A specialized model from the Archivist suite, designed to remove linear artifacts. It excels at eliminating horizontal lines that other denoisers often mistake for part of the line art.

This model is optimized for input resolutions between 720p and 1080p. Using it on significantly different resolutions may produce suboptimal results.

All Archivist models are trained on a custom dataset generated by a physics-based degradation simulator. Recommended Workflow: Use Archivist to fix physical defects, then pass the result through DRUNet (low strength) to stabilize.

Archiver AntiLines

A general-purpose model from the Archivist suite, providing a balanced approach to removing common film grain and dirt while preserving the original drawing texture. It is the recommended starting point for most footage.

This model is optimized for input resolutions between 720p and 1080p. Using it on significantly different resolutions may produce suboptimal results.

All Archivist models are trained on a custom dataset generated by a physics-based degradation simulator. Recommended Workflow: Use Archivist to fix physical defects, then pass the result through DRUNet (low strength) to stabilize.

Archiver Medium

A specialized model from the Archivist suite, specifically tuned for tackling heavy chromatic (color) noise and severe color channel degradation, often seen in Metrocolor films. Note: its capabilities overlap with the Rough model, but it is better suited for color-based artifacts.

This model is optimized for input resolutions between 720p and 1080p. Using it on significantly different resolutions may produce suboptimal results.

All Archivist models are trained on a custom dataset generated by a physics-based degradation simulator. Recommended Workflow: Use Archivist to fix physical defects, then pass the result through DRUNet (low strength) to stabilize.

Archiver RGB

An aggressive restoration model from the Archivist suite for severely degraded footage. It attempts to reconstruct heavily damaged or lost details through hallucination. Note: its capabilities overlap with the RGB model, but it focuses more on structural integrity than color noise.

This model is optimized for input resolutions between 720p and 1080p. Using it on significantly different resolutions may produce suboptimal results.

All Archivist models are trained on a custom dataset generated by a physics-based degradation simulator. Recommended Workflow: Use Archivist to fix physical defects, then pass the result through DRUNet (low strength) to stabilize.

Archiver Rough

A light-touch restoration model from the Archivist suite, designed for high-quality sources. It focuses on gentle denoising while preserving the original film grain aesthetic. CAVEAT: In some scenarios, a standard DRUNet might yield subjectively better results. Always compare on your specific footage.

This model is optimized for input resolutions between 720p and 1080p. Using it on significantly different resolutions may produce suboptimal results.

All Archivist models are trained on a custom dataset generated by a physics-based degradation simulator. Recommended Workflow: Use Archivist to fix physical defects, then pass the result through DRUNet (low strength) to stabilize.

Archiver Soft

Pretrained: 4xPSNR

Texture retaining denoiser and sharpener for digital artwork

Sample images: https://1drv.ms/u/s!Aip-EMByJHY28xKFp93VIGydNsoN?e=vN2WYh

ArtClarity

A fast upscaler that is a replacement for standard methods of cleaning noise from the picture. Ideal for BDRemux 720p and 1080p, but only if the noise is monochrome or close to it. So far, attempts are underway to create a dataset that could eliminate color noise at the same speed.

1x BW Denoise

Halo Removal + edge removal. This model softens edges without blurring other parts of the image.

Comparison and use case: https://imgsli.com/MjgxMDU

DeEdge

[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 

This is a 1x model for removing halos in animation. I was training this a while back and considered it a failed project, but I recently found it useful on a few things I was working on, so I decided to release it. I don't remember any details regarding how it was trained.

Dehalo_v1_Compact

This model is far from perfect, but it does a decent job on removing dot crawl and dehalo\deblock old anime at fast rate without removing many details. chaining it with models like 2x-anifilm-compact or 2xLD-ANIME or 2x_AnimeClassics_UltraLite_510K will give good results i think.

Epsilon-one compact

Film-Degrainer_1-000

ISO denoise v1

ISO denoise v2

Takes composite/RF/VHS NES footage and attempts to restore it to RGB quality. Assumes footage has been properly deinterlaced via field duplication from 240p to 480p/720p/etc. Note that:

* All footage was captured in 240p/480p/720p NTSC.
* RGB footage was captured via an AV Famicom with the RGB Blaster via the Retrotink 2x or GBS Control.
* The model was trained exclusively on individual frames, so it can't fix things like dropouts.
* The even and odd fields of NES composite tend to be a bit...different from each other, so there will be some jitter at 60fps.
* I don't have access to an NES Toploader, so I wouldn't expect it to fix the jailbars very well.

