MFlux Skill for OpenClaw

Local image generation using Apple MLX via mflux — FLUX.2 Klein 4B (fast, Apache 2.0) and Z-Image Turbo (quality) models

Security Scan

The skill is internally consistent: it documents local Apple‑Silicon image generation with mflux, requires no secrets, and only instructs installing the mflux package and using its CLI/Python API.

Purpose & Capability

Name/description (local image generation on Apple Silicon) match the instructions: install mflux (pip/uv) and run mflux CLI or Python API. Required platform and RAM guidance are appropriate for the claimed capabilities.

Instruction Scope

SKILL.md stays on‑topic (installing and using mflux, model selection, quantization, LoRA, image-to-image workflows). It does implicitly require network access to download models and caches them in ~/.cache/huggingface/hub/ and may require a Hugging Face token for private models — this is expected but worth noting.

Install Mechanism

No install spec in the registry; SKILL.md recommends installing via uv (a third‑party tool) or pip. No direct downloads from unknown URLs or archive extraction are included in the skill files themselves.

Credentials

The skill declares no required environment variables or credentials. The instructions do not request secrets. A possible real‑world need for HF credentials (only for private Hugging Face models) is noted in the SKILL.md behavior and is proportionate to model downloads.

Persistence & Privilege

Skill does not request always:true, does not claim persistent elevated privileges, and is user‑invocable. It does instruct installing third‑party software (mflux) which will persist as any pip/uv install would.

Assessment

This skill appears coherent, but before installing: (1) verify the origin and integrity of the mflux package (PyPI project page or official repo) rather than blindly running installs; (2) confirm what 'uv' is and prefer a controlled environment (virtualenv) when using pip; (3) be prepared for large model downloads and ~8–32GB+ storage usage (models cached in ~/.cache/huggingface/hub/); (4) some models or LoRA files may be third‑party or require a Hugging Face token for private repos—only provide tokens if you trust the model source; (5) review licenses of models you plan to use (SKILL.md lists some as 'Custom'); and (6) avoid using LoRA/safetensors from untrusted sources. If you want higher assurance, ask the publisher for a source repository or official homepage and inspect the mflux project directly before installation.

SKILL.md — mflux

Name

mflux

Description

Generate images locally using Apple Silicon via the mflux MLX implementation. Supports FLUX.2-klein-4B (default, fastest 4-step generation, Apache 2.0 licensed) and Z-Image-Turbo (6B, highest quality). All processing is on-device — no cloud, no API keys, no data leaving your Mac.

Requirements

• Apple Silicon Mac (M1 or later)
• Python 3.10+
• macOS 13.5+
• Recommended: 16GB+ RAM (8GB works with quantization)

Installation

1. Install mflux (via uv - recommended)

uv tool install --upgrade mflux --prerelease=allow

With faster downloads (optional):

uv tool install --upgrade mflux --with hf_transfer --prerelease=allow

2. Alternative: Install via pip

pip install -U mflux

3. Verify installation

mflux-generate --help
mflux-generate-z-image-turbo --help
mflux-generate-flux2 --help

Python API Usage

Quick Start — FLUX.2 Klein 4B (Default, Fastest)

from mflux.models.flux2.variants import Flux2Klein
from mflux.models.common.config import ModelConfig

model = Flux2Klein(model_config=ModelConfig.flux2_klein_4b())
image = model.generate_image(
    prompt="A serene Japanese garden with cherry blossoms, golden afternoon light",
    num_inference_steps=4,  # Only 4 steps needed!
    width=1024,
    height=768,
    seed=42,
)
image.save("garden.png")

Z-Image Turbo (Highest Quality)

from mflux.models.z_image import ZImage
from mflux.models.common.config import ModelConfig

model = ZImage(
    model_config=ModelConfig.z_image_turbo(),
    model_path="filipstrand/Z-Image-Turbo-mflux-4bit",  # 4-bit quantized
)
image = model.generate_image(
    prompt="A majestic eagle soaring over snow-capped mountains at sunset",
    num_inference_steps=9,
    width=1280,
    height=720,
    seed=42,
)
image.save("eagle.png")

With Quantization (Lower RAM)

from mflux.models.flux2.variants import Flux2Klein
from mflux.models.common.config import ModelConfig

model = Flux2Klein(
    model_config=ModelConfig.flux2_klein_4b(),
    quantize=8,  # 8-bit quantization
)
# ... generate image

Image-to-Image

from PIL import Image
from mflux.models.flux2.variants import Flux2Klein

model = Flux2Klein(model_config=ModelConfig.flux2_klein_4b())
image = model.generate_image(
    prompt="Transform into a watercolor painting",
    num_inference_steps=4,
    init_image=Image.open("source.jpg"),
    init_image_strength=0.3,  # 0.0-1.0, higher = more change
)
image.save("watercolor.png")

FLUX.2 Image Editing

from mflux.models.flux2.variants import Flux2KleinEdit
from mflux.models.common.config import ModelConfig

model = Flux2KleinEdit(model_config=ModelConfig.flux2_klein_4b())
image = model.generate_image(
    prompt="Make the person wear sunglasses",
    image_paths=["person.jpg", "sunglasses.jpg"],
    num_inference_steps=4,
    seed=42,
)
image.save("edited.png")

