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002_lightweightSR_DIV2K_s64w8_SwinIR-S_x4.pth
(SHA-256 09fad24e32ae62722e1a055efde9921328f4137981bab0a42a4a3a806306c58e).
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 762b804abc7b2d49739fc05c8c10ce434fccbb4a0dff26a958b17e8ac5bf2601).1from libreyolo import LibreYOLO
2
3model = LibreYOLO("LibreSwinIRs-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