ComfyUI MLX Extension - 70% Faster on Apple Silicon Complete Guide

Apple Silicon Macs offer remarkable AI capabilities with their unified memory architecture and powerful Neural Engine, but standard PyTorch with MPS backend doesn't fully exploit this potential. MLX, Apple's array framework designed specifically for their chips, unlocks performance that PyTorch cannot match. ComfyUI MLX extensions use this framework to accelerate image generation by 50-70%, transforming the Mac from a compromise platform into a genuinely capable AI generation workstation. This guide explains how MLX achieves these speedups and walks through the complete setup and optimization process.

Understanding Why MLX Is Faster

To appreciate what MLX offers, you need to understand how Apple Silicon differs from traditional GPU computing and why standard frameworks leave performance untapped.

Apple Silicon's Unique Architecture

Apple Silicon chips use a unified memory architecture (UMA) where CPU and GPU share the same physical memory. In traditional systems, data must be copied between separate CPU RAM and GPU VRAM, creating bottlenecks. UMA eliminates these copies since both processors access the same memory directly.

However, simply sharing memory doesn't automatically mean frameworks know how to use it efficiently. The memory controller, cache hierarchy, and optimal access patterns differ from what PyTorch and CUDA were designed for. PyTorch's MPS backend translates operations to Metal, Apple's GPU API, but this translation adds overhead and doesn't take full advantage of UMA.

How MLX Differs from PyTorch MPS

MLX was written from scratch for Apple Silicon rather than adapted from CUDA. This native design means several things in practice:

Lazy Evaluation: MLX uses lazy evaluation where operations aren't executed immediately. Instead, the framework builds a computation graph and executes it optimally when results are needed. This allows automatic kernel fusion, where multiple operations combine into single GPU passes, reducing memory bandwidth and kernel launch overhead.

Unified Memory Awareness: MLX understands that CPU and GPU share memory. It avoids unnecessary copies and uses optimal access patterns for Apple's memory controller. Data lives in one place and both processors access it efficiently.

Optimized Kernels: MLX includes hand-tuned Metal kernels for common ML operations on Apple Silicon. These kernels use Apple's specific GPU architecture features rather than generic implementations.

Stream-Based Execution: MLX uses streams for concurrent execution, overlapping computation and data movement effectively on Apple's architecture.

Performance Impact

These differences translate to substantial speedups. In practical ComfyUI usage with compatible models, you can expect:

• SD 1.5 models: 60-70% faster than PyTorch MPS
• SDXL models: 40-50% faster than PyTorch MPS
• Memory efficiency: 20-30% reduction in peak memory usage
• Consistency: More stable generation times with less variance

The exact improvement depends on your specific chip (M1, M2, M3, or their Pro/Max/Ultra variants), model size, and generation parameters. Higher-end chips see larger absolute improvements, but even base M1 Macs benefit significantly.

Installation Guide

Setting up ComfyUI with MLX support requires installing the MLX framework, the ComfyUI extension, and MLX-format models. Here's the complete process.

Prerequisites

Before starting, ensure you have:

• Apple Silicon Mac (M1, M2, M3, or variants)
• macOS 13.5 or later (earlier versions have incomplete MLX support)
• Python 3.10 or later
• ComfyUI already installed and working with MPS

If you don't have ComfyUI installed yet, set that up first using standard guides for Mac installation and verify it works with PyTorch MPS before adding MLX.

Installing MLX Framework

Install MLX and its dependencies:

# Activate your ComfyUI Python environment
source /path/to/comfyui/venv/bin/activate

# Install MLX
pip install mlx

# Install MLX-LM for language model support (optional but recommended)
pip install mlx-lm

# Install additional MLX libraries
pip install mlx-data

Verify installation:

import mlx.core as mx

# Check MLX sees your device
print(f"MLX default device: {mx.default_device()}")

# Quick test
a = mx.array([1, 2, 3])
print(f"MLX test array: {a}")

This should print your device (usually gpu) and the test array.

Installing ComfyUI MLX Extension

Several community extensions provide MLX support for ComfyUI. The most maintained options are available through GitHub:

# Navigate to ComfyUI custom_nodes directory
cd /path/to/ComfyUI/custom_nodes

# Clone MLX nodes (example - check for current best option)
git clone https://github.com/apple/ml-stable-diffusion.git mlx-nodes

# Or use ComfyUI Manager
# Search for "MLX" in the extension browser

After cloning, install any additional requirements:

cd mlx-nodes
pip install -r requirements.txt

Restart ComfyUI and look for MLX-prefixed nodes in the node browser to confirm installation.

