Boogu-Image-0.1 (MLX)
Collection
MLX conversions of Boogu-Image-0.1 (OmniGen2-lineage T2I/edit, Apache-2.0) for Apple Silicon. • 6 items • Updated • 1
How to use mlx-community/Boogu-Image-0.1-Turbo-bf16 with MLX:
# Download the model from the Hub pip install huggingface_hub[hf_xet] huggingface-cli download --local-dir Boogu-Image-0.1-Turbo-bf16 mlx-community/Boogu-Image-0.1-Turbo-bf16
MLX (bf16) conversion of Boogu-Image-0.1-Turbo (Apache-2.0)
for Apple Silicon. 4-step Decoupled-DMD distilled (guidance 1.0) — ~6x faster. OmniGen2-lineage pipeline (DiT + FLUX.1 VAE + FlowMatchEuler).
The Qwen3-VL-8B-Instruct text encoder is stock (bit-identical) — referenced from
mlx-community/Qwen3-VL-8B-Instruct, not re-hosted.
Reference precision (bf16). ~19 GB DiT.
pip install mlx mlx-vlm && git clone https://github.com/xocialize/boogu-image-mlx && cd boogu-image-mlx && pip install -e .
from boogu_image_mlx.pipeline_mlx import BooguImagePipeline
pipe = BooguImagePipeline.from_pretrained("<this repo dir>", "mlx-community/Qwen3-VL-8B-Instruct")
img = pipe.generate("a red panda surfing on a wave, photorealistic", steps=4, guidance=1.0) # 4-step DMD
Quantized
Base model
Boogu/Boogu-Image-0.1-Turbo