ComfyUI on Mac M4 Max Complete Setup Guide

Apple's M4 Max chip brings remarkable unified memory and GPU compute capabilities to macOS, making it a genuinely viable platform for local AI image generation. With up to 128GB of unified memory accessible to both CPU and GPU, the M4 Max can load and run models that would require expensive professional-tier NVIDIA cards. However, successfully running ComfyUI Mac M4 requires specific setup procedures different from the typical NVIDIA-focused guides, and understanding both the capabilities and limitations helps you get optimal results.

The M4 Max uses Metal Performance Shaders (MPS) rather than CUDA for GPU acceleration, which means PyTorch's MPS backend provides the performance for ComfyUI Mac M4 installations. While not as mature or fast as CUDA, MPS delivers reasonable generation speeds and continues to improve. The unified memory architecture eliminates traditional VRAM constraints entirely; your system RAM and GPU memory are the same pool, meaning a 64GB M4 Max can load models that would require a 48GB NVIDIA card, with memory to spare.

This guide provides complete instructions for setting up ComfyUI Mac M4: installation procedures, performance optimization, understanding what works and what doesn't compared to NVIDIA systems, and practical benchmarks to set realistic expectations. Whether you're migrating from an NVIDIA setup or setting up your first ComfyUI Mac M4 environment, this guide will get you running efficiently.

Understanding M4 Max Architecture for AI Workloads

Before diving into installation, understanding how M4 Max differs from traditional GPU setups helps you optimize effectively.

Unified Memory Architecture

Traditional discrete GPUs have their own dedicated VRAM separate from system RAM. A 24GB NVIDIA card has 24GB of VRAM regardless of how much system RAM you have. Models must fit in VRAM, and data must be copied between system RAM and VRAM.

Apple Silicon unifies memory: the same physical RAM is accessible by CPU and GPU with equal performance. On a 64GB M4 Max, both CPU and GPU can access all 64GB without copying data between pools. This creates several advantages:

• Large models load without VRAM constraints
• No memory copying overhead for CPU/GPU transfers
• More efficient memory use when running multiple applications

However, unified memory is also a shared resource. If macOS and other applications are using 20GB, your AI workloads have 44GB available from your 64GB total, not a dedicated 24GB.

GPU Core Architecture

The M4 Max GPU uses a tile-based deferred rendering architecture optimized for graphics workloads. For compute (like neural network inference), Apple provides Metal Performance Shaders, a GPU-accelerated framework. PyTorch's MPS backend translates tensor operations to Metal commands.

The M4 Max has 40 GPU cores in its full configuration. Performance scales with core count, so the 30-core M4 Pro variant will be correspondingly slower. The GPU also has hardware acceleration units (the Neural Engine) that some operations can use through Core ML, though ComfyUI primarily uses MPS.

Thermal and Power Considerations

Unlike desktop GPUs with unlimited power budgets, the M4 Max runs in a laptop with thermal constraints. Sustained workloads may throttle as the chip warms up. Generation times may vary between the first generation (cool chip) and subsequent generations (warm chip). Adequate ventilation and avoiding lap use during intensive work helps maintain performance.

Complete ComfyUI Mac M4 Installation Procedure

Follow these steps for a clean, functional ComfyUI Mac M4 installation on your system.

Installing Prerequisites

1. Install Homebrew

Homebrew provides package management on macOS:

/bin/bash -c "$(curl -fsSL https://raw.githubusercontent.com/Homebrew/install/HEAD/install.sh)"

Follow the post-install instructions to add Homebrew to your PATH. Verify installation:

brew --version

2. Install Python

ComfyUI requires Python. Use Homebrew to install a specific version:

brew install python@3.10

Python 3.10 has the best compatibility with AI libraries. Version 3.11 may work but some packages have issues. Verify:

python3.10 --version

3. Install Git

For cloning repositories:

brew install git
git --version

Creating Python Environment

Create a dedicated virtual environment for ComfyUI to isolate its dependencies:

# Navigate to where you want ComfyUI
cd ~/Projects

# Create virtual environment
python3.10 -m venv comfyui-env

# Activate environment
source comfyui-env/bin/activate

# Verify Python
which python
# Should show: /Users/[you]/Projects/comfyui-env/bin/python

Always activate this environment before working with ComfyUI.

Installing PyTorch with MPS Support

PyTorch 2.0+ includes MPS backend by default for Apple Silicon. Install the latest stable version:

# With environment activated
pip install --upgrade pip
pip install torch torchvision torchaudio

Verify MPS is available:

python -c "import torch; print(torch.backends.mps.is_available())"
# Should print: True

If this returns False, your PyTorch installation doesn't have MPS support. Ensure you're on Apple Silicon and using ARM Python (not x86 through Rosetta).