Obtaining MLX Models

MLX uses a different model format than standard SafeTensors. You need MLX-converted versions of models. These are available from:

HuggingFace: Search for "mlx" along with the model name. Apple and community contributors maintain MLX versions of popular models:

# Example: Download MLX SDXL using huggingface-cli
huggingface-cli download apple/SDXL-mlx --local-dir models/mlx/sdxl

Manual Conversion: If an MLX version doesn't exist, you can convert models yourself using MLX conversion tools:

# This is a simplified example - actual conversion depends on model type
from mlx_lm import convert

convert(
    "path/to/safetensors/model",
    "output/mlx/model",
    quantize=True  # Optional: quantize for smaller size
)

Conversion requires understanding the model architecture and may need custom scripts for certain models.

Directory Structure

Organize MLX models in your ComfyUI models directory:

ComfyUI/
├── models/
│   ├── checkpoints/          # Standard models
│   ├── mlx/                   # MLX-converted models
│   │   ├── sdxl/
│   │   ├── sd15/
│   │   └── flux/
│   ├── vae/
│   └── loras/

Configure the MLX extension to look in the mlx subdirectory for its models.

Using MLX Nodes in Workflows

With everything installed, you can build workflows using MLX nodes. These work alongside standard nodes but require some understanding of what can mix and what can't.

Basic MLX Workflow

A simple MLX workflow mirrors a standard workflow but uses MLX-specific nodes:

1. MLX Model Loader: Loads MLX-format checkpoint
2. MLX CLIP Encoder: Encodes text prompts using MLX
3. MLX KSampler: Performs diffusion sampling with MLX
4. MLX VAE Decode: Decodes latents to image

Each MLX node operates on MLX tensors rather than PyTorch tensors. The entire pipeline stays in MLX for maximum performance.

Mixing MLX and Standard Nodes

You can't directly connect MLX tensor outputs to standard PyTorch nodes or vice versa. If you need to mix them, use conversion nodes:

• MLX to Torch: Converts MLX array to PyTorch tensor (incurs overhead)
• Torch to MLX: Converts PyTorch tensor to MLX array

Every conversion adds overhead, so minimize these transitions. Ideally, your entire generation pipeline is either all MLX or all standard, not mixed.

When to Use MLX vs Standard

Use MLX when:

• The model has an MLX version available
• Your entire pipeline can stay in MLX
• Speed is a priority

Use standard PyTorch MPS when:

• No MLX version of the model exists
• You need nodes that only work with PyTorch tensors
• Compatibility with specific features matters more than speed

Many users keep both available and choose based on the task.

Example Workflow Configuration

Here's how you might set up an SDXL workflow with MLX:

MLX Load Checkpoint (SDXL-mlx)
        ↓
    ┌───────────┬───────────┐
    ↓           ↓           ↓
MLX CLIP    MLX CLIP    (model)
(prompt)    (negative)      ↓
    ↓           ↓           ↓
    └───────────┼───────────┘
                ↓
        MLX KSampler
        (20 steps, DPM++ 2M)
                ↓
        MLX VAE Decode
                ↓
           Save Image

This keeps everything in MLX from loading to output, maximizing performance.

Available MLX Models

The MLX model ecosystem is growing but doesn't cover everything. Here's the current space:

Well-Supported Models

Stable Diffusion 1.5 Family: Good MLX coverage including base model and popular fine-tunes. The smaller model size shows the largest relative speedups.

SDXL: Official MLX SDXL available from Apple. Works well with substantial speedups over PyTorch MPS.

Flux (emerging): MLX Flux support is actively developing. Check current availability before relying on it.

Limited Support

ControlNet: Some ControlNet models have MLX versions but coverage is spotty. Verify specific model availability.

VAE Models: Standard VAE is available. Specialized VAE variants may need conversion.

LoRAs: LoRA support in MLX is complicated. Some extensions support it, others don't. Check your specific extension's documentation.

Models Without MLX Versions

For models without MLX versions, you have options:

1. Convert yourself: Requires technical knowledge but gives you exactly what you need
2. Request conversion: Community members often fulfill requests for popular models
3. Use standard PyTorch: Fall back to MPS for incompatible models

The ecosystem is growing rapidly. Models that don't have MLX versions today may have them soon.

Performance Benchmarking

To understand what MLX provides on your specific setup, benchmark systematically.

Benchmarking Method

Test the same generation task with identical parameters on both MLX and PyTorch MPS:

import time

def benchmark_generation(workflow, runs=5):
    times = []
    for i in range(runs):
        start = time.time()
        # Execute workflow
        elapsed = time.time() - start
        if i > 0:  # Skip first run (warmup)
            times.append(elapsed)
    return sum(times) / len(times)

Run multiple times and average, discarding the first run which includes startup overhead.

