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Model Database
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. |
Contents
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 | 268 | 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 "
| |
---|---|---|---|---|---|---|---|---|---|---|---|
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 "
|
---|---|---|---|---|---|---|---|---|---|---|
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
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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.
Testing captcha. Again. Testing again with basic account.