The best place to find AI Upscaling models

This model is designed to upscale the standard definition digital era of the Pokémon anime, which runs from late season 5 (Master Quest) to early season 12 (Galactic Battles). During this time, the show was animated digitally in a 4:3 ratio. This process was also used for Mewtwo Returns, most of Pokémon Chronicles, and the Mystery Dungeon specials. Advice/Known Limitations: * This OmniSR model can occasionally produce black frames when run in fp16 mode. This seems to be more common in the TPCi era (seasons 9 and later). The issue is sporadic enough that it probably makes sense to do a first pass in fp16, then re-upscale any affected shots in fp32. * I recommend using QTGMC on a preset of "Slow" or slower for deinterlacing. While the show is primarily animated at 12/24 fps, some elements like backgrounds are animated at a full 60i. * The model is not great at handling fonts, particularly the italicized text in the episode credits. This is despite including font images in the training data,.

This model is designed to upscale the standard definition digital era of the Pokémon anime, which runs from late season 5 (Master Quest) to early season 12 (Galactic Battles). During this time, the show was animated digitally in a 4:3 ratio. This process was also used for Mewtwo Returns, most of Pokémon Chronicles, and the Mystery Dungeon specials. Advice/Known Limitations: * I recommend using QTGMC on a preset of "Slow" or slower for deinterlacing. While the show is primarily animated at 12/24 fps, some elements like backgrounds are animated at a full 60i. * The model is not great at handling fonts, particularly the italicized text in the episode credits. This is despite including font images in the training data,.

Takes composite/RF/VHS NES footage and attempts to restore it to RGB quality. Assumes footage has been properly deinterlaced via field duplication from 240p to 480p. Note that: * All footage was captured in 240p/480p NTSC. * RGB footage was captured via an AV Famicom with the RGB Blaster via the Retrotink 2x. * The model was trained exclusively on individual frames, so it can't fix things like dropouts. * The even and odd fields of NES composite tend to be a bit...different from each other, so there will be some jitter at 60fps. * I don't have access to an NES Toploader, so I wouldn't expect it to fix the jailbars very well.

GitHub Link: https://github.com/Kim2091/Kim2091-Models/releases/tag/2x-AnimeSharpV2_Set This is my first anime model in years. Hopefully you guys can find a good use-case for it. Included are 4 models: 1. RealPLKSR (Higher quality, slower) 2. MoSR (Lower quality, faster) There are Sharp and Soft versions of both When to use each: - __Sharp:__ For heavily degraded sources. Sharp models have issues depth of field but are best at removing artifacts - __Soft:__ For cleaner sources. Soft models preserve depth of field but may not remove other artifacts as well __Notes:__ - MoSR doesn't work in chaiNNer currently - To use MoSR: 1. Use the ONNX version in tools like VideoJaNai 2. Update spandrel in the latest version of ComfyUI The ONNX version may produce slightly different results than the .pth version. If you have issues, try the .pth model.

GitHub Link: https://github.com/Kim2091/Kim2091-Models/releases/tag/2x-AnimeSharpV2_Set This is my first anime model in years. Hopefully you guys can find a good use-case for it. Included are 4 models: 1. RealPLKSR (Higher quality, slower) 2. MoSR (Lower quality, faster) There are Sharp and Soft versions of both When to use each: - __Sharp:__ For heavily degraded sources. Sharp models have issues depth of field but are best at removing artifacts - __Soft:__ For cleaner sources. Soft models preserve depth of field but may not remove other artifacts as well __Notes:__ - MoSR doesn't work in chaiNNer currently - To use MoSR: 1. Use the ONNX version in tools like VideoJaNai 2. Update spandrel in the latest version of ComfyUI The ONNX version may produce slightly different results than the .pth version. If you have issues, try the .pth model.

GitHub Link: https://github.com/Kim2091/Kim2091-Models/releases/tag/2x-AnimeSharpV2_Set This is my first anime model in years. Hopefully you guys can find a good use-case for it. Included are 4 models: 1. RealPLKSR (Higher quality, slower) 2. MoSR (Lower quality, faster) There are Sharp and Soft versions of both When to use each: - __Sharp:__ For heavily degraded sources. Sharp models have issues depth of field but are best at removing artifacts - __Soft:__ For cleaner sources. Soft models preserve depth of field but may not remove other artifacts as well __Notes:__ - MoSR doesn't work in chaiNNer currently - To use MoSR: 1. Use the ONNX version in tools like VideoJaNai 2. Update spandrel in the latest version of ComfyUI The ONNX version may produce slightly different results than the .pth version. If you have issues, try the .pth model.

