Difference between revisions of "Model Database"

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{{ModelDBColumns|Name|Author|Scale|License|Purpose|Iterations|Batch Size|HR Size|Dataset Size|Dataset|Pretrained Model}}
 
{{ModelDBColumns|Name|Author|Scale|License|Purpose|Iterations|Batch Size|HR Size|Dataset Size|Dataset|Pretrained Model}}
 
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|[https://drive.google.com/file/d/1KToK9mOz05wgxeMaWj9XFLOE4cnvo40D/view?usp=sharing Box]||buildist||4||GNU GPLv3||Realistic||390K||8||192||268||11.577K||Flickr2K+Div2K+OST||PSNR model from same data
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|[https://drive.google.com/file/d/1KToK9mOz05wgxeMaWj9XFLOE4cnvo40D/view?usp=sharing Box]||buildist||4||GNU GPLv3||Realistic||390K||8||192||11.577K||Flickr2K+Div2K+OST||PSNR model from same data
 
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|[https://drive.google.com/file/d/1d89zvzC6BKf5EKapBtc4MH2DX5GClKzl/view Nickelback]||[[User:BlackScout|BlackScout]]||4||GNU GPLv3||Photographs||70k||8||128||44.55k||Wallpapers||4xESRGAN
 
|[https://drive.google.com/file/d/1d89zvzC6BKf5EKapBtc4MH2DX5GClKzl/view Nickelback]||[[User:BlackScout|BlackScout]]||4||GNU GPLv3||Photographs||70k||8||128||44.55k||Wallpapers||4xESRGAN
 
|-
 
|-
|[https://drive.google.com/file/d/1dGmhHUPmb3lO9buX_Bt2nq97Nk5MCTb4/view Ground]||ZaphodBreeblebox||4|| ||Ground Textures||305K||?||128||?||?||Custom (Ground textures Google)||?
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|[https://drive.google.com/file/d/1dGmhHUPmb3lO9buX_Bt2nq97Nk5MCTb4/view Ground]||ZaphodBreeblebox||4|| ||Ground Textures||305K||?||128||?||Custom (Ground textures Google)||?
 
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|[https://mega.nz/#!KmpAXaJb!MoWN4XArM9n1xZEyeyS9Tt9yQkYcDvbZIszHTNzfZlo Misc]||[[User:Alsa|Alsa]]||4||[https://creativecommons.org/licenses/by-nc-sa/4.0/ CC BY-NC-SA 4.0]||Surface Textures||220K||32||128||20.797K||Custom (Photos)||[http://www.mediafire.com/file/w3jujtm752hvdj1/Manga109Attempt.pth.zip/file Manga109Attempt]
 
|[https://mega.nz/#!KmpAXaJb!MoWN4XArM9n1xZEyeyS9Tt9yQkYcDvbZIszHTNzfZlo Misc]||[[User:Alsa|Alsa]]||4||[https://creativecommons.org/licenses/by-nc-sa/4.0/ CC BY-NC-SA 4.0]||Surface Textures||220K||32||128||20.797K||Custom (Photos)||[http://www.mediafire.com/file/w3jujtm752hvdj1/Manga109Attempt.pth.zip/file Manga109Attempt]

Revision as of 17:42, 10 July 2020

If a model has no license, that means all rights reserved. You will need a private license or explicit permission from the creator to use it commercially or with modifications. However you may use all model here on non-commercial projects, that may not in any way cost money. If you gain permission to use a model in another way including commercially, you should still credit the author and link to the original model source.

ESRGAN ("old Architecture") Models

Models that use the "old" ESRGAN architecture. They can be used either with the official ESRGAN repository (old arch tag) and BasicSR (old arch or preferably victorc's fork). You can also use tools that wrap around one of them, like IEU from Honh.

Image scaling and Video upscaling

In computer graphics and digital imaging, image scaling refers to the resizing of a digital image. In video technology, the magnification of digital material is known as upscaling or resolution enhancement.

Drawings

Drawing is a form of visual art in which a person uses various drawing instruments to mark paper or another two-dimensional medium. Instruments include graphite pencils, pen and ink, various kinds of paints, inked brushes, colored pencils, crayons, charcoal, chalk, pastels, various kinds of erasers, markers, styluses, and various metals (such as silverpoint). Digital drawing is the act of using a computer to draw. Common methods of digital drawing include a stylus or finger on a touchscreen device, stylus- or finger-to-touchpad, or in some cases, a mouse. There are many digital art programs and devices.

