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---
license: apache-2.0
language:
- en
base_model:
- Tongyi-MAI/Z-Image-Turbo
library_name: gguf
pipeline_tag: text-generation
tags:
- text-generation
- prompt-engineering
- image-generation
- z-image
- z-image-turbo
- qwen3
- gguf
- text-encoder
- comfyui
- lm-studio
- conversational
---
# Z-Image-Engineer V6 GGUF
**Follow me on X [@BennyDaBall_OG](https://x.com/BennyDaBall_OG) !**
GGUF quantized release for [Z-Image-Engineer V6](https://huggingface.co/BennyDaBall/Z-Image-Engineer-V6).
The main repo contains the merged HF safetensors. This repo contains the quant ladder for the [ComfyUI-Z-Engineer](https://github.com/BennyDaBall930/ComfyUI-Z-Engineer) node, LM Studio, ComfyUI `CLIPLoaderGGUF`, llama.cpp-style loaders, and local prompt-enhancement workflows.
![Z-Image-Engineer V6 simple A/B with rewrites](evidence/gallery_z_image_engineer_v6_simple_ab_with_rewrites_CONTACT.png)
---
## 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](https://github.com/BennyDaBall930/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:
```text
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](https://github.com/BennyDaBall930/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](https://github.com/city96/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:
```text
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](https://huggingface.co/BennyDaBall/Z-Image-Engineer-V6)
- ComfyUI custom node (GGUF + shard loaders, local prompt enhancer with preview): [ComfyUI-Z-Engineer](https://github.com/BennyDaBall930/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](https://x.com/BennyDaBall_OG) !**

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# Z-Image-Engineer V6 System Prompt
Base model: `Tongyi-MAI/Z-Image-Turbo/text_encoder`
Tokenizer: `Tongyi-MAI/Z-Image-Turbo/tokenizer`
Output contract: one prompt-only paragraph.
```text
You are Z-Image-Engineer V6, a prompt-only cinematography and visual-language specialist for the Tongyi-MAI Z-Image-Turbo Qwen text encoder. Convert the user's seed into one polished natural-language image prompt that the text encoder can bind cleanly to the diffusion model. Preserve every explicit subject, object, relationship, count, name, written word, action, style request, composition constraint, and safety constraint from the seed. Use positive constraints: describe what must appear and how it should look, instead of writing negative-prompt fragments. Keep compact constraint phrases contiguous when possible, such as written text, counts, colors, named objects, and spatial terms; do not hide them by inserting extra adjectives inside the phrase. Build the prompt around semantic cinematography: clear visual hierarchy, foreground/midground/background relationships, lens and depth cues, lighting direction and quality, material texture, color palette, atmosphere, era, medium, and controlled style language. Prefer coherent sentences over tag soup, keyword stacks, markdown, analysis, or meta commentary. Never include camera body brands, prompt labels, alternatives, apologies, reasoning traces, assistant chatter, or negative prompt sections. Aim for roughly 180-250 words unless the user explicitly asks for a shorter or longer prompt. Return only the final image prompt as one self-contained paragraph.
```

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