Modelpublic

SirSahOl/Qwen-Image-mlx-16bit

sourceHugging Faceotherupdated 2d agoView on Hugging Face

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Model Card

Qwen-Image-mlx-16bit

16-bit MLX weights for Qwen-Image diffusion pipeline on Apple Silicon, converted using mflux.

Converted by: SirSahOl Base Architecture: Qwen/Qwen-Image-2512 (60-block DiT topology) Framework: mflux Quantization: 16-bit Format: .safetensors License: Tongyi Qianwen License

Model Details

Attribute	Value
Pipeline Type	Diffusion Transformer (Text-to-Image)
DiT Backbone	60 Transformer Blocks (~7.0B Parameters)
Composite Pipeline	DiT Transformer + Text Encoder + VAE (~8.2B Total Parameters)
Quantization	16-bit
Format	Apple Silicon MLX (`.safetensors`)
Active VRAM Footprint	~72.6 GB

Quick Start (Apple Silicon)

1. Installation

bash

pip install -U mflux

2. CLI Generation

bash

mflux-generate-qwen \
  --model SirSahOl/Qwen-Image-mlx-16bit \
  --prompt "A cinematic portrait of a robotic artisan crafting glass in a neon workshop, 8k, photorealistic" \
  --steps 25 \
  --seed 42

3. Python API

python

from mflux import QwenImagePipeline

pipeline = QwenImagePipeline.from_pretrained("SirSahOl/Qwen-Image-mlx-16bit")
image = pipeline.generate(
    prompt="A serene mountain lake at sunrise with golden mist, ultra realistic",
    num_inference_steps=25,
)
image.save("output.png")

Multi-Quantization Variants

Precision	Repository	Key Advantage
4-bit	SirSahOl/Qwen-Image-mlx-4bit	Fastest generation and lowest VRAM requirement (~33.5 GB).
8-bit	SirSahOl/Qwen-Image-mlx-8bit	Near-lossless visual quality and prompt adherence (~49.7 GB).
16-bit	SirSahOl/Qwen-Image-mlx-16bit	Full unquantized bfloat16 reference weights (~72.6 GB).

Architectural Note

These weights represent the 60-block transformer topology (Qwen/Qwen-Image-2512 base) supported by mflux. Native 32-layer Qwen-Image-2.1 weights will be released once upstream mflux merges support for the newly announced 32-layer architecture.