SirSahOl

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Qwen-Image-mlx-4bit

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then why in readme it says

Parameters: ~60.2B ?

The Real Catch: The Weights Are Qwen-Image-1.0, Not 2.1
Inspection of transformer/model.safetensors.index.json in SirSahOl/Qwen-Image-2.1-mlx-4bit reveals:

It contains 60 transformer blocks (transformer_blocks.0 through transformer_blocks.59), per-block img_mod_linear, and txt_norm.
That is the old Qwen-Image-1.0 (Qwen/Qwen-Image-2512) architecture from last year, which mflux supports.
The new Qwen-Image-2.1 released today is completely different:
32 layers (not 60)
32 heads (not 24)
patch_size: 1 (not 2)
Unified shared modulation
Qwen3-VL prompt encoder
mflux does not yet support Qwen-Image-2.1

Hi @ethicalvats ,

Sharp eye, and thank you for taking the time to inspect transformer/model.safetensors.index.json and call this out. You are 100% correct on both counts.

Here is the exact breakdown of what happened and what has been updated:

1. The Architecture (60 Blocks vs. 32 Layers)

You are completely right: the weights in this repository follow the 60-block transformer topology (transformer_blocks.0 through transformer_blocks.59 with per-block modulation and txt_norm), which is the architecture supported by mflux (Qwen/Qwen-Image-2512 base).

The newly announced Qwen-Image-2.1 uses a redesigned 32-layer backbone with a unified modulation scheme and a Qwen3-VL prompt encoder, which mflux does not yet natively parse. We have updated the model card and repository description to explicitly document that these weights are compiled for the 60-block topology to prevent any community confusion. As soon as upstream mflux merges support for the new 32-layer 2.1 architecture, we will convert and release native 2.1 weights under a dedicated branch/repo.

2. The Parameter Count Correction (~7B vs. ~60.2B)

The ~60.2B figure in the initial automated card was an erroneous metric produced by an early card-generation script that calculated composite byte footprint across uncompressed sharded weights rather than counting active layer parameters.

The DiT transformer itself is ~7.0B parameters, with the complete end-to-end pipeline (DiT transformer + text encoder + VAE) totaling ~8.2B parameters. The README table and metadata have now been corrected to reflect the accurate parameter count.

Thank you again for the rigorous review—this kind of community diligence keeps open-source weights accurate and dependable. Please let us know if you run into any inference or generation issues on Apple Silicon.