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README.md

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+---
+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](https://huggingface.co/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](https://huggingface.co/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`](https://github.com/john-rocky/CoreML-Models/tree/master/sample_apps/NitroEDemo), a standalone SwiftUI project.
+- **Models Zoo** — this model is downloadable and runnable inside the [Models Zoo app](https://apps.apple.com/app/id6762083207) on the App Store, no build required.
+
+## Conversion
+
+- Script: [`convert_nitro_e_emmdit.py`](https://github.com/john-rocky/CoreML-Models/blob/master/conversion_scripts/convert_nitro_e_emmdit.py)
+- Pitfalls hit during conversion (FP16 overflow, ANE buffer limits, stride handling): [`docs/coreml_conversion_notes.md`](https://github.com/john-rocky/CoreML-Models/blob/master/docs/coreml_conversion_notes.md)
+- Model index: [CoreML-Models](https://github.com/john-rocky/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](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](https://huggingface.co/amd/Nitro-E), 2025
+- Core ML conversion: john-rocky (Daisuke Majima)