AI Colorization of Black-and-White Photos: Mobile App Integration Guide

import coremltools as ct import torch from deoldify.visualize import get_image_colorizer colorizer = get_image_colorizer(artistic=True) model = colorizer.learn.model.eval() # Export via torch.jit.trace example = torch.zeros(1, 3, 256, 256) traced = torch.jit.trace(model, example) mlmodel = ct.convert( traced, inputs=[ct.TensorType(name="input", shape=(1, 3, 256, 256))], compute_precision=ct.precision.FLOAT16, minimum_deployment_target=ct.target.iOS16 ) mlmodel.save("DeOldify_artistic.mlpackage") 

An important detail: DeOldify expects RGB input (even for grayscale — it internally converts to Lab and works with AB channels). Before feeding a grayscale image, you must replicate it into a 3-channel RGB: gray → [gray, gray, gray]. In Core ML this is done in preprocessing, not in the model itself.

iOS: Running Inference

let config = MLModelConfiguration() config.computeUnits = .cpuAndNeuralEngine // ANE for FLOAT16 let model = try DeOldify_artistic(configuration: config) // Prepare: grayscale CVPixelBuffer → RGB func prepareInput(from grayImage: UIImage) -> CVPixelBuffer? { // Create RGB pixel buffer from grayscale, replicating channel var pixelBuffer: CVPixelBuffer? CVPixelBufferCreate(nil, width, height, kCVPixelFormatType_32BGRA, nil, &pixelBuffer) // ... copy gray channel into all three return pixelBuffer } let input = DeOldify_artisticInput(input: pixelBuffer) let output = try model.prediction(input: input) let colorizedImage = UIImage(cvPixelBuffer: output.output) 

On iPhone 13, a 512×512 image processes in 0.8–1.2 seconds. For Full HD (1920×1080), we use tiled inference with 512×512 patches and blending at seams. The tile-based approach creates a problem of color inconsistency between patches: the model might color one sky fragment blue and a neighboring one gray. The solution is global color histogram matching: normalize each tile's histogram to the global histogram.

Android: TFLite with ONNX Runtime as an Alternative

// ONNX Runtime gives flexibility: one model for iOS and Android val env = OrtEnvironment.getEnvironment() val session = env.createSession( "deoldify_optimized.onnx", OrtSession.SessionOptions().apply { addNnapi() // ONNX Runtime NNAPI delegate for acceleration } ) val inputTensor = OnnxTensor.createTensor(env, inputArray, longArrayOf(1, 3, 512, 512)) val results = session.run(mapOf("input" to inputTensor)) val outputArray = (results[0].value as Array<*>) 

ONNX Runtime Mobile is a good choice when the same model is needed on both platforms — no need for double conversion. The NNAPI delegate works on devices with Android 8.1+. ONNX Runtime reduces deployment complexity by 3x compared to separate Core ML and TFLite builds.

UX: What to Consider

Colorization is a slow operation (1–5 seconds). Show an animated progress indicator. A good pattern is a before/after slider: the user drags a divider and sees the original and result side by side. This is both UX and a clear demonstration of the model's work.

Save the result in .heic or .jpg with maximum quality. The colorization result poorly tolerates repeated JPEG compression: artifacts on color transitions become noticeable.

What's Included in the Work

• Selection and conversion of the model for the target platform (Core ML / TFLite / ONNX)
• Optimization for specific devices (Neural Engine, GPU, NNAPI)
• Implementation of tiled inference with color normalization
• UI development with before/after comparison and progress
• Integration of saving to gallery and sharing
• Documentation on the model, preprocessing, and instructions for App Store Review (according to App Store Review Guidelines section 5.1 for photo processing)
• Testing on historical photos of varying quality

Savings on licenses: you don't pay for open-source models. Typical development cost for one platform is $8,000–$12,000, saving you up to $20,000 compared to hiring an in-house team. Development cost is comparable to one developer's monthly salary.

We provide turnkey integration (под ключ) in 2–8 weeks. The work includes (входит) all steps above. Contact us to evaluate your project for free. Write to us for a quote.

Timeline Estimates

We can implement the solution for you in as little as 2 weeks for a single platform. Get a consultation — we will evaluate your project and propose the optimal stack within two days.