metadata
license: apache-2.0
library_name: coreml
pipeline_tag: image-to-image
tags:
- coreml
- core-ml
- ios
- macos
- apple
- on-device
- inpainting
- fourier-convolution
- arxiv:2109.07161
LaMa — Core ML
Image Inpainting, 2022
Resolution-robust large mask inpainting. Draw over unwanted objects to remove them. Fast Fourier convolutions for global context. 800×800 input.
Core ML conversion of advimman/lama for on-device inference on iPhone, iPad and Mac. Converted with coremltools; the packages are stateless, so all sequencing and buffering lives in your Swift code.
| Task | image to image |
| Upstream | advimman/lama |
| Packages | 1 |
| Download size | 187 MB |
| Minimum iOS | 17.0 |
| Peak RAM | ~600 MB |
Files
| File | Size | Compute units | SHA-256 |
|---|---|---|---|
LaMa.mlpackage.zip |
187 MB | all |
b57b8451a1a86c00… |
| Total | 187 MB |
compute_units is not a suggestion -- it is the configuration the conversion was verified against. Moving a package to a different compute unit can silently change the numerics (FP16 attention overflow) or crash on the GPU.
Download
hf download mlboydaisuke/coreml-zoo --include "lama/*" --local-dir ./lama
unzip './lama/lama/*.zip' -d ./lama
Use in Swift
import CoreML
let config = MLModelConfiguration()
config.computeUnits = .all // as converted — see the table above
// Unzip the .mlpackage, drop it into your Xcode target and Xcode compiles it
// at build time:
let model = try LaMa(configuration: config)
// ...or compile a downloaded .mlpackage at runtime:
let compiled = try await MLModel.compileModel(at: mlpackageURL)
let model = try MLModel(contentsOf: compiled, configuration: config)
Demo
- Sample app — lama-cleaner-iOS, a standalone iOS project.
- Models Zoo — this model is downloadable and runnable inside the Models Zoo app on the App Store, no build required.
Conversion
- Pitfalls hit during conversion (FP16 overflow, ANE buffer limits, stride handling):
docs/coreml_conversion_notes.md - Model index: CoreML-Models
License
The conversion inherits the upstream license: Apache-2.0.
Credits
- Upstream authors: advimman/lama, 2022
- Core ML conversion: john-rocky (Daisuke Majima)