mlboydaisuke/DDColor-Tiny-CoreML

DDColor Tiny — Core ML

Image Colorization, 2023

Automatic grayscale image colorization via dual decoders. 512×512 input.

Core ML conversion of piddnad/DDColor 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	piddnad/DDColor
Packages	1
Download size	203 MB
Minimum iOS	17.0
Peak RAM	~400 MB

Files

File	Size	Compute units	SHA-256
DDColor_Tiny.mlpackage.zip	203 MB	all	bfecea37d66005f6…
Total	203 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 "ddcolor/*" --local-dir ./ddcolor
unzip './ddcolor/ddcolor/*.zip' -d ./ddcolor

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 DDColor_Tiny(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 — sample_apps/DDColorDemo, a standalone SwiftUI project.
• Models Zoo — this model is downloadable and runnable inside the Models Zoo app on the App Store, no build required.

Conversion

• Script: convert_ddcolor.py
• 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: piddnad/DDColor, 2023
• Core ML conversion: john-rocky (Daisuke Majima)