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See the information about the two models below: Author: Philip Hofmann License: CC BY 4.0 Release Date: 11.01.2024 (dd/mm/yy) Trained with musl's [neosr](https://github.com/muslll/neosr), kim's [Datasetdestroyer](https://github.com/Kim2091/helpful-scripts/tree/main/Dataset%20Destroyer), and the Real-ESRGAN pipeline. --- Name: 2xNomosUni_compact_multijpg_ldl Author: Philip Hofmann Release Date: 11.01.2024 (dd/mm/yy) License: CC BY 4.0 Network: SRVGGNetCompact Scale: 2 Purpose: 2x fast universal DoF preserving upscaler Iterations: 218'000 epoch: 168 batch_size: 12 HR_size: 128 Dataset: nomosuni Number of train images: 2989 OTF Training: No Pretrained_Model_G: 2x-Compact-Pretrain Description: 2x compact fast universal DoF preserving upscaler, pair trained with jpg degradation (down to 40) and multiscale (down_up, bicubic, bilinear, box, nearest, lanczos). Examples: [ani](https://slow.pics/c/Zd7UCmLP) [face](https://slow.pics/c/wKiu7Orf) [dofwheat](https://imgsli.com/MjMyNTU4) --- Name: 2xNomosUni_compact_otf_medium Author: Philip Hofmann Release Date: 11.01.2024 (dd/mm/yy) License: CC BY 4.0 Network: SRVGGNetCompact Scale: 2 Purpose: 2x fast universal upscaler with medium degradation handling (jpg compression, noise, blur) Iterations: 276'000 epoch: 218 batch_size: 12 HR_size: 128 Dataset: nomosuni Number of train images: 2989 OTF Training: Yes Pretrained_Model_G: 2xNomosUni_compact_otf_strong Description: 2x compact fast universal upscaler with medium degradation handling using the Real-ESRGAN training pipeline, based off 2xNomosUni_compact_otf_strong. Handles jpg compression, some noise, and some blur (so dejpgs, denoises and deblurs). Examples: [RealPhoto](https://imgsli.com/MjMyNTUy) [Noisy](https://imgsli.com/MjMyNTUz) |