The best place to find AI Upscaling models
OpenModelDB is a community driven database of AI Upscaling models. We aim to provide a better way to find and compare models than existing sources.
Found 567 models
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A 4x model for Illustrations, digital art, manga covers. Model for color images including manga covers and color illustrations, digital art, visual novel art, artbooks, and more. The DAT2 version is the highest quality version but also the slowest. See the ESRGAN version for faster performance. https://slow.pics/c/GfArurPG
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A 4x model for Illustrations, digital art, manga covers. Model for color images including manga covers and color illustrations, digital art, visual novel art, artbooks, and more. The ESRGAN version is high quality with balanced performance. See the DAT2 version for maximum quality. https://slow.pics/c/GfArurPG
A 1x model for Upscaling older cartoons. This is yet another retrain of SaurusX's SwatKats_Lite model. The dataset was reprocessed with my Find Misaligned Images script, along with the new ImgAlign update, which drastically reduced artifacts and increased the model's capabilities. This particular model is roughly on par with or slightly behind the original, doing better in some spots and worse in others. Refer to the attached examples to see this. The advantage of this over the original is the speed improvement of Compact over ESRGAN-lite. In a 480p test on an RTX 4090, the original ESRGAN-lite model took 0.28 seconds to process a frame vs Compact's 0.13 seconds. https://slow.pics/s/dF3Icjpv OR <https://imgsli.com/MjQxMzc1/0/1>
NomosUni rgt multijpg
A 4x model for 4x universal DoF preserving upscaler. 4x universal DoF preserving upscaler, pair trained with jpg degradation (down to 40) and multiscale (down_up, bicubic, bilinear, box, nearest, lanczos) in neosr with adamw, unet and pixel, perceptual, gan and color losses. Similiar to the last model I released, with same dataset, this is a full RGT model in comparison. FP32 ONNX conversion is provided in the google drive folder for you to run it. 6 Examples (To check JPG compression handling see Example Nr.4, to check Depth of Field handlin see Example Nr.1 & Nr.6): Slowpics
wtp manga cover pretrained
A 2x model for pre-workout to speed up your workouts. While experimenting with color models for cugan, I just ran into problems that the official models are not imported correctly and have additional processing in addition to resizing. As a result, for my project I trained 2 x cugans for neosr; only resizing losses were applied to the dataset `(“lanczos”, “bicubic”, “bilenear”, “HAMMING”, “NEAREST”)`
NomosUni rgt s multijpg
A 4x model for 4x universal DoF preserving upscaler. 4x universal DoF preserving upscaler, pair trained with jpg degradation (down to 40) and multiscale (down_up, bicubic, bilinear, box, nearest, lanczos) in neosr with adamw, unet and pixel, perceptual, gan, color and ldl losses. Examples: Imgsli Slowpics
A 2x model for Upscale Evangelion episode 16. An ESRGAN Lite model trained on the dataset provided by pwnsweet. I had to make the adjustment of transfering average brightness and contrast from the LR's to the HR's to avoid flashing in the final model. Otherwise the dataset is as provided. https://imgsli.com/MjM5Njk5
A 2x model for Upscale Evangelion episode 16. An earlier iteration of my other ESRGAN lite model. After some closer examination at the video output of the other model I think this one is also worth considering. For being at such an early stage, this model is extremely stable, which makes me think the other model may be exhibiting some overfitting issues. Definitely worth a look.
A 2x model for 2x upscaler for evangelion episodes. For the evangelion upscale project still, this time a dat2 model. A 2x upscaler for evangelion episodes on the evangelion dataset provided by pwnsweet, which called for model trainers to train models on it. Slowpoke Pics 4 Examples
A 1x model for For restoring the non blu-ray Evangelion episodes.. Restoration model for de-cruddifying Evangelion episodes from their non blu-ray source. Has issues with eyes in some cases, where it thinks the whites are haloing. Comparison images are of the interpolated model.