Installing ComfyUI

Clone ComfyUI and install its dependencies:

# Clone the repository
git clone https://github.com/comfyanonymous/ComfyUI.git
cd ComfyUI

# Install requirements
pip install -r requirements.txt

This installs all Python packages ComfyUI needs.

First Launch

Start ComfyUI:

python main.py

You should see output indicating that MPS is being used:

Using MPS
...
Starting server
To see the GUI go to: http://127.0.0.1:8188

Open http://127.0.0.1:8188 in your browser to access the interface.

Installing ComfyUI Manager

ComfyUI Manager makes installing custom nodes much easier:

cd custom_nodes
git clone https://github.com/ltdrdata/ComfyUI-Manager.git
cd ..

Restart ComfyUI to load Manager. You'll see a "Manager" button in the interface.

Downloading and Configuring Models

With ComfyUI installed, you need models to generate images.

Directory Structure

ComfyUI expects models in specific directories:

ComfyUI/
  models/
    checkpoints/    # Main SD/SDXL/Flux models
    loras/          # LoRA adapters
    vae/            # VAE models
    controlnet/     # ControlNet models
    embeddings/     # Textual inversions
    upscale_models/ # Upscalers

Downloading Checkpoint Models

Download Stable Diffusion models from HuggingFace or CivitAI. For M4 Max, you can use full-size models that wouldn't fit in typical GPU VRAM:

SDXL (full model 6.5GB): Works great with unified memory Flux.1 (large model): Fits comfortably in 64GB+ M4 Max SD 1.5 (4GB): Lighter option for faster iteration

Download and place in models/checkpoints/.

Memory Considerations for Model Loading

With unified memory, you're not constrained to typical VRAM limits, but consider:

• Each loaded model consumes memory from your total pool
• macOS needs memory for itself and other applications
• Loading multiple models simultaneously is possible but uses memory

For a 64GB M4 Max, loading SDXL plus ControlNet plus LoRAs is completely feasible. For 128GB configurations, you can keep multiple checkpoints loaded simultaneously.

Optimizing ComfyUI Mac M4 Performance

Several settings and techniques improve ComfyUI Mac M4 generation speed and quality.

Memory Mode Configuration

ComfyUI has command-line flags for memory management:

# For M4 Max with ample unified memory:
python main.py --highvram

# If experiencing issues:
python main.py --normalvram

The --highvram flag keeps more data in VRAM (which for M4 Max is just unified memory), reducing memory operations. Since there's no separate VRAM to overflow, --lowvram mode's aggressive offloading to CPU actually provides no benefit and should be avoided.

Disabling Incompatible Optimizations

Some NVIDIA-specific optimizations cause issues on MPS:

xFormers: Requires CUDA, doesn't work on MPS. Don't install it.

Flash Attention: NVIDIA-specific optimization. Not available on M4 Max.

ComfyUI should automatically skip these when MPS is detected, but if you see errors mentioning them, ensure they're not installed.

Precision Settings

M4 Max supports FP16 and FP32 computation:

FP16: Half precision, faster and uses less memory. Preferred for most generation.

FP32: Full precision, more stable but slower. Use if you encounter numerical issues.

Most SDXL and Flux models work fine in FP16 on M4 Max. If you see NaN errors or completely black outputs, try forcing FP32 for specific operations.

Batch Size Considerations

For image batches:

• Larger batches are more efficient per-image
• But require more memory
• Start with batch_size 4 and adjust

For video frames (AnimateDiff):

• Memory scales with frame count
• 16 frames typically fits comfortably
• Higher frame counts may need more memory management

Custom Nodes Compatibility

Not all custom nodes work on M4 Max. Before installing, check if the node:

• Requires CUDA (won't work)
• Has MPS/CPU fallback (will work)
• Is pure Python/PyTorch (usually works)

Some nodes have Apple Silicon-specific versions or forks. Check GitHub issues for compatibility reports.

Performance Benchmarks and Expectations

Understanding realistic performance helps plan your workflow.

Generation Speed Benchmarks

Benchmarks on M4 Max (40 GPU cores, 64GB unified memory):

SDXL at 1024x1024, 25 steps:

• Generation time: 10-14 seconds
• Roughly comparable to RTX 3080

SDXL at 1024x1024, 50 steps:

• Generation time: 20-28 seconds

SD 1.5 at 512x512, 25 steps:

• Generation time: 3-5 seconds

Flux.1 Schnell at 1024x1024, 4 steps:

• Generation time: 8-12 seconds

These times are slower than high-end NVIDIA cards (4090 would be roughly 2x faster) but competitive with mid-range cards.

