The best place to find AI Upscaling models

A 4x model for Upscaler . Link to Github Release # 4xNomos2_hq_dat2 Scale: 4 Architecture: DAT Architecture Option: dat2 Author: Philip Hofmann License: CC-BY-0.4 Subject: Photography Input Type: Images Release Date: 29.08.2024 Dataset: nomosv2 Dataset Size: 6000 OTF (on the fly augmentations): No Pretrained Model: DAT_2_x4 Iterations: 140'000 Batch Size: 4 Patch Size: 48 Description: A dat2 4x upscaling model, similiar to the 4xNomos2_hq_mosr model, trained and for usage on non-degraded input to give good quality output. I scored 7 validation outputs of each of the 21 checkpoints (10k-210k) of this model training with 68 metrics. The metric scores can be found in this google sheet. The corresponding image files for this scoring can be found here Screenshot of the google sheet: !|100 Release checkpoint has been selected by looking at the scores, manually inspecting, and then getting responses on discord to this quick visual test, A B or C, which denote different checkpoints: https://slow.pics/c/8Akzj6rR Checkpoint B is the one released here, but you can also try out Checkpoint A or Checkpoint C if you like them better. ## Model Showcase: Slowpics

A 2x model for Upscale text in very low quality to normal quality.. The upscale model is specifically designed to enhance lower-quality text images, improving their clarity and readability by upscaling them by 2x. It excels at processing moderately sized text, effectively transforming it into high-quality, legible scans. However, the model may encounter challenges when dealing with very small text, as its performance is optimized for text of a certain minimum size. For best results, input images should contain text that is not excessively small.

A 2x model for VHS Restoration. An advanced VHS recording model designed to enhance video quality by reducing artifacts such as haloing, ghosting, and noise patterns. Optimized primarily for PAL resolution (NTSC might work good as well).

A 4x model for Upscaler . Link to Github Release # 4xNomos2_hq_mosr Scale: 4 Architecture: MoSR Architecture Option: mosr Author: Philip Hofmann License: CC-BY-0.4 Subject: Photography Input Type: Images Release Date: 25.08.2024 Dataset: nomosv2 Dataset Size: 6000 OTF (on the fly augmentations): No Pretrained Model: 4xmssim_mosr_pretrain Iterations: 190'000 Batch Size: 6 Patch Size: 64 Description: A 4x MoSR upscaling model, meant for non-degraded input, since this model was trained on non-degraded input to give good quality output. If your input is degraded, use a 1x degrade model first. So for example if your input is a .jpg file, you could use a 1x dejpg model first. Model Showcase: Slowpics

A fast upscaler that is a replacement for standard methods of cleaning noise from the picture. Ideal for BDRemux 720p and 1080p, but only if the noise is monochrome or close to it. So far, attempts are underway to create a dataset that could eliminate color noise at the same speed.

A fast upscaler that is a replacement for standard methods of cleaning noise from the picture. Ideal for BDRemux or WEB-DL 720p or 1080p it. It gets rid of floral noise well, the smaller its grain size, the more uniform the resolution for all videos will be. If you need a more stable video, then you can also add 2x-DigitalFlim-SuperUltraCompact after this upscaler. My upscaler can slightly simplify the geometry of very small objects.

A 1x model for Restoration, H264 . Link to Github Release # 1xDeH264_realplksr Scale: 1 Architecture: RealPLKSR Architecture Option: realplksr Author: Philip Hofmann License: CC-BY-0.4 Subject: Photography Input Type: Images Release Date: 08.08.2024 Dataset: nomosv2 Dataset Size: 6000 OTF (on the fly augmentations): No Pretrained Model: 1xDeJPG_realplksr_otf Iterations: 210'000 Batch Size: 8 Patch Size: 64 Description: A 1x de h264 model to remove h264 compression. Showcase: Slowpics Imgsli