Expected Results by Hardware

M1/M2 Base (8GB):

• SD 1.5 512x512: ~4-5 sec/image (vs 7-8 with MPS)
• SDXL 1024x1024: Memory constrained but works with optimizations

M1/M2/M3 Pro (16-18GB):

• SD 1.5 512x512: ~3-4 sec/image
• SDXL 1024x1024: ~12-15 sec/image (vs 20-25 with MPS)

M1/M2/M3 Max (32-64GB):

• SD 1.5 512x512: ~2-3 sec/image
• SDXL 1024x1024: ~8-12 sec/image
• Batch processing becomes practical

M1/M2 Ultra (64-128GB):

• All models run comfortably
• Batch sizes that match or exceed many dedicated GPUs
• Competitive with mid-range NVIDIA cards

These are rough estimates; your results will vary based on exact chip variant, thermal conditions, and background activity.

Memory Efficiency

MLX typically uses memory more efficiently than PyTorch MPS. Monitor memory usage with Activity Monitor during generation:

• Note peak memory usage
• Compare between MLX and MPS for same task
• MLX often enables larger batches in same memory

On memory-constrained systems (8GB), this efficiency can make the difference between a model running or not.

Optimization Techniques

Beyond basic setup, several techniques maximize MLX performance.

Quantization

MLX supports quantized models that use less memory and compute faster:

# Load quantized model
model = load_model("model-4bit-mlx")  # 4-bit quantization

4-bit quantization reduces memory by ~4x with modest quality impact. 8-bit offers a middle ground. Use quantization when memory is tight or when speed matters more than maximum quality.

Generation Parameters

Certain parameters affect MLX performance differently than PyTorch:

Step Count: MLX overhead is per-step, so very low step counts show smaller improvements. At 20+ steps, the per-step speedup dominates.

Resolution: Higher resolutions benefit more from MLX's efficient memory handling. This is where unified memory really shines.

Batch Size: MLX handles batches efficiently. If you need multiple images, batching is often faster than sequential generation.

System Optimization

Maximize MLX performance with system settings:

• Close unnecessary applications to reduce memory pressure
• Ensure good thermal conditions (MacBooks throttle when hot)
• Use "High Performance" mode on laptops when available
• Disable "Low Power Mode" which can limit GPU

Caching and Reuse

MLX efficiently caches compiled operations. Reusing the same generation parameters uses this caching:

# First generation compiles operations
image1 = generate(params)  # Slower

# Subsequent with same params reuse compilation
image2 = generate(params)  # Faster
image3 = generate(params)  # Faster

If you're generating many images with identical parameters (different seeds), later generations are faster.

Troubleshooting Common Issues

MLX setup can encounter several issues. Here are solutions to common problems.

Extension Not Loading

If MLX nodes don't appear in ComfyUI:

1. Check Python environment matches ComfyUI's
2. Verify MLX installed correctly (import mlx.core as mx)
3. Check extension directory is in custom_nodes
4. Review ComfyUI console for error messages
5. Try reinstalling extension dependencies

Model Loading Failures

If MLX models won't load:

1. Confirm model is MLX format (not standard SafeTensors)
2. Check model path configuration in extension
3. Verify model files are complete (not corrupted downloads)
4. Ensure sufficient memory for model size

Performance Worse Than Expected

If MLX is slower than expected:

1. Verify you're using MLX nodes (not standard nodes with MLX model)
2. Check for tensor conversion overhead (mixing MLX and PyTorch)
3. Monitor thermal throttling during generation
4. Ensure sufficient free memory (swap kills performance)

Out of Memory Errors

If running out of memory:

1. Use quantized models (4-bit or 8-bit)
2. Reduce batch size
3. Lower resolution
4. Close other applications
5. Try memory-optimized attention if available

Inconsistent Results

If results differ between MLX and PyTorch:

1. Numerical differences are normal (different implementations)
2. Use same seed for comparison
3. Slight variations in output are expected
4. Large differences may indicate a bug - report to extension developer

Comparing with Other Mac Acceleration Options

MLX isn't the only option for faster Mac generation. Here's how it compares.

vs. PyTorch MPS (Standard)

MPS is the default Apple Silicon support in PyTorch. It works with all PyTorch models without conversion but is slower than MLX. Use MPS for compatibility, MLX for speed.

vs. ONNX Runtime

ONNX Runtime has a CoreML execution provider for Apple Silicon. It requires ONNX model conversion and can be faster than MPS for some models. MLX is generally faster and more actively developed for ML use cases.

vs. CoreML Direct

Converting models to CoreML format can provide good performance. However, this requires significant model-specific work and loses flexibility. MLX offers better developer experience while approaching similar performance.

