| license: apache-2.0 | |
| library_name: libreyolo | |
| tags: | |
| - super-resolution | |
| - image-to-image | |
| - swinir | |
| pipeline_tag: image-to-image | |
| # LibreSwinIRl-restore | |
| SwinIR-L x4 real-world super-resolution (large GAN generator), repackaged for LibreYOLO. | |
| ## Source | |
| Weights derived from [JingyunLiang/SwinIR](https://github.com/JingyunLiang/SwinIR) | |
| release `003_realSR_BSRGAN_DFOWMFC_s64w8_SwinIR-L_x4_GAN.pth` | |
| (SHA-256 `99adfa91350a84c99e946c1eb3d8fce34bc28f57d807b09dc8fe40a316328c0a`). | |
| Copyright (c) 2021 Jingyun Liang. Licensed under the Apache License, Version 2.0. | |
| Architecture reference commit: `6545850fbf8df298df73d81f3e8cba638787c8bd`. | |
| ## 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=4`). | |
| Tensor-level parity vs the official model is exact (`max_abs_diff == 0`, fp32). | |
| Converted with `weights/convert_swinir_weights.py` from the | |
| [LibreYOLO source repository](https://github.com/LibreYOLO/libreyolo) | |
| (converted-file SHA-256 `373e2397ab8c8fc71308808683da025e98c5f845ea7588e3f65009a516ed985e`). | |
| ## Usage | |
| ```python | |
| from libreyolo import LibreYOLO | |
| model = LibreYOLO("LibreSwinIRl-restore.pt") | |
| res = model.predict("small.jpg") # res.restored is 4x the input | |
| res.save("upscaled.png") | |
| # large images: model.predict("big.jpg", tile=256) # halo-padded tiling | |
| ``` | |
| ## License | |
| Apache-2.0 (code and weights). See the [`LICENSE`](./LICENSE) and | |
| [`NOTICE`](./NOTICE) files in this repository. | |