Z-Image-Engineer-V6-GGUF

Z-Image-Engineer V6 GGUF

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GGUF quantized release for Z-Image-Engineer V6.

The main repo contains the merged HF safetensors. This repo contains the quant ladder for the ComfyUI-Z-Engineer node, LM Studio, ComfyUI CLIPLoaderGGUF, llama.cpp-style loaders, and local prompt-enhancement workflows.

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What is this?

Z-Image-Engineer V6 is a SMART DoRA fine-tuned 4B Qwen text encoder from Tongyi-MAI/Z-Image-Turbo.

Use these GGUF files when you want:

• ComfyUI Z-Image text-encoder replacement and in-ComfyUI prompt enhancement through ComfyUI-Z-Engineer (no external server needed)
• LM Studio prompt enhancement
• ComfyUI text-encoder loading through plain CLIPLoaderGGUF
• smaller local files than the merged HF safetensors
• the same V6 prompt style and conditioning behavior in a quantized format

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Quantization Ladder

Full recursive validation hashes are in HASHES.sha256.

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Quick Start

LM Studio

Download a GGUF quant, load it, and prompt it directly:

Enhance this image prompt for Z-Image Turbo: a unicorn

The comparison examples were generated from direct LM Studio user requests like this, with no separate system prompt. V6_SYSTEM_PROMPT.md is included only as an optional preset for people who want a stricter prompt-only chat setup.

ComfyUI (recommended: ComfyUI-Z-Engineer)

1. Install the ComfyUI-Z-Engineer custom node (v2.0+).
2. Place a GGUF file into ComfyUI/models/text_encoders/.
3. Add Z-Engineer CLIP Loader (GGUF) and pick the quant - use the clip output where the stock Z-Image Qwen text encoder would normally go.
4. Optional: add Z-Engineer Prompt Enhancer (Local) with the same clip to rewrite seed prompts in-process, previewed on the node. No LM Studio or external server required.

A ready-made workflow ships with the node repo: example_workflows/z_image_turbo_z_engineer.json. With ComfyUI-GGUF installed the quant stays quantized in VRAM.

Alternative without the node: add a plain CLIPLoaderGGUF node (ComfyUI-GGUF), set model type to lumina2, and use it as the text encoder only.

Verified image settings:

UNET: z_image_turbo_bf16.safetensors
VAE: ae.safetensors
Text Encoder: Z-Image-Engineer-V6-Q8_0.gguf
Resolution: 1024x1024
Steps: 8
CFG: 1.0
Sampler: res_multistep
Scheduler: simple
Shift: 3.0

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SMART DoRA

V6 was trained with BennyDaBall's SMART DoRA system:

• DoRA for direction/magnitude-separated adapter updates.
• Entropic regularization for less repetition and broader output variety.
• Holographic regularization for cleaner depth-wise feature structure.
• Topological regularization for more coherent latent trajectories.
• Manifold regularization for stable weight behavior during refinement.

The final V6 build used master-corpus SMART DoRA training, retention pressure, SceneClean SFT32 style restoration, AntiRepeat Binary24 refinement, and a 25% style-restoration / 75% anti-repeat DoRA blend.

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Related Repos

• Main merged HF release: BennyDaBall/Z-Image-Engineer-V6
• ComfyUI custom node (GGUF + shard loaders, local prompt enhancer with preview): ComfyUI-Z-Engineer

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Acknowledgements

• Tongyi-MAI for the Z-Image Turbo ecosystem.
• Qwen for the adaptable text encoder backbone.
• The open-source maintainers behind LM Studio, ComfyUI, llama.cpp, PEFT, and Transformers.

Built & trained locally with care by BennyDaBall.

Follow me on X @BennyDaBall_OG !