LibreQuickSRNetm2-restore

LibreQuickSRNetm2-restore

QuickSRNet Medium 2x compact real-time super-resolution, packaged for LibreYOLO. It has 50,604 parameters and produces an RGB image at twice the input height and width.

Source

The architecture and official checkpoint come from quic/aimet-model-zoo at commit 1bd2bf5b17cdda9251437c444009b29e1a25054b, BSD-3-Clause. Copyright (c) 2022 Qualcomm Innovation Center, Inc.

The maintained Qualcomm integration was verified against qualcomm/ai-hub-models at commit 16dbeb5e2805d4ada7218026de72e36878717d46, BSD-3-Clause.

Official source artifact: quicksrnet_medium_2x_checkpoint_float32.pth.tar

SHA-256: a0d176b40a649e45a176c3b53f45e0237015f4f2c17b157ef5c81e38c4442a0d

The model card records DIV2K as the training dataset.

Modifications

The 14 learned state-dict tensors are unchanged. Conversion discards the training-only epoch, optimizer, PSNR, and SSIM objects, then wraps the tensors in the LibreYOLO v1.0 checkpoint schema with task=restore, size=m2, and scale=2. FP32 tensor output matches the pinned upstream implementation exactly (max_abs_diff == 0).

Converted checkpoint SHA-256: 3f779b461d200704ab82904a110aaa37f41e8258b285e02b7e413b955deb150b

Usage

from libreyolo import LibreYOLO

model = LibreYOLO("LibreQuickSRNetm2-restore.pt")
result = model.predict("small.jpg")
print(result.restore_scale)  # 2
result.save("upscaled.png")

Native PyTorch prediction accepts arbitrary positive image dimensions. Dynamic spatial ONNX and fixed-canvas TorchScript export are supported.

Reference latency

Synchronized model-forward timings on an NVIDIA GeForce RTX 5070 Ti with PyTorch 2.11.0 and CUDA 12.8, batch 1, cuDNN benchmarking enabled, 10 warmups, and 30 timed iterations:

Image decoding, transfer, and result conversion are excluded.

License

BSD 3-Clause. See LICENSE and NOTICE.