Manga/Anime

Name
Author
Scale
License
Purpose
Iterations
Batch Size
HR Size
Dataset Size
Dataset
Pretrained Model
"
Manga109Attempt Kingdomakrillic 4 CC BY 4.0 Anime / Manga ? 4 ? 0.1K Manga109 RRDB_PSNR_x4
Falcon Fanart LyonHrt 4 CC BY-NC-SA 4.0 Anime / Manga 125K 8 128 3.393K Falcon Fanart RRDB_PSNR_x4
WaifuGAN v3 DinJerr 4 CC BY-NC 4.0 Anime / Manga 30K 2 128 0.173K CG-Painted Anime Manga109v2
De-Toon LyonHrt 4 CC BY-NC-SA 4.0 Toon Shading / Sprite 225K 8 128 7.117K Custom Cartoon-style photos RRDB_PSNR_x4
DigiPaint TheAtheistGod 4 CC0 Digital Art Upscaler 35K 6 128 48.493K Misc digital art, material studies 4xfalcoon300(manga)
DeviantPixelHD Raulsangonzalo 4 CC BY-NC 4.0 Digital Art Upscaler 250K 16 128 2.797K Digital Art from Deviant Art RRDB_PSNR_x4
BS_Deviance BlackScout 4 GNU GPLv3 Digital Art Upscaler 60K 12 128 5.435K Digital Art from DeviantArt 4xESRGAN
FireAlpha BlueAmulet 4 CC BY-NC 4.0 Artwork with an alpha channel 1.285M 128 104k tiles, 1.2k imgs Fire Emblem artwork with transparency None

Manga109Attempt is slightly blurry, but performs well as a general upscaler.

Falcoon Fanart tries to improve upon it with the goal of removing checkerboard patterns / and dithering. It has oil color based shading with sharp lines.

WaifuGAN v3 is DinJerr’s third attempt at training from a mostly anime dataset sourced from image boards and is intended for upscaling CG-painted anime with variable outlines. Only PNGs were used, mainly with brush strokes and gradients. Texturised images avoided as much as possible. If too generative, tone down by interpolating with a softer model.

De-Toon, is a model that does the opposite of tooning an image. It takes toon style shading and detail, and attempts to make it realistic. Its very sensitive, and can be used on small sprites, to large images. Also included is a alt version, which is less sharp.

DigiPaint is a digital art upscaler designed to take brush strokes into account, based off of the Falcoon Fanart model.

DeviantPixelHD was designed to upscale original LucasArts games' backgrounds. High Definition digital art from Deviant Art was used, reducing the LR training to 32 colors so the training images looked pixelated. Can be used with other environments such as manga or digital art. Examples can be found on Raúl Sangonzalo's YouTube account (https://www.youtube.com/channel/UCwBfuiHdSPQ-zslOmiW8OHg).

FireAlpha is an attempt to create a 4 channel model that handles transparency without splitting and upscaling separately.

  • Notice: IEU can use the model but will attempt to split and handle transparency itself, BlueAmulet's fork of ESRGAN is required to use this model. https://github.com/BlueAmulet/ESRGAN

Cartoon / Comic

Name
Author
Scale
License
Purpose
Iterations
Batch Size
HR Size
Dataset Size
Dataset
Pretrained Model
"
Comic Book LyonHrt 4 CC BY-NC-SA 4.0 Comic / Drawings 115K 8 128 1.548K Custom (Spider-Man) none (no interpolation)
DigitalFrames 1.0 Klexos 4 CC BY-NC-SA 4.0 Clean Digital Animation 1.06M 15 128 0.25K - 2.5K Digital Cartoon Images RRDB_PSNR_x4
DigitalFrames 2.0 Klexos 4 CC BY-NC-SA 4.0 Digital Animation 905K 27 128 1.636M Digital Cartoon Images RRDB_PSNR_x4
DigitalFrames 2.1_Aggressive Klexos 4 CC BY-NC-SA 4.0 Flat Digital Animation 230K 27 128 1.8M Digital Cartoon Images DigitalFrames 2.0
DigitalFrames 2.1_Final Klexos 4 CC BY-NC-SA 4.0 Digital & Traditional Animation 230K 27 128 1.8M Cartoon Images DigitalFrames 2.0
FatalimiX Twittman 4 CC BY-SA 4.0 Comic and Cartoon style images 260k 10 128 69k/79k Digital Comics 4x_Fatality_MKII_90000_G_02.pth
SpongeBob Joey 4 CC BY-NC-SA 4.0 Cartoons & SpongeBob Games 235k 20 128 8k Scene-detected frames from random Season 11 episodes 4xESRGAN
SpongeBob v6 Joey 4 CC BY-NC-SA 4.0 Cartoons 190k 20 128 4,803 1 frame of every scene of season 11, downscaled 50% with nearest neighbor 4xESRGAN
Spongebob v6 De-Quantize Joey 4 CC BY-NC-SA 4.0 Quantized Cartoons 90k 20 128 4,803 1 frame of every scene of season 11, downscaled 50% with nearest neighbor 4x Spongebob v6
Spongebob v6 Deblur Joey 4 CC BY-NC-SA 4.0 Cartoons 65k 20 128 4,803 1 frame of every scene of season 11, downscaled 50% with nearest neighbor 4x Spongebob v6
HugePeeps DinJerr 8 CC BY-NC 4.0 Painted humans 360k 4 256 1,000+ A bunch of pretty girls (and guys) from ArtStation TGHQFace8x
Fatal_Anime Twittman 4 CC BY-SA 4.0 Anime and Cartoons 500k 6 128 183k Anime and Manga images 4x_Fatality_Comix_MKII_430000_G.pth
HugePaint DinJerr 8 CC BY-NC 4.0 Digital Illustrations 500k 4 256 1,000+ Variety of images from ArtStation HugePeeps

