RunPod/FLUX.2-klein-4B-mflux-4bit

FLUX.2 [klein] 4B — mflux 4-bit quantized

4-bit quantized weights of FLUX.2 [klein] 4B by Black Forest Labs, optimized for mflux on Apple Silicon.

	Full precision	This repo (4-bit)
Size	~8 GB	4.3 GB
Framework	diffusers / mflux	mflux only
Hardware	CUDA / MLX	Apple Silicon (MLX)

Quickstart

Install mflux

pip install mflux

Generate an image

mflux-generate-flux2 \
  --model RunPod/FLUX.2-klein-4B-mflux-4bit \
  --prompt "A cute robot standing in a field of flowers, digital art" \
  --width 1024 \
  --height 1024 \
  --steps 4 \
  --seed 42 \
  --output output.png

Python usage

from mflux import Flux2

flux = Flux2(
    model="RunPod/FLUX.2-klein-4B-mflux-4bit",
    base_model="flux2-klein-4b",
)

image = flux.generate_image(
    prompt="A cute robot standing in a field of flowers, digital art",
    width=1024,
    height=1024,
    num_inference_steps=4,
    seed=42,
)

image.save("output.png")

Details

• Base model: black-forest-labs/FLUX.2-klein-4B (Apache 2.0)
• Quantization: 4-bit via MLX nn.quantize (group_size=64), created with mflux-save --quantize 4
• Requirements: mflux v0.16.0+, Apple Silicon Mac
• Performance: ~11s for 512x512 (4 steps) on M3 Pro 18GB

How this was created

pip install mflux
mflux-save \
  --path ./FLUX.2-klein-4B-mflux-4bit \
  --model flux2-klein-4b \
  --quantize 4

About FLUX.2 [klein]

FLUX.2 [klein] 4B is a 4 billion parameter rectified flow transformer by Black Forest Labs for fast image generation and editing. It delivers state-of-the-art quality with sub-second inference on consumer hardware.

• Ultra-fast inference (4 steps)
• Text-to-image and multi-reference image editing
• Apache 2.0 — fully open for commercial use
• Blog post | GitHub

License

Apache 2.0, inherited from the original model.

Credits

• Model: Black Forest Labs
• mflux: Filip Strand
• Quantization & upload: Runpod / OpenClaw2Go