Measured on device (edge-compat): Galaxy S26 · LiteRT 2.2.0 · GPU (ML Drift) · 26.6 ms p50 (2026-08-26); Galaxy S26 · LiteRT 2.2.0 · NPU (QNN/HTP) · 30.9 ms p50 (2026-08-26); Raspberry Pi 5 · LiteRT 2.2.0.dev20260804 · CPU/XNNPACK, 4 threads · 1788 ms p50 (2026-08-31); browser · Chromium 151 on M4 Max · LiteRT.js 2.5.3 · WebGPU · 22.0 ms p50 · output differs from CPU (max rel diff 0.7) (2026-08-11). Record: https://github.com/john-rocky/edge-compat/blob/main/cards/mi-gan-512-places2/CARD.md

MI-GAN — LiteRT (on-device image inpainting / object removal, fully-GPU)

MI-GAN (Picsart AI Research, ICCV 2023) — a mobile "magic eraser": paint over an object and it is removed and inpainted. Converted to LiteRT and running fully on the CompiledModel GPU (ML Drift) on Android (512×512, Places2).

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

in[1,4,512,512] = concat(mask-0.5, rgb·mask)  →[GPU: MI-GAN]→  out[1,3,512,512] (inpainted, [-1,1])

How it converts (litert-torch) — clean in one shot, no re-authoring

The MI-GAN inference generator (the re-parametrized mobile model) is already GPU-friendly: depthwise-separable Conv2d, nn.Upsample(nearest) + a fixed FIR-filter grouped conv (no transposed conv), leaky-ReLU with gain/clamp (→ MAXIMUM/MINIMUM), and no normalization layers (StyleGAN-style). Banned ops NONE, all tensors ≤4D, tflite-vs-torch corr 1.0, device-vs-torch corr 0.99998.

I/O

• Input (4 ch): concat(mask − 0.5, rgb · mask) — rgb ∈ [−1,1] (pixel/127.5 − 1); mask = 1 keep, 0 erase.
• Output (3 ch): generated image in [−1,1]; composite as rgb·mask + out·(1−mask).

Preprocessing: center-crop, resize 512×512.

Minimal usage

Android (Kotlin, CompiledModel GPU)

val model = CompiledModel.create(context.assets, "migan_fp16.tflite",
    CompiledModel.Options(Accelerator.GPU), null)
val inputs = model.createInputBuffers()
val outputs = model.createOutputBuffers()
inputs[0].writeFloat(x)            // [1,4,512,512] = concat(mask-0.5, rgb*mask)
model.run(inputs, outputs)
val out = outputs[0].readFloat()    // [1,3,512,512] in [-1,1]; composite rgb*mask + out*(1-mask)

Python (desktop verification)

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

rgb = (np.asarray(Image.open("photo.jpg").convert("RGB").resize((512, 512)), np.float32)
       / 127.5 - 1).transpose(2, 0, 1)                            # [3,512,512], [-1,1]
m = np.asarray(Image.open("mask.png").convert("L").resize((512, 512)), np.float32)
mask = (m < 128).astype(np.float32)[None]                          # 1 = keep, 0 = erase (painted)
x = np.concatenate([mask - 0.5, rgb * mask])[None]                 # [1,4,512,512]

it = Interpreter(model_path="migan_fp16.tflite"); it.allocate_tensors()
it.set_tensor(it.get_input_details()[0]["index"], x); it.invoke()
out = it.get_tensor(it.get_output_details()[0]["index"])[0]        # [3,512,512], [-1,1]
comp = rgb * mask + out * (1 - mask)
Image.fromarray(((comp.transpose(1, 2, 0) + 1) * 127.5).clip(0, 255).astype(np.uint8)).save("inpainted.png")

Performance

Measured on a Pixel 8a (Tensor G3, Android 16) with the standard TFLite benchmark_model tool — 10 warm-up runs then 50 timed runs, reported as the tool's mean.

The two GPU rows are different runtimes, not a contradiction. The LITERT_CL figure is the one recorded when this model shipped, taken through LiteRT's own CompiledModel accelerator — the path the Kotlin sample app and the LiteRT API use. The TfLiteGpuDelegateV2 figure is the classic TFLite OpenCL delegate, measured with a tool anyone can download and re-run. They agree on how much of the graph the GPU takes; they disagree on speed, and the classic delegate is the slower of the two here. Read the TfLiteGpuDelegateV2 row as a reproducible floor, not as this model's speed on LiteRT.

XNNPACK declines these fp16 graphs — it reports failed to delegate DEPTHWISE_CONV_2D and then fails to allocate tensors — so there is no usable CPU number. Disabling XNNPACK falls back to reference kernels, which measured about 20× slower than the GPU on models of this size and would not represent CPU inference anyone would ship.

Snapdragon NPU (Hexagon)

The GPU is faster: 26.64 ms against 30.85 ms on the NPU, a factor of 1.16. The NPU still loads 7.34x faster (168 ms against 1231 ms).

Measured on a Samsung Galaxy S26 (Snapdragon 8 Elite Gen 5 / SM8850, Hexagon v81, Android 16) with LiteRT CompiledModel 2.2.0, one accelerator per process, 5 warm-up runs then N=50 timed runs, median reported. Every run held thermal status NONE throughout. Headroom 0.80, where 1.0 is the throttling threshold.

The NPU rows ran the published file unchanged. LiteRT compiled it for the Hexagon on the device at first load. That first compile took 35 s here. The load column above is the cached load every later run pays. Recipe and the runtime libraries it needs: NPU guide.

GPU wiring: GPU guide.

Raspberry Pi 5 (CPU)

Measured on a Raspberry Pi 5 Model B Rev 1.1 (8 GB, Raspberry Pi OS 64-bit) with the LiteRT benchmark_model tool from litert-cli-nightly 0.2.0.dev20260805: CPU inference (XNNPACK, 4 threads), 3 invocations per file of 10 warm-up plus 50 timed runs (the tool caps a phase at 150 s, so very slow graphs run fewer — the Runs column is the actual timed total). The latency is the median across invocations; the spread is the min–max over all timed runs. No thermal throttling occurred during these runs (vcgencmd get_throttled stayed 0x0).

License

MIT. Upstream: Picsart-AI-Research/MI-GAN.