Upscaling models, as ONNX
The model files used by Upscale Agent, the desktop app, and UpsizerAI, the web app it is the desktop version of. Both download from here on demand; nothing needs fetching by hand.
Each model is the original author's weights converted to ONNX by UpsizerAI'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.
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 | Architecture | Original weights |
|---|---|---|---|---|---|
APISR_GRL_x4 |
x4 | 27.1 MB | 17.5 MB | GRL | HikariDawn/APISR |
APISR_RRDB_x2 |
x2 | 18.7 MB | 9.8 MB | RRDBNet-6B | HikariDawn/APISR |
AnimeSharpV2_ESRGAN_Soft_x2 |
x2 | 70.0 MB | 36.6 MB | ESRGAN | Kim2091/AnimeSharpV2 |
AnimeSharpV2_MoSR_Sharp_x2 |
x2 | 18.0 MB | 9.4 MB | MoSR | Kim2091/AnimeSharpV2 |
AnimeSharpV2_MoSR_Soft_x2 |
x2 | 18.0 MB | 9.4 MB | MoSR | Kim2091/AnimeSharpV2 |
AnimeSharpV2_RPLKSR_Sharp_x2 |
x2 | 30.8 MB | 16.1 MB | RealPLKSR | Kim2091/AnimeSharpV2 |
AnimeSharpV2_RPLKSR_Soft_x2 |
x2 | 30.8 MB | 16.1 MB | RealPLKSR | Kim2091/AnimeSharpV2 |
AnimeSharpV3_x2 |
x2 | 70.0 MB | 36.6 MB | ESRGAN | Kim2091/AnimeSharpV3 |
BSRGAN_x2 |
x2 | 69.7 MB | 36.4 MB | RRDBNet | kadirnar/BSRGANx2 |
RealESRGAN_anime_x4 |
x4 | 18.7 MB | 9.8 MB | RRDBNet-6B | xinntao/Real-ESRGAN |
RealESRGAN_x2 |
x2 | 70.0 MB | 36.6 MB | RRDBNet | ai-forever/Real-ESRGAN |
RealESRGAN_x4 |
x4 | 69.9 MB | 36.5 MB | RRDBNet | ai-forever/Real-ESRGAN |
RealESRGAN_x4plus |
x4 | 69.9 MB | 36.5 MB | RRDBNet | xinntao/Real-ESRGAN |
RealESRGAN_x8 |
x8 | 70.0 MB | 36.6 MB | RRDBNet | ai-forever/Real-ESRGAN |
RealESR_General_x4 |
x4 | 5.0 MB | 2.5 MB | SRVGGNetCompact | xinntao/Real-ESRGAN |
RealWebPhoto_RGT_x4 |
x4 | 92.3 MB | - | RGT | Phips/4xRealWebPhoto_RGT |
Swin2SR_Classical_x2 |
x2 | 72.4 MB | 40.0 MB | Swin2SR | mv-lab/swin2sr |
Swin2SR_Classical_x4 |
x4 | 73.0 MB | 40.3 MB | Swin2SR | mv-lab/swin2sr |
Swin2SR_RealWorld_x4 |
x4 | 72.3 MB | 39.9 MB | Swin2SR | mv-lab/swin2sr |
SwinIR_BSRGAN_x4 |
x4 | 55.2 MB | - | SwinIR-M | mikestealth/SwinIR |
UltraMix_Smooth_x4 |
x4 | 69.9 MB | 36.5 MB | ESRGAN | Kim2091/UltraSharp |
UltraSharp_x4 |
x4 | 69.9 MB | 36.5 MB | ESRGAN | 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 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.