GitHub Link: https://github.com/Kim2091/Kim2091-Models/releases/tag/2x-AnimeSharpV2_Set This is my first anime model in years. Hopefully you guys can find a good use-case for it. Included are 4 models: 1. RealPLKSR (Higher quality, slower) 2. MoSR (Lower quality, faster) There are Sharp and Soft versions of both When to use each: - __Sharp:__ For heavily degraded sources. Sharp models have issues depth of field but are best at removing artifacts - __Soft:__ For cleaner sources. Soft models preserve depth of field but may not remove other artifacts as well __Notes:__ - MoSR doesn't work in chaiNNer currently - To use MoSR: 1. Use the ONNX version in tools like VideoJaNai 2. Update spandrel in the latest version of ComfyUI The ONNX version may produce slightly different results than the .pth version. If you have issues, try the .pth model.

PBRify Github: https://github.com/Kim2091/PBRify_Remix Release Link: https://github.com/Kim2091/Kim2091-Models/releases/tag/4x-PBRify_RPLKSRd_V3 This update brings a new upscaling model, 4x-PBRify_RPLKSRd_V3. This model is roughly 8x faster than the current DAT2 model, while being *higher quality*. It produces far more natural detail, resolves lines and edges more smoothly, and cleans up compression artifacts better. As a result of those improvements, PBR is also much improved. It tends to be clearer with less defined artifacts. However, this model is currently **only compatible with ComfyUI**. chaiNNer has not yet been updated to support this architecture. More Comparisons

A 2x model for A 2x mosr upscaling model for game textures . Link to Github Release with more infos to the process 2xAoMR_mosr Scale: 4 Architecture: MoSR Architecture Option: mosr Author: Philip Hofmann License: CC-BY-0.4 Subject: Game Textures Input Type: Images Release Date: 21.09.2024 (dd/mm/yy) Dataset: Game Textures from Age of Mythology: Retold Dataset Size: 13'847 OTF (on the fly augmentations): No Pretrained Model: 4xNomos2_hq_mosr Iterations: 510'000 Batch Size: 4 Patch Size: 64 ## Description: In short: A 2x game texture mosr upscaling model, trained on and for (but not limited to) Age of Mythology: Retold textures. Since I have been playing Age of Mythology: Retold (casual player), I thought it would be interesting to train an single image super resolution model on (and for) game textures of AoMR, but this model should be usable for other game textures aswell. This is a 2x model, since the biggest texture images are already 4096x4096, I thought going 4x on those would be overkill (also there are already 4x game texture upscaling models, so this model can be used for similiar cases where 4x is not needed). Model Showcase: Slowpics

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.

A 4x model for Upscaler . Link to Github Release 4xNomos2_hq_drct-l Scale: 4 Architecture: DRCT Architecture Option: drct_l Author: Philip Hofmann License: CC-BY-0.4 Subject: Photography Input Type: Images Release Date: 08.09.2024 Dataset: nomosv2 Dataset Size: 6000 OTF (on the fly augmentations): No Pretrained Model: DRCT-L_X4 Iterations: 200'000 Batch Size: 2 Patch Size: 64 Description: An drct-l 4x upscaling model, similiar to the 4xNomos2_hq_atd, 4xNomos2_hq_dat2 and 4xNomos2_hq_mosr models, trained and for usage on non-degraded input to give good quality output. Model Showcase: Slowpics

A 4x model for Upscaler . Link to Github Release 4xNomos2_hq_atd Scale: 4 Architecture: ATD Architecture Option: atd Author: Philip Hofmann License: CC-BY-0.4 Subject: Photography Input Type: Images Release Date: 05.09.2024 Dataset: nomosv2 Dataset Size: 6000 OTF (on the fly augmentations): No Pretrained Model: 003_ATD_SRx4_finetune Iterations: 180'000 Batch Size: 2 Patch Size: 48 Norm: true Description: An atd 4x upscaling model, similiar to the 4xNomos2_hq_dat2 or 4xNomos2_hq_mosr models, trained and for usage on non-degraded input to give good quality output.