| --- |
| license: apache-2.0 |
| library_name: onnxruntime |
| pipeline_tag: image-to-image |
| tags: |
| - inpainting |
| - object-removal |
| - lama |
| - onnx |
| - webgpu |
| --- |
| |
| # LaMa inpainting, WebGPU-ready ONNX |
|
|
| LaMa ("Resolution-robust Large Mask Inpainting with Fourier Convolutions", Suvorov et al., WACV 2022) |
| as a single fp32 ONNX graph that runs on **onnxruntime-web's WebGPU execution provider**. It is used by |
| the on-device AI eraser of the ToolsMonk Photo Editor and Background Remover. |
|
|
| ## Why this build exists |
|
|
| The published LaMa ONNX exports run in the browser on WASM only. On WebGPU (onnxruntime-web 1.30) every |
| run fails inside the Fourier unit ("Failed to run JSEP kernel"). This file is the same model with three |
| **numerically equivalent** graph rewrites: |
|
|
| 1. constant folding at a fixed batch of 1, so the DFT matrices the export builds at run time become constants; |
| 2. every `Einsum` (`ij,jk->ik`, 216 nodes) rewritten as `MatMul`; |
| 3. every negative-step `Slice` (72 nodes) rewritten as `Gather` with constant reversed indices. |
|
|
| Maximum output difference against the source on a 512x512 test input: 0.00026 on the 0..255 scale. |
| Measured in Chrome: ~1.7-2.2 s per 512x512 inpaint on WebGPU, against ~30 s on single-threaded WASM. |
|
|
| ## Inputs and outputs |
|
|
| - `image`: float32 `[1, 3, 512, 512]`, RGB in 0..1 |
| - `mask`: float32 `[1, 1, 512, 512]`, 1 = area to fill, 0 = keep |
| - `output`: float32 `[1, 3, 512, 512]`, RGB in 0..255 |
|
|
| ## Provenance and licence |
|
|
| - Model: [advimman/lama](https://github.com/advimman/lama), Apache-2.0. |
| - Source ONNX: [Carve/LaMa-ONNX](https://huggingface.co/Carve/LaMa-ONNX) `lama_fp32.onnx`, Apache-2.0 |
| (sha256 `1faef5301d78db7dda502fe59966957ec4b79dd64e16f03ed96913c7a4eb68d6`). |
| - Conversion script: `build-lama-webgpu.py` in this repository (requires `onnx`, `onnxruntime`, `numpy`). |
|
|
| This derivative is distributed under the same Apache-2.0 licence. The rewrites change the graph's |
| structure, not its weights. |
|
|
