README.md

---
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).