Krea 2 is a single-stream MMDiT text-to-image model built on the Qwen-Image stack: it reuses the Qwen-Image VAE and conditions on a 12-layer hidden-state tap from a Qwen3-VL-4B text encoder. The Turbo variant is distilled and produces high-quality images in 8 steps.

Details


Base model	`krea/Krea-2-Turbo`
Format	MLX `safetensors` (sharded)
Quantization	8-bit
Parameters	~13B dense (HF badge shows ~4B — see note below)
Saved with	mflux `0.18.0`
Pipeline	Text-to-image
Hardware	Apple Silicon (Metal / MLX)

This is a ready-to-run quantized snapshot, so it loads without re-quantizing at runtime. It contains the transformer, the Qwen3-VL-4B text encoder, the tokenizer, and the Qwen-Image VAE. At 8-bit it is a ~13 GB snapshot — larger and higher fidelity than the 4-bit build, smaller than the unquantized weights.

Why does Hugging Face show ~4B parameters? The params badge counts stored tensor elements, and MLX packs 8-bit weights four-to-a-uint32 word. The Hub reads ~3.6B packed elements and rounds the badge to 4B, but the model is ~13B parameters (transformer + Qwen3-VL-4B text encoder + VAE). The small badge is an artifact of quantized packing, not a truncated model.

Usage

Download this model:

hf download mflux-community/krea-2-turbo-mlx-q8 --local-dir /path/to/krea-2-turbo-mlx-q8

Install mflux:

pip install mflux

Generate from the local model directory:

mflux-generate-krea2 \
  --model /path/to/krea-2-turbo-mlx-q8 \
  --prompt "a photograph of a red fox sitting in a sunlit forest clearing, sharp focus, bokeh" \
  --width 1024 \
  --height 1024 \
  --seed 42 \
  --steps 8

Turbo defaults: 8 steps, guidance 1.0 (CFG off), er_sde sampler. The plain flow-matching Euler sampler — which matches the official diffusers FlowMatchEulerDiscreteScheduler — is available via --scheduler euler.

Standard mflux CLI options are supported (--metadata, --stepwise-image-output-dir, multiple --seed values). Image conditioning (edit / reference) is not yet implemented.

Python API

python

from mflux.models.krea2 import Krea2

model = Krea2(model_path="/path/to/krea-2-turbo-mlx-q8")
image = model.generate_image(
    seed=42,
    prompt="a photograph of a red fox sitting in a sunlit forest clearing, sharp focus, bokeh",
    num_inference_steps=8,
    width=1024,
    height=1024,
    guidance=1.0,
)
image.save("krea2_fox.png")

Architecture

Transformer: 28-layer single-stream MMDiT — hidden 6144, GQA (48 query / 12 KV heads, head_dim 128), SwiGLU, 3-axis Flux-style RoPE [32, 48, 48], per-head QK-norm + sigmoid-gated attention, AdaLN-single 6-way modulation, and a txtfusion adapter that fuses the 12 text-encoder hidden states.
Text encoder: Qwen3-VL-4B, 12-layer tap [2, 5, …, 35] flattened layer-major; the chat-template prefix is stripped so only prompt tokens condition the DiT.
VAE: Qwen-Image VAE (Wan2.1 16-channel latent).

License

This conversion inherits the license of the base model, krea/Krea-2-Turbo. Review and accept the original model's terms before use.

Acknowledgements

Krea for the original Krea-2-Turbo model
mflux for the MLX implementation and conversion tooling
MLX by Apple

mflux-community/krea-2-turbo-mflux-q8

Krea-2-Turbo · MLX · 8-bit (mflux)