README.md

metadata

license: other
license_name: mit-and-llama3.2-community
license_link: https://huggingface.co/amd/Nitro-E
library_name: coreml
pipeline_tag: text-to-image
base_model: amd/Nitro-E
base_model_relation: quantized
tags:
  - coreml
  - core-ml
  - ios
  - macos
  - apple
  - on-device
  - text-to-image
  - diffusion
  - mmdit
  - few-step

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

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

Use in 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)