Instructions to use SceneWorks/lens-turbo-mlx with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- MLX
How to use SceneWorks/lens-turbo-mlx with MLX:
# Download the model from the Hub pip install huggingface_hub[hf_xet] huggingface-cli download --local-dir lens-turbo-mlx SceneWorks/lens-turbo-mlx
- Notebooks
- Google Colab
- Kaggle
- Local Apps Settings
- LM Studio
| 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 β MLX pre-quantized tiers (SceneWorks) | |
| Native-MLX, **pre-quantized** re-host of [`microsoft/Lens-Turbo`](https://huggingface.co/microsoft/Lens-Turbo) | |
| 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-8763). | |
| ## Tiers | |
| Each subdirectory is a full, self-contained turnkey snapshot (the diffusers multi-component tree β | |
| `transformer/`, `text_encoder/`, `vae/`, `tokenizer/`, `scheduler/`, `model_index.json`): | |
| | Tier | Dir | What is packed | | |
| |------|-----|----------------| | |
| | **Q4** (default) | `q4/` | DiT + gpt-oss encoder MoE experts β MLX group-64 affine 4-bit | | |
| | **Q8** | `q8/` | DiT + gpt-oss encoder MoE experts β 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** β `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** β 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). | |