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Model: BennyDaBall/Z-Image-Engineer-V6 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: transformers
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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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- 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 (4B)
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**Follow me on X [@BennyDaBall_OG](https://x.com/BennyDaBall_OG) !**
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## Model Metadata
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| Key | Value |
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|---|---|
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| **License** | Apache-2.0 |
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| **Language** | English (`en`) |
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| **Base Model** | `Tongyi-MAI/Z-Image-Turbo` |
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| **Library** | `transformers` |
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| **Pipeline Tag** | `text-generation` |
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| **Format** | HF Safetensors |
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---
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The **Z-Engineer** returns, fully rebuilt around the **SMART DoRA** training system for Z-Image Turbo.
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Yes, we jump from V4 to V6. Unlike the usual guy math, this one actually brought the extra two inches.
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**Z-Image-Engineer V6** is a fine-tuned 4B Qwen text encoder (`Tongyi-MAI/Z-Image-Turbo`) optimized for dual-role performance: a local prompt-enhancement model and a merged HF text encoder for Z-Image workflows. The [ComfyUI-Z-Engineer](https://github.com/BennyDaBall930/ComfyUI-Z-Engineer) node runs both roles fully inside ComfyUI from this release.
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---
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## What is Z-Image-Engineer V6?
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V6 transforms minimal seed prompts into rich, highly structured visual narratives. It adds explicit scene composition, lighting direction, material texture, and depth separation while stripping out empty prompt sludge like *"8k, masterpiece, trending on ArtStation."*
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It can also be used directly as a Z-Image text encoder. This repo contains the merged HF safetensors. The GGUF quantized release lives in the companion repo: [Z-Image-Engineer-V6-GGUF](https://huggingface.co/BennyDaBall/Z-Image-Engineer-V6-GGUF).
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### Key Use Cases
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- **Prompt Enhancement:** Upgrade simple concepts into descriptive, high-fidelity visual prompts locally.
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- **Text Encoder Swap:** Replace the stock Z-Image Qwen text encoder to generate different conditioning from the same seed.
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- **Hybrid Mode:** Use V6 to rewrite your prompt, then use V6 again to encode it. It writes the scene and drives the image model.
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- **Private Local Workflow:** Built for LM Studio, ComfyUI, and `llama.cpp`. No API logs, no external telemetry.
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---
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## Under the Hood: SMART DoRA
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V4 pioneered SMART training. V6 adapts that system into a **Weight-Decomposed Low-Rank Adaptation (DoRA)** framework.
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DoRA provides surgical adapter updates by decoupling directional and magnitude adjustments. SMART adds auxiliary pressure so the model does not collapse into repetitive prompt loops or superficial sentence patterns.
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| Regularizer | What it Does | Why it Matters |
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|---|---|---|
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| **Entropic** | Broadens output probability diversity. | Reduces repetitive loops and generic vocabulary. |
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| **Holographic** | Enforces structured, depth-wise feature logic. | Improves foreground/background hierarchy. |
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| **Topological** | Stabilizes coherent latent trajectories. | Keeps prompts flowing naturally instead of stalling out. |
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| **Manifold** | Regulates overall weight distributions. | Keeps model behavior stable under high-pressure refinement. |
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### The Refinement Pipeline
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V6 was not a simple one-and-done training run. The final architecture is a blended composite:
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1. **Base Pass:** Master-corpus SMART DoRA training on the native Z-Image Turbo text encoder.
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2. **Retention Pass:** Preservation pressure for numbers, color accuracy, text signage, named objects, actions, and spatial tracking.
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3. **SceneClean SFT32:** Supervised refinement to restore the cinematic V4/base-V6 voice.
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4. **AntiRepeat Binary24:** Binary anti-repeat refinement to reduce loops, abrupt fragments, and bad endings.
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5. **Final Blend:** A 25% style-restoration / 75% anti-repeat DoRA adapter blend, balancing vivid descriptions with tighter syntax.
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---
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## Quick Start
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### LM Studio: Prompt Enhancement
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Use this merged HF release directly where supported, or download a GGUF quant from [Z-Image-Engineer-V6-GGUF](https://huggingface.co/BennyDaBall/Z-Image-Engineer-V6-GGUF) for LM Studio. No complex system prompt is required.
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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: Text Encoder + Local Prompt Enhancer
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Use the [ComfyUI-Z-Engineer](https://github.com/BennyDaBall930/ComfyUI-Z-Engineer) custom node (v2.0+). It loads this repo's sharded safetensors release directly and runs V6 as both the Z-Image text encoder and an in-ComfyUI prompt enhancer - no LM Studio or external server required.
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1. Download this repo into `ComfyUI/models/text_encoders/Z-Image-Engineer-V6/` (the three `model-0000X-of-00003.safetensors` shards plus `model.safetensors.index.json`).
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2. Add **Z-Engineer CLIP Loader (Safetensors / Shards)** and pick `Z-Image-Engineer-V6/` from the dropdown.
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3. Wire `clip` into your Z-Image **CLIP Text Encode** - V6 replaces the stock Qwen text encoder.
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4. Optional: add **Z-Engineer Prompt Enhancer (Local)** with the same `clip` to rewrite seed prompts in-process; the enhanced prompt is previewed right on the node.
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A ready-made workflow ships with the node repo: `example_workflows/z_image_turbo_z_engineer.json`.
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Prefer smaller files? Use a quant from [Z-Image-Engineer-V6-GGUF](https://huggingface.co/BennyDaBall/Z-Image-Engineer-V6-GGUF) with the node's **Z-Engineer CLIP Loader (GGUF)** instead.
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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 (this repo's sharded safetensors, or a GGUF quant)
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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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## Training Specifics
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| Parameter | Specification |
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|---|---|
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| **Base Text Encoder** | `Tongyi-MAI/Z-Image-Turbo/text_encoder` |
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| **Tokenizer** | `Tongyi-MAI/Z-Image-Turbo/tokenizer` |
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| **Method** | SMART DoRA / PEFT Adapter Training |
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| **Rank / Alpha / Dropout** | 64 / 64 / 0.03 |
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| **Target Modules** | `q_proj`, `k_proj`, `v_proj`, `o_proj`, `gate_proj`, `down_proj`, `up_proj` |
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| **Refinement Stack** | Supervised Style SFT + Binary Anti-Repeat |
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| **Final Packaging** | Merged HF safetensors |
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---
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## GGUF Quantization Ladder
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The quantized release is separate on purpose:
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[BennyDaBall/Z-Image-Engineer-V6-GGUF](https://huggingface.co/BennyDaBall/Z-Image-Engineer-V6-GGUF)
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That repo contains the full GGUF ladder: F16, Q8_0, Q6_K, Q5_K_M, Q4_K_M, Q3_K_M, and MXFP4.
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---
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## Verification & Proof
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The bundled comparison image is:
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```text
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evidence/gallery_z_image_engineer_v6_simple_ab_with_rewrites_CONTACT.png
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```
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It compares foundational prompts across four isolated control paths:
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1. Stock Encoder + Raw Prompt
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2. V6 Encoder + Raw Prompt
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3. Stock Encoder + V6 LM Studio Rewrite
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4. V6 Encoder + V6 LM Studio Rewrite
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---
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## Disclaimer & Acknowledgements
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This model is a prompt engineer and text encoder. Diffusion is still diffusion; structural expansion improves compositional adherence, but it does not mathematically guarantee a perfect seed every single time. Use creative judgment locally.
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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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- My local power utility provider, for sustaining the research grid.
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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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