Alloy ⚡️

A unified toolchain for converting open-source diffusion models (Stable Diffusion, Wan 2.1/2.2) into Apple's Core ML format for hardware-accelerated inference on macOS.

Features

• Core ML Conversion: Optimize models for Apple Silicon (Neural Engine/GPU).
• Wan 2.x Support:
  • Supports Wan 2.1 and Wan 2.2 (Text-to-Video).
  • Supports Image-to-Video / Edit models (automatic 36-channel input detection).
  • Implements Int4 Quantization to run 14B models on consumer Macs (64GB RAM recommended).
• Hunyuan Video Support:
  • Supports HunyuanVideo (Transformer conversion).
  • Hybrid Runner: PyTorch Text Encoder + Core ML Transformer + PyTorch VAE.
• LTX-Video Support:
  • Supports Lightricks/LTX-Video.
  • Efficient Core ML implementation for video generation.
• Flux ControlNet Support:
  • Full support for Flux ControlNet residuals (Base Model + ControlNet Model).
  • ComfyUI Nodes: Dedicated nodes for loading and applying Core ML ControlNets.
• Stable Diffusion Support: Wraps Apple's python_coreml_stable_diffusion for SDXL and SD3. (Requires optional [sd] extra)
• Lumina-Image 2.0 Support: Implements Next-Gen DiT conversion using Gemma 2B text encoder.
• Full Pipeline: Automates Download -> Convert -> Upload to Hugging Face.
• Progress Tracking: Real-time conversion progress with phases, steps, elapsed time, and ETA estimation.
• Memory Monitoring: Pre-flight memory checks warn if system resources are low before starting conversion.
• Dependency Management: Uses uv to resolve complex conflicts between legacy Core ML scripts and modern Hugging Face libraries.

Installation

1. Install uv:

   curl -LsSf https://astral.sh/uv/install.sh | sh
   

2. Install the Tool:

   uv sync
   # Or install globally:
   # uv tool install .
   

3. Hugging Face Login (Required for Uploads/Gated Models):

   uv run huggingface-cli login
   

   Or configure it via .env (recommended).

4. Configuration: Copy the example environment file:

   cp .env.example .env
   

   Edit .env to set your HF_TOKEN and OUTPUT_DIR.

Usage

You can run the tool using uv run alloy.

Convert Stable Diffusion (SDXL/SD3)

Note: SD support requires Apple's python-coreml-stable-diffusion package (not on PyPI). Install manually: pip install git+https://github.com/apple/ml-stable-diffusion.git

uv run alloy convert stabilityai/stable-diffusion-xl-base-1.0 \
  --type sd \
  --output-dir converted_models/sdxl \
  --quantization float16

Convert Lumina-Image 2.0

uv run alloy convert Alpha-VLLM/Lumina-Image-2.0 \
  --type lumina \
  --quantization int4

Convert Flux ControlNet (New!)

Support for converting Flux ControlNet models (X-Labs, InstantX, etc.) and preparing Base Flux models to accept them.

1. Convert Base Model (ControlNet Ready) You must re-convert your base model with --controlnet to inject the residual inputs.

uv run alloy convert black-forest-labs/FLUX.1-schnell \
  --output-dir converted_models/flux_controlnet \
  --quantization int4 \
  --controlnet

2. Convert ControlNet Model

uv run alloy convert x-labs/flux-controlnet-canny \
  --type flux-controlnet \
  --output-dir converted_models/flux_canny \
  --quantization int4

Convert Wan 2.1 / 2.2

Supports both T2V (Text-to-Video) and I2V (Image-to-Video). The converter automatically detects the input channels from the model config.