LoRA Support

from mflux.models.flux2.variants import Flux2Klein
from mflux.models.common.config import ModelConfig

model = Flux2Klein(
    model_config=ModelConfig.flux2_klein_4b(),
    lora_paths=["path/to/lora.safetensors"],
    lora_scales=[0.8],
)
# ... generate image

Supported Models

Model	CLI Command	Size	Steps	Speed	Quality	License
FLUX.2-klein-4b (default)	mflux-generate-flux2	4B	4	⚡ Fastest	⭐⭐⭐⭐	Apache 2.0
FLUX.2-klein-9b	mflux-generate-flux2	9B	4	⚡ Fast	⭐⭐⭐⭐⭐	Apache 2.0
Z-Image-Turbo	mflux-generate-z-image-turbo	6B	9	⚡ Fast	⭐⭐⭐⭐⭐	Custom
Z-Image (base)	mflux-generate-z-image	6B	50	🐢 Slow	⭐⭐⭐⭐⭐	Custom
FLUX.2-klein-base-4b	mflux-generate-flux2	4B	50	🐢 Slowest	⭐⭐⭐⭐⭐	Apache 2.0
Qwen-Image	mflux-generate-qwen	20B	20	🐢 Slow	⭐⭐⭐⭐⭐⭐	Custom

CLI Reference

Generate with FLUX.2 Klein

# Default 4B model, 4 steps
mflux-generate-flux2 \
  --prompt "A photorealistic portrait of a wise old sailor" \
  --width 1024 \
  --height 768 \
  --steps 4 \
  --seed 42

# 9B model for higher quality
mflux-generate-flux2 \
  --model flux2-klein-9b \
  --prompt "A cyberpunk cityscape with neon lights" \
  --steps 4

# Base model (non-distilled, more steps)
mflux-generate-flux2 \
  --model flux2-klein-base-4b \
  --prompt "A detailed oil painting of a forest" \
  --steps 50 \
  --guidance 1.5

Generate with Z-Image Turbo

mflux-generate-z-image-turbo \
  --prompt "A minimalist logo design for a coffee shop" \
  --width 1280 \
  --height 720 \
  --steps 9 \
  --seed 42

# With LoRA
mflux-generate-z-image-turbo \
  --prompt "Art nouveau style portrait of a woman" \
  --steps 9 \
  --lora-paths "renderartist/Art-Nouveau-Style" \
  --lora-scales 0.7

With Quantization

mflux-generate-flux2 \
  --prompt "A serene landscape" \
  --quantize 8  # 8-bit quantization (reduces RAM)

# Or 4-bit for lowest RAM
mflux-generate-flux2 \
  --prompt "A serene landscape" \
  --quantize 4

Parameters

Parameter	Type	Default	Description
prompt	str	required	Text description of image
width	int	1024	Image width in pixels
height	int	768	Image height in pixels
num_inference_steps	int	4 (Klein), 9 (Z-Image)	Number of denoising steps
seed	int	random	Random seed for reproducibility
quantize	int	None	Quantization level (4, 8)
guidance	float	1.0 (Klein) / 4.0 (Z-Image)	Guidance scale (base models only)
lora_paths	list	None	List of LoRA file paths
lora_scales	list	None	LoRA blending scales
init_image	PIL.Image	None	Source image for img2img
init_image_strength	float	0.3	Strength of transformation

Aspect Ratios (Recommended Sizes)

Aspect Ratio	Dimensions	Use Case
1:1	1024×1024	Profile photos, icons
4:3	1024×768	Photo standard
16:9	1024×576 or 1280×720	Landscape, videos
3:4	768×1024	Portrait orientation
9:16	720×1280	Mobile vertical
21:9	1280×550	Cinematic widescreen

Performance & RAM Guide

Configuration	RAM	Speed	Best For
FLUX.2-klein-4b, q=8	~5 GB	~8 sec	8GB Macs
FLUX.2-klein-4b, q=4	~4 GB	~5 sec	Low RAM
FLUX.2-klein-4b, q=None	~8 GB	~15 sec	Quality on 16GB
FLUX.2-klein-9b, q=8	~12 GB	~20 sec	Best quality 16GB
Z-Image-Turbo, q=4	~5 GB	~12 sec	All-around 8GB

Model Weights

Models are downloaded automatically on first use:

• FLUX.2-klein-4b: ~15GB
• FLUX.2-klein-9b: ~32GB
• Z-Image-Turbo quantized: ~8GB

Cache location: ~/.cache/huggingface/

Comparison: When to Use Which

Choose FLUX.2-klein-4b when:

• Speed is priority (4 steps, ~5-8 sec)
• Apache 2.0 license needed (commercial use)
• Generating many images fast
• 8GB+ RAM available

Choose Z-Image-Turbo when:

• Quality is priority
• Realism matters most
• You have 16GB+ RAM
• Time per image acceptable

Choose FLUX.2-klein-9b when:

• Best quality from Apache-licensed model
• 16GB+ RAM available
• Commercial use required

Error Handling

Error	Cause	Fix
OutOfMemoryError	Not enough RAM	Use quantization (q=8, q=4)
ValueError: Model not found	First run / cache issue