Files read on Hugging Face: 2026-10-24

Z-Image-Turbo on Mac: files, RAM and published times

Turbo checkpoint of the 6B Z-Image family, released under Apache 2.0. The card example makes a 1024 x 1024 image in 9 scheduler steps, which run 8 DiT forwards, with guidance 0.

Tongyi-MAI · Updated 2026-10-24
Answer

Z-Image-Turbo runs on a Mac with 64 GB or more. A published run on M1 Max · 64 GB used the mflux 4-bit build (source, 2026-10-03).

Smallest Mac with a published run
64 GBM1 Max · 64 GB · mflux 4-bit
Smallest complete download
13.23 GBQ4_K_M GGUF
Files read
2026-10-24Hugging Face

Apache 2.0. Commercial use: allowed. Excluded territories: none listed.

See the exact GGUF files and sizes

Table 1

What do you download?

Only builds with every required component and an exact probed file size are shown.

mflux 4-bit

safetensors · 4-bit (mflux --quantize 4) · 32.83 GB complete · Recommended

Mac routes: mflux

Exact component files for mflux 4-bit
FileComponentRequiredDownloadSource
transformer/diffusion_pytorch_model-00001-of-00003.safetensorsTongyi-MAI/Z-Image-TurboZ-Image transformerditYes9.97 GBHugging FaceRead 2026-10-24
transformer/diffusion_pytorch_model-00002-of-00003.safetensorsTongyi-MAI/Z-Image-TurboZ-Image transformerditYes9.97 GBHugging FaceRead 2026-10-24
transformer/diffusion_pytorch_model-00003-of-00003.safetensorsTongyi-MAI/Z-Image-TurboZ-Image transformerditYes4.67 GBHugging FaceRead 2026-10-24
text_encoder/model-00001-of-00003.safetensorsTongyi-MAI/Z-Image-TurboQwen3 text encodertext_encoderYes3.96 GBHugging FaceRead 2026-10-24
text_encoder/model-00002-of-00003.safetensorsTongyi-MAI/Z-Image-TurboQwen3 text encodertext_encoderYes3.99 GBHugging FaceRead 2026-10-24
text_encoder/model-00003-of-00003.safetensorsTongyi-MAI/Z-Image-TurboQwen3 text encodertext_encoderYes0.10 GBHugging FaceRead 2026-10-24
vae/diffusion_pytorch_model.safetensorsTongyi-MAI/Z-Image-TurboVAEvaeYes0.17 GBHugging FaceRead 2026-10-24

Build sources: mflux model registry · mflux README.

Q4_K_M GGUF

gguf · Q4_K_M · 13.23 GB complete

Mac routes: stable-diffusion.cpp

Exact component files for Q4_K_M GGUF
FileComponentRequiredDownloadSource
z-image-turbo-Q4_K_M.ggufunsloth/Z-Image-Turbo-GGUFZ-Image transformerditYes5.02 GBHugging FaceRead 2026-10-24
text_encoder/model-00001-of-00003.safetensorsTongyi-MAI/Z-Image-TurboQwen3 text encodertext_encoderYes3.96 GBHugging FaceRead 2026-10-24
text_encoder/model-00002-of-00003.safetensorsTongyi-MAI/Z-Image-TurboQwen3 text encodertext_encoderYes3.99 GBHugging FaceRead 2026-10-24
text_encoder/model-00003-of-00003.safetensorsTongyi-MAI/Z-Image-TurboQwen3 text encodertext_encoderYes0.10 GBHugging FaceRead 2026-10-24
vae/diffusion_pytorch_model.safetensorsTongyi-MAI/Z-Image-TurboVAEvaeYes0.17 GBHugging FaceRead 2026-10-24

Build sources: Z-Image-Turbo GGUF card · stable-diffusion.cpp README.

