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