Recommendation

For most ComfyUI users on Apple Silicon:

1. Use MLX where available for best performance
2. Use PyTorch MPS as fallback for models without MLX versions
3. Don't bother with ONNX or CoreML unless you have specific compatibility needs

This gives you the best balance of speed and flexibility with minimal configuration.

Future of MLX for AI Generation

MLX is actively developed with significant Apple investment. Expected improvements include:

• More models converted to MLX format
• Better LoRA support
• Training capabilities (not just inference)
• Performance optimizations for newer chips
• Broader community extension support

As the ecosystem matures, MLX will become the default choice for Mac-based AI generation rather than an optimization option.

For users who want Apple Silicon optimization without managing MLX setup, or who need access to models and capabilities beyond what MLX currently supports, Apatero.com provides optimized generation across platforms.

Conclusion

MLX transforms ComfyUI performance on Apple Silicon from adequate to impressive. The 50-70% speedups make generation genuinely practical on Mac hardware, not just possible. Combined with Apple Silicon's silent operation, unified memory (no VRAM limitations in the traditional sense), and laptop portability, MLX makes Macs compelling AI generation platforms.

Setup requires installing MLX, obtaining converted models, and using MLX-specific nodes, but the process is straightforward. The main limitation is model availability - not everything has an MLX version yet. For supported models, the performance improvement justifies the setup effort.

If you're running ComfyUI on Apple Silicon and want the best possible performance, MLX is the clear choice for compatible models. Keep standard PyTorch MPS available for models without MLX versions, and monitor the ecosystem as coverage grows. The Mac AI generation experience is better than it's ever been, and MLX is a major reason why.

Getting Started with MLX for ComfyUI

For users new to ComfyUI on Apple Silicon, understanding the fundamentals before adding MLX acceleration ensures a solid foundation. Our essential nodes guide covers core concepts that apply regardless of whether you use MLX or standard MPS.

Recommended Learning Path

Step 1 - Verify Basic ComfyUI Operation: Before adding MLX, ensure ComfyUI runs correctly with standard PyTorch MPS. This baseline helps you isolate MLX-specific issues later.

Step 2 - Understand Your Hardware: Know your specific Mac's chip variant, memory configuration, and performance characteristics. M4 Max with 64GB has different optimal configurations than M2 Pro with 16GB.

Step 3 - Install MLX Incrementally: Add MLX framework first, verify it works, then add ComfyUI extension, verify again, then add models. This step-by-step approach isolates problems.

Step 4 - Benchmark Systematically: Compare MLX performance to MPS for your specific workflows. Not all workflows benefit equally from MLX.

First MLX Workflow Recommendations

Start Simple: Begin with a basic text-to-image workflow using SD 1.5 MLX models. This smaller model shows the largest relative speedup and helps you learn MLX node operation without complexity.

Verify Results: Compare MLX outputs to MPS outputs using identical seeds and prompts. Slight numerical differences are expected, but major visual differences indicate configuration issues.

Scale Gradually: Once SD 1.5 MLX works correctly, move to SDXL MLX. The larger model provides more practical test of your system's MLX performance and memory handling.

Common Beginner Issues

Issue: Can't find MLX nodes in ComfyUI Solution: Verify MLX extension installed in custom_nodes, restart ComfyUI, check console for errors during loading.

Issue: MLX models fail to load Solution: Confirm models are MLX format (not safetensors), check path configuration in extension, ensure sufficient memory available.

Issue: Performance worse than expected Solution: Verify entire workflow uses MLX nodes (not mixing with standard nodes), close other applications, check for thermal throttling.

For complete beginners to AI image generation, our beginner's guide provides foundational knowledge that makes MLX optimization more understandable.

Advanced MLX Configuration

Beyond basic setup, advanced configuration options maximize performance for your specific use cases.

Memory Management Optimization

Configure MLX Memory Pool: MLX allows configuration of its memory allocator for different workloads:

import mlx.core as mx

# Set memory limit (in bytes)
mx.metal.set_memory_limit(48 * 1024**3)  # 48GB for 64GB M4 Max

# Enable memory debugging for optimization
mx.metal.set_cache_limit(8 * 1024**3)  # 8GB cache limit

For optimal performance, leave 8-16GB free for macOS and other applications.

