dat2   
Scale: 4   
Architecture: [DAT](https://github.com/zhengchen1999/dat)   
Architecture Option: [dat2](https://github.com/muslll/neosr/blob/5fba7f162d36052010169e6517dec3b406c569ab/neosr/archs/dat_arch.py#L1111)   

Author: Philip Hofmann   
License: CC-BY-0.4   
Purpose: Upscaler   
Subject: Photography   
Input Type: Images   
Release Date: 29.08.2024   

Dataset: [nomosv2](https://github.com/muslll/neosr/?tab=readme-ov-file#-datasets)   
Dataset Size: 6000   
OTF (on the fly augmentations): No   
Pretrained Model: DAT_2_x4   
Iterations: 140'000   
Batch Size: 4   
Patch Size: 48   

Description:    
A dat2 4x upscaling model, similiar to the [4xNomos2_hq_mosr](https://github.com/Phhofm/models/releases/tag/4xNomos2_hq_mosr) model, trained and for usage on non-degraded input to give good quality output.    

I scored 7 validation outputs of each of the 21 checkpoints (10k-210k) of this model training with 68 metrics.    
[The metric scores can be found in this google sheet](https://docs.google.com/spreadsheets/d/1NL-by7WvZyDMHj5XN8UeDALVSSwH70IKvwV65ATWqrA/edit?usp=sharing).   
The corresponding image files for this scoring can be [found here](https://drive.google.com/file/d/1ZTp9fBMeawftNqzg4RN9_zIvHtul5jVc/view?usp=sharing)     
Screenshot of the google sheet:     
![|100](https://i.slow.pics/VZJTsrUv.webp)

Release checkpoint has been selected by looking at the scores, manually inspecting, and then getting responses on discord to this quick visual test, A B or C, which denote different checkpoints: https://slow.pics/c/8Akzj6rR   

Checkpoint B is the one released here, but you can also try out [Checkpoint A](https://github.com/Phhofm/models/releases/download/4xNomos2_hq_dat2/4xNomos2_hq_dat2_150000.pth) or [Checkpoint C](https://github.com/Phhofm/models/releases/download/4xNomos2_hq_dat2/4xNomos2_hq_dat2_10000.pth) if you like them better.

## Model Showcase:
[Slowpics](https://slow.pics/c/yuue9WpF)

4xNomos2_hq_dat2

[Link to Github Release](https://github.com/Phhofm/models/releases/tag/4xNomos2_hq_drct-l)

4xNomos2_hq_drct-l  
Scale: 4  
Architecture: [DRCT](https://github.com/ming053l/DRCT)  
Architecture Option: [drct_l](https://github.com/muslll/neosr/blob/dc4e3742132bae2c2aa8e8d16de3a9fcec6b1a74/neosr/archs/drct_arch.py#L937)  

Author: Philip Hofmann  
License: CC-BY-0.4  
Purpose: Upscaler  
Subject: Photography  
Input Type: Images  
Release Date: 08.09.2024  

Dataset: [nomosv2](https://github.com/muslll/neosr/?tab=readme-ov-file#-datasets)  
Dataset Size: 6000  
OTF (on the fly augmentations): No  
Pretrained Model: DRCT-L_X4  
Iterations: 200'000  
Batch Size: 2  
Patch Size: 64  

Description: 
An drct-l 4x upscaling model, similiar to the [4xNomos2_hq_atd](https://github.com/Phhofm/models/releases/tag/4xNomos2_hq_atd), [4xNomos2_hq_dat2](https://github.com/Phhofm/models/releases/tag/4xNomos2_hq_dat2) and [4xNomos2_hq_mosr](https://github.com/Phhofm/models/releases/tag/4xNomos2_hq_mosr) models, trained and for usage on non-degraded input to give good quality output.   

Model Showcase:
[Slowpics](https://slow.pics/c/1mODuuUS)

4xNomos2_hq_drct-l

Purpose: Intended as a pretrain model, can be used to upscale undegraded photos.

Original Link: https://github.com/terrainer/AI-Upscaling-Models/tree/main/4xSPANkendata

4x realistic upscaler that may also work for general purpose usage. Trained on Bicubic downscaled tiles from very high quality images.

Iterations: 500k, fine-tuned with 120k, then 110k, then 75k

batch_size: 32, 16, 12, 10

HR_size: 192, 256, 320, 192

Epoch: Unknown, 32, 20, 12

Dataset_size: 58,420 512x512 tiles, downscaled with a variety of traditional algorithms and CAR

SPANkendata

Good for

Model

Rights

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