Purpose: Anime

This is a successor to AnimeSharpV3 based on RCAN instead of ESRGAN. It outperforms both versions of AnimeSharpV3 in every capacity. It's sharper, retains *even more* detail, and has very few artifacts. It is __extremely faithful__ to the input image, even with heavily compressed inputs. 

To use this model, you must update to the [**latest chaiNNer nightly build**](https://github.com/chaiNNer-org/chaiNNer-nightly/releases)

The `2x-AnimeSharpV4_Fast_RCAN_PU` model is trained on RCAN PixelUnshuffle. This is much faster, but comes at the cost of quality. I believe the model is ~95% the quality of the full V4 RCAN model, but ~6x faster in Pytorch and ~4x faster in TensorRT. This model is ideal for video processing, and as such was trained to handle MPEG2 & H264 compression.

__Comparisons:__ https://slow.pics/c/63Qu8HTN
https://slow.pics/c/DBJPDJM9
![1736292155 679079](https://github.com/user-attachments/assets/875be696-0948-4de2-9d03-85a581891a49)

AnimeSharpV4

AnimeSharpV4_Fast_RCAN_PU

[Link to Github Release with more infos to the process](https://github.com/Phhofm/models/releases/tag/2xAoMR_mosr)

2xAoMR_mosr  
Scale: 4  
Architecture: [MoSR](https://github.com/umzi2/MoSR)  
Architecture Option: [mosr](https://github.com/umzi2/MoSR/blob/95c5bf73cca014493fe952c2fbc0bdbe593da08f/neosr/archs/mosr_arch.py#L117)  

Author: Philip Hofmann  
License: CC-BY-0.4  
Purpose: A 2x mosr upscaling model for game textures  
Subject: Game Textures  
Input Type: Images  
Release Date: 21.09.2024 (dd/mm/yy)  

Dataset: Game Textures from Age of Mythology: Retold  
Dataset Size: 13'847  
OTF (on the fly augmentations): No  
Pretrained Model: [4xNomos2_hq_mosr](https://github.com/Phhofm/models/releases/tag/4xNomos2_hq_mosr)  
Iterations: 510'000  
Batch Size: 4  
Patch Size: 64  

## Description:   
In short: A 2x game texture mosr upscaling model, trained on and for (but not limited to) Age of Mythology: Retold textures.

Since I have been playing Age of Mythology: Retold (casual player), I thought it would be interesting to train an single image super resolution model on (and for) game textures of AoMR, but this model should be usable for other game textures aswell.
This is a 2x model, since the biggest texture images are already 4096x4096, I thought going 4x on those would be overkill (also there are already 4x game texture upscaling models, so this model can be used for similiar cases where 4x is not needed).

Model Showcase:
[Slowpics](https://slow.pics/s/IlkmsToH)

2xAoMR_mosr

Purpose: 2x anime upscaler which tries to preserve dof

2x anime upscaler, a non-otf version of the HFA2kCompact model which tries to preserve dof by not deblurring.

Original Link: https://drive.google.com/drive/folders/1RZbALUTCf4DYDTVMBR74b8Nt-7m6TEyk?usp=drive_link

HFA2k compact multijpg

2x SRFormer_light anime upscale model that handles AVC (h264) compression since h264 crf 20-28 degradation together with bicubic, bilinear, box and lanczos downsampling has been applied on musl's HFA2k dataset with Kim's dataset destroyer for training.


If you want to run this model with chaiNNer (or another application) you need to use the onnx files with an onnx upscale node. All onnx conversions can be found in the [onnx folder on my repo](https://github.com/Phhofm/models/tree/main/2xHFA2kAVCSRFormer_light/onnx).

Example 1: https://imgsli.com/MTkxMTQz

Example 2: https://imgsli.com/MTkxMTQ0

2xHFA2kAVCSRFormer_light

Upscaling frames from Irodori anime (namely kemono friends) from 540p (the source render resolution) to 1080p, low resolution flat shaded art, de-JPEG of the aforementioned

KemonoScale v2

Upscale Anime while keeping as much of the original detail as possible. Isn't among the sharpest models and if the anime is somewhat blurry it will retain that detail but add more pixels to give a smoother edge. Doesn't change the color very much and may make some scene's very slightly darker.

Pretrained model: Pony Models

Loyaldk Giroro

Loyaldk Keroro

Loyaldk Kororo

Upscale MLP episodes. Liter version. Able to handle compression better compared to V1.0 and no longer creates halo's/rainbows. Good for Vector 2D art however converts detail to blobs.

Loyaldk LitePony V2.0

Upscale MLP episodes. Liter version. Able to handle compression better compared to V1.0 and no longer creates halo's/rainbows. Good for Vector 2D art however converts detail to blobs. Leaves more blobs when converting detail to blobs compared to litepony

Loyaldk MediumPony V2.0

Upscale MLP episodes. Full version. Able to handle compression better compared to V1.0 and no longer creates halo's/rainbows. Good for Vector 2D art however converts detail to blobs.

Loyaldk SuperPony V2.0

Made to restore screentones and remove compression artifacts in manga images with widths between 650 and 900 pixels (can be more than 900).

2x Manga Ora

2x compact fast universal upscaler pair trained with jpg degradation (down to 40) and multiscale (down_up, bicubic, bilinear, box, nearest, lanczos).

2x-NomosUni_compact_multijpg

Purpose: 2x fast universal upscaler with medium degradation handling (jpg compression, noise, blur)

2x compact fast universal upscaler with medium degradation handling using the Real-ESRGAN training pipeline, based off 2xNomosUni_compact_otf_strong. Handles jpg compression, some noise, and some blur (so dejpgs, denoises and deblurs).

