The wiki has been moved, here is the new Model Database.

Name	Author	Scale	License	Purpose	Iterations	Batch Size	HR Size	Epoch	Dataset Size	Dataset	Pretrained Model
Manga109Attempt	kingdomakrillic	4		Anime / Manga	?	4	?	?	0.1K	Manga109	RRDB_PSNR_x4
Falcon Fanart	LyonHrt	4		Anime / Manga	125K	8	128	?	3.393K	Falcon Fanart	RRDB_PSNR_x4
Comic Book	LyonHrt	4		Comic / Drawings	115K	8	128	592	1.548K	Custom (Spider-Man)	none
ad_test_tf	PRAGMA	4		Cartoon / Netflix	5K	16	128	?	30K	Custom (American Dad)	PSNRx4
De-Toon	LyonHrt	4		Toon Shading / Sprite	225K	8	128	525	7.117K	Custom Cartoon-style photos	RRDB_PSNR_x4
Unholy02	DinJerr	4		Anime / Manga	?	?	?	?	?	CG-Painted Anime	Several, see notes
Unholy03	DinJerr	4		Anime / Manga	?	?	?	?	?	CG-Painted Anime	Several, see notes
WaifuGAN v3	DinJerr	4		Anime / Manga	30K	2	128	?	0.173K	CG-Painted Anime	Manga109v2
Lady0101	DinJerr	4		Anime / Manga	208K	?	?	?	~7K	CG-Painted Anime	WaifuGAN v3
DigitalFrames	Klexos	4		Digital Cartoon	1.06M	15	128	96.275	0.25K - 2.5K	Digital Cartoon Images	RRDB_PSNR_x4

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

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

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

The ad_test_tf model was designed for upscaling American Dad NTSC DVD frames (originally at 480p) to match the quality and style of Netflix's equivalent 1080p WEB-DL, which includes a slight desaturation of colors.

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.

Unholy02 and Unholy03 were created by interpolating a whole bunch of models about 30 times, mainly with the Dinjerr's own WaifuGAN model and RRDB_esrgan. It's intended for upscaling CG-painted anime images with light outlines and produces sharper, cleaner, and more aggressive results than manga109, but may produce unnecessary outlines or details when faced with noise, so be wary of jpegs.

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.

Lady0101 was trained on digital paintings of ladies (mostly). Strong anti-staircasing, mediocre undithering and slight blending. It is meant to be used to upscale pixel art/paintings and transform it into digital painting style.

Name	Author	Scale	License	Purpose	Iterations	Batch Size	HR Size	Epoch	Dataset Size	Dataset	Pretrained Model
Box	buildist	4	GNU GPLv3	Realstic	390K	8	192	268	11.577K	Flickr2K+Div2K+OST	PSNR model from same data
Ground	ZaphodBreeblebox	4		Ground Textures	305K	?	128	?	?	Custom (Ground textures Google)	?
Misc	alsa64	4	GNU GPLv3	Surface Textures	220K	32	128	338	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.

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, wook, bark, metal and a few others.

Name	Author	Scale	License	Purpose	Iterations	Batch Size	HR Size	Epoch	Dataset Size	Dataset	Pretrained Model
Trixie	LyonHrt	4		Star Wars	275K	8	192	87	19.814K	?	None
Face Focus	LyonHrt	4		Face De-blur	275K	8	192	455	4.157K	Custom (Faces)	RRDB_PSNR_x4
Face	Twittman	4		Face Upscaling	250K	10	128	967	3.765K	Custom (Faces)	4xESRGAN

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.

