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A 4x model for Pretrained . 4xNomos2_realplksr_dysample Scale: 4 Architecture: RealPLKSR with Dysample Architecture Option: realplksr Github Release Author: Philip Hofmann License: CC-BY-0.4 Subject: Photography Input Type: Images Release Date: 30.06.2024 Dataset: nomosv2 Dataset Size: 6000 OTF (on the fly augmentations): No Pretrained Model: 4xmssim_realplksr_dysample_pretrain Iterations: 185'000 Batch Size: 8 GT Size: 256, 512 Description: A Dysample RealPLKSR 4x upscaling model that was trained with / handles jpg compression down to 70 on the Nomosv2 dataset, preserves DoF. Based on the 4xmssim_realplksr_dysample_pretrain I released 3 days ago. This model affects / saturate colors, which can be counteracted a bit by using wavelet color fix, as used in these examples. Showcase: Slowpics

A 4x model for Restoration . 4xNomos2_otf_esrgan Scale: 4 Architecture: ESRGAN Architecture Option: esrgan Github Release Link Author: Philip Hofmann License: CC-BY-0.4 Subject: Photography Input Type: Images Release Date: 22.06.2024 Dataset: Nomos-v2 Dataset Size: 6000 OTF (on the fly augmentations): Yes Pretrained Model: RealESRGAN_x4plus Iterations: 246'000 Batch Size: 8 GT Size: 256 Description: 4x ESRGAN model for photography, trained using the Real-ESRGAN otf degradation pipeline. Showcase: Slow Pics 8 Examples

A 4x model for Restoration . I simply wanted to release an ESRGAN model just because I had not trained one for quite a while and just wanted to revisit this older arch for the current series. 4xNomosWebPhoto_esrgan Scale: 4 Architecture: ESRGAN Architecture Option: esrgan Github Release Link Author: Philip Hofmann License: CC-BY-0.4 Subject: Photography Input Type: Images Release Date: 16.06.2024 Dataset: Nomos-v2 Dataset Size: 6000 OTF (on the fly augmentations): No Pretrained Model: RealESRGAN_x4plus Iterations: 210'000 Batch Size: 12 GT Size: 256 Description: 4x ESRGAN model for photography, trained with realistic noise, lens blur, jpg and webp re-compression. ESRGAN version of 4xNomosWebPhoto_RealPLKSR, trained on the same dataset and in a similiar way. For more information look into the 4xNomosWebPhoto_RealPLKSR release, and the pdf file in its attachments.

A 4x model for Restoration . 4xNomosWebPhoto_atd Scale: 4 Architecture: ATD Architecture Option: atd Github Release Link Author: Philip Hofmann License: CC-BY-0.4 Subject: Photography Input Type: Images Release Date: 07.06.2024 Dataset: Nomos-v2 Dataset Size: 6000 OTF (on the fly augmentations): No Pretrained Model: 003_ATD_SRx4_finetune.pth Iterations: 460'000 Batch Size: 6, 2 GT Size: 128, 192 Description: 4x ATD model for photography, trained with realistic noise, lens blur, jpg and webp re-compression. ATD version of 4xNomosWebPhoto_RealPLKSR, trained on the same dataset and in the same way. For more information look into the 4xNomosWebPhoto_RealPLKSR release, and the pdf file in its attachments. Showcase: Slow Pics 18 Examples

A 2x model for Compression Removal, Noise Reduction, Line Correction, MPEG2 / LD Artifact Removal. This is my first model release. The model was inspired from a personal project which I have been pursuing for some time now, which this model aims to solve. This model will upscale low resolution hand drawn animation from 1970s to 2000. The colors are retained with effective noise control. Details and Textures are maintained to a good degree considering animations. Color Spills are also corrected depending on the colors. Shades of white and yellows have been difficult. It also makes the lines slightly sharper and thinner. This could be a plus depending on your source. The model is also temporally stable across my tests with little observable issues. **Showcase:** Images - https://imgsli.com/MjYwNzY1/12/13 Video Sample - https://t.ly/Jp7-w vs Upscale - https://t.ly/PdsKs

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

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

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

A 4x model for Restoration . 4xNomosWebPhoto_RealPLKSR Scale: 4 Architecture: RealPLKSR Architecture Option: realplksr Link to Github Release Author: Philip Hofmann License: CC-BY-0.4 Subject: Photography Input Type: Images Release Date: 28.05.2024 Dataset: Nomos-v2 Dataset Size: 6000 OTF (on the fly augmentations): No Pretrained Model: 4x_realplksr_gan_pretrain Iterations: 404'000, 445'000 Batch Size: 12, 4 GT Size: 128, 256, 512 Description: short: 4x RealPLKSR model for photography, trained with realistic noise, lens blur, jpg and webp re-compression. full: My newest version of my RealWebPhoto series, this time I used the newly released Nomos-v2 dataset by musl. I then made 12 different low resolution degraded folders, using kim's datasetdestroyer for scaling and compression, my ludvae200 model for realistic noise, and umzi's wtp_dataset_destroyer with its floating point lens blur implementation for better control. I then mixed them together in a single lr folder and trained for 460'000 iters, checked the results, and upon kims suggestion of using interpolation, I tested and am releasing this interpolation between the checkpoints 404'000 and 445'000. This model has been trained on neosr using mixup, cutmix, resizemix, cutblur, nadam, unet, multisteplr, mssim, perceptual, gan, dists, ldl, ff, color and lumaloss, and interpolated using the current chaiNNer nightly version. This model took multiple retrainings and reworks of the dataset, until I am now satisfied enough with the quality to release this version. For more details on the whole process see the pdf file in the attachement. I am also attaching the 404'000, 445'000 and 460'000 checkpoints for completeness. PS in general degradation strengths have been reduced/adjusted in comparison to my previous RealWebPhoto models Showcase: Slow Pics 10 Examples

This is a model for the restoration of My Little Pony: Friendship is Magic, however it also works decently well on all similar art. It was trained in 2x on ground truth 3840x2160 HRs and 1920x1080 LRs of varying compression, so it is able to upscale from 1080p to 2160p, where its detail retention is great, however it may create noticeable artifacting if looked at closely, like areas of randomly coloured pixels along edges. In 1x or 1.5x (2x upscaled and then downscaled back down) it performs extremely well, almost perfectly in fact, in correcting colours, removing compression, and crisping up lines - and this is the way the model is intended to be used (hence the acronym of its name being "SS", or "supersampling"). **Github Release** **Showcase:** https://slow.pics/s/1ixqCSjy

This is part of my PBRify_Remix project. This is a much more capable model based on SwinIR Medium, which should strike a balance between capacity for learning + inference speed. It appears to have done so :)