A 1x model designed for skin contrast (although some backgrounds suffer a little modification), also try the other SkinContrast models

1xSkinContrast-High-SuperUltraCompact

A 1x model designed for skin contrast (although some backgrounds suffer a little modification), some images may suffer from the creation of artifacts, High Alternative is the model that generates the most artifacts, also try the other SkinContrast models

1xSkinContrast-HighAlternative-SuperUltraCompact

1xSkinContrast-SuperUltraCompact

2x upscaler for the AI generated image output. Trained on 43856 images from lexica.art, so its trained specifically on that model but should work in general on ai generated images.

2xLexicaRRDBNet

Its like 2xLexicaRRDBNet model, but trained for some more with l1_gt_usm and percep_gt_usm set to true, resulting in sharper outputs. I provide both so they can be chosen based on preferrence of the user.

2xLexicaRRDBNet_Sharp

2x upscaler for AI generated images. Trained on 43856 images from lexica.art, so its trained specifically on that model but should work in general on ai generated images.

2xLexicaSwinIR

4x ai generated image upscaler trained with otf

The 4xLexicaDAT2_hb generated some weird lines on some edges. 4xNomosUniDAT is a different checkpoint of 4xNomosUniDAT_otf (145000), I liked the result a bit more in that example.

LexicaDAT2_otf

4x upscaler for AI generated images. Trained on 43856 images from lexica.art, so its trained specifically on that model but should work in general on ai generated images.

4xLexicaHAT

Skilled in working with cats, hair, parties, and creating clear images. Also proficient in resizing photos and enlarging large, sharp images. Can effectively improve images from small sizes as well (300px at smallest on one side, depending on the subject). Experienced in experimenting with techniques like upscaling with this model twice and then reducing it by 50% to enhance details, especially in features like hair or animals.

Architecture	ESRGAN
Scale	2x
Color Mode	RGB
License	CC-BY-4.0 Private use Commercial use Distribution Modifications Credit required State Changes No Liability & Warranty Disclaimer
Date	2023-06-01
Dataset	lexica-aperture-v3-small
Dataset size	43856
Training iterations	220000
Training epochs	18
Training batch size	4
Training HR size	128

2xLexicaRRDBNet_Sharp

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