Original Link: https://drive.google.com/drive/folders/1YdOPyb0Ht9YjoRZILf87hSocyAx43CLc

Examples:
[RealPhoto](https://imgsli.com/MjMyNTUy)
[Noisy](https://imgsli.com/MjMyNTUz)

NomosUni compact otf medium

Purpose: 2x universal upscaler

2x esrgan universal upscaler, pair trained with jpg degradation (down to 40) and multiscale (down_up, bicubic, bilinear, box, nearest, lanczos).

Original Link: https://drive.google.com/drive/folders/12zKVS74mz0NtBKGlkx_r0ytUVoeDiTX3?usp=drive_link

Try it out in [this huggingface space](https://huggingface.co/spaces/Phips/upscale) I made (since the spaces uses cpu, testing locally on your gpu would be faster)

Slow Pics crops examples:
[ani](https://slow.pics/c/LLP9p89w)
[face](https://slow.pics/c/4IsxVMAB)
[dof_wheat](https://slow.pics/c/zj4q6Mvi)

NomosUni esrgan multijpg

2x span fast universal upscaler pair trained with jpg degradation (down to 40) and multiscale (down_up, bicubic, bilinear, box, nearest, lanczos).

2x-NomosUni_span_multijpg

Purpose: 2x fast universal DoF preserving upscaler

2x span fast universal DoF preserving upscaler, pair trained with jpg degradation (down to 40) and multiscale (down_up, bicubic, bilinear, box, nearest, lanczos).

Original Link: https://drive.google.com/drive/folders/1_P56B3k1XrBsa-f698zPHAuLaqQ8sSWF

Examples:
[dofwheat](https://slow.pics/c/k0U59uJC)
[digitalart](https://slow.pics/c/yyY0xMAn)  
[face](https://slow.pics/c/MdV3Lr7N)

NomosUni span multijpg ldl

4x anime image upscaler with a bit of otf jpg compression and blur. Trained on musl's hfa2k dataset release for Anime SISR, which has been extracted from modern anime films, where the selection criteria was high SNR, no DOF and high frequency information.

Examples: https://imgsli.com/MTc2NDgx (PS Example input images are small, each around 500x280 px) \
Example input files: https://drive.google.com/drive/folders/1RI6gGqRy-KxDujbaIrpEMdvFkIWHvfPt \
Example output files: https://drive.google.com/drive/folders/1GqHwPlFp6bIQl4R1AxmrmJ6vUUD8FUxH \
All my model files can be found on my github repo https://github.com/Phhofm/models

HFA2k

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

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

4xLSDIRplusC

4xNomos2_otf_esrgan

Scale: 4  
Architecture: ESRGAN  
Architecture Option: esrgan  
[Github Release Link](https://github.com/Phhofm/models/releases/tag/4xNomos2_otf_esrgan)  

Author: Philip Hofmann  
License: CC-BY-0.4  
Purpose: Restoration  
Subject: Photography  
Input Type: Images  
Release Date: 22.06.2024  

Dataset: Nomos-v2  
Dataset Size: 6000  
OTF (on the fly augmentations): Yes  
Pretrained Model: RealESRGAN_x4plus  
Iterations: 246'000  
Batch Size: 8  
GT Size: 256  

Description:  

4x ESRGAN model for photography, trained using the Real-ESRGAN otf degradation pipeline.  

Showcase:
[Slow Pics 8 Examples](https://slow.pics/s/Y5wdyTPh?canvas-mode=fit-width&image-fit=contain)

4xNomos2_otf_esrgan

4xNomos2_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)  
[Github Release](https://github.com/Phhofm/models/releases/tag/4xNomos2_realplksr_dysample)  

Author: Philip Hofmann  
License: CC-BY-0.4  
Purpose: Pretrained  
Subject: Photography  
Input Type: Images  
Release Date: 30.06.2024  

Dataset: [nomosv2](https://github.com/muslll/neosr/?tab=readme-ov-file#-datasets)  
Dataset Size: 6000  
OTF (on the fly augmentations): No  
Pretrained Model: [4xmssim_realplksr_dysample_pretrain](https://github.com/Phhofm/models/releases/tag/4xmssim_realplksr_dysample_pretrain)  
Iterations: 185'000  
Batch Size: 8  
GT Size: 256, 512  

Description: 
A Dysample RealPLKSR 4x upscaling model that was trained with / handles jpg compression down to 70 on the Nomosv2 dataset, preserves DoF.   
Based on the [4xmssim_realplksr_dysample_pretrain](https://github.com/Phhofm/models/releases/tag/4xmssim_realplksr_dysample_pretrain) I released 3 days ago.  
This model affects / saturate colors, which can be counteracted a bit by using wavelet color fix, as used in these examples.  

Showcase:
[Slowpics](https://slow.pics/s/p3sMnd5l)

4xNomos2_realplksr_dysample

4x photo upscaler with otf jpg compression and blur, trained on musl's Nomos8k_sfw dataset for realisic sr

Architecture	Compact
Scale	2x
Size	64nf16nc
Color Mode	RGB
License	CC-BY-4.0 Private use Commercial use Distribution Modifications Credit required State Changes No Liability & Warranty Disclaimer
Date	2024-01-11
Dataset	nomosuni
Dataset size	2989
Training iterations	276000
Training epochs	218
Training batch size	12
Training HR size	128
Training OTF	Yes

NomosUni compact otf medium

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