Comparison to NVIDIA

The M4 Max falls between RTX 3080 and 3070 in raw speed, but its memory advantage enables workloads impossible on those cards.

Where M4 Max Excels

Large models: Load Flux or SDXL with plenty of memory to spare. No VRAM constraints.

Multiple models: Keep several models loaded simultaneously for rapid switching.

Long video generation: More frames fit in memory without swapping.

Stability: Consistent performance without thermal throttling worries of some desktop GPUs.

Where NVIDIA Excels

Raw speed: Even mid-range NVIDIA cards are faster for pure generation.

Optimization ecosystem: xFormers, Flash Attention, and other speed optimizations.

Training: CUDA-optimized training code is significantly faster.

Custom nodes: More nodes work without compatibility issues.

Working with MLX-Optimized Models

Apple's MLX framework provides potentially better performance than PyTorch MPS for some operations.

What is MLX?

MLX is Apple's array computation framework specifically optimized for Apple Silicon. It can be faster than PyTorch MPS for certain operations because it's designed specifically for the M-series architecture.

Using MLX Models

Some model providers offer MLX-optimized versions of models. These are converted to MLX format and run through MLX rather than PyTorch.

Currently, MLX support in ComfyUI is limited but growing. Some community node packs provide MLX integration for specific models.

Future MLX Development

As the MLX ecosystem matures, expect:

• More models converted to MLX format
• Better ComfyUI integration
• Potentially significant speed improvements

Monitor the ComfyUI and MLX communities for developments.

Handling Compatibility Issues

Some things don't work on M4 Max, and workarounds exist for some limitations.

Nodes Requiring CUDA

Nodes that call CUDA-specific functions won't work. Look for:

• GPU-specific imports (pycuda, numba.cuda)
• xFormers dependencies
• Flash Attention requirements

Solutions:

• Find alternative nodes with MPS support
• Use CPU fallback if available
• Look for Mac-specific forks

Training Limitations

LoRA training works on M4 Max but is slower than NVIDIA:

• No Kohya optimizations (CUDA-specific)
• Longer training times
• But feasible for small-scale training

For production LoRA training, consider cloud NVIDIA instances. For experimentation, local M4 Max training is fine.

Video Generation Performance

AnimateDiff and similar video generation works but slowly:

• Each frame takes generation time
• 16 frames at 10s each = nearly 3 minutes
• Long videos are time-consuming

Use faster models (Lightning variants) for iteration, standard for final quality.

Troubleshooting Common Issues

Black images or NaN errors:

• Precision issues
• Try forcing FP32 for problematic operations
• Ensure latest PyTorch version

Out of memory errors:

• Despite unified memory, you can still exceed available
• Reduce batch size or resolution
• Restart ComfyUI to clear cached models

Slow first generation:

• Model compilation and caching on first run
• Subsequent generations will be faster

MPS backend errors:

• Ensure MPS-compatible PyTorch
• Some operations may not be implemented for MPS
• Check for newer PyTorch versions with fixes

Recommended Workflow Optimizations

Best practices for efficient M4 Max workflows.

Model Management

Keep frequently used models loaded:

# Use --highvram to maintain models in memory
python main.py --highvram

Unload models you're not using to free memory for others.

Resolution and Quality Strategy

For iteration:

• Work at lower resolution (768x768 instead of 1024x1024)
• Use fewer steps (15 instead of 25)
• Use Lightning models

For final output:

• Full resolution
• Full steps
• Standard models

This balances speed during development with quality for delivery.

Batch Processing

For large batch jobs:

• Set up workflow to run overnight
• Generate at standard resolution and quality
• M4 Max runs cool enough for sustained operation

Saving and Loading Workflows

Save optimized workflows for different tasks:

• Fast iteration workflow (lower settings)
• Quality final workflow (full settings)
• Specific task workflows (inpainting, ControlNet, etc.)

Running ComfyUI Persistently

For regular use, streamline how you launch ComfyUI.

Creating Launch Script

Create a shell script to activate environment and launch:

#!/bin/bash
# File: ~/launch_comfyui.sh

cd ~/Projects/ComfyUI
source ~/Projects/comfyui-env/bin/activate
python main.py --highvram

Make executable:

chmod +x ~/launch_comfyui.sh

Launch with ~/launch_comfyui.sh.

Creating macOS Application

You can create an app icon for convenience:

1. Open Automator
2. Create new Application
3. Add "Run Shell Script" action
4. Paste the launch script contents
5. Save as application

Double-clicking the app launches ComfyUI.

