Z-Image-Turbo — GPTQ, 4-bit (MLX / mflux)
▶ Run it locally in LLMTray
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A 4-bit MLX quantization of Z-Image-Turbo
(Tongyi-MAI, 6.15B diffusion transformer, 9-step distilled), for
mflux on Apple Silicon — using
GPTQ Hessian-based error correction instead of mflux's own
round-to-nearest (RTN) --quantize 4. Same on-disk size and speed as plain
mflux-save --quantize 4; this is where GPTQ's correction actually earns
its keep (see comparison below — at 8-bit the gap is small, at 4-bit it's
not).
Method
Same pipeline as the 8-bit release:
GPTQ-correct all 30 transformer blocks' attention and feed-forward linears
using activations captured from the model's own real denoising loop, then
re-quantize the corrected weights and splice them into mflux's own MLX
checkpoint format (zero format changes vs. plain --quantize 4). Full
method + code: zimage-quant.
Honest quality comparison
Same prompt, same seed (42), same 9 steps, RTN-4bit vs this GPTQ-4bit:
RTN (plain --quantize 4) | GPTQ (this repo) |
|---|---|
 |  |
At 4 bits, RTN's larger per-weight rounding error is enough to visibly shift the entire generation to a different composition/camera angle for the same prompt+seed, compared to what the model reliably produces at higher precision (see the 8-bit card's matching image). GPTQ-4bit stays much closer to that same composition — measured against the 8-bit GPTQ output as a stable reference, GPTQ-4bit scores modestly higher PSNR (17.1dB vs 16.6dB for RTN-4bit), but the qualitative difference (same picture vs. a different picture) is the more telling signal here than the pixel-level number. Judge for yourself from the images — quantization-induced composition drift is inherently a qualitative failure mode, not just a noise-level one.
This is a genuinely aggressive quantization (4 bits + a 64-wide group scale, ~4.25 bits/weight effective) — expect visibly softer detail than 8-bit or full precision even with GPTQ's correction. Use this where memory is the binding constraint, not where you need the sharpest possible output.
If +0.8GB is affordable, z-image-turbo-gptq-mlx-mixed (attention at 8-bit, feed-forward at 4-bit, 6.3GB) noticeably better preserves the model's generation composition than this uniform 4-bit release — worth checking if this one's outputs feel unstable for your prompts.
Usage
pip install mflux
mflux-generate-z-image-turbo \
--model /path/to/local/checkout/of/this/repo \
--base-model z-image-turbo \
--prompt "your prompt" --steps 9
Or use it directly from LLMTray's
built-in image generation (the model chooses to call generate_image
mid-conversation; LLMTray drives mflux under the hood) — point LLMTray's
image-model setting at this repo.
Size
~5.5GB total (transformer + text encoder + VAE + tokenizer), same as plain
mflux-save --quantize 4 — GPTQ's win here is quality-at-this-size, not a
smaller file. Peak MLX memory during generation: ~5.6GB (vs. ~8.7GB for the
8-bit release).
The IPSupport local-AI stack
Local-first AI tools for macOS by IPSupport — nothing leaves your Mac.
- LLMTray — your local AI workstation for macOS: chat with local LLMs, generate and edit images, make music, run agents, and serve an OpenAI-compatible API. Downloads models from Hugging Face in-app, with per-model profiles.
- IPSupport Code — your AI coding agent for real repositories: analyze, fix, test, report.
LLMTray uses this model for in-chat image generation (it drives mflux under the hood).
License
Licensed under the Apache License 2.0, the same license as the base model — see LICENSE.
Modified from Tongyi-MAI/Z-Image-Turbo: the attention and feed-forward linears of all 30 transformer blocks were GPTQ-corrected and quantized to 4-bit (group size 64), and the model was saved in mflux's MLX checkpoint format (same layout as mflux-save --quantize 4). The weights and configuration files in this repo are therefore modified versions of the original, not the original files.