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