LibreRealESRGANx2 Restore
RRDBNet Real-ESRGAN super-resolution (2x upscaling), repackaged for LibreYOLO.
Fonte do modelo
- Tipo
- Other
- Tamanho do arquivo
- 64,0 MB
- Versão
- vRevision 09c092b
- Atualizado
- 8 de out. de 2026
- Downloads
- 0
- Fonte
- Hugging Face
Descrição da fonte
LibreRealESRGANx2-restore
RRDBNet Real-ESRGAN super-resolution (2x upscaling), repackaged for LibreYOLO.
Source
Weights derived from xinntao/Real-ESRGAN
release RealESRGAN_x2plus.pth.
Copyright (c) 2021 Xintao Wang. Licensed under the BSD 3-Clause License.
The RRDBNet architecture lineage is XPixelGroup/BasicSR (Apache-2.0, Copyright 2018-2022 BasicSR Authors).
Modifications
State-dict metadata-wrap only: keys and learned parameters are unchanged; the
checkpoint is wrapped in the LibreYOLO v1.0 schema (task=restore, scale=2).
Tensor-level parity vs the official model is exact (max_abs_diff == 0, fp32).
See weights/convert_realesrgan_weights.py in the
LibreYOLO source repository.
Usage
from libreyolo import LibreYOLO
model = LibreYOLO("LibreRealESRGANx2-restore.pt")
res = model.predict("small.jpg") # res[0].restored is 2x the input
res[0].save("upscaled.png")
# large images: model.predict("big.jpg", tile=512) # seam-free tiling
License
BSD 3-Clause (weights) with Apache-2.0 architecture lineage. See the
LICENSE and NOTICE files in this repository.
Fontes
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Artefatos de modelo
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Artefatos de modelo
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Trechos de fonte
2 trechos--- license: bsd-3-clause library_name: libreyolo tags: - super-resolution - image-to-image - real-esrgan pipeline_tag: image-to-image --- # LibreRealESRGANx2-restore RRDBNet Real-ESRGAN super-resolution (2x upscaling), repackaged for LibreYOLO. ## Source Weights derived from [xinntao/Real-ESRGAN](https://github.com/xinntao/Real-ESRGAN) release `RealESRGAN_x2plus.pth`. Copyright (c) 2021 Xintao Wang. Licensed under the BSD 3-Clause License. The RRDBNet architecture lineage is [XPixelGroup/BasicSR](https://github.com/XPixelGroup/BasicSR) (Apache-2.0, Copyright 2018-2022 BasicSR Authors). ## Modifications State-dict metadata-wrap only: keys and learned parameters are unchanged; the checkpoint is wrapped in the LibreYOLO v1.0 schema (`task=restore`, `scale=2`). Tensor-level parity vs the official model is exact (`max_abs_diff == 0`, fp32). See `weights/convert_realesrgan_weights.py` in the [LibreYOLO source repository](https://github.com/LibreYOLO/libreyolo). ## Usage ```python from libreyolo import LibreYOLO model = LibreYOLO("LibreRealESRGANx2-restore.pt") res = model.predict("small.jpg") # res[0].restored is 2x the input res[0].save("upscaled.png") # large images: model.predict("big.jpg", tile=512) # seam-free tiling ``` ## License BSD 3-Clause (weights) with Apache-2.0 architecture lineage. See the [`LICENSE`](./LICENSE) and [`NOTICE`](./NOTICE) files in this repository.
Source context: 0 downloads · 0 likes · Pipeline image-to-image · Library libreyolo · Repo LibreYOLO/LibreRealESRGANx2-restore
