LibreSwinIRm Restore

--- license: apache-2.0 library_name: libreyolo tags: - super-resolution - image-to-image - swinir pipeline_tag: image-to-image --- # LibreSwinIRm-restore SwinIR-M x4 real-world super-resolution (BSRGAN-degradation GAN generator), repackaged for LibreYOLO. ## Source Weights derived from [JingyunLiang/SwinIR](https://github.com/JingyunLiang/SwinIR) release `003_realSR_BSRGAN_DFO_s64w8_SwinIR-M_x4_GAN.pth` (SHA-256 `b9afb61e65e04eb7f8aba5095d070bbe9af28df76acd0c9405aeb33b814bcfc6`). 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 `bc86620d0ff09b259e414473b65825858a97418e57f1f8065c57696e41f5ece1`). ## Usage ```python from libreyolo import LibreYOLO model = LibreYOLO("LibreSwinIRm-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.

Source context: 0 downloads · 1 likes · Pipeline image-to-image · Library libreyolo · Repo LibreYOLO/LibreSwinIRm-restore