Add QuickSRNet Medium 2x weights

.gitattributes

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LICENSE

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+BSD 3-Clause License
+
+Copyright (c) 2018, the respective contributors, as shown by the AUTHORS file.
+All rights reserved.
+
+Redistribution and use in source and binary forms, with or without
+modification, are permitted provided that the following conditions are met:
+
+* Redistributions of source code must retain the above copyright notice, this
+  list of conditions and the following disclaimer.
+
+* Redistributions in binary form must reproduce the above copyright notice,
+  this list of conditions and the following disclaimer in the documentation
+  and/or other materials provided with the distribution.
+
+* Neither the name of the copyright holder nor the names of its
+  contributors may be used to endorse or promote products derived from
+  this software without specific prior written permission.
+
+THIS SOFTWARE IS PROVIDED BY THE COPYRIGHT HOLDERS AND CONTRIBUTORS "AS IS"
+AND ANY EXPRESS OR IMPLIED WARRANTIES, INCLUDING, BUT NOT LIMITED TO, THE
+IMPLIED WARRANTIES OF MERCHANTABILITY AND FITNESS FOR A PARTICULAR PURPOSE ARE
+DISCLAIMED. IN NO EVENT SHALL THE COPYRIGHT HOLDER OR CONTRIBUTORS BE LIABLE
+FOR ANY DIRECT, INDIRECT, INCIDENTAL, SPECIAL, EXEMPLARY, OR CONSEQUENTIAL
+DAMAGES (INCLUDING, BUT NOT LIMITED TO, PROCUREMENT OF SUBSTITUTE GOODS OR
+SERVICES; LOSS OF USE, DATA, OR PROFITS; OR BUSINESS INTERRUPTION) HOWEVER
+CAUSED AND ON ANY THEORY OF LIABILITY, WHETHER IN CONTRACT, STRICT LIABILITY,
+OR TORT (INCLUDING NEGLIGENCE OR OTHERWISE) ARISING IN ANY WAY OUT OF THE USE
+OF THIS SOFTWARE, EVEN IF ADVISED OF THE POSSIBILITY OF SUCH DAMAGE.

LibreQuickSRNetm2-restore.pt

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+version https://git-lfs.github.com/spec/v1
+oid sha256:3f779b461d200704ab82904a110aaa37f41e8258b285e02b7e413b955deb150b
+size 208945

NOTICE

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+LibreQuickSRNetm2-restore weights
+---------------------------------
+
+Architecture and checkpoint source:
+  https://github.com/quic/aimet-model-zoo
+  commit: 1bd2bf5b17cdda9251437c444009b29e1a25054b
+  license: BSD-3-Clause
+  copyright: Copyright (c) 2022 Qualcomm Innovation Center, Inc.
+
+Maintained integration audited for current metadata:
+  https://github.com/qualcomm/ai-hub-models
+  commit: 16dbeb5e2805d4ada7218026de72e36878717d46
+  license: BSD-3-Clause
+  copyright: Copyright (c) 2025 Qualcomm Technologies, Inc. and/or its subsidiaries.
+
+Official source artifact:
+  https://github.com/quic/aimet-model-zoo/releases/download/phase_2_january_artifacts/quicksrnet_medium_2x_checkpoint_float32.pth.tar
+  SHA-256: a0d176b40a649e45a176c3b53f45e0237015f4f2c17b157ef5c81e38c4442a0d
+
+Converted artifact:
+  LibreQuickSRNetm2-restore.pt
+  SHA-256: 3f779b461d200704ab82904a110aaa37f41e8258b285e02b7e413b955deb150b
+
+Conversion preserves all 14 learned tensors unchanged, discards training-only
+optimizer and history objects, and adds LibreYOLO checkpoint metadata. The
+model was trained on DIV2K. The dataset is provenance only and is not included
+in this repository.

README.md

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+---
+license: bsd-3-clause
+library_name: libreyolo
+tags:
+  - super-resolution
+  - image-to-image
+  - quicksrnet
+  - real-time
+pipeline_tag: image-to-image
+---
+
+# 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](https://github.com/quic/aimet-model-zoo/tree/1bd2bf5b17cdda9251437c444009b29e1a25054b/aimet_zoo_torch/quicksrnet)
+at commit `1bd2bf5b17cdda9251437c444009b29e1a25054b`, BSD-3-Clause.
+Copyright (c) 2022 Qualcomm Innovation Center, Inc.
+
+The maintained Qualcomm integration was verified against
+[qualcomm/ai-hub-models](https://github.com/qualcomm/ai-hub-models/tree/16dbeb5e2805d4ada7218026de72e36878717d46/src/qai_hub_models/models/quicksrnetmedium)
+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
+
+```python
+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:
+
+| Input to output | Precision | Median | p95 |
+|---|---|---:|---:|
+| 360p to 720p | FP32 | 1.746 ms | 1.770 ms |
+| 360p to 720p | FP16 | 0.937 ms | 0.966 ms |
+| 720p to 1440p | FP32 | 10.536 ms | 10.931 ms |
+| 720p to 1440p | FP16 | 5.437 ms | 5.855 ms |
+
+Image decoding, transfer, and result conversion are excluded.
+
+## License
+
+BSD 3-Clause. See [`LICENSE`](./LICENSE) and [`NOTICE`](./NOTICE).