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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.

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).