| --- |
| library_name: onnx |
| pipeline_tag: image-to-image |
| tags: |
| - super-resolution |
| - upscaling |
| - onnx |
| - image-upscaling |
| --- |
| |
| # Upscaling models, as ONNX |
|
|
| The model files run by [upscale](https://github.com/benbaker76/upscale), the command line |
| upscaler, and by the two front ends built on it: [Upscale Agent](https://github.com/benbaker76/UpscaleAgent), |
| the desktop app, and [UpsizerAI](https://github.com/benbaker76/UpsizerAI), the web app. All |
| three download from here on demand; nothing needs fetching by hand. |
|
|
| Each model is the original author's weights converted to ONNX by upscale's |
| `python/export_onnx.py`: opset and input/output naming are the same across all of them, and |
| height and width are dynamic so an image can be run whole or in tiles. Most come in two |
| precisions, `<name>.onnx` (float32 weights) and `<name>_fp16.onnx` (float16 weights, float32 |
| input and output), which give the same picture to within one 8-bit level. A few also come as |
| `<name>_int8.onnx`: 8-bit weights, turned back to float32 as the network runs, a quarter of the |
| size and, on most images, within two levels of float32. |
|
|
| **Nothing here was trained by this organization.** Every file is a conversion, and each |
| remains under the license its author released the original weights under - follow the source |
| link for the terms, which for some models restrict commercial use. |
|
|
| | Model | Scale | fp32 | fp16 | int8 | Architecture | Original weights | |
| | --- | --- | --- | --- | --- | --- | --- | |
| | `APISR_GRL_x4` | x4 | 27.1 MB | 17.5 MB | - | GRL | [HikariDawn/APISR](https://huggingface.co/HikariDawn/APISR) | |
| | `APISR_RRDB_x2` | x2 | 18.7 MB | 9.8 MB | - | RRDBNet-6B | [HikariDawn/APISR](https://huggingface.co/HikariDawn/APISR) | |
| | `AnimeSharpV2_ESRGAN_Soft_x2` | x2 | 70.0 MB | 36.6 MB | - | ESRGAN | [Kim2091/AnimeSharpV2](https://huggingface.co/Kim2091/AnimeSharpV2) | |
| | `AnimeSharpV2_MoSR_Sharp_x2` | x2 | 18.0 MB | 9.4 MB | - | MoSR | [Kim2091/AnimeSharpV2](https://huggingface.co/Kim2091/AnimeSharpV2) | |
| | `AnimeSharpV2_MoSR_Soft_x2` | x2 | 18.0 MB | 9.4 MB | - | MoSR | [Kim2091/AnimeSharpV2](https://huggingface.co/Kim2091/AnimeSharpV2) | |
| | `AnimeSharpV2_RPLKSR_Sharp_x2` | x2 | 30.8 MB | 16.1 MB | - | RealPLKSR | [Kim2091/AnimeSharpV2](https://huggingface.co/Kim2091/AnimeSharpV2) | |
| | `AnimeSharpV2_RPLKSR_Soft_x2` | x2 | 30.8 MB | 16.1 MB | - | RealPLKSR | [Kim2091/AnimeSharpV2](https://huggingface.co/Kim2091/AnimeSharpV2) | |
| | `AnimeSharpV3_x2` | x2 | 70.0 MB | 36.6 MB | - | ESRGAN | [Kim2091/AnimeSharpV3](https://huggingface.co/Kim2091/AnimeSharpV3) | |
| | `BSRGAN_x2` | x2 | 69.7 MB | 36.4 MB | - | RRDBNet | [kadirnar/BSRGANx2](https://huggingface.co/kadirnar/BSRGANx2) | |
| | `MewZoom_V1_x2` | x2 | 23.2 MB | 12.5 MB | 7.3 MB | MewZoom TrunkNet | [andrewdalpino/MewZoom-V1-2X](https://huggingface.co/andrewdalpino/MewZoom-V1-2X) | |
| | `MewZoom_V1_UNet_x4` | x4 | 517.2 MB | 258.9 MB | 129.9 MB | MewZoom U-Net | [andrewdalpino/MewZoom-V1-4X-Unet](https://huggingface.co/andrewdalpino/MewZoom-V1-4X-Unet) | |
| | `RealESRGAN_anime_x4` | x4 | 18.7 MB | 9.8 MB | - | RRDBNet-6B | [xinntao/Real-ESRGAN](https://github.com/xinntao/Real-ESRGAN) | |
| | `RealESRGAN_x2` | x2 | 70.0 MB | 36.6 MB | - | RRDBNet | [ai-forever/Real-ESRGAN](https://huggingface.co/ai-forever/Real-ESRGAN) | |
| | `RealESRGAN_x4` | x4 | 69.9 MB | 36.5 MB | - | RRDBNet | [ai-forever/Real-ESRGAN](https://huggingface.co/ai-forever/Real-ESRGAN) | |
| | `RealESRGAN_x4plus` | x4 | 69.9 MB | 36.5 MB | - | RRDBNet | [xinntao/Real-ESRGAN](https://github.com/xinntao/Real-ESRGAN) | |
| | `RealESRGAN_x8` | x8 | 70.0 MB | 36.6 MB | - | RRDBNet | [ai-forever/Real-ESRGAN](https://huggingface.co/ai-forever/Real-ESRGAN) | |
| | `RealESR_General_x4` | x4 | 5.0 MB | 2.5 MB | - | SRVGGNetCompact | [xinntao/Real-ESRGAN](https://github.com/xinntao/Real-ESRGAN) | |
| | `RealWebPhoto_RGT_x4` | x4 | 92.3 MB | - | - | RGT | [Phips/4xRealWebPhoto_RGT](https://huggingface.co/Phips/4xRealWebPhoto_RGT) | |
| | `Swin2SR_Classical_x2` | x2 | 72.4 MB | 40.0 MB | - | Swin2SR | [mv-lab/swin2sr](https://github.com/mv-lab/swin2sr) | |
| | `Swin2SR_Classical_x4` | x4 | 73.0 MB | 40.3 MB | - | Swin2SR | [mv-lab/swin2sr](https://github.com/mv-lab/swin2sr) | |
| | `Swin2SR_RealWorld_x4` | x4 | 72.3 MB | 39.9 MB | - | Swin2SR | [mv-lab/swin2sr](https://github.com/mv-lab/swin2sr) | |
| | `SwinIR_BSRGAN_x4` | x4 | 55.2 MB | - | - | SwinIR-M | [mikestealth/SwinIR](https://huggingface.co/mikestealth/SwinIR) | |
| | `UltraMix_Smooth_x4` | x4 | 69.9 MB | 36.5 MB | - | ESRGAN | [Kim2091/UltraSharp](https://huggingface.co/Kim2091/UltraSharp) | |
| | `UltraSharp_x4` | x4 | 69.9 MB | 36.5 MB | - | ESRGAN | [Kim2091/UltraSharp](https://huggingface.co/Kim2091/UltraSharp) | |
|
|
| ## Using a file directly |
|
|
| ``` |
| https://huggingface.co/baker76/upscale-models/resolve/main/RealESRGAN_x4.onnx |
| ``` |
|
|
| Input is `[1, 3, H, W]` float32, RGB, 0 to 1; output is the same layout at the model's |
| scale. The x2 RRDBNet models need even sides, and the window-attention models and MewZoom's |
| U-Net need sides that are a multiple of 8 or 32; `models.json` in the UpsizerAI repository records the |
| multiple, and the peak activation, of each file. |
|
|