Revision History:

* 1.5.0 (06/29/2025): Applied additional augmentations to reduce overfitting. Also added a small amount of 720p training data.
* 1.0.0 (11/03/2024): Initial release.

NES Composite to RGB

Takes composite/RF/VHS NES footage and attempts to restore it to RGB quality. Assumes footage has been properly deinterlaced via field duplication from 240p to 480p/720p/etc. Note that:

* All footage was captured in 240p/480p/720p NTSC.
* RGB footage was captured via an AV Famicom with the RGB Blaster via the Retrotink 2x or GBS Control.
* The model was trained exclusively on individual frames, so it can't fix things like dropouts.
* The even and odd fields of NES composite tend to be a bit...different from each other, so there will be some jitter at 60fps.
* I don't have access to an NES Toploader, so I wouldn't expect it to fix the jailbars very well.

Compared to the OmniSR variant of this model, this version runs more quickly (despite the larger model file size), and should do a good job on footage captured directly from the console using composite. If your footage is from a VHS tape or was captured over RF, the slower OmniSR model is probably a better choice.

Revision History:

* 1.5.0 (09/17/2025): Added "Small" version of model

NES-Composite-2-RGB-Small

NoiseToner Poisson

Attempts to remove the damages done from noise. Successor of sorts to Noisetoner_Poisson_150000_G

NoiseToner Poisson Detailed

NoiseToner Poisson Soft

NoiseToner Uniform

A fast upscaler that is a replacement for standard methods of cleaning noise from the picture. Ideal for BDRemux or WEB-DL 720p or 1080p it. It gets rid of floral noise well, the smaller its grain size, the more uniform the resolution for all videos will be. If you need a more stable video, then you can also add 2x-DigitalFlim-SuperUltraCompact after this upscaler. My upscaler can slightly simplify the geometry of very small objects.

1x RGB max Denoise

Takes composite/RF/RGB/VHS Genesis footage and attempts to restore it to RGB quality. Assumes footage has been properly deinterlaced via field duplication from 240p to 480p/720p/etc. Note that:

* All footage was captured in 240p/480p/720p NTSC.
* Ground truth RGB footage was captured via a triple-bypassed Model 2 Genesis with new capacitors via the Retrotink 2x or GBS Control. Other footage was captured from various Model 1 Genesis consoles (with and without the "High Definition Graphics" mark).
* The model was trained exclusively on individual frames, so it can't fix things like dropouts.
* Should help fix color, blur, and jailbars.
* Cannot un-dither fully blended pixels.

Compared to the OmniSR variant of this model, this version runs more quickly (despite the larger model file size), and should do a good job on footage captured directly from the console using composite or RGB. If your footage is from a VHS tape or was captured over RF, the slower OmniSR model is probably a better choice.

Revision History:

1.0.0 (09/16/2025): Initial release.

Sega Genesis Cleanup-Small

upscaling old anime. help to denoise and find lines and dehalo

BIGOLDIES

I trained Skr's great LD-Anime model on compact architecture. It upscales while fixing numerous video problems, including: noise/grain, compression artifacts, rainbows, dot crawl, halos and color bleed. This compact version may look slightly worse than Skr's original model, but runs significantly faster and also retains the correct colors better than the original model did.

LD-Anime Compact

Purpose: Denoise/Dehalo

Denoise, dehalo, derainbow old anime

Architecture	ESRGAN
Scale	1x
Size	48nf16nb
Color Mode	RGB
License	MIT Private use Commercial use Distribution Modifications Credit required No Liability & Warranty Disclaimer
Date	2025-12-17
Dataset	Custom dataset (~1800 images) of classic cel animation (1940s-1980s) processed with the Project Degrader physics-based pipeline.
Dataset size	1800
Training iterations	193000
Training HR size	256
Training OTF	Yes

Archiver RGB

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