# Text-to-Video
uv run alloy convert Wan-AI/Wan2.1-T2V-14B-720P-Diffusers \
  --type wan \
  --output-dir converted_models/wan_t2v \
  --quantization int4

# Image-to-Video (36 channels)
uv run alloy convert Wan-AI/Wan2.1-I2V-14B-720P-Diffusers \
  --type wan \
  --output-dir converted_models/wan_i2v \
  --quantization int4

Convert Flux

# Basic Conversion
uv run alloy convert black-forest-labs/FLUX.1-schnell \
  --output-dir converted_models/flux \
  --quantization int4

# With LoRA Baking
uv run alloy convert black-forest-labs/FLUX.1-schnell \
  --output-dir converted_models/flux_style \
  --quantization int4 \
  --lora "path/to/style.safetensors:0.8:1.0" \
  --lora "path/to/fix.safetensors:1.0"

Civitai / Single-File Models

Support for directly loading .safetensors files (e.g., from Civitai) for Flux and LTX-Video.

Auto-Detection: The CLI automatically detects the model architecture (Flux vs LTX) from the file header, so you can often skip the --type argument.

# Convert a single file checkpoint (Type auto-detected!)
uv run alloy convert /path/to/flux_schnell.safetensors \
  --output-dir converted_models/flux_civiai \
  --quantization int4

Full Pipeline

Downloads a model, converts it, and uploads the Core ML package to your Hugging Face account.

Run Locally

Verify your converted models by generating an image directly.

uv run alloy run converted_models/wan2.2  \
  --prompt "A astronaut riding a horse on mars, photorealistic, 4k" \
  --type wan \
  --output result.png

Benchmark Performance

Measure real performance on your hardware:

uv run alloy run converted_models/flux \
  --prompt "test image" \
  --benchmark \
  --benchmark-runs 5 \
  --benchmark-output benchmarks.json

This will run 5 iterations and report:

• Mean/median generation time
• Memory usage
• Per-step timing breakdown
• Statistical variance

Results are saved to JSON for further analysis.

ComfyUI Integration

Alloy includes custom nodes for seamless ComfyUI integration with Core ML acceleration!

Installation

# 1. Install silicon-alloy
pip install -e .

# 2. Link to ComfyUI
ln -s /path/to/alloy/comfyui_custom_nodes /path/to/ComfyUI/custom_nodes/alloy

# 3. Restart ComfyUI

Quick Start (ComfyUI)

1. Install Alloy via ComfyUI Manager
2. Convert Model using the CoreMLQuickConverter node
   • Select preset (e.g., "Flux Schnell")
   • Run once to convert (caches automatically)
3. Load Model using CoreMLFluxWithCLIP
4. Generate!

Example Workflows

Check out comfyui_custom_nodes/example_workflows/ for ready-to-use examples:

• flux_txt2img.json: Basic Flux text-to-image workflow
• flux_img2img.json: Flux image-to-image transformation
• flux_allinone.json: Simplified workflow with integrated CLIP/T5/VAE
• convert_quick.json: One-click model conversion in ComfyUI
• convert_lora.json: Bake multiple LoRAs into one model

See the ComfyUI Node Reference for full documentation of all 13 nodes.

Utility Commands

Validate Models

alloy validate converted_models/flux/Flux_Transformer.mlpackage

Show Model Info

alloy info converted_models/flux/Flux_Transformer.mlpackage

List All Converted Models

alloy list-models
# or specify directory
alloy list-models --dir /path/to/models

Architecture

• src/alloy/cli.py: CLI entry point.
• src/alloy/converters/: Model conversion logic (Flux, Wan, LTX, Hunyuan, Lumina, Stable Diffusion).
  • All converters use 2-phase subprocess isolation to prevent OOM during large model conversion.
  • Intermediate files enable resume capability for interrupted conversions.
  • Worker modules (*_workers.py) handle subprocess conversion logic.
• src/alloy/runners/: Inference runners (PyTorch/Core ML hybrids).
• src/alloy/utils/: Utilities for file handling, Hugging Face auth, and benchmarking.
• pyproject.toml: Dependency overrides to force compatibility between coremltools and diffusers.

Requirements

• macOS 14+ (Sonoma) or newer.
• Python 3.11+.
• Apple Silicon (M1/M2/M3/M4).