Add MLX-converted ScaleLSD v1/v2 weights for mlx-swift-ScaleLSD

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README.md

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+---
+license: apache-2.0
+base_model:
+  - cherubicxn/scalelsd
+tags:
+  - mlx
+  - mlx-swift
+  - line-segment-detection
+  - wireframe-parsing
+pipeline_tag: image-feature-extraction
+library_name: mlx
+---
+
+# ScaleLSD — MLX weights
+
+MLX-format weights for [ScaleLSD](https://github.com/ant-research/scalelsd), converted for use
+with [**mlx-swift-ScaleLSD**](https://github.com/mnmly/mlx-swift-ScaleLSD) on Apple Silicon.
+
+These are **converted redistributions** of the original checkpoints published at
+[cherubicxn/scalelsd](https://huggingface.co/cherubicxn/scalelsd). No retraining or
+fine-tuning was performed — the numerical content of the network is unchanged.
+
+## Contents
+
+| folder | LayerScale | parameters | source checkpoint |
+|---|---|---|---|
+| `scalelsd-vitbase-v1/` | no | 122,525,833 | `scalelsd-vitbase-v1-train-sa1b.pt` |
+| `scalelsd-vitbase-v2/` | yes | 122,544,265 | `scalelsd-vitbase-v2-train-sa1b.pt` |
+
+Each folder holds `config.json` + `model.safetensors`. Upstream recommends **v2** by default.
+
+## Usage
+
+```swift
+import MLXScaleLSD
+
+// Downloads from this repo on first use, then caches locally.
+let directory = try await ModelStore.download(.v2)
+let session = try ScaleLSDSession.load(directory: directory)
+
+let image = try ScaleLSDSession.loadImage(at: imageURL)
+let result = try session.detect(image)
+for segment in result.segments(minimumScore: 10) {
+    print(segment.x1, segment.y1, segment.x2, segment.y2, segment.score)
+}
+```
+
+Or from the command line:
+
+```bash
+scalelsd detect -m <directory> -i image.jpg -e png --save-to out/
+```
+
+## What was changed in conversion
+
+The original checkpoints are PyTorch pickles, which MLX cannot read. `Scripts/convert.py` in
+the Swift repo performs a format conversion plus several inference-only graph simplifications,
+each of which is numerically equivalent (verified to ~2e-6 relative against the PyTorch
+reference):
+
+- **Weight standardisation baked in.** timm's `StdConv2dSame` re-standardises its weight on
+  every forward pass; inference weights are frozen, so the standardised tensor is stored
+  directly. (Note: the hybrid ViT uses `eps=1e-8`, not the class default `1e-6`.)
+- **conv + BatchNorm folded.** The 16 `conv(bias=False) -> BatchNorm2d` pairs in DPT's
+  `ResidualConvUnit_custom` collapse into single biased convolutions.
+- **`nn.Sequential` indices renamed** to named submodules, so keys read structurally.
+- **Conv weights transposed** from PyTorch `(O, I, kH, kW)` to MLX `(O, kH, kW, I)`.
+- **The 1000-class ImageNet classifier head dropped** — ScaleLSD never calls it.
+
+## Accuracy
+
+Verified stage by stage against the PyTorch reference. The final 9-channel HAT field matches to
+**1.1e-05** (v1) / **1.4e-05** (v2) maximum relative error. End-to-end on `assets/indoor.jpg`:
+
+| | v1 | v2 |
+|---|---|---|
+| junctions matched within 0.01 px | 512/512 | 511/512 |
+| segments matched within 0.01 px | 1879/1880 | 1581/1590 |
+
+Detections are not bit-exact by construction: the 512-junction cap and the nearest-junction
+assignment are discrete choices that a sub-noise perturbation can flip. See
+[docs/PARITY.md](https://github.com/mnmly/mlx-swift-ScaleLSD/blob/main/docs/PARITY.md).
+
+## Performance
+
+Apple M5 Max, 512×512 input, Release build, median of 20 runs:
+
+| runtime | per image |
+|---|---|
+| mlx-swift (this port) | **57 ms** |
+| PyTorch 2.13, MPS | 88 ms |
+| PyTorch 2.13, CPU | 652 ms |
+
+## License and attribution
+
+Apache-2.0, inherited from the original checkpoints at
+[cherubicxn/scalelsd](https://huggingface.co/cherubicxn/scalelsd). The upstream ScaleLSD source
+is MIT (Copyright © 2023 Nan Xue). Original work and all model credit belong to the ScaleLSD
+authors; this repository contributes only a format conversion.
+
+```bibtex
+@inproceedings{ScaleLSD,
+    title = {ScaleLSD: Scalable Deep Line Segment Detection Streamlined},
+    author = {Zeran Ke and Bin Tan and Xianwei Zheng and Yujun Shen and Tianfu Wu and Nan Xue},
+    booktitle = {IEEE Conference on Computer Vision and Pattern Recognition (CVPR)},
+    year = {2025},
+}
+```

scalelsd-vitbase-v1/config.json

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+{
+  "architecture": "scalelsd-vitbase",
+  "use_layer_scale": false,
+  "vit_features": 768,
+  "vit_depth": 12,
+  "vit_heads": 12,
+  "patch_size": 16,
+  "features": 256,
+  "head_size": [
+    3,
+    1,
+    1,
+    2,
+    2
+  ],
+  "stride": 2,
+  "distance_threshold": 5.0,
+  "source_checkpoint": "scalelsd-vitbase-v1-train-sa1b.pt"
+}

scalelsd-vitbase-v1/model.safetensors

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+size 490153692

scalelsd-vitbase-v2/config.json

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+{
+  "architecture": "scalelsd-vitbase",
+  "use_layer_scale": true,
+  "vit_features": 768,
+  "vit_depth": 12,
+  "vit_heads": 12,
+  "patch_size": 16,
+  "features": 256,
+  "head_size": [
+    3,
+    1,
+    1,
+    2,
+    2
+  ],
+  "stride": 2,
+  "distance_threshold": 5.0,
+  "source_checkpoint": "scalelsd-vitbase-v2-train-sa1b.pt"
+}

scalelsd-vitbase-v2/model.safetensors

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