coreml-diffusion 0.1.1

Convert diffusion-model checkpoints (SD1.5/SDXL) to Core ML for Apple Neural Engine — framework-free, ComfyUI-independent.

coreml-diffusion

Convert diffusion-model checkpoints into Core ML .mlpackage artifacts for the Apple Neural Engine (ANE) — framework-free and standalone.

coreml-diffusion takes a single-file Stable Diffusion checkpoint and produces a Core ML UNet you can run on-device (macOS/iOS) via Core ML, in a Python pipeline, or load into any host that consumes the artifact.

What this is

A standalone toolkit and knowledge base for running diffusion models on the Apple Neural Engine via Core ML. The niche is diffusion on the ANE: low-power, GPU-free, embeddable in a Swift/iOS app. ANE is the differentiator — this is about feasibility and power efficiency for SD1.5/SDXL on ANE, not a raw-throughput claim against desktop GPUs.

The scope is diffusion architectures generally, not Stable Diffusion specifically. The project aims to gather, in one place: the conversion path, a reproducible benchmarking suite for objective comparison, a per-model catalogue documenting the quirks of each architecture on the ANE, and the sources behind it all.

Supported today: SD1.5 and SDXL (verified). SDXL refiner and LCM convert but are not yet golden-verified (experimental).

Install

uv pip install coreml-diffusion          # from PyPI
uv pip install -e .                       # from a checkout

Requires Python 3.12 and (for conversion) coremltools 9 — conversion runs on macOS; the package imports and its CLI parse on any platform.

CLI

coreml-diffusion convert \
    --ckpt path/to/model.safetensors \
    --model-version SD15 \
    --out unet.mlpackage \
    --height 512 --width 512 \
    --attn-impl SPLIT_EINSUM \
    --quantize none

Options: --batch-size, --controlnet, --lora PATH[:STRENGTH] (repeatable), --config (original-config YAML). --quantize {none,8,6,4} applies k-means weight palettization. Run coreml-diffusion convert --help for the full list.

The output .mlpackage is the deliverable: load it natively in Swift/Core ML, run it through the Python inference pipeline below, or hand it to any consuming host.

Model sources

Register directories so --ckpt accepts a bare name instead of a full path:

coreml-diffusion sources add comfy /path/to/ComfyUI/models   # --kind comfy|flat
coreml-diffusion sources list                                # sources + checkpoints
coreml-diffusion convert --ckpt v1-5-pruned-emaonly --model-version SD15 --out unet.mlpackage

Sources are recorded in ~/.config/coreml-diffusion/sources.toml. comfy knows the models/{checkpoints,loras,vae,...} layout; flat is a plain checkpoint directory.

Library

import coreml_diffusion
from coreml_diffusion import ModelVersion

coreml_diffusion.convert(
    "model.safetensors", ModelVersion.SD15, "unet.mlpackage",
    height=512, width=512, attn_impl="SPLIT_EINSUM",
)

Inference (in progress)

A framework-free inference path lets a converted .mlpackage generate images with no host framework: a diffusers pipeline runs the stock VAE / text encoder on torch while the UNet is served from Core ML on the ANE. This doubles as the package's own regression anchor — the Tier 2 (m2) golden image, asserted on an Apple Silicon runner — and as the reference for the on-device write-up. See tests/m2/.

Discovery API

list_model_versions, list_attention_impls, list_quant_modes, and CONTRACT_VERSION report what this build can convert. The identifiers are an additive-only contract: removing or renaming one is a major version bump, because downstream consumers reference these strings verbatim.

ComfyUI

ComfyUI-CoreMLSuite consumes this package for its conversion path and drives its node dropdowns from the discovery API above — installing a newer coreml-diffusion surfaces new conversion types in the node with no Suite change. The Suite is one consumer; this package neither depends on nor requires ComfyUI.

License

MIT