license: mit
language:
- en
pipeline_tag: text-to-image
library_name: mlx-gen
base_model: microsoft/Lens-Turbo
tags:
- mlx
- apple-silicon
- lens
- text-to-image
- quantized
Lens-Turbo — MLX pre-quantized tiers (SceneWorks)
Native-MLX, pre-quantized re-host of microsoft/Lens-Turbo
for on-device Apple-Silicon inference via mlx-gen's
mlx-gen-lens provider (SceneWorks). The heavy components are packed offline so a tier loads
directly with no dense transient and no in-app quantization (epic 8506, sc-8763).
Tiers
Each subdirectory is a full, self-contained turnkey snapshot (the diffusers multi-component tree —
transformer/, text_encoder/, vae/, tokenizer/, scheduler/, model_index.json):
| Tier | Dir | What is packed |
|---|---|---|
| Q4 (default) | q4/ |
DiT + gpt-oss encoder MoE experts → MLX group-64 affine 4-bit |
| Q8 | q8/ |
DiT + gpt-oss encoder MoE experts → MLX group-64 affine 8-bit |
| bf16 | bf16/ |
dense mirror of the source (no quantization) |
Two components are quantized (matching the load-time .quantize scope):
- DiT —
img_in/txt_in/proj_out+ every block's fused-QKV attention projections (img_qkv/txt_qkv/to_out.0/to_add_out) and SwiGLU MLPs. The timestep embedder, AdaLN modulations, and all norms stay full precision. - gpt-oss-20b encoder MoE experts — the source ships these as MXFP4; the packed tiers store them
as MLX group-64 affine Q4/Q8 (stacked
experts.{gate_up,down}_proj.{weight,scales,biases}). The router / attention / embeddings / norms stay dense.
The VAE (the shared Flux.2 decoder) always runs f32 and is shipped dense in every tier.
The pack is byte-identical to what the load-time quantizer produces (bf16 cast, group 64), verified
in-repo (mlx-gen-lens convert/quant byte-identity tests) and by an on-device render gate.
License
MIT, inherited from microsoft/Lens-Turbo. This is a format re-host; all model weights and credit
belong to the original authors (Microsoft Research).