Launch at Login

To start ComfyUI automatically:

1. System Settings > General > Login Items
2. Add your launch script or app

ComfyUI will be ready when you open your browser.

Troubleshooting Common M4 Max Issues

Even with proper setup, you may encounter specific issues on Apple Silicon. Here are solutions for the most common problems.

Memory Management Issues

Symptoms: Slow generation, system becomes unresponsive, swap usage increases.

Solutions:

• Close memory-intensive applications before generation
• Reduce batch size or resolution
• Restart ComfyUI to clear loaded models
• Use Activity Monitor to check memory pressure

The unified memory architecture means system and AI workloads compete for the same RAM. When memory pressure is high, macOS swaps to disk, devastating performance.

Generation Quality Issues

Black or corrupted outputs: Usually precision-related. Try forcing FP32 for the problematic operation or updating PyTorch to a newer version with better MPS support.

Inconsistent colors or artifacts: Some models are sensitive to MPS computation differences. Use the same seed on MPS and CUDA to compare results. Differences are normal; major artifacts indicate issues.

Custom Node Compatibility

Before installing custom nodes, check for MPS compatibility:

1. Search the node's GitHub issues for "MPS" or "Mac" or "Apple Silicon"
2. Look for explicit compatibility statements in README
3. Check if the node has CUDA-specific imports (pycuda, numba.cuda)

For nodes without MPS support, look for alternative nodes that accomplish similar tasks with pure PyTorch operations.

Performance Regression After Updates

PyTorch and macOS updates occasionally cause performance regression:

1. Note your current working versions
2. Test performance after updates
3. If performance drops, you can pin to previous versions
4. Report regressions to PyTorch GitHub for MPS backend fixes

Advanced Workflows for M4 Max

use the M4 Max's strengths for advanced generation workflows.

Multi-Model Workflows

With unified memory, you can keep multiple models loaded simultaneously:

Style Transfer Workflow:

1. Load both source and style models
2. Generate with first model
3. Pass result to second model without reloading
4. Faster iteration than switching models

Ensemble Generation:

1. Load multiple checkpoints
2. Generate same prompt with each
3. Blend or select best results
4. All models remain available for re-generation

This approach is impractical on NVIDIA cards with limited VRAM but works well with 64GB+ unified memory.

Video Generation Considerations

AnimateDiff and video generation benefit from large memory:

Memory Advantages:

• Higher frame counts fit without optimization tricks
• Full resolution video generation is feasible
• Multiple video models can stay loaded

Speed Considerations:

• Each frame takes generation time
• Long videos require patience
• Use Lightning/Turbo models for iteration

For complete video generation guidance, see our WAN 2.2 ComfyUI guide which covers video workflows in detail.

ControlNet and Complex Compositions

ControlNet workflows benefit from memory headroom:

Multiple ControlNet Stacking: Load depth + pose + canny simultaneously for precise control. On constrained VRAM systems, this combination requires careful memory management. On M4 Max, simply load all and compose freely.

High-Resolution ControlNet: Generate at 1024x1024 or higher with multiple ControlNet guidance. The memory capacity enables quality levels difficult on consumer NVIDIA cards.

LoRA Experimentation

The memory allows extensive LoRA experimentation:

LoRA Stacking: Stack multiple LoRAs to combine characters, styles, and concepts. Test combinations without memory concerns.

Rapid LoRA Switching: Keep multiple LoRAs loaded and switch between them instantly. Useful for comparing style LoRAs or finding optimal combinations.

For LoRA training on Mac, the speed is slower than NVIDIA but feasible for small-scale training. See our LoRA training guide for training fundamentals that apply across platforms.

Optimizing for Specific Use Cases

Different use cases benefit from different optimization approaches on M4 Max.

Rapid Iteration Workflow

For quick experimentation and iteration:

• Use SD 1.5 or SDXL Lightning variants
• Generate at 768x768 or lower
• Keep step count low (8-15 steps)
• Save high-quality generation for finals

This workflow emphasizes speed over quality, letting you explore ideas quickly before committing to longer generation times.

Production Quality Workflow

For final deliverables:

• Use full-size models (SDXL, Flux)
• Generate at native resolution (1024x1024+)
• Full step counts (25-50 steps)
• Batch overnight for large projects

The M4 Max handles production workflows; they just take longer than on dedicated GPUs.

Batch Processing Workflow

For processing many images:

• Set up queue with multiple generations
• Let batch run unattended
• M4 Max runs cool for sustained operation
• Excellent for overnight or multi-day batches

The quiet operation and thermal stability make M4 Max excellent for long batch runs that would stress desktop GPU cooling.

