91 lines
4.9 KiB
Markdown
91 lines
4.9 KiB
Markdown
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---
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license: apache-2.0
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base_model: Tongyi-MAI/Z-Image-Turbo
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tags:
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- image-generation
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- prompt-engineering
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- qwen
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- photography
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new_version: BennyDaBall/Qwen3-4b-Z-Image-Engineer-V4
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---
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# 🚀 Z-Engineer V2.5 (4B)
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**Follow me on X [@BennyDaBall_OG](https://x.com/BennyDaBall_OG) !**
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**The "Z-Engineer" is back — longer, deeper, and smarter.**
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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.
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## 🧠 What is this?
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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:
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* **Expanding Concepts**: Turn "dog on a bike" into a cinematic narrative.
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* **Technical Precision**: It understands lenses (35mm vs 85mm), lighting (rembrandt, volumetric), and film stocks.
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* **Stylistic Consistency**: It avoids the robotic "AI feel" and writes with a distinct, creative voice.
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## 🔑 Key Use Cases
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* **✨ Prompt Enhancement**: A lightweight, low-VRAM solution to create, edit, and enrich simple image ideas into detailed narratives.
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* **🔌 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.
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* **🛡️ Local & Private**: Runs entirely on your machine. No API fees, no data logging, no censorship.
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* **⚡ Hybrid Power**: Use it to expand a prompt, then use the model itself as the encoder for the generation stage.
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## 📉 Key Improvements
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* **Base Model Upgrade**: Switched from standard Qwen3 Instruct to the native text encoder from **Z-Image-Turbo** for perfect alignment.
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* **All-Layer Training**: Unlike typical lightweight LoRAs, I trained adapters on **all 36 layers** of the model, ensuring deep behavioral alignment.
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* **Massive Iteration Count**: Trained for **10,000 iterations** to fully saturate the weights with the dataset concepts.
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## 📊 CLIP Model Comparison
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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:
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| Model | Result |
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| :--- | :--- |
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| **Z-Engineer V2.5** | ✅ Clean, natural output with excellent detail and coherence. |
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| **Z-Engineer V2** | ✅ Good quality, but V2.5 shows improved texture and lighting. |
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| **Z-Engineer V1** | ❌ **Broken**: Produces severe visual artifacts and distortions. |
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| **Base Qwen3 4B** | ⚠️ Functional but generic; lacks the specialized prompt understanding. |
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### Visual Comparison
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*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.*
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## 🔌 ComfyUI Integration (Recommended)
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I have released a custom node for seamless integration with ComfyUI!
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* **Features**: Optimized for local OpenAI API compatible backends (LM Studio, Ollama, etc.).
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* **Get it here**: [ComfyUI-Z-Engineer](https://github.com/BennyDaBall930/ComfyUI-Z-Engineer)
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## 💻 Training Facts
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I believe in open science. Here is exactly how this was built:
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* **Hardware**: Trained locally on a Mac with **48GB Unified Memory** (Apple Silicon).
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* **Framework**: **MLX** (Apple's native machine learning framework).
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* **Dataset**: Generated locally using [Qwen3 VL 30B A3B Instruct](https://huggingface.co/Qwen/Qwen3-VL-30B-A3B-Instruct)
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* **Size**: ~34,678 high-quality examples.
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* **Content**: A curated mix of "Prompt Enhancement" pairs, teaching the model how to take a seed idea and "engineer" it into a final prompt.
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* **Hyperparameters**:
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* **Iterations**: 10,000
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* **Batch Size**: 4
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* **LoRA Layers**: 36 (All Linear Layers)
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* **Learning Rate**: 1e-5
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## 📦 GGUF & Quantization
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I provide a full suite of GGUF quantizations for use with `llama.cpp`, Ollama, and LM Studio.
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| Quantization | Size | Use Case |
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| :--- | :--- | :--- |
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| **Q4_K_S** | **2.2 GB** | 🔻 Max Compression |
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| **Q4_K_M** | **2.3 GB** | ⚡️ Fast / Mobile / Edge |
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| **Q5_K_M** | **2.7 GB** | ⚖️ **Recommended Balance** |
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| **Q6_K** | **3.1 GB** | 💎 High Quality |
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| **Q8_0** | **4.0 GB** | 🎬 Near-Lossless |
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| **F16** | **7.5 GB** | 🧪 Reference / Conversion |
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## ⚠️ Disclaimer
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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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**Follow me on X [@BennyDaBall_OG](https://x.com/BennyDaBall_OG) !**
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