Clearing Memory Between Generations: When switching between models or after large batch processing:

# Clear MLX cache
mx.metal.clear_cache()

This releases memory held by MLX's internal caches.

Stream Configuration for Concurrency

MLX supports multiple concurrent streams for advanced workflows:

# Create separate streams for different operations
stream1 = mx.Stream(mx.gpu)
stream2 = mx.Stream(mx.gpu)

# Run operations on different streams
with mx.stream(stream1):
    # First operation
    pass

with mx.stream(stream2):
    # Second operation (concurrent)
    pass

This enables parallel preparation and generation in sophisticated workflows.

Custom Quantization Configuration

Create custom quantization settings for specific quality/performance tradeoffs:

from mlx_lm import convert

# Fine-tuned quantization
convert(
    "path/to/model",
    "output/path",
    quantize=True,
    q_group_size=64,  # Smaller groups = better quality, larger size
    q_bits=8          # Use 8-bit for better quality than default 4-bit
)

Higher bit quantization (8-bit) provides better quality with less speed improvement. Lower bit (4-bit) maximizes speed and memory savings with more quality loss.

Integration with Broader Workflows

MLX works within larger AI workflows on Mac.

Combining with Vision Models

Use MLX image generation with local vision-language models for intelligent workflows:

1. Generate image with MLX-accelerated ComfyUI
2. Analyze with Qwen VL for automatic captioning
3. Use caption for refined regeneration
4. Iterate until satisfied

This creates feedback loops where AI evaluates its own output.

Batch Processing with MLX

MLX's efficient memory management enables larger batches:

Batch Generation Strategy:

# MLX handles batches efficiently
batch_size = 4  # Can be larger on M4 Max than with MPS

for batch in batches:
    results = generate_batch(batch, size=batch_size)
    save_results(results)

For extensive batch processing techniques, see our batch processing guide.

Model Pipeline Architecture

Build pipelines using MLX throughout:

1. Text encoding: MLX CLIP encoder
2. Diffusion: MLX KSampler
3. VAE decode: MLX VAE
4. Upscaling: MLX-based upscaler (if available)

Keeping the entire pipeline in MLX avoids conversion overhead. Mixed pipelines still work but lose some performance at conversion points.

Troubleshooting Advanced Issues

Solutions for less common problems encountered with MLX.

Numerical Instability

Symptom: NaN values or corrupted outputs.

Solutions:

• Try different precision (FP32 instead of FP16)
• Reduce batch size
• Update to latest MLX version (fixes often address numerical issues)
• Test with simpler prompts to isolate the problem

Kernel Compilation Failures

Symptom: Errors during first model use mentioning Metal shader compilation.

Solutions:

• Ensure macOS is updated (Metal improvements in updates)
• Delete MLX cache: rm -rf ~/.cache/mlx
• Re-download model (may be corrupted)

Performance Inconsistency

Symptom: Speed varies significantly between generations.

Solutions:

• Check for thermal throttling (Activity Monitor > CPU > Temperature)
• Close other GPU-intensive applications
• Ensure power adapter is connected (performance mode)
• Check memory pressure (swap usage kills performance)

Comparison with NVIDIA Performance

Understanding how MLX compares to NVIDIA alternatives helps set expectations.

Speed Comparison

MLX brings M4 Max from roughly 3x slower than RTX 4090 to roughly 1.7x slower, making it competitive with RTX 3080/4070.

Use Case Recommendations

MLX on Mac excels for:

• Workflow development and iteration
• Moderate batch processing
• Large model usage (unified memory advantage)
• Mobile/portable generation
• Quiet operation requirements

NVIDIA excels for:

• Production-scale batch processing
• LoRA training
• Maximum speed requirements
• Ecosystem compatibility

For comparison with Mac setup fundamentals, see our M4 Max setup guide.

Future Development Trajectory

MLX development continues rapidly with Apple investment.

Expected Near-Term Improvements

Model Coverage: More models being converted to MLX format monthly. Expect SDXL, Flux, and newer models to have official or community MLX versions.

Performance Optimization: Each MLX release includes performance improvements for specific operations. Keep MLX updated for automatic speed gains.

ComfyUI Integration: Better native support in ComfyUI nodes as MLX matures. Less manual configuration needed.

Long-Term Outlook

Training Support: MLX is gaining training capabilities. LoRA training on Mac will become more practical.

Hardware Optimization: New Apple Silicon chips bring MLX improvements. M5 series will likely include AI-specific hardware features that MLX uses.

Ecosystem Growth: As MLX usage grows, more tools and models will support it natively. The Apple Silicon AI ecosystem is developing rapidly.