Future Improvements to Expect

The M4 Max AI generation experience will improve through software updates.

PyTorch MPS Improvements

PyTorch actively develops the MPS backend. Expect:

• Better operation coverage (fewer fallbacks to CPU)
• Performance optimizations for specific operations
• Reduced numerical differences from CUDA
• Better memory management

Each PyTorch release typically improves MPS performance. Update regularly to benefit.

MLX Integration

As covered in our MLX extension guide, MLX provides substantial speedups for compatible models. Expect:

• More models converted to MLX format
• Better ComfyUI node integration
• Approaching NVIDIA-competitive speeds for supported models

MLX represents the future of high-performance AI on Apple Silicon.

macOS Optimizations

Apple continues optimizing macOS for AI workloads:

• Neural Engine improvements
• Better memory management for AI
• Metal performance improvements

These system-level improvements benefit all AI applications automatically.

Conclusion

ComfyUI Mac M4 runs effectively with proper setup, providing a capable local AI generation environment. The unified memory architecture eliminates VRAM constraints that limit NVIDIA cards, allowing you to load and run large models that would require expensive professional GPUs. While ComfyUI Mac M4 raw generation speed is slower than high-end NVIDIA cards, it's competitive with mid-range options and comes with the benefits of the Mac ecosystem.

Success with ComfyUI Mac M4 requires understanding the differences from NVIDIA systems: use MPS backend instead of CUDA, avoid incompatible optimizations like xFormers, and accept that some custom nodes won't work. The ComfyUI Mac M4 performance is best used for workflows that benefit from large memory: loading multiple models, generating long videos, or working with large models like Flux.

The ComfyUI Mac M4 ecosystem continues to improve as PyTorch MPS matures and MLX-optimized models become available. A ComfyUI Mac M4 setup today will get better through software improvements without hardware changes.

For users committed to the Mac ecosystem or needing large memory capacity without the cost of professional NVIDIA cards, ComfyUI Mac M4 provides a genuinely viable platform. Set realistic expectations for generation speed, optimize your workflow for the architecture's strengths, and you'll have an effective AI generation environment.

Getting Started: First Steps for M4 Max Users

For users new to ComfyUI setting up their first AI generation environment on M4 Max, following a structured approach prevents common pitfalls and builds skills progressively.

Recommended Initial Learning Path

Step 1 - Verify Installation: Before attempting any generation, confirm ComfyUI launches correctly and shows "Using MPS" in the console. This baseline verification prevents troubleshooting confusion later.

Step 2 - Learn Core Concepts: Understand fundamental ComfyUI concepts including nodes, connections, conditioning, and sampling. Our essential nodes guide covers these foundations that apply regardless of platform.

Step 3 - Start with SD 1.5: Begin with smaller models before moving to SDXL or Flux. SD 1.5 generates quickly, uses less memory, and provides fast iteration for learning. Mistakes cost less time with fast generation.

Step 4 - Progress to SDXL: Once comfortable with basics, move to SDXL. This is where M4 Max's unified memory advantage becomes apparent, as you can run full SDXL comfortably.

Step 5 - Explore Advanced Features: Add ControlNet, IP-Adapter, LoRAs, and other enhancements once base generation works well. The memory headroom enables combinations that stress consumer NVIDIA cards.

First Project Recommendations

Project 1 - Basic Text-to-Image: Generate simple images from prompts using SD 1.5. Focus on understanding prompt writing and sampler behavior.

Project 2 - SDXL Generation: Generate high-resolution images with SDXL at native 1024x1024. Observe memory usage and generation time as baseline.

Project 3 - ControlNet Guidance: Add ControlNet for pose or depth control. This tests multi-model memory loading that benefits from unified memory.

Project 4 - Multi-Model Workflow: Create a workflow using multiple models simultaneously (checkpoint + ControlNet + LoRA). This showcases M4 Max's strength.

Understanding M4 Max Performance Characteristics

Expectation Setting: M4 Max is not faster than high-end NVIDIA cards for raw generation. Its advantages are memory capacity, stability, and the Mac ecosystem. Understanding this prevents disappointment.

Memory Advantage: The ability to load multiple models simultaneously without VRAM limitations enables workflows impossible on 8-12GB NVIDIA cards. This is where M4 Max shines.

Thermal Behavior: Unlike desktop GPUs with active cooling, M4 Max may throttle under sustained load. Expect first generations to be fastest, with some slowdown during extended sessions.

For complete beginners to AI image generation, our beginner's guide provides foundational knowledge that makes ComfyUI concepts clearer.