Q8_0 GGUF

gguf · Q8_0 · 15.44 GB complete

Mac routes: stable-diffusion.cpp

Exact component files for Q8_0 GGUF
FileComponentRequiredDownloadSource
z-image-turbo-Q8_0.ggufunsloth/Z-Image-Turbo-GGUFZ-Image transformerditYes7.22 GBHugging FaceRead 2026-10-24
text_encoder/model-00001-of-00003.safetensorsTongyi-MAI/Z-Image-TurboQwen3 text encodertext_encoderYes3.96 GBHugging FaceRead 2026-10-24
text_encoder/model-00002-of-00003.safetensorsTongyi-MAI/Z-Image-TurboQwen3 text encodertext_encoderYes3.99 GBHugging FaceRead 2026-10-24
text_encoder/model-00003-of-00003.safetensorsTongyi-MAI/Z-Image-TurboQwen3 text encodertext_encoderYes0.10 GBHugging FaceRead 2026-10-24
vae/diffusion_pytorch_model.safetensorsTongyi-MAI/Z-Image-TurboVAEvaeYes0.17 GBHugging FaceRead 2026-10-24

Build sources: Z-Image-Turbo GGUF card · stable-diffusion.cpp README.

Original safetensors

safetensors · Original weights · 32.83 GB complete

Mac routes: Diffusers

Exact component files for Original safetensors
FileComponentRequiredDownloadSource
transformer/diffusion_pytorch_model-00001-of-00003.safetensorsTongyi-MAI/Z-Image-TurboZ-Image transformerditYes9.97 GBHugging FaceRead 2026-10-24
transformer/diffusion_pytorch_model-00002-of-00003.safetensorsTongyi-MAI/Z-Image-TurboZ-Image transformerditYes9.97 GBHugging FaceRead 2026-10-24
transformer/diffusion_pytorch_model-00003-of-00003.safetensorsTongyi-MAI/Z-Image-TurboZ-Image transformerditYes4.67 GBHugging FaceRead 2026-10-24
text_encoder/model-00001-of-00003.safetensorsTongyi-MAI/Z-Image-TurboQwen3 text encodertext_encoderYes3.96 GBHugging FaceRead 2026-10-24
text_encoder/model-00002-of-00003.safetensorsTongyi-MAI/Z-Image-TurboQwen3 text encodertext_encoderYes3.99 GBHugging FaceRead 2026-10-24
text_encoder/model-00003-of-00003.safetensorsTongyi-MAI/Z-Image-TurboQwen3 text encodertext_encoderYes0.10 GBHugging FaceRead 2026-10-24
vae/diffusion_pytorch_model.safetensorsTongyi-MAI/Z-Image-TurboVAEvaeYes0.17 GBHugging FaceRead 2026-10-24

Build sources: Z-Image-Turbo model card.

Table 2

Does it fit your Mac's RAM?

Published names the exact measured machine. est. is a memory calculation. unknown means the run verdict is not established, not that the model fails.

Run verdicts by Mac memory tier
Mac RAMComplete buildDownloadRun verdictBasis
8 GBmflux 4-bit4-bit (mflux --quantize 4) · mflux32.83 GBDoes not fitest.
16 GBQ4_K_M GGUFQ4_K_M · stable-diffusion.cpp13.23 GBunknownunknown
24 GBQ4_K_M GGUFQ4_K_M · stable-diffusion.cpp13.23 GBunknownunknown
32 GBQ4_K_M GGUFQ4_K_M · stable-diffusion.cpp13.23 GBunknownunknown
48 GBmflux 4-bit4-bit (mflux --quantize 4) · mflux32.83 GBunknownunknown
64 GBmflux 4-bit4-bit (mflux --quantize 4) · mflux32.83 GBRuns · PublishedPublishedApple M1 Max · 64 GB RAM
96 GBmflux 4-bit4-bit (mflux --quantize 4) · mflux32.83 GBRuns · Published on M1 Max · 64 GBPublishedApple M1 Max · 64 GB RAM
128 GBmflux 4-bit4-bit (mflux --quantize 4) · mflux32.83 GBRuns · Published on M1 Max · 64 GBPublishedApple M1 Max · 64 GB RAM

How does it rank in blind votes?

