LibreSwinIRl-restore

SwinIR-L x4 real-world super-resolution (large GAN generator), repackaged for LibreYOLO.

Source

Weights derived from 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 (converted-file SHA-256 373e2397ab8c8fc71308808683da025e98c5f845ea7588e3f65009a516ed985e).

Usage

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 and NOTICE files in this repository.