Instructions to use SceneWorks/Mage-Flow-Components-mlx with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- MLX
How to use SceneWorks/Mage-Flow-Components-mlx with MLX:
# Download the model from the Hub pip install huggingface_hub[hf_xet] huggingface-cli download --local-dir Mage-Flow-Components-mlx SceneWorks/Mage-Flow-Components-mlx
- Notebooks
- Google Colab
- Kaggle
- Local Apps Settings
- LM Studio
| license: mit | |
| library_name: mlx | |
| tags: | |
| - text-to-image | |
| - image-to-image | |
| - mlx | |
| - quantized | |
| base_model: microsoft/Mage-Flow-Base | |
| # Mage-Flow — shared components (MLX, per-tier) | |
| The **Qwen3-VL-4B text encoder** and **Mage-VAE** shared by every | |
| [Mage-Flow](https://huggingface.co/microsoft/Mage-Flow) variant, re-hosted for | |
| [SceneWorks](https://github.com/SceneWorks/SceneWorks) as physically distinct MLX quantization tiers. | |
| ## Why this repo exists | |
| Microsoft publishes six Mage-Flow checkpoints (`Base`, RL, `Turbo`, and the three `Edit` twins). The | |
| text encoder (8.875 GB) and VAE (0.345 GB) are **bit-identical across all six** — only the 8.232 GB | |
| DiT differs. Mirroring each variant as a complete snapshot would cost **105.04 GB** for a full | |
| install. Hosting the shared components once here, and only the per-tier DiT in each variant mirror, | |
| brings that to **58.65 GB**, and makes a second variant an ~8.2 GB delta rather than another 17.5 GB. | |
| ## Layout | |
| Each tier directory is self-contained: | |
| ``` | |
| <tier>/text_encoder/ Qwen3-VL-4B — config, tokenizer/processor assets, model.safetensors | |
| <tier>/vae/ Mage-VAE | |
| ``` | |
| | tier | text_encoder | vae | total | | |
| |---|---|---|---| | |
| | `bf16` | 8.876 GB | 0.345 GB | 9.232 GB | | |
| | `q8` | 4.720 GB | 0.345 GB | 5.076 GB | | |
| | `q4` | 2.503 GB | 0.345 GB | 2.860 GB | | |
| **The VAE is dense in every tier, deliberately.** 59 of its 60 quantizable 2-D weights are | |
| `adaLN_modulation` projections that the decoder *folds away at load* (adaLN depends only on `t`, and | |
| the one-step decode always runs `t = 0`). Packing them on disk would fold quantized codes and | |
| silently corrupt the decode. The disk cost of that correctness is ~0 — the VAE is conv-dominated, and | |
| the comparable Z-Image VAE shrinks only 1.8% under q4. | |
| Quantization is MLX `q4`/`q8` at group size 64, packed with the same | |
| `mlx_rs::ops::quantize(bf16, 64)` call the load-time path uses — a pre-quantized tier is | |
| **bit-identical** to quantizing the dense weights at load. | |
| ## Provenance | |
| Converted from `microsoft/Mage-Flow-Base` at revision | |
| `59a9cfd58cf6ecef28245852c6bdace3f12428a2` with | |
| [`mlx-gen-mage`](https://github.com/SceneWorks/inference)'s `examples/mage_prequant.rs`. The text | |
| encoder and VAE tensors are identical in all six upstream repos, so the choice of source variant is | |
| immaterial. | |
| Upstream: [microsoft/Mage](https://github.com/microsoft/Mage) · arXiv:2607.19064 · MIT. | |