#59 of 82 in text to image blind votes, from 24,808 votes.

Quality: blind human votes from z-image-turbo (arena.ai), CC BY 4.0, snapshot 2026-10-07. Scores are for the Arena checkpoint, not your local quantized build.

Table 3

What published Mac times are available?

Third-party results keep their original machine and settings. ModelFit does not run these benchmarks.

Published Mac generation measurements
Machine and RAMSettingsAppResultSource
Apple M1 Max · 64 GB RAM1024x1024 · 8 steps · 4-bit (mflux --quantize 4) · Seed 1, macOS 26, MLX 0.32.3, on AC power. Run before mflux changed its default float32 stream. Two runs took 102.4 s and 94.8 s; the slower is shown.mfluxSuccess102.4 s26.1 GB peakmflux pull request 820Published 2026-10-03 · Grade A

Which Mac software routes are listed?

  • Diffusers

    Official

    Formats: safetensors, gguf. Memory policy: component stages stay resident.

    Runs on the PyTorch mps device on Mac and CUDA on NVIDIA. GGUF loads through from_single_file on the model class, not the pipeline. Resident size is the sum of the weights, an upper bound.

    Sources: Diffusers MPS guide · Diffusers GGUF guide

  • Draw Things

    Official

    Formats: safetensors. Memory policy: component stages stay resident.

    Native offline app for Mac. It picks a weight-streaming tier from physical memory. No official Mac timing or peak-memory table was found, so there is no run verdict here.

    Sources: Draw Things on the App Store · Draw Things App Store listing data · Draw Things model importer

  • mflux

    Community · 0.23.0

    Formats: safetensors. Memory policy: component stages stay resident.

    MLX-native runner. It reads the original Hugging Face weights and quantizes them while loading (--quantize 3, 4, 5, 6 or 8). --low-ram lowers memory use at a performance cost.

    Install with uv

    uv tool install --upgrade mflux

    Sources: mflux README, install · mflux pyproject · mflux common README, quantization

  • stable-diffusion.cpp

    Official

    Formats: gguf, safetensors. Memory policy: component stages stay resident.

    C/C++ runner with Metal on Mac and CUDA on NVIDIA. It reads GGUF and safetensors, and its docs keep a page per model family. Resident size is the sum of the weights, an upper bound.

    Sources: stable-diffusion.cpp README, backends

What does the official configuration say?

"The card says the model fits comfortably within 16G VRAM consumer devices. This is a GPU statement for the Z-Image family, not a Mac minimum."

Source: Z-Image-Turbo model card · Read 2026-10-24.

What known issues should you check?

  • Known issue 1

    The only Mac run is a pre-release test in an mflux pull request, made before mflux changed its default float32 stream. Treat it as one data point.

    Source: mflux pull request 820 · Read 2026-10-24

Frequently asked questions

Can I run Z-Image-Turbo on a Mac?

One published Mac run exists. mflux with 4-bit quantization made a 1024x1024 image in 8 steps on an M1 Max with 64 GB in 102.4 s, peaking at 26.13 GB of MLX memory. It predates a later mflux change, so treat it as one data point, not a minimum.

Is Z-Image-Turbo free for commercial use?

Yes. Z-Image-Turbo is released under Apache 2.0, which allows commercial use. The card describes the 6B Z-Image family and says it fits comfortably within 16G VRAM consumer devices, a GPU statement rather than a Mac requirement.

Which Z-Image-Turbo build should I download?

The measured Mac path is mflux, which loads the original weights and quantizes them to 4-bit while loading. The Q8_0 and Q4_K_M GGUF files cover the transformer only, so they need the text encoder and VAE from the original repository.

Sources