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)
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