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 |
204.8 | 1.000000 | 833.8 |
*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: 638.2 ms).
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] | 7.510e-06 | 1.000000 |
XNNPACK delegate coverage (fp32): 49.8% (2020/4056 ops); ops left on the portable kernels: aten.abs.default x460, aten.sub.Tensor x460, aten.expand_copy.default x288, aten.arange.start_step x230, aten.index.Tensor x230, aten.select_copy.int x144, dim_order_ops._to_dim_order_copy.default x116, aten.view_as_real_copy.default x72, aten._fft_r2c.default x36
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.
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