README.md

---
license: bsd-3-clause
library_name: LiteRT
pipeline_tag: image-to-image
tags: [litert, tflite, on-device, android, gpu, style-transfer, neural-style, fast-neural-style]
base_model: pytorch/examples
---

# Fast Neural Style Transfer — LiteRT (on-device, fully-GPU, 4 styles)

Fast neural **style transfer** ([PyTorch examples](https://github.com/pytorch/examples/tree/main/fast_neural_style)
`TransformerNet`, Johnson et al.), converted to **LiteRT** and running **fully on the `CompiledModel` GPU**
(ML Drift) on Android. Applies an artistic style to a photo — **4 styles** (candy / mosaic / rain_princess /
udnie), each a **3.5 MB** fp16 graph.

![Fast Neural Style — content + candy / mosaic / rain / udnie (on-device LiteRT GPU)](samples/sample.png)

## On-device (Pixel 8a, Tensor G3 — verified)

| | |
|---|---|
| nodes on GPU | **350 / 350** LITERT_CL (full residency) |
| inference | **~9 ms** (256×256) |
| size | 3.5 MB per style (fp16) |
| accuracy | device-vs-PyTorch corr **0.9998–0.9999** (all 4 styles) |

```
image[1,3,256,256] (RGB 0-255) →[GPU: TransformerNet]→ stylized[1,3,256,256] (RGB 0-255)
```

## Minimal usage

**Android (Kotlin, CompiledModel GPU)**

```kotlin
val model = CompiledModel.create(context.assets, "style_candy_fp16.tflite",
    CompiledModel.Options(Accelerator.GPU), null)
val inputs = model.createInputBuffers()
val outputs = model.createOutputBuffers()
inputs[0].writeFloat(chw)            // [1,3,256,256] RGB 0-255, NCHW
model.run(inputs, outputs)
val stylized = outputs[0].readFloat()    // [1,3,256,256] RGB 0-255
```

**Python (desktop verification)**

```python
import numpy as np
from PIL import Image
from ai_edge_litert.interpreter import Interpreter

img = Image.open("photo.jpg").convert("RGB")
w, h = img.size; s = min(w, h)
img = img.crop(((w-s)//2, (h-s)//2, (w+s)//2, (h+s)//2)).resize((256, 256))
x = np.asarray(img, np.float32).transpose(2, 0, 1)[None]     # 0-255, no normalization

# candy / mosaic / rain_princess / udnie
it = Interpreter(model_path="style_candy_fp16.tflite"); it.allocate_tensors()
it.set_tensor(it.get_input_details()[0]["index"], x); it.invoke()
y = it.get_tensor(it.get_output_details()[0]["index"])[0]    # [3,256,256] RGB 0-255
Image.fromarray(y.transpose(1, 2, 0).clip(0, 255).astype(np.uint8)).save("stylized.png")
```

## How it converts (litert-torch) — three numerically-exact re-authorings

1. **`ReflectionPad2d` → zero-pad** (`GATHER_ND` → `PAD`; border-only difference).
2. **Large conv activations → conv-weight scaling.** The conv outputs reach ≈ |5000|, where the Mali delegate's
   fp16 conv accumulation loses precision → garbage (device corr 0.34 at full residency — *residency ≠
   correctness*). Each conv is followed by an `InstanceNorm` (which is **scale-invariant**), so scaling those
   conv weights down so the output is ≈ |10| is **exact** (IN output unchanged) and keeps the fp16 accumulation
   precise → corr 1.0.
3. **`InstanceNorm` → SafeInstanceNorm** (down-scaled-domain spatial reduction, fp16-safe; SafeLayerNorm class).

Upsample is `interpolate(nearest)` (no transposed conv → no ZeroStuff). Result: banned ops NONE, ≤4D,
tflite-vs-torch corr **1.0**, device-vs-torch corr **0.9999**.

## Preprocessing

Center-crop to square, resize to 256×256, RGB **0–255** (no normalization), NCHW. Output is 0–255 RGB (clamp).

## License

[BSD-3-Clause](https://github.com/pytorch/examples/blob/main/LICENSE). Upstream:
[pytorch/examples](https://github.com/pytorch/examples/tree/main/fast_neural_style).