The Comic Book model was trained using stills from the film spiderman into the spiderverse, has a comic book crosshatch shading effect to the images. Sample

DigitalFrames 1.0: The purpose of this model is upscaling digital *only* animation frames while cleaning and restoring textures and details, but it can also works well on a variety of other things such as medium sized sprites and digital art. This model is highly sensitive to grain and other type of noise so if an improper input image is feed, the results can have severe blobs of noise if the source image has any type of grain, even if barely noticeable.

DigitalFrames 2.0: Was trained completely separately from 1.0 with a completely new and bigger dataset. It has a reduced sensitivity to noise which reduces the blobs of noise phenomena. It can also work reasonably well on Cel/Film Toons now. Overall it's a big leap from 1.0.

DigitalFrames 2.1_Aggressive: Provides the sharpest results yet. About 30% better than the base 2.0 version. But as the title says it's the most aggressive version of DF. Does it's best on animation with little to no textures. The model as well as any version of DF is not suitable at all for overly deteriorated/blurred and low quality TVRips and VHSRips copies of animation pieces.

DigitalFrames 2.1_Final: The most universally "compatible" from the 2.0 branch that works well for all types of animation. If your piece has few textures or none at all, 2.1_Aggressive can perform better, but for all other scenarios _Final takes the cake with a much higher texture retention and "natural" look.

FatalimiX is specialized in making those low res comics high res again!

The SpongeBob model was trained for the purpose of upscaling Battle for Bikini Bottom's textures, but it works well for upscaling scenes from the show as well. It was initially trained on the clean downscaled images but later JPEG, quantization, dithering, and Gaussian blur were added to the LR OTF options to help it upscale the textures better.

Spongebob v6 is ideally a lot sharper and cleaner, but Joey's still not sure if it works better than the old one. It still seems to be better in many cases.

Spongebob v6 De-Quantize is for de-quantizing as well as upscaling. The results are pretty blurry but it works decently enough.

Spongebob v6 Deblur was created as Joey wasn't entirely happy with the main Spongebob v6 model. It was trained with blurring OTF options and two different downscale types.

HugePeeps v1 was created by DinJerr to test out a theory on how to properly train 8x models. This particular model best works on painted pics of ladies.

HugePaint v1 is trained on a more generic dataset with a balance of illustrated humans, vegetation, minerals and some animals.

Some of the first cartoon painted models for ESRGAN can still be found here, but have been mostly superseded by newer models.

Pixel Art

Pixel art is a form of digital art, created through the use of software, where images are edited on the pixel level. The aesthetic for this kind of graphics comes from 8-bit and 16-bit computers and video game consoles, in addition to other limited systems such as graphing calculators. In most pixel art, the color palette used is extremely limited in size, with some pixel art using only two colors.

Creating or modifying pixel art characters or objects for video games is sometimes called spriting, a term that arose from the hobbyist community. The term likely came from the term sprite, a term used in computer graphics to describe a two-dimensional bitmap that is used in tandem with other bitmaps to construct a larger scene.

Name
Author
Scale
License
Purpose
Iterations
Batch Size
HR Size
Dataset Size
Dataset
Pretrained Model
"
Xbrz LyonHrt 4 CC BY-NC-SA 4.0 Xbrz style pixel art upscaler 90K 8 128 1.897K custom xbrz up-scaled RRDB_PSNR_x4
Xbrz+DD LyonHrt 4 CC BY-NC-SA 4.0 Xbrz style pixel art upscaler with de-dithering 90K 8 128 1.523K custom de-dithered xbrz xbrz
ScaleNX LyonHrt 4 CC BY-NC-SA 4.0 Scalenx style pixel art upscaler 80K 8 128 1.070K custom scalenx up-scaled from retroarch shader RRDB_PSNR_x4
Fatality Twittman 4 CC BY-SA 4.0 (dithered) spirites 265K 10 128 19.7K ? Face
Rebout LyonHrt 4 CC BY-NC-SA 4.0 Character Sprites 325K 8 128 23.808K Custom prepared sprites from kof 94 rebout Detoon
Lady0101 DinJerr 4 CC BY-NC 4.0 Painting-style 340K 4 128 7K CG-Painted Pinups & Landscapes WaifuGAN v3
Faithful 2x Joey 2 CC0 General small pixel textures 130K 80 32 2.858K Faithful 32x32 (HR) / default Minecraft 1.13 and 1.14 (LR) Private model trained from 2xESRGAN
FArtDIV3 Suite DinJerr 4 CC BY-NC 4.0 Pixels to digital painting 700K~ 4 128 ~10K DIV2K, FatalityFaces, ArtStation paintings ArtStation1337
BigFArt Suite DinJerr 4 CC BY-NC 4.0 Larger-scaled pixels to digital painting 800+ 6 192 ~12K DIV2K, FatalityFaces, ArtStation paintings Face Focus
Fatal Pixels Twittman 4 CC BY-SA 4.0 Sprites 340K 22 96 49K Anime, Manga Fatality_MKII_90k
SmoothRealism Joey 4 CC BY-NC 4.0 Pixel art, rocky/grainy textures 140K 2 64 679 R3DCraft Smooth Realism Texture Pack 64x RRDB_PSNR_x4

