# Omni-Section: Z-Axis Geometric Reasoning LoRA

## 🚀 Overview
**Omni-Section** is a commercial-grade Low-Rank Adaptation (LoRA) fine-tuned on the **Qwen-Image 2.0** architecture. It introduces **Z-axis geometric reasoning** to the generative pipeline, enabling the instant generation of precise 3D spatial disaggregation (exploded views) of complex objects.

Unlike standard diffusion fine-tunes that treat "exploded views" as stylistic noise, Omni-Section enforces **structural coherence**, isolating individual components while maintaining photorealistic material fidelity.

## 📈 Impact & Utility
*   **Rapid Prototyping:** Reduces 3D modeling/rendering workflows from hours to seconds.
*   **Industrial Design:** Provides alpha-ready, text-free 8K assets for product manuals, technical brochures, and marketing visualization.
*   **Geometric Intelligence:** Bridges the gap between aesthetic generation and structural logic, proving Qwen-Image 2.0's latent space is capable of complex mechanical understanding.

## 🛠 Training Methodology
*   **Base Model:** Qwen-Image 2.0
*   **Dataset:** 16 high-resolution, text-free, 3D industrial exploded view renders.
*   **Resolution:** 1024x1024 (native training resolution).
*   **Hyperparameters:** Rank 64 / Alpha 64 / 3,200 Steps.
*   **Data Purity:** Dataset was scrubbed of all typographic annotations to force the model to focus 100% of its capacity on spatial geometry and material texture.

## 📊 Performance Verification (A/B Test)
| Prompt | Base Qwen-Image | Omni-Section LoRA |
| :--- | :--- | :--- |
| **Nvidia RTX 6090** | Generic aesthetic render | Structurally coherent 3D diagram |
| **Modular House** | Non-functional house | Functional, assembly-ready modular render |

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## 🔗 How to Use
Use the trigger word: `omni-section style`
**Recommended Settings:**
- **LoRA Weight:** 0.85
- **CFG Scale:** 7.0
- **Negative Prompt:** `text, words, labels, diagram lines, pointers, watermark, messy, merged components`

## 📁 Dataset
You can explore the clean, text-free dataset used to train this model

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*Built for the Qwen-Image LoRA Training Competition (Track 1: AI for Production).*


Omni-Section-V2-Clean

Files with SHA256: 75bc9a9842ac2e145af777c8a6030cbbf2050b5cb81bdd95137639a607b9425c