4.9 KiB
license, language, base_model, library_name, pipeline_tag, tags
| license | language | base_model | library_name | pipeline_tag | tags | |||||||||||||
|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|
| apache-2.0 |
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|
gguf | text-generation |
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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.
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
Quantization Ladder
| Filename | Size | Target Use Case |
|---|---|---|
Z-Image-Engineer-V6-F16.gguf |
7.498 GiB | Full precision reference. |
Z-Image-Engineer-V6-Q8_0.gguf |
3.986 GiB | Near-lossless; used for local A/B testing. |
Z-Image-Engineer-V6-Q6_K.gguf |
3.079 GiB | High-fidelity balanced footprint. |
Z-Image-Engineer-V6-Q5_K_M.gguf |
2.697 GiB | Daily-driver performance-to-size ratio. |
Z-Image-Engineer-V6-Q4_K_M.gguf |
2.331 GiB | Reliable 4-bit standard. |
Z-Image-Engineer-V6-Q3_K_M.gguf |
1.933 GiB | Lightweight option for tighter setups. |
Z-Image-Engineer-V6-MXFP4.gguf |
2.101 GiB | Alternative compact quantization. |
Full recursive validation hashes are in HASHES.sha256.
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)
- Install the ComfyUI-Z-Engineer custom node (v2.0+).
- Place a GGUF file into
ComfyUI/models/text_encoders/. - Add Z-Engineer CLIP Loader (GGUF) and pick the quant - use the
clipoutput where the stock Z-Image Qwen text encoder would normally go. - Optional: add Z-Engineer Prompt Enhancer (Local) with the same
clipto 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
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.
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
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 !