Fatality is meant to be used for upscaling medium resolution Sprites, dithered or un-dithered, it can also upscale manga/anime and gameboy camera images.

Rebout is trained to give detail to character models, with faces and hands improved. Based on the snk game kof94 rebout, although best for snk style games, does work on a variety of sprites. Also included is a interpolated version that may provide a cleaner upscale for certain sprites.

Lady0101 was trained on digital paintings (mostly of pinups) and landscapes. It is meant to upscale pixel art into painting-style images with some canvas-like texturing for certain elements. Included is a version that has strong blending but weak undithering.

Faithful 2x was trained on the faithful 32x32 Minecraft texture/resource pack, using both the 1.13 and 1.14 versions (which have different textures). For those not familiar, this texture pack is meant to upscale the 16x16 textures to 32x32 faithfully, as in make them as close as possible to the original, but higher resolution. The model does a decent job at replicating this.

  • NOTE: Due to an error in the dataset, sometimes the model turns jagged white lines green. Only encountered this in one texture tried, so it appears unlikely to happen.

FArtDIV3 Suite is a group of models based on a large image dataset trained using OTF tile generation. Most of the models are based off the Base model, and suite different purposes. Fine is used to generate details, but may be noisy/grainy at times. Blend will attempt to blend colour steps into proper gradients. UnholyFArt is a interpolated model that reduces details in favor of sharp blends.

BigFArt Suite is similar to FArtDIV3, but for larger pixel sprites/art in the context of tile size.

Photographs and Photorealism

A photograph (also known as a photo) is an image created by light falling on a photosensitive surface, usually photographic film or an electronic image sensor, such as a CCD or a CMOS chip. Most photographs are created using a camera, which uses a lens to focus the scene's visible wavelengths of light into a reproduction of what the human eye would see. The process and practice of creating such images is called photography. The word photograph was coined in 1839 by Sir John Herschel and is based on the Greek φῶς (phos), meaning "light," and γραφή (graphê), meaning "drawing, writing," together meaning "drawing with light." Photorealism is a genre of art that encompasses painting, drawing and other graphic media, in which an artist studies a photograph and then attempts to reproduce the image as realistically as possible in another medium. Although the term can be used broadly to describe artworks in many different media, it is also used to refer specifically to a group of paintings and painters of the American art movement that began in the late 1960s and early 1970s.

Misc / Kitchen Sink

All kinds of photographs or photorealistic images. Those models aren't specialized.

Name
Author
Scale
License
Purpose
Iterations
Batch Size
HR Size
Dataset Size
Dataset
Pretrained Model
"
Box buildist 4 GNU GPLv3 Realistic 390K 8 192 11.577K Flickr2K+Div2K+OST PSNR model from same data
Nickelback BlackScout 4 GNU GPLv3 Photographs 70k 8 128 44.55k Wallpapers 4xESRGAN
Ground ZaphodBreeblebox 4 Ground Textures 305K ? 128 ? Custom (Ground textures Google) ?
Misc Alsa 4 CC BY-NC-SA 4.0 Surface Textures 220K 32 128 20.797K Custom (Photos) Manga109Attempt

Box was meant to be an improvement on the RRDB_ESRGAN_x4 model (comparison). It’s also trained on photos, but with a much larger dataset which was downscaled with linear interpolation (box filter) instead of bicubic.

Deoldify is intended for denoising and deblurring photos. All outputs are greyscale. This was initially a proof of concept. (Need clarification on specific subject matter.)

The Ground model was trained on various pictures of stones, dirt and grass using Google’s image search.

The Misc model is trained on various pictures shoot by myself, including bricks, stone, dirt, grass, plants, wood, bark, metal and a few others.

Characters and Faces

For images of humans, creatures, faces, ...

Name
Author
Scale
License
Purpose
Iterations
Batch Size
HR Size
Dataset Size
Dataset
Pretrained Model
"
Trixie LyonHrt 4 CC BY-NC-SA 4.0 Star Wars 275K 8 192 87 19.814K ? None
Face Focus LyonHrt 4 CC BY-NC-SA 4.0 Face De-blur 275K 8 192 455 4.157K Custom (Faces) RRDB_PSNR_x4
Face Twittman 4 CC BY-SA 4.0 Face Upscaling 250K 10 128 3.765K Custom (Faces) 4xESRGAN
Face-Ality V1 Twittman 4 CC BY-SA 4.0 Face Upscaling 310k 10 128 13.3k Custom (Faces) 4x_Faces_04_N_180000_G.pth
TGHQFace8x Torrentguy 8 GNU GPL3 Face Upscaling 500k 8 128 54 70k Flickr Cropped Faces 8xESRGAN

Trixie was made to bring balance to the force… Also to upscale character textures for star wars games, including the heroes, rebels, sith and imperial. Plus a few main aliens…Why called trixie? Because jar jars big adventure would be too long of a name…This also provides good upscale for face textures for general purpose as well as basic star wars textures.