Name	Author	Scale	License	Purpose	Iterations	Batch Size	HR Size	Epoch	Dataset Size	Dataset	Pretrained Model
Xbrz	LyonHrt	4		Xbrz style pixel art upscaler	90K	8	128	368	1.897K	custom xbrz up-scaled	RRDB_PSNR_x4
Xbrz+DD	LyonHrt	4		Xbrz style pixel art upscaler with de-dithering	90K	8	128	470	1.523K	custom de-dithered xbrz	xbrz
ScaleNX	LyonHrt	4		Scalenx style pixel art upscaler	80K	8	128	599	1.070K	custom scalenx up-scaled from retroarch shader	RRDB_PSNR_x4
Fatality	twittman	4		(dithered) spirites	265K	10	128	160	19.7K	?	Face
Rebout	LyonHrt	4		Character Sprites	325K	8	128	106	23.808K	Custom prepared sprites from kof 94 rebout	Detoon

Fatality is meant to be used for upscaling medium resolution Sprites, dithered or undithered, 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.

Name	Author	Scale	License	Purpose	Iterations	Batch Size	HR Size	Epoch	Dataset Size	Dataset	Pretrained Model
Map	LyonHrt	4		Map / Old Paper with text	120K	8	192	361	2.311K	Custom(Scans)	none
Forest	LyonHrt	4		Wood / Leaves	160K	8	192	590	2.2K	Custom(?)	none
Skyrim Armory	alsa64	4	GNU GPLv3	Armor, Clothes and Weapons	80K	26	128	2.6K	0.8K	Skyrim Mod textures	Manga109Attempt
Skyrim Wood	Laeris	4		Wood	75K	?	?	?	?	?	?
Skyrim Misc	Deorder	4		Skyrim Diffuse Textures	105K	?	128	?	?	Skyrim Diffuse Textures	?
Fallout 4 Weapons	Bob	4		Fallout Weapon Diffuse Textures	120K	13	128	2.973K	532(OTF)	Fallout 4 HDDLC Weapon Diffuse Textures	Manga109Attempt

The map model was trained on maps, old documents, papers and various styles of typefaces/fonts. Based on a dataset contributed by alsa64. 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 hd armor/weapon textures but could be used on other weapon and armor textures.

Name	Author	Scale	License	Purpose	Iterations	Batch Size	HR Size	Epoch	Dataset Size	Dataset	Pretrained Model
Normal Maps	alsa64	4	GNU GPLv3	Normal Maps	36K	27	128	?	?	Custom (Normal Maps)	Normal Maps - Skyrim artifacted
Normal Maps - Skyrim artifacted	Deorder	4		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.

Name	Author	Scale	License	Purpose	Iterations	Batch Size	HR Size	Epoch	Dataset Size	Dataset	Pretrained Model
Skyrim Alpha	Deorder	4		Alpha Channel	105K	?	128	?	?	Alpha Channels from Skyrim	?

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

Name	Author	Scale	License	Purpose	Iterations	Batch Size	HR Size	Epoch	Dataset Size	Dataset	Pretrained Model
BC1 take 1	alsa64	1	GNU GPLv3	BC1 Compression	100K	2	128	111	1,8K	Custom (Photos)	Failed Attempts
BC1 take 2	alsa64	1	GNU GPLv3	BC1 Compression	261K	2	128	106	4,7K	Custom (Photos / Manga)	JPG (0-20%)
BC1 take 3 Noise Aggressive	alsa64	1	GNU GPLv3	BC1 Compression	400K	2	128	26	28.985K	Custom (just about everything)	BC1 take 2
JPG (0-20%)	alsa64	1	GNU GPLv3	JPG compressed Images	178K	2	128	52	6.23K	Custom (Photos / Manga)	JPG (20-40%)
JPG (20-40%)	alsa64	1	GNU GPLv3	JPG compressed Images	141K	2	128	42	6.23K	Custom (Photos / Manga)	JPG (40-60%)
JPG (40-60%)	alsa64	1	GNU GPLv3	JPG compressed Images	100K	2	128	31	~6.5K	Custom (Photos / Manga)	JPG (60-80%)
JPG (60-80%)	alsa64	1	GNU GPLv3	JPG compressed Images	91K	2	128	27	~6.5K	Custom (Photos / Manga)	JPG (80-100%)
JPG (80-100%)	alsa64	1	GNU GPLv3	JPG compressed Images	162K	2	128	51	~6.5K	Custom (Photos / Manga)	BC1 take 1
JPG PlusULTRA	twittman	1		JPG compressed Images	130K	1	?	150	0.937K	Custom (Manga)	Failed Attempts
Cinepak	twittman	1		Cinepak, msvideo1 and Roq	200K	1	128	21	~8K	Custom (Manga)	none
DeDither	alsa64	1	GNU GPLv3	Dithered Images	127K	2	128	53	4.7K	Custom (Photos / Manga)	JPG (0-20%)
dither_4x_flickr2k_esrgan, dither_4x_flickr2k_psnr	buildist	4		Ordered dithering	280K	16	128	?	2.64K, ~8K	Flickr2K, OST dithered with GIMP	none
DeSharpen	loinne	1		Oversharpened Images	310K	1	128	48	~3K	Custom (?)	Failed Attempts
AntiAliasing	twittman	1		Images with pixelated edges	200K	1	128	440	0.656K	Custom (?)	none

