mlx-community/Boogu-Image-0.1-Base-4bit

MLX (int4) conversion of Boogu-Image-0.1-Base (Apache-2.0) for Apple Silicon — bilingual (EN/ZH) text-to-image. OmniGen2-lineage pipeline (DiT + FLUX.1 VAE + FlowMatchEuler scheduler). The Qwen3-VL-8B-Instruct text encoder is the stock model (verified bit-identical) — referenced from mlx-community/Qwen3-VL-8B-Instruct, not re-hosted.

Quantization: attn+FFN Linears int4 (group_size=32); per-pass cosine vs bf16 0.99896. ~7.4 GB. Quant auto-detected via transformer/quant_config.json.

Parity (CPU stream, fp32)

  • FLUX VAE decode: max_abs 6.7e-6 · encode 1.97e-4
  • Scheduler (flow-match + time-shift): bit-exact
  • Full DiT (40-layer): max_abs 1.56e-5

Use

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
from PIL import Image
pipe = BooguImagePipeline.from_pretrained("<this repo dir>", "mlx-community/Qwen3-VL-8B-Instruct")
img = pipe.generate("a red panda surfing on a wave, photorealistic", height=1024, width=1024, steps=30, guidance=3.5)
Image.fromarray(img).save("out.png")

Code: https://github.com/xocialize/boogu-image-mlx

Downloads last month

-

Downloads are not tracked for this model. How to track
MLX
Hardware compatibility
Log In to add your hardware

Quantized

Inference Providers NEW
This model isn't deployed by any Inference Provider. 🙋 Ask for provider support

Model tree for mlx-community/Boogu-Image-0.1-Base-4bit

Finetuned
(3)
this model

Collection including mlx-community/Boogu-Image-0.1-Base-4bit