Instructions to use mlx-community/Lens-Turbo-3.8B-bf16 with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- MLX
How to use mlx-community/Lens-Turbo-3.8B-bf16 with MLX:
# Download the model from the Hub pip install huggingface_hub[hf_xet] huggingface-cli download --local-dir Lens-Turbo-3.8B-bf16 mlx-community/Lens-Turbo-3.8B-bf16
- Notebooks
- Google Colab
- Kaggle
- Local Apps Settings
- LM Studio
Lens-Turbo-3.8B-bf16 (MLX)
Apple MLX conversion of microsoft/Lens-Turbo — the distilled 4-step sibling of Lens (identical 3.8B DiT architecture; sample at 4 steps, guidance 1.0). ~8.2 GB. DiT-only (MIT); the GPT-OSS-20B encoder (Apache-2.0) and FLUX.2 VAE load from source. Architecture is byte-identical to base Lens, which is parity-locked vs the PT reference (DiT cosine 0.999999); this variant inherits that port.
Usage
from lens_mlx.pipeline_mlx import LensPipeline # github.com/xocialize/lens-mlx
# `base` = a microsoft/Lens snapshot (tokenizer + GPT-OSS encoder + FLUX.2 VAE).
pipe = LensPipeline.from_pretrained(base, dit_repo="mlx-community/Lens-Turbo-3.8B-bf16")
img = pipe("A serene lake below snow-capped mountains, golden hour.",
height=1024, width=1024, num_inference_steps=4, guidance_scale=1.0, seed=42)
img.save("out.png")
Tip: page weights into memory before the first forward (
mx.evalthe params) when loading from slow/external storage, to avoid a Metal command-buffer watchdog timeout at large sizes.
License
DiT weights MIT (from microsoft/Lens-Turbo) · GPT-OSS-20B encoder Apache-2.0 (not re-hosted) · FLUX.2 VAE under its own terms (not re-hosted). Upstream: microsoft/Lens-Turbo.
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Model tree for mlx-community/Lens-Turbo-3.8B-bf16
Base model
microsoft/Lens-Turbo