Models to remove compression artifacts.

The BC1 take 2 model is better than my first BC1 model (BC1 take 1), It also might improve edges and tone differences between before and after somewhat. The Dataset was based on the JPG dataset, slightly balanced to contain less manga styled images. Note that BC1 compression is also used for the RGB channel in BC3. BC1=DXT1, BC3=DXT5. Do not use any of them for uncompressed textures.

JPG gets compressed witch 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 resaved.

The Cinepak model removes movie compressions artifacts from older video compression methods like Cinepak, msvideo1 and Roq.

Dithering is an older compression method, where the amount of color gets reduced, if your image has few colors or banding try the Dedither 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. For the 4x model, start with the ESRGAN model, and interpolate with the PSNR model if the result is too sharp.

The DeSharpen 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 oversharpening, 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 oversharpened, for example in image consisting of several combined images, 1 of them with sharpen noise. It is made to remove sharpen noise, particulary 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.

AntiAliasing is for smoothing jagged edges in images and textures.

Name	Author	Scale	License	Purpose	Iterations	Batch Size	HR Size	Epoch	Dataset Size	Dataset	Pretrained Model
1xESRGAN	victorca25	1		Pretrained model		1	128		65k	Combination of DIV2K, Flickr2K and GOPRO	RRDB_ESRGAN_x4.pth
2xESRGAN	victorca25	2		Pretrained model		4	128		65k	Combination of DIV2K, Flickr2K and GOPRO	RRDB_ESRGAN_x4.pth
4xESRGAN	victorca25	4		Pretrained model		8	128		65k	Combination of DIV2K, Flickr2K and GOPRO	RRDB_ESRGAN_x4.pth
8xESRGAN	victorca25	8		Pretrained model		16	128		65k	Combination of DIV2K, Flickr2K and GOPRO	RRDB_ESRGAN_x4.pth
16xESRGAN	victorca25	16		Pretrained model		16	128		65k	Combination of DIV2K, Flickr2K and GOPRO	RRDB_ESRGAN_x4.pth

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.

Name	Author	Scale	License	Purpose	Iterations	Batch Size	HR Size	Epoch	Dataset Size	Dataset	Pretrained Model
normal generator	LyonHrt	1		Difuse to Normal	215K	1	128	45	4.536K	Custom (?)	none

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

Cartoon Painted Models

Name	Author	Scale	License	Purpose	Iterations	Batch Size	HR Size	Epoch	Dataset Size	Dataset	Pretrained Model
Pixie	victorca25	4		Pixel art (and some cartoon) Upscaling	80K	8	192			Drawings downscaled and degraded on the fly	PPON
xBRZ+	victorca25	4		Pixel art Upscaling	60K	8	128			xBRZ images (phase 1 and 2) and drawings downscaled and degraded on the fly (phase 3)	Pixie PPON

• GNU GLPv3:
  • You can't sell the model under that license
  • 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, meaning that you can't sell it.
  • 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.

In addition to that all models by:

• alsa/alsa64

have the following additional restriction:

• You can't sell results generated with a model using that license.