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Model: BennyDaBall/Z-Image-Engineer-V6-GGUF Source: Original Platform
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README.md
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README.md
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---
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license: apache-2.0
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language:
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- en
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base_model:
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- Tongyi-MAI/Z-Image-Turbo
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library_name: gguf
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pipeline_tag: text-generation
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tags:
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- text-generation
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- prompt-engineering
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- image-generation
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- z-image
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- z-image-turbo
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- qwen3
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- gguf
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- text-encoder
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- comfyui
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- lm-studio
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- conversational
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---
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# Z-Image-Engineer V6 GGUF
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**Follow me on X [@BennyDaBall_OG](https://x.com/BennyDaBall_OG) !**
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GGUF quantized release for [Z-Image-Engineer V6](https://huggingface.co/BennyDaBall/Z-Image-Engineer-V6).
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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.
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---
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## What is this?
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**Z-Image-Engineer V6** is a SMART DoRA fine-tuned 4B Qwen text encoder from `Tongyi-MAI/Z-Image-Turbo`.
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Use these GGUF files when you want:
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- 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)
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- LM Studio prompt enhancement
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- ComfyUI text-encoder loading through plain `CLIPLoaderGGUF`
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- smaller local files than the merged HF safetensors
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- the same V6 prompt style and conditioning behavior in a quantized format
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---
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## Quantization Ladder
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| Filename | Size | Target Use Case |
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|---|---:|---|
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| `Z-Image-Engineer-V6-F16.gguf` | 7.498 GiB | Full precision reference. |
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| `Z-Image-Engineer-V6-Q8_0.gguf` | 3.986 GiB | Near-lossless; used for local A/B testing. |
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| `Z-Image-Engineer-V6-Q6_K.gguf` | 3.079 GiB | High-fidelity balanced footprint. |
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| `Z-Image-Engineer-V6-Q5_K_M.gguf` | 2.697 GiB | Daily-driver performance-to-size ratio. |
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| `Z-Image-Engineer-V6-Q4_K_M.gguf` | 2.331 GiB | Reliable 4-bit standard. |
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| `Z-Image-Engineer-V6-Q3_K_M.gguf` | 1.933 GiB | Lightweight option for tighter setups. |
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| `Z-Image-Engineer-V6-MXFP4.gguf` | 2.101 GiB | Alternative compact quantization. |
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Full recursive validation hashes are in `HASHES.sha256`.
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---
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## Quick Start
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### LM Studio
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Download a GGUF quant, load it, and prompt it directly:
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```text
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Enhance this image prompt for Z-Image Turbo: a unicorn
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```
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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.
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### ComfyUI (recommended: ComfyUI-Z-Engineer)
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1. Install the [ComfyUI-Z-Engineer](https://github.com/BennyDaBall930/ComfyUI-Z-Engineer) custom node (v2.0+).
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2. Place a GGUF file into `ComfyUI/models/text_encoders/`.
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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.
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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.
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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.
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Alternative without the node: add a plain `CLIPLoaderGGUF` node (ComfyUI-GGUF), set model type to `lumina2`, and use it as the text encoder only.
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Verified image settings:
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```text
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UNET: z_image_turbo_bf16.safetensors
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VAE: ae.safetensors
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Text Encoder: Z-Image-Engineer-V6-Q8_0.gguf
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Resolution: 1024x1024
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Steps: 8
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CFG: 1.0
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Sampler: res_multistep
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Scheduler: simple
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Shift: 3.0
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```
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---
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## SMART DoRA
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V6 was trained with BennyDaBall's SMART DoRA system:
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- **DoRA** for direction/magnitude-separated adapter updates.
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- **Entropic regularization** for less repetition and broader output variety.
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- **Holographic regularization** for cleaner depth-wise feature structure.
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- **Topological regularization** for more coherent latent trajectories.
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- **Manifold regularization** for stable weight behavior during refinement.
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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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---
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## Related Repos
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- Main merged HF release: [BennyDaBall/Z-Image-Engineer-V6](https://huggingface.co/BennyDaBall/Z-Image-Engineer-V6)
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- ComfyUI custom node (GGUF + shard loaders, local prompt enhancer with preview): [ComfyUI-Z-Engineer](https://github.com/BennyDaBall930/ComfyUI-Z-Engineer)
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---
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## Acknowledgements
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- **Tongyi-MAI** for the Z-Image Turbo ecosystem.
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- **Qwen** for the adaptable text encoder backbone.
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- The open-source maintainers behind **LM Studio**, **ComfyUI**, **llama.cpp**, **PEFT**, and **Transformers**.
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**Built & trained locally with care by BennyDaBall.**
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**Follow me on X [@BennyDaBall_OG](https://x.com/BennyDaBall_OG) !**
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