---
license: apache-2.0
base_model: Tongyi-MAI/Z-Image-Turbo
tags:
- image-generation
- prompt-engineering
- qwen
- photography
new_version: BennyDaBall/Qwen3-4b-Z-Image-Engineer-V4
---

# 🚀 Z-Engineer V2.5 (4B)

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**The "Z-Engineer" is back — longer, deeper, and smarter.**

This is **Z-Engineer V2.5**, a specialized 4B parameter model fine-tuned on the **Qwen 3** architecture. It serves as a dedicated **Creative Director** for your image generation workflow, capable of extrapolating complex, cohesive visual narratives from minimal seed concepts. It doesn't just describe a scene; it engineers the light, lens, and atmosphere necessary to render it.

## 🧠 What is this?
Z-Engineer V2.5 is a merged LoRA fine-tuned version of high-performance text encoder from `Tongyi-MAI/Z-Image-Turbo`. It has been trained to specifically understand the nuances of **AI Image Generation** (Z-Image-Turbo, Flux2 Klein). It excels at:
*   **Expanding Concepts**: Turn "dog on a bike" into a cinematic narrative.
*   **Technical Precision**: It understands lenses (35mm vs 85mm), lighting (rembrandt, volumetric), and film stocks.
*   **Stylistic Consistency**: It avoids the robotic "AI feel" and writes with a distinct, creative voice.

## 🔑 Key Use Cases
*   **✨ Prompt Enhancement**: A lightweight, low-VRAM solution to create, edit, and enrich simple image ideas into detailed narratives.
*   **🔌 Z-Image Turbo Encoder**: Fully backwards compatible as a drop-in CLIP text encoder for Z-Image Turbo workflows, producing varied and unique results from the same seed.
*   **🛡️ Local & Private**: Runs entirely on your machine. No API fees, no data logging, no censorship.
*   **⚡ Hybrid Power**: Use it to expand a prompt, then use the model itself as the encoder for the generation stage.

## 📉 Key Improvements
*   **Base Model Upgrade**: Switched from standard Qwen3 Instruct to the native text encoder from **Z-Image-Turbo** for perfect alignment.
*   **All-Layer Training**: Unlike typical lightweight LoRAs, I trained adapters on **all 36 layers** of the model, ensuring deep behavioral alignment.
*   **Massive Iteration Count**: Trained for **10,000 iterations** to fully saturate the weights with the dataset concepts.

## 📊 CLIP Model Comparison
Z-Engineer V2.5 can be used as a **drop-in CLIP text encoder** for Z-Image-Turbo workflows. Here's how it compares to previous versions and the base model:

| Model | Result |
| :--- | :--- |
| **Z-Engineer V2.5** | ✅ Clean, natural output with excellent detail and coherence. |
| **Z-Engineer V2** | ✅ Good quality, but V2.5 shows improved texture and lighting. |
| **Z-Engineer V1** | ❌ **Broken**: Produces severe visual artifacts and distortions. |
| **Base Qwen3 4B** | ⚠️ Functional but generic; lacks the specialized prompt understanding. |

### Visual Comparison

![CLIP Comparison 1](z-image-gguf-comparison_00023_.png)

![CLIP Comparison 2](z-image-gguf-comparison_00026_.png)

*Note: V1 exhibits catastrophic artifacts (bottom-left in each grid) due to training instabilities. V2.5 (top-left) consistently produces the cleanest, most natural results.*

## 🔌 ComfyUI Integration (Recommended)
I have released a custom node for seamless integration with ComfyUI!

*   **Features**: Optimized for local OpenAI API compatible backends (LM Studio, Ollama, etc.).
*   **Get it here**: [ComfyUI-Z-Engineer](https://github.com/BennyDaBall930/ComfyUI-Z-Engineer)

## 💻 Training Facts
I believe in open science. Here is exactly how this was built:

*   **Hardware**: Trained locally on a Mac with **48GB Unified Memory** (Apple Silicon).
*   **Framework**: **MLX** (Apple's native machine learning framework).
*   **Dataset**: Generated locally using [Qwen3 VL 30B A3B Instruct](https://huggingface.co/Qwen/Qwen3-VL-30B-A3B-Instruct)
    *   **Size**: ~34,678 high-quality examples.
    *   **Content**: A curated mix of "Prompt Enhancement" pairs, teaching the model how to take a seed idea and "engineer" it into a final prompt.
*   **Hyperparameters**:
    *   **Iterations**: 10,000
    *   **Batch Size**: 4
    *   **LoRA Layers**: 36 (All Linear Layers)
    *   **Learning Rate**: 1e-5

## 📦 GGUF & Quantization
I provide a full suite of GGUF quantizations for use with `llama.cpp`, Ollama, and LM Studio.

| Quantization | Size | Use Case |
| :--- | :--- | :--- |
| **Q4_K_S** | **2.2 GB** | 🔻 Max Compression |
| **Q4_K_M** | **2.3 GB** | ⚡️ Fast / Mobile / Edge |
| **Q5_K_M** | **2.7 GB** | ⚖️ **Recommended Balance** |
| **Q6_K** | **3.1 GB** | 💎 High Quality |
| **Q8_0** | **4.0 GB** | 🎬 Near-Lossless |
| **F16** | **7.5 GB** | 🧪 Reference / Conversion |

## ⚠️ Disclaimer
This model generates text for image prompts. While I have filtered the dataset, users should use their best judgment. I am not responsible for the content you generate.

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