Mage-Flow β€” shared components (MLX, per-tier)

The Qwen3-VL-4B text encoder and Mage-VAE shared by every Mage-Flow variant, re-hosted for 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 β€” 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 β€” 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 β€” 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 β€” 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'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 Β· arXiv:2607.19064 Β· MIT.

Downloads last month

-

Downloads are not tracked for this model. How to track
MLX
Hardware compatibility
Log In to add your hardware

Quantized

Inference Providers NEW
This model isn't deployed by any Inference Provider. πŸ™‹ Ask for provider support

Model tree for SceneWorks/Mage-Flow-Components-mlx

Finetuned
(2)
this model

Paper for SceneWorks/Mage-Flow-Components-mlx