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README.md

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+---
+license: mit
+language:
+- en
+pipeline_tag: text-to-image
+library_name: mlx-gen
+base_model: microsoft/Lens
+tags:
+- mlx
+- apple-silicon
+- lens
+- text-to-image
+- quantized
+---
+
+# Lens (base) — MLX pre-quantized tiers (SceneWorks)
+
+Native-MLX, **pre-quantized** re-host of the **base** Lens model (`microsoft/Lens`, MIT)
+for on-device Apple-Silicon inference via [`mlx-gen`](https://github.com/SceneWorks/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-8767).
+
+Microsoft removed `microsoft/Lens` from the Hub; the base DiT here was recovered from the public
+ungated re-package [`Comfy-Org/Lens`](https://huggingface.co/Comfy-Org/Lens)
+(`diffusion_models/lens_bf16.safetensors`), whose keys are byte-identical to the diffusers
+`LensTransformer2DModel` state dict. Base Lens and Lens-Turbo differ **only** in the DiT weights;
+this re-host reuses the **shared** gpt-oss-20b text encoder + Flux.2 VAE + tokenizer + scheduler
+from [`SceneWorks/lens-turbo-mlx`](https://huggingface.co/SceneWorks/lens-turbo-mlx).
+
+Base Lens is **undistilled** — use a higher step count (~20–26) with CFG ~5.0 (the `mlx-gen-lens`
+`lens` id defaults to 20 steps / CFG 5.0), unlike the distilled Turbo (4 steps / guidance 1.0).
+
+## 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`. The shared text encoder is `openai/gpt-oss-20b` (Apache-2.0)
+and the VAE is `black-forest-labs/FLUX.2-dev` (Apache-2.0). This is a format re-host; all model
+weights and credit belong to the original authors (Microsoft Research; OpenAI; Black Forest Labs).