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README.md

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+---
+license: apache-2.0
+library_name: litert
+pipeline_tag: image-to-image
+tags:
+  - litert
+  - tflite
+  - android
+  - on-device
+  - gpu
+  - portrait
+  - sketch
+  - line-drawing
+  - u2net
+  - creative
+---
+
+# U²-Net Portrait — Photo → pencil line drawing (LiteRT GPU)
+
+On-device **portrait sketch generation** running **fully on the LiteRT `CompiledModel` GPU**
+delegate (no CPU fallback). The [U²-Net](https://github.com/xuebinqin/U-2-Net) portrait model
+turns a face photo into a **hand-drawn pencil line portrait** — a fun creative / AR filter.
+~12 ms/frame on a Pixel 8a.
+
+- **Architecture:** U²-Net (RSU / nested residual U-blocks) — pure CNN.
+- **Weights:** [xuebinqin/U-2-Net](https://github.com/xuebinqin/U-2-Net) (`u2net_portrait`) · Apache-2.0.
+- **Size:** 176 MB.
+
+![U2-Net portrait sketch](hero.png)
+
+*Input (left) → generated pencil portrait (right). Photo: Unsplash (free license).*
+
+## I/O
+
+- **Input:** `[1, 3, 512, 512]` NCHW, RGB, `x/max` then ImageNet-normalized
+  (mean `[0.485,0.456,0.406]`, std `[0.229,0.224,0.225]`). A centered face works best.
+- **Output:** `[1, 1, 512, 512]` in `[0,1]`. Min-max normalize, then **invert** (`1 − x`)
+  for dark strokes on white paper.
+
+## GPU conversion
+
+U²-Net is a pure CNN → fully GPU-compatible (**893/893 nodes on the delegate, 1
+partition**; device corr 0.998683, ~12 ms) with **one defensive patch**: `align_corners=True`
+→ `False` on the bilinear upsamples. CPU-exact vs PyTorch (corr 1.0).
+
+## Minimal usage
+
+### Kotlin (Android, LiteRT CompiledModel GPU)
+
+```kotlin
+val options = CompiledModel.Options(Accelerator.GPU)
+val model = CompiledModel.create(context.assets, "portrait.tflite", options, null)
+val inBufs = model.createInputBuffers()
+val outBufs = model.createOutputBuffers()
+
+inBufs[0].writeFloat(inputNCHW)          // [1,3,512,512] RGB, /max then ImageNet-norm
+model.run(inBufs, outBufs)
+val d = outBufs[0].readFloat()           // [512*512] 0..1; min-max normalize then 1-x -> pencil sketch
+```
+
+### Python (LiteRT / ai-edge-litert)
+
+```python
+import numpy as np
+from ai_edge_litert.interpreter import Interpreter
+
+it = Interpreter(model_path="portrait.tflite"); it.allocate_tensors()
+inp, out = it.get_input_details(), it.get_output_details()
+it.set_tensor(inp[0]["index"], x)        # [1,3,512,512] float32, RGB, /max, ImageNet-norm
+it.invoke()
+d = it.get_tensor(out[0]["index"])[0, 0]
+d = (d - d.min()) / (d.max() - d.min()); sketch = 1.0 - d   # dark strokes on white
+```
+
+## Conversion
+
+Converted with **litert-torch** (`build_portrait.py`): loads the Apache-2.0 `u2net_portrait`
+weights and exports the sketch map.
+
+## License
+
+Apache-2.0 (U²-Net / xuebinqin).