The Face Focus modes was designed for slightly out of focus / blurred images of faces. It is aimed at faces / hair, but it can help to improve other out of focused images too as always just try it.

Specialized

For Purposes that didn't fit anywhere else (for now).

Name
Author
Scale
License
Purpose
Iterations
Batch Size
HR Size
Dataset Size
Dataset
Pretrained Model
"
Map LyonHrt 4 CC BY-NC-SA 4.0 Map / Old Paper with text 120K 8 192 2.311K Custom(Scans) none
Forest LyonHrt 4 CC BY-NC-SA 4.0 Wood / Leaves 160K 8 192 2.2K Custom(?) none
Skyrim Armory Alsa 4 CC BY-NC-SA 4.0 Armor, Clothes and Weapons 80K 26 128 0.8K Skyrim Mod textures Manga109Attempt
Skyrim Wood Laeris 4 Wood 75K ? ? ? ? ?
Skyrim Misc Deorder 4 CC0 Skyrim Diffuse Textures 105K ? 128 ? Skyrim Diffuse Textures ?
Fallout 4 Weapons Bob 4 CC BY-NC-SA 4.0 Fallout Weapon Diffuse Textures 120K 13 128 532(OTF) Fallout 4 HDDLC Weapon Diffuse Textures Manga109Attempt
Fallout Weapons V2 Bob 4 CC BY-NC-SA 4.0 Video game textures, mostly metal rusty, clean or painted. 180K 13 128 1.999K(OTF) Fallout 4 HD DLC(Weapon,Armor/Clothes,Vehicle and Interior/Architecture textures to be exact) Fallout 4 Weapons
Nickelfront BlackScout 4 GNU GPLv3 Money, Coins 14k 8 128 1.176k Images of Coins 4xESRGAN
BS_Wolly BlackScout 2 GNU GPLv3 Pixar Movies / Wall-E 38k 1 256 9.0k BD 1080p Wall-E Frames 2xESRGAN
Morrowind Mixed mkultra 4 CC BY-NC-SA 4.0 Morrowind Mod Textures 115K-130K 16 128 56K Morrowind Mod Textures RRDB_PSNR_x4/RRDB_ESRGAN_x4

The map model was trained on maps, old documents, papers and various styles of typefaces/fonts. Based on a dataset contributed by alsa. Sample

The Forest model is focused on trees, leaves, bark and stone can be used for double upscaling for even more detail. Sample

The Armory model was trained with modded textures form Skyrim, including Clothing, Armor and Weapons. (Leather, Canvas and Metal should all work - maybe too sharp so interpolate)

The wood model was trained for Skyrim by Laeris.

The Skyrim Diffuse models is supposed to be used with Skyrim’s diffuse textures. It is a bit too sharp so I recommend to interpolating with the RDDB_ESRGAN_x4 model or the mangaAttempt109 model, look in Deorder’s Skyrim Model Google Drive for an already interpolated version.

Fallout 4 weapons was trained using Fallout 4’s official HDarmor/weapon textures but could be used on other weapon and armor textures.

Texture Maps

Normal Maps

In 3D computer graphics, normal mapping, or Dot3 bump mapping, is a technique used for faking the lighting of bumps and dents – an implementation of bump mapping. It is used to add details without using more polygons. A common use of this technique is to greatly enhance the appearance and details of a low polygon model by generating a normal map from a high polygon model or height map.

Normal maps are commonly stored as regular RGB images where the RGB components correspond to the X, Y, and Z coordinates, respectively, of the surface normal.

The models here have been specifically trained on Normal Maps, but beware, this approach is considered deprecated by many of us. Instead of using any of those models, you can just split the R, G and B channels of the normal map you want to upscale and use any other model on them.

Name
Author
Scale
License
Purpose
Iterations
Batch Size
HR Size
Dataset Size
Dataset
Pretrained Model
"
Normal Maps Alsa 4 CC BY-NC-SA 4.0 Normal Maps 36K 27 128 ? Custom (Normal Maps) Normal Maps - Skyrim artifacted
Normal Maps - Skyrim artifacted Deorder 4 CC0 Skyrim Normal Maps 145K ? 128 ? Skyrim Normal Maps ?

The first one is based on the second one it was trained, with a higher learning rate and insane n_workers and batch_size values. It is meant to replace the old Normal Map model from Deorder, but without adding BC1 compression to your normal maps.

