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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.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
(converted-file SHA-256 bc86620d0ff09b259e414473b65825858a97418e57f1f8065c57696e41f5ece1).1from libreyolo import LibreYOLO
2
3model = LibreYOLO("LibreSwinIRm-restore.pt")
4res = model.predict("small.jpg") # res.restored is 4x the input
5res.save("upscaled.png")
6# large images: model.predict("big.jpg", tile=256) # halo-padded tiling