How To Upscale

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The best place to find AI Upscaling models

OpenModelDB is a community driven database of AI Upscaling models. We aim to provide a better way to find and compare models than existing sources.

Found 586 models
ESRGAN
2x
2x Pooh V4
2x Pooh V4
2x Pooh V4
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
Compact
1x
1xSkinContrast-HighAlternative-SuperUltraCompact
1xSkinContrast-HighAlternative-SuperUltraCompact
1xSkinContrast-HighAlternative-SuperUltraCompact
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
Compact
2x
MLP StarSample V1.0
MLP StarSample V1.0
MLP StarSample V1.0
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
SwinIR
4x
4x-PBRify_UpscalerSIR-M_V2
4x-PBRify_UpscalerSIR-M_V2
4x-PBRify_UpscalerSIR-M_V2
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 :)
RGT
4x
4xTextures_GTAV_rgt-s
4xTextures_GTAV_rgt-s
4xTextures_GTAV_rgt-s
Github Release Link ## 4xTextures_GTAV_rgt-s **Scale:** 4 **Architecture:** RGT **Architecture Option:** RGT-S **License:** CC-BY-0.4 **Purpose:** Restoration **Subject:** Game Textures **Input Type:** Images **Release Date:** 04.05.2024 **Dataset:** GTAV_512_Textures **Dataset Size:** 8492 **OTF (on the fly augmentations):** No **Pretrained Model:** RGT_S_x4 **Iterations:** 165'000 **Batch Size:** 6,4 **GT Size:** 128,256 **Description:** A model to upscale game textures, trained on GTAV Textures, handles jpg compression down to 80. **Showcase:** Slow Pics
DRCT
4x
4xRealWebPhoto_v4_drct-l
4xRealWebPhoto_v4_drct-l
4xRealWebPhoto_v4_drct-l
Link to Github Release ## 4xRealWebPhoto_v4_drct-l **Scale:** 4 **Architecture:** DRCT **Architecture Option:** DRCT-L **Author:** Philip Hofmann **License:** CC-BY-0.4 **Purpose:** Restoration **Subject:** Realistic, Photography **Input Type:** Images **Release Date:** 02.05.2024 **Dataset:** 4xRealWebPhoto_v4 **Dataset Size:** 8492 **OTF (on the fly augmentations):** No **Pretrained Model:** 4xmssim_drct-l_pretrain **Iterations:** 260'000 **Batch Size:** 6,4 **GT Size:** 128,192 **Description:** The first real-world drct model, so I am releasing it, or at least my try at it, maybe others will be able to get better results than me, I think I'd recommend my 4xRealWebPhoto_v3_atd model over this one if a real-world model for upscaling photos downloaded from the web is desired. This model is based on my previously released drct pretrain. Used mixup, cutmix, resizemix augmentations, and mssim, perceptual, gan, dists, ldl, focalfrequency, gradvar, color and luma losses. **Showcase:** Slow.pics
DAT
4x
4xRealWebPhoto_v4_dat2
4xRealWebPhoto_v4_dat2
4xRealWebPhoto_v4_dat2
Link to Github Release ## 4xRealWebPhoto_v4_dat2 **Scale:** 4 **Architecture:** DAT **Author:** Philip Hofmann **License:** CC-BY-4.0 **Purpose:** Compression Removal, Deblur, Denoise, JPEG, WEBP, Restoration **Subject:** Photography **Input Type:** Images **Date:** 04.04.2024 **Architecture Option:** DAT-2 **I/O Channels:** 3(RGB)->3(RGB) **Dataset:** Nomos8k **Dataset Size:** 8492 **OTF (on the fly augmentations):** No **Pretrained Model:** DAT_2_x4 **Iterations:** 243'000 **Batch Size:** 4-6 **GT Size:** 128-256 **Description:** 4x Upscaling Model for Photos from the Web. The dataset consists of only downscaled photos (to handle good quality), downscaled and compressed photos (uploaded to the web and compressed by service provider), and downscale, compressed, rescaled, recompressed photos (downloaded from the web and re-uploaded to the web). Applied lens blur, realistic noise with my ludvae200 model, JPG and WEBP compression (40-95), and down_up, linear, cubic_mitchell, lanczos, gaussian and box downsampling algorithms. For details on the degradation process, check out the pdf with its explanations and visualizations. This is basically a dat2 version of my previous 4xRealWebPhoto_v3_atd model, but trained with a bit stronger noise values, and also a single image per variant so drastically reduced training dataset size. **Showcase:** 12 Slowpics Examples
SPAN
1x
1x-PBRify_NormalV3
1x-PBRify_NormalV3
1x-PBRify_NormalV3
This is part of a larger model set, PBRify_Remix. PBRify_Remix is an easy way to upscale and generate PBR textures using existing low quality textures. The dataset consists of ethically sourced textures from websites like ambientCG and Polyhaven, which sets it apart from most other models. It's intended for use with RTX Remix (hence the name) but it'll work for other things as well.
SPAN
1x
1x-PBRify_Height
1x-PBRify_Height
1x-PBRify_Height
This is part of a larger model set, PBRify_Remix. PBRify_Remix is an easy way to upscale and generate PBR textures using existing low quality textures. The dataset consists of ethically sourced textures from websites like ambientCG and Polyhaven, which sets it apart from most other models. It's intended for use with RTX Remix (hence the name) but it'll work for other things as well. Note: The height map model is best used after applying a "delighting" pipeline to your game textures. Currently PBRify_Remix does not have a method for this, so you need your own.
SPAN
1x
1x-PBRify_RoughnessV2.pth
1x-PBRify_RoughnessV2.pth
1x-PBRify_RoughnessV2.pth
This is part of a larger model set, PBRify_Remix. PBRify_Remix is an easy way to upscale and generate PBR textures using existing low quality textures. The dataset consists of ethically sourced textures from websites like ambientCG and Polyhaven, which sets it apart from most other models. It's intended for use with RTX Remix (hence the name) but it'll work for other things as well.
SPAN
4x
4x-PBRify_UpscalerSPANV4
4x-PBRify_UpscalerSPANV4
4x-PBRify_UpscalerSPANV4
This is part of a larger model set, PBRify_Remix. PBRify_Remix is an easy way to upscale and generate PBR textures using existing low quality textures. The dataset consists of ethically sourced textures from websites like ambientCG and Polyhaven, which sets it apart from most other models. It's intended for use with RTX Remix (hence the name) but it'll work for other things as well.
SPAN
2x
ModernSpanimationV1
ModernSpanimationV1
ModernSpanimationV1
Upscale modern animation images/videos. Compression, blur, and un-sharpening degradations were used on the dataset. First model I trained and I think it came out pretty well.