The second one was trained on Skyrim’s Normal Maps, including compression artifacts, so it will have to be redone.

Grayscale

In digital photography, computer-generated imagery, and colorimetry, a grayscale or greyscale image is one in which the value of each pixel is a single sample representing only an amount of light, that is, it carries only intensity information. Grayscale images, a kind of black-and-white or gray monochrome, are composed exclusively of shades of gray. The contrast ranges from black at the weakest intensity to white at the strongest.[1]

Name
Author
Scale
License
Purpose
Iterations
Batch Size
HR Size
Dataset Size
Dataset
Pretrained Model
"
Skyrim Alpha Deorder 4 CC0 Alpha Channel 105K ? 128 ? Alpha Channels from Skyrim ?
Deoldify TheAtheistGod 4 CC0 B/W Photo Restoration 790K 16 128 2.931M ADE20K, DIV2K, Library of Congress Falcon Fanart (?)

Trained to upscale grayscale images, like specular or alpha etc.

Artifact Removal

The models in this section were made to remove compression artifacts in images and textures.

Blur

Name
Author
Scale
License
Purpose
Iterations
Batch Size
HR Size
Dataset Size
Dataset
Pretrained Model
"
Fatality DeBlur Twittman 1 CC BY-SA 4.0 Deblurring 270K 1 128 41K Mix of anime, manga, and photos 1x_DeJpeg_Fatality_01_175000_G.pth
LADDIER1 Alexander Syring 4 CC0 Remove noise, grain, box/lens/guassian blur 282K 8 128 12.7K Mostly images of Nirvana, some of other images RRDB_ESRGAN_x4.pth

Fatality DeBlur is intended for general deblurring of images, and has some robustness against compression and noise.

LADDIER1 upscales images with noise, grain and different types of blur.

Noise

Name
Author
Scale
License
Purpose
Iterations
Batch Size
HR Size
Dataset Size
Dataset
Pretrained Model
"
ISO denoise v1 Alpha 1 CC0 Remove high ISO noise 372K 1 128 170K Custom (DSLR photos) 1xESRGAN.pth
ISO denoise v2 Alpha 1 CC0 Remove high ISO noise 308K 1 128 239K Custom (DSLR photos) 1x_ISO_denoise_v1.pth

High ISO is required to capture images at very high shutter speeds and/or in poor lighting. However, increasing ISO also results in noise that is challenging to remove without also losing fine details. To remove this noise without damaging details, one can apply the technique of median stacking: combine 10-100 photos of the exact same object to separate the signal (clear photo) from noise. ISO denoise v1 is trained on a handcraft set of pairs between original noisy photos and median stacked counterparts. It tends to be conservative, avoiding modifying anything that does not closely resemble camera ISO noise. ISO denoise v2 uses a larger dataset, handling some cases better and some cases worse than v1. If results are unsatisfactory with one model, try the other model.

JPG Compression

Name
Author
Scale
License
Purpose
Iterations
Batch Size
HR Size
Dataset Size
Dataset
Pretrained Model
"
JPG (0-20%) A Alsa 1 CC BY-NC-SA 4.0 JPG compressed Images 178K 2 128 6.23K Custom (Photos / Manga) JPG (20-40%)
JPG (0-20%) B BlueAmulet 1 CC BY-NC 4.0 JPG compressed Images 610K 1 128 52.789K Custom (CC0 Textures) JPG (20-40%)
JPG (20-40%) A Alsa 1 CC BY-NC-SA 4.0 JPG compressed Images 141K 2 128 6.23K Custom (Photos / Manga) JPG (40-60%)
JPG (20-40%) B BlueAmulet 1 CC BY-NC 4.0 JPG compressed Images 535K 1 128 52.789K Custom (CC0 Textures) JPG (40-60%)
JPG (40-60%) A Alsa 1 CC BY-NC-SA 4.0 JPG compressed Images 100K 2 128 ~6.5K Custom (Photos / Manga) JPG (60-80%)
JPG (40-60%) B BlueAmulet 1 CC BY-NC 4.0 JPG compressed Images 550K 1 128 52.789K Custom (CC0 Textures) JPG (60-80%)
JPG (60-80%) A Alsa 1 CC BY-NC-SA 4.0 JPG compressed Images 91K 2 128 ~6.5K Custom (Photos / Manga) JPG (80-100%)
JPG (60-80%) B BlueAmulet 1 CC BY-NC 4.0 JPG compressed Images 545K 1 128 52.789K Custom (CC0 Textures) JPG (80-100%)
JPG (80-100%) A Alsa 1 CC BY-NC-SA 4.0 JPG compressed Images 162K 2 128 ~6.5K Custom (Photos / Manga) BC1 take 1
JPG (80-100%) B BlueAmulet 1 CC BY-NC 4.0 JPG compressed Images 550K 1 128 52.789K Custom (CC0 Textures) BC1 take 1
JPG PlusULTRA Twittman 1 CC BY-SA 4.0 JPG compressed Images 130K 1 ? 0.937K Custom (Manga) Failed Attempts
DeJpeg Fatality PlusULTRA Twittman 1 CC BY-SA 4.0 JPG compressed Images 200K 1 128 26K Real life, Manga and digital text 1x_DeJpeg_Fatality_01_200000_G.pth

JPG gets compressed with a Quality Percentage between 0 and 100. So depending on how bad your JPEGs are compressed, choose the model of your choice. You can use ImageMagick to guess the Quality percentage, but keep in mind that it might be wrong, since the image might have been re-saved.

