litert-community/ormbg-LiteRT

ormbg — Open Remove-Background (LiteRT GPU)

On-device background removal running fully on the LiteRT CompiledModel GPU delegate (no CPU fallback). ormbg is a fully open, Apache-2.0 foreground/alpha matte model (an ISNet trained for photorealistic subject cut-out) — the permissively-licensed alternative to the non-commercial RMBG-1.4. ~10 ms/frame on a Pixel 8a.

• Architecture: ISNet (RSU / U²-Net-style nested residual blocks) — pure CNN.
• Weights: schirrmacher/ormbg · Apache-2.0.
• Size: 176 MB.

Input (left) → alpha cut-out on transparency (right). Subject photo: Unsplash (free license).

I/O

• Input: [1, 3, 1024, 1024] NCHW, RGB, x / 255 (no mean/std).
• Output: [1, 1, 1024, 1024] alpha matte in [0,1]. Post-process: min-max normalize, resize to the original size, use as the foreground alpha.

GPU conversion

ormbg is a pure CNN (ISNet RSU blocks). It converts fully GPU-compatible (246/246 nodes on the delegate, 1 partition; device corr 0.999881, ~10 ms) with one defensive patch: align_corners=True → False on the bilinear upsamples (the GPU delegate rejects align_corners=True). CPU-exact vs PyTorch (corr 0.9999999999).

Minimal usage

Kotlin (Android, LiteRT CompiledModel GPU)

val options = CompiledModel.Options(Accelerator.GPU)
val model = CompiledModel.create(context.assets, "ormbg.tflite", options, null)
val inBufs = model.createInputBuffers()
val outBufs = model.createOutputBuffers()

inBufs[0].writeFloat(inputNCHW)          // [1,3,1024,1024] RGB, x/255
model.run(inBufs, outBufs)
val mask = outBufs[0].readFloat()        // [1024*1024] alpha; min-max normalize -> foreground alpha

Python (LiteRT / ai-edge-litert)

import numpy as np
from ai_edge_litert.interpreter import Interpreter

it = Interpreter(model_path="ormbg.tflite"); it.allocate_tensors()
inp, out = it.get_input_details(), it.get_output_details()
it.set_tensor(inp[0]["index"], x)        # [1,3,1024,1024] float32, RGB, x/255
it.invoke()
m = it.get_tensor(out[0]["index"])[0, 0] # [1024,1024] alpha
m = (m - m.min()) / (m.max() - m.min())  # normalize; resize to original, composite

Conversion

Converted with litert-torch (build_ormbg.py): loads the ISNet weights and exports.

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.

Runtime	Backend	Graph on GPU	Latency
LiteRT CompiledModel (LITERT_CL)	GPU	246 / 246	~10 ms
TFLite benchmark_model (TfLiteGpuDelegateV2)	GPU (OpenCL)	246 / 246	244.4 ms
TFLite benchmark_model	CPU (XNNPACK, 4 threads)	—	5263.2 ms

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.

License

Apache-2.0 (ormbg / schirrmacher). ISNet architecture.