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How do I run Flux locally on a Mac? On Apple silicon, the documented Mac-focused route is Argmax DiffusionKit, which runs FLUX with MLX. Create a Python environment, install DiffusionKit, then generate an image from Terminal. This guide covers that route, a separate setup from Black Forest Labs (BFL), and the practical differences between FLUX.1 schnell and FLUX.1 dev. The commands are not a promise of identical performance across Mac models.
How do I install Flux on a Mac?
For an Apple silicon Mac, use DiffusionKit if you want the documented MLX route. MLX is the runtime used in Argmax’s Mac-oriented instructions. The following commands create an isolated Conda environment with Python 3.11 and install the package:
conda create -n diffusionkit python=3.11 -yconda activate diffusionkitpip install diffusionkit
These are the setup commands in the Argmax DiffusionKit documentation. They assume Conda is already available. The documentation describes an Apple silicon Core ML/MLX project and demonstrates FLUX inference with MLX; it does not establish that the same setup or performance applies to every Mac.
How do I run Flux locally on a Mac?
Generate an image from the command line
With the environment active, try the documented first-image command:
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diffusionkit-cli --prompt "a photo of a cat" --output-path ./cat.pngIf it completes successfully, the output is written to cat.png in the current directory. The CLI documentation lists options including --seed, --height, and --width. Run diffusionkit-cli -h in your installed environment to check the flags supported by your package version; command-line options can change between releases. See the DiffusionKit project documentation.
Generate images from Python
DiffusionKit also documents a Python pipeline. This example uses FLUX.1 schnell, a 512-by-512 image, and four generation steps:
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from diffusionkit.mlx import FluxPipeline
HEIGHT = 512
WIDTH = 512
pipeline = FluxPipeline(
shift=1.0,
model_version="argmaxinc/mlx-FLUX.1-schnell",
low_memory_mode=True,
a16=True,
w16=True,
)
image = pipeline.generate_image(
"a photo of a cat",
cfg_weight=0.0,
num_steps=4,
latent_size=(HEIGHT // 8, WIDTH // 8),
)
image.save("cat.png")The 512-by-512 dimensions and four-step setting are values in the project’s example, not a required resolution or a timing guarantee. For dev, select the dev model version shown in the DiffusionKit documentation; its example uses 50 steps. Changing the model or step count changes the example configuration, not a Mac performance benchmark.
What is the alternative BFL setup?
Black Forest Labs publishes a general Python setup in its FLUX repository. It is a separate route from DiffusionKit, not the documented Apple silicon-optimized recipe. The repository’s demo defaults to CUDA when available and otherwise CPU, so do not assume it uses MLX on a Mac.
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- BUCKLE UP — Along with a next-generation CPU, faster unified memory, and up to 2x faster SSD storage,* M5 Pro and M5 Max feature a more powerful GPU with a Neural Accelerator built into each core, delivering faster AI performance and on-device training capabilities. So you can blaze through demanding workloads at mind-bending speeds.
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- Clone the official repository:
git clone https://github.com/black-forest-labs/flux.git, then change into the clonedfluxdirectory. - Create and activate the documented Python 3.10 virtual environment:
python3.10 -m venv .venv, then usesource .venv/bin/activateon macOS. - Install the project and its optional dependencies:
pip install -e ".[all]". - Start a text-to-image demo, choosing a model name:
python -m flux t2i --name flux-schnell --looporpython -m flux t2i --name flux-dev --loop.
The repository says model weights download from Hugging Face when a demo starts. It also documents FLUX_MODEL and FLUX_AE for supplying manual weight paths. Follow the repository’s current instructions for the precise environment and model files; the generic setup does not establish Mac-specific speed or compatibility guarantees. Sources: BFL repository and its README.
FLUX.1 schnell vs. FLUX.1 dev
Both model cards describe 12-billion-parameter models, but their training approach, documented step counts, and licenses differ. The listed steps are documentation examples, not controlled measurements of image quality or generation speed on Mac hardware.
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| Comparison | FLUX.1 schnell | FLUX.1 dev |
|---|---|---|
| Model size | 12 billion parameters, according to the BFL model card | 12 billion parameters, according to the BFL model card |
| Documented generation steps | One to four inference steps in the model card; the DiffusionKit example uses four | The DiffusionKit example uses 50 steps; the BFL Diffusers example also shows 50 |
| Distillation | Latent adversarial diffusion distillation, per the model card | Guidance-distilled, per the model card |
| License | Apache 2.0 | FLUX.1-dev Non-Commercial License; model access requires accepting the listed conditions |
| Practical distinction | A low-step option when minimizing the number of generation steps is a priority | A non-schnell comparison point; the example’s higher step count does not establish how long it takes on a given Mac |
Sources: the schnell model card, the dev model card, and DiffusionKit documentation.
Independent reader supportYour contribution helps us test, update, and keep practical guides available for everyone.Check the license before using generated images commercially
Local execution does not change a model’s license. The schnell model card lists Apache 2.0; dev is governed by the FLUX.1-dev Non-Commercial License, and its model page requires accepting access conditions. Review the applicable terms for your intended use before choosing a model, especially for commercial work. Sources: schnell model card and dev model card.
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- HAPPILY EVER FASTER — Along with its faster CPU and unified memory, M5 features a more powerful GPU with a Neural Accelerator built into each core, delivering faster AI performance. So you can blaze through demanding workloads at mind-bending speeds.
- BUILT FOR APPLE INTELLIGENCE — Apple Intelligence is the personal intelligence system that helps you write, express yourself, and get things done effortlessly. With groundbreaking privacy protections, it gives you peace of mind that no one else can access your data — not even Apple.*
- ALL-DAY BATTERY LIFE — MacBook Pro delivers the same exceptional performance whether it’s running on battery or plugged in.
- APPS FLY WITH APPLE SILICON — All your favorites, including Microsoft 365 and Adobe Creative Cloud, run lightning fast in macOS.*
What Mac performance can you expect?
The cited project and model documentation does not establish a universal minimum unified-memory requirement or a dependable image-generation time for a particular Mac. It also does not provide a controlled Mac comparison proving that one model produces better images than the other. Results depend on the device and configuration; treat step counts as example settings, not elapsed-time or quality scores.
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