JPG PlusULTRA, while not considered successful in its initial purpose, may find some use on certain unique images.

DeJpeg Fatality PlusULTRA is its burly strongman big brother. Anything and everything that is JPG will tremble before you!

DDS Files with BC1/DXT1, BC3/DXT5 Compression

Name
Author
Scale
License
Purpose
Iterations
Batch Size
HR Size
Dataset Size
Dataset
Pretrained Model
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BC1 smooth 2.0 BlueAmulet 1 CC BY-NC 4.0 BC1 Compression 1M 20 32 4184 4.767K Custom (? including normal maps) none (no interpolation)
BC1 free 1.0 Alsa 1 CC BY-NC-SA 4.0 BC1 Compression 400K 2 128 28.985K Custom (just about everything) BC1 take 2
BC1 restricted v1.0 Alsa 1 CC BY-NC-SA 4.0 BC1 Compression 100K 2 128 1.8K Custom (Photos) Failed Attempts
BC1 restricted v2.0 Alsa 1 CC BY-NC-SA 4.0 BC1 Compression 261K 2 128 4.7K Custom (Photos / Manga) JPG (0-20%)

BC1 (DXT1) compression is commonly used in dds textures, which are utilized in most PC games today, it allows to shrink the texture to 1/6 of the original size, reducing VRAM usage. There is also BC3 (DXT5) which uses BC1 compression for the color channels and leaves the alpha channel uncompressed, this one reduces the file size to 1/3 of the original. But this compression comes at a cost. The BC1 models are designed to reverse the damage done by the compression. This is a must if you want to upscale a dds file that uses either BC1 or BC3 compression. The free variant has more freedom when dealing with the images and should lead to better results. The restricted version only deals with perfect images compressed once (ideal case) so will perform worse in any other scenario, but it tries to preserve the original colors more. You can interpolate between restricted and unrestricted if you want.

Cinepak, msvideo1 and Roq

Name
Author
Scale
License
Purpose
Iterations
Batch Size
HR Size
Dataset Size
Dataset
Pretrained Model
"
Cinepak Twittman 1 CC BY-SA 4.0 Cinepak, msvideo1 and Roq 200K 1 128 ~8K Custom (Manga) none (no interpolation)
Cinepak_alt buildist 1 GNU GPLv3 Cinepak (only) 240K 1 128 2,640 Flickr2K none (no interpolation)

The Cinepak model removes movie compressions artifacts from older video compression methods like Cinepak, msvideo1 and Roq. Cinepak_alt may work better for video frames with realistic content.

Color Reduction

Dithering

Name
Author
Scale
License
Purpose
Iterations
Batch Size
HR Size
Dataset Size
Dataset
Pretrained Model
"
DeDither Alsa 1 CC BY-NC-SA 4.0 Dithered Images 127K 2 128 4.7K Custom (Photos / Manga) JPG (0-20%)
1x_ordered_dither buildist 1 GNU GPLv3 Ordered dithering 280K 16 128 ? ~8K Flickr2K, OST dithered with GIMP none (no interpolation)

Dithering is an older compression method, where the amount of colors gets reduced, if your image has few colors and have a noise like pattern in them try a De-Dither model. Ordered dithering is a less common form of dithering that results in distinctive checkerboard/crosshatch patterns, which are misinterpreted as texture by models not trained on it. It’s often used on GIFs because the pattern is stable between frames.

Banding

Name
Author
Scale
License
Purpose
Iterations
Batch Size
HR Size
Dataset Size
Dataset
Pretrained Model
"
BS_DeBandizer BlackScout 1 GNU GPLv3 Images with Banding 34k 1 128 changing ~900 Custom (Wallpapers) old attempts based on 1xESRGAN

Banding is like dithering, but instead of adding patterns of pixels to simulate missing colors, it just clips them off.

Color Removal / Grayscale

Name
Author
Scale
License
Purpose
Iterations
Batch Size
HR Size
Dataset Size
Dataset
Pretrained Model
"
BS_Colorizer BlackScout 1 GNU GPLv3 Coloring B/W images 22k 1 128 4.5045k Custom (800 Wallpapers) 1xESRGAN

Just removes all colors and makes the image grayscale, the models here should restore some color to them, should also work for old black and white images.

