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mlx-community/Lens-Turbo-3.8B-4bit
Lens-Turbo-3.8B-4bit is a text-to-image model from mlx-community. Use it when you need an image from a text prompt. It is set up for mlx. The card lists the license as mit.
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). int4 (groupsize 64, keeping in/out/time at bf16), ~2.35 GB. DiT-o…
Downloads · 30 days
25
15% of all-time downloads
All-time downloads
163
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Parameters
4.1B
2.3 GB on disk
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Public
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.safetensors2.3 GB · 100%
How the weights are stored.
U324.1B · 99%
From the Hugging Face model README
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). int4 (group_size 64, keeping in/out/time at bf16), ~2.35 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.

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-4bit")
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.
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.