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mlboydaisuke/Nitro-E-CoreML

sourceHugging Faceotherupdated 1mo agoView on Hugging Face

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Model Card

Nitro-E (4-Step) — Core ML

AMD, 2025

AMD's 304M E-MMDiT text-to-image model (Oct 2025). 4-step distilled variant, 512×512. Llama 3.2 1B text encoder + E-MMDiT denoiser + DC-AE VAE decoder. ~1.04 GB bundled after INT4/INT8 palettization. ~2–3 s / image on iPhone 15+.

Core ML conversion of amd/Nitro-E 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	text to image
Upstream	amd/Nitro-E
Packages	3
Download size	987 MB
Minimum iOS	18.0
Peak RAM	~2500 MB

Files

File	Size	Compute units	SHA-256
`NitroE_TextEncoder.mlpackage.zip`	545 MB	`cpuAndNeuralEngine`	`9b366b29d790ab98…`
`NitroE_EMMDiT.mlpackage.zip`	283 MB	`cpuAndNeuralEngine`	`93a7ed971c5c419d…`
`NitroE_VAEDecoder.mlpackage.zip`	153 MB	`cpuAndNeuralEngine`	`4837023736d82b49…`
`Llama3Vocab.json`	2 MB	`-`	`f8f40517934d6f5d…`
`Llama3Merges.txt`	3 MB	`-`	`0cd100e0ab7dbd83…`
Total	987 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

bash

hf download mlboydaisuke/coreml-zoo --include "nitroe/*" --local-dir ./nitroe
unzip './nitroe/nitroe/*.zip' -d ./nitroe

Use in Swift

swift

import CoreML

let config = MLModelConfiguration()
config.computeUnits = .cpuAndNeuralEngine   // 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 NitroE_TextEncoder(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)

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 the MLMultiArray buffers and release each model before loading the next — two large Core ML models resident at once will OOM on an iPhone.

Demo

—Sample app — `sample_apps/NitroEDemo`, 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_nitro_e_emmdit.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: MIT (Nitro-E) + Llama 3.2 Community License (text encoder). See https://huggingface.co/amd/Nitro-E.

Nitro-E itself is MIT; the bundled text encoder is Llama 3.2 and carries the Llama 3.2 Community License.

Credits

—Upstream authors: amd/Nitro-E, 2025
—Core ML conversion: john-rocky (Daisuke Majima)

More models in this format: Core ML Model Zoo — 46 models, each with the recipe that produced it.

Want a different model on-device? Open a request — free, open weights only; the export and its measured numbers get published publicly.