Over-Sharpening

Name
Author
Scale
License
Purpose
Iterations
Batch Size
HR Size
Dataset Size
Dataset
Pretrained Model
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DeSharpen loinne 1 CC BY-NC-SA 4.0 Oversharpened Images 310K 1 128 ~3K Custom (?) Failed Attempts

The De-Sharpen model was made for rare particular cases when the image was destroyed by applying noise, i.e. game textures or any badly exported photos. If your image does not have any over-sharpening, it won’t hurt them, leaving as is. In theory, this model knows when to activate and when to skip, also can successfully remove artifacts if only some parts of the image are over-sharpened, for example in image consisting of several combined images, 1 of them with sharpen noise. It is made to remove sharpen noise, particularly made with Photoshop “sharpen” or “sharpen more” filters OR ImageMagick’s -sharpen directive with several varying parameters of Radius and Sigma, from subtle 0.3x0.5 to something extreme like 8x2, somewhere about that.

Aliasing

Name
Author
Scale
License
Purpose
Iterations
Batch Size
HR Size
Dataset Size
Dataset
Pretrained Model
"
AntiAliasing Twittman 1 CC BY-SA 4.0 Images with pixelated edges 200K 1 128 0.656K Custom (?) none (no interpolation)
SSAntiAlias9x BlueAmulet 1 CC BY-NC 4.0 Anti aliasing 125K~ 12 32 4096 Custom (9x Supersampling AA) none (no interpolation)

AntiAliasing is for smoothing jagged edges in images and textures.

Image Generation

Texture Maps

Name
Author
Scale
License
Purpose
Iterations
Batch Size
HR Size
Dataset Size
Dataset
Pretrained Model
"
normal generator LyonHrt 1 CC BY-NC-SA 4.0 Diffuse to Normal 215K 1 128 4.536K Custom (?) none (no interpolation)

The model was trained on pairs of diffuse textures and normal maps.

Pretrained models for different scales

Name Author Scale License Dataset Size Dataset Pretrained Model
1xESRGAN victorca25 1 Apache License 2.0 65k Combination of DIV2K, Flickr2K and GOPRO 4xPSNR
2xESRGAN victorca25 2 Apache License 2.0 65k Combination of DIV2K, Flickr2K and GOPRO 4xPSNR
4xESRGAN victorca25 4 Apache License 2.0 65k Combination of DIV2K, Flickr2K and GOPRO 4xPSNR
8xESRGAN victorca25 8 Apache License 2.0 65k Combination of DIV2K, Flickr2K and GOPRO 4xPSNR
16xESRGAN victorca25 16 Apache License 2.0 65k Combination of DIV2K, Flickr2K and GOPRO 4xPSNR
1xPSNR BlueAmulet 1 Apache License 2.0 65k Combination of DIV2K, Flickr2K and GOPRO none
2xPSNR BlueAmulet 2 Apache License 2.0 65k Combination of DIV2K, Flickr2K and GOPRO none
4xPSNR BlueAmulet 4 Apache License 2.0 65k Combination of DIV2K, Flickr2K and GOPRO none
8xPSNR BlueAmulet 8 Apache License 2.0 65k Combination of DIV2K, Flickr2K and GOPRO none
16xPSNR BlueAmulet 16 Apache License 2.0 65k Combination of DIV2K, Flickr2K and GOPRO none

https://drive.google.com/drive/folders/1ldwajXL50uC7PCS63B4Wato6Dnk-svNL

These models were transformed from the original RRDB_ESRGAN_x4.pth model into the other scales, in order to be used as pretrained models for new models in those scales.

More information can be found here.

ESRGAN ("new Architecture") Models

Models that use the "new" ESRGAN architecture. It has no advantages over the old architecture, but breaks compatibility with old arch models and scales other than 1 (if you use the official ESRGAN repo). In the future victorc plans to make his fork compatible with both, providing the option to convert between them. They can be used either with the official ESRGAN repository and BasicSR (not victorc's fork).

If you want to train your own model, please use the "old" architecture instead. There really are no disadvantages to it.

PPON Models

Upscaling

Pixel Art

Name Author Scale License Purpose Iterations (Phase 1; 2; 3) Batch Size (Phase 1; 2; 3) HR Size (Phase 1; 2; 3) Dataset Size (Phase 1; 2; 3) Dataset (Phase 1; 2; 3) Pretrained Model
Pixie victorca25 4 Pixel Art / some Cartoons 80K(?; ?; ?) 8; 8; 8 192; 192; 192 ?; ?; ? Custom(Drawings); Custom(Drawings); Custom(Drawings) PPON
xBRZ+ victorca25 4 Pixel Art 60K(?; ?; ?) 8; 8; 8 128; 128; 128 ?; ?; ? Custom (xBRZ images); Custom (xBRZ images); Custom (Drawings) Pixie

Pretrained models for different scales

Name
Author
Scale
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PPON Zheng Hui (惠政) 4

Licenses Links

  • GNU GPLv3:
    • If you modify, interpolate or use the model as a pretrained model for your own model and share results of your resulting model, it will have to be under the same license.
    • You have to state that you used the model and its author for your results.
    • You have to state any changes you made to the model.
    • There are other points, but those are the main ones.

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