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coremltools; the packages are stateless, so all sequencing and buffering lives in your Swift code.| Task | image to image |
| Upstream | wyf0912/SinSR |
| Packages | 3 |
| Download size | 517 MB |
| Minimum iOS | 17.0 |
| Peak RAM | ~600 MB |
| File | Size | Compute units | SHA-256 |
|---|---|---|---|
SinSR_Encoder.mlpackage.zip | 39 MB | cpuAndGPU | fdec09d17561ec1b… |
SinSR_Denoiser.mlpackage.zip | 420 MB | cpuOnly | b31374c2d539b2cd… |
SinSR_Decoder.mlpackage.zip | 58 MB | cpuAndGPU | b8b9a7b52d6b240c… |
| Total | 517 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.1hf download mlboydaisuke/coreml-zoo --include "sinsr/*" --local-dir ./sinsr
2unzip './sinsr/sinsr/*.zip' -d ./sinsr1import CoreML
2
3let config = MLModelConfiguration()
4config.computeUnits = .cpuAndGPU // as converted — see the table above
5
6// Unzip the .mlpackage, drop it into your Xcode target and Xcode compiles it
7// at build time:
8let model = try SinSR_Encoder(configuration: config)
9
10// ...or compile a downloaded .mlpackage at runtime:
11let compiled = try await MLModel.compileModel(at: mlpackageURL)
12let model = try MLModel(contentsOf: compiled, configuration: config)This model is split into 3 Core ML packages that are driven in sequence from Swift. Load them one at a time, copy the outputs out of theMLMultiArraybuffers and release each model before loading the next — two large Core ML models resident at once will OOM on an iPhone.
sample_apps/SinSRDemo, a standalone SwiftUI project.convert_sinsr.pydocs/coreml_conversion_notes.mdNon-commercial, attribution, share-alike.