mlboydaisuke/LaMa-Inpainting-ExecuTorch

lama_512 — ExecuTorch

• Source: advimman/lama + smartywu/big-lama weights
• License: Apache-2.0
• Input: [[1, 3, 512, 512], [1, 1, 512, 512]] — image RGB 0-1 [1,3,512,512] + mask [1,1,512,512] where 1 marks the region to fill
• Output: inpainted image [1,3,512,512] RGB 0-1, already composited with the untouched region

Variants

All variants take and return fp32 tensors — swap the .pte file, keep your app code.

build	file	size (MB)	parity vs fp32 eager (worst corr)	Mac median (ms)*
fp32	lama_512_xnnpack_fp32.pte	205.0	1.000000	816.0
Core ML (fp16, iOS)	lama_512_coreml_all.pte	105.3	0.999553	59.2

The Core ML build is the same graph lowered to Apple's Neural Engine instead of XNNPACK, which is CPU-only. Measured on an iPhone 17 Pro across seven models, it runs 3.5x to 13.9x faster (median 12x) at roughly half the file size — for example Depth-Anything-V2-Small at 500.8 ms against 42.7 ms, and MODNet at 81.7 ms against 5.9 ms. It computes in fp16 and is iOS-only; the XNNPACK files stay the portable option and are what runs on Android.

*Mac arm64, single process, median of 10 — a reference point for relative cost only, not a device number (torch eager fp32 on the same machine: 526.7 ms).

Checked in the task's own units

Correlation is a first filter. These are the numbers that decide:

• Core ML (fp16, iOS) — measured in the units that matter for this model — PSNR vs the fp32 .pte (dB): median 36.8776 over 5 real images, worst 36.6448.

Builds that did not earn a slot

• int8 is not shipped: measured in the units that matter for this model — PSNR vs the fp32 .pte (dB): median 22.4666 over 5 real images, worst 21.3621.

Verification (executorch 1.4.0, torch 2.13.0)

Parity is measured against the fp32 eager model on real image input; corr is the correlation over all elements of each output tensor.

output	shape	max_abs_diff	corr
0	[1, 3, 512, 512]	7.629e-06	1.000000

XNNPACK delegate coverage (fp32): 57.4% (2884/5028 ops); ops left on the portable kernels: aten.expand_copy.default x576, aten.abs.default x460, aten.sub.Tensor x460, aten.arange.start_step x230, aten.index.Tensor x230, dim_order_ops._to_dim_order_copy.default x116, aten.select_copy.int x72

Conversion

torch.export -> to_edge_transform_and_lower(partitioner) -> .pte (conversion scripts: executorch-models)

Notes: The inverse FFT inside every FourierUnit is replaced with the real matmul form from convert/fft_ops.py; ExecuTorch cannot lower torch.fft.irfftn. Spatial size is fixed because those matrices are built per size.

Notes (int8): The inverse FFT inside every FourierUnit is replaced with the real matmul form from convert/fft_ops.py; ExecuTorch cannot lower torch.fft.irfftn. Spatial size is fixed because those matrices are built per size.

Notes (coreml_all): The inverse FFT inside every FourierUnit is replaced with the real matmul form from convert/fft_ops.py; ExecuTorch cannot lower torch.fft.irfftn. Spatial size is fixed because those matrices are built per size.

---

More models in this format: ExecuTorch Model Zoo — 31 models, each with the recipe that produced it.

Want a different model on-device? Open a request — free, open weights only; the export and its measured numbers get published publicly.