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README.md
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---
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license: creativeml-openrail-m
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base_model: black-forest-labs/FLUX.1-dev
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tags:
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- lora
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- flux
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- diffusers
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- ai-toolkit
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- z-image-turbo
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datasets:
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- cowl1
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- cowl2
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instance_prompt: cowl neck
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---
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# Model Card: Flux LoRA - cowl (Z-Image De-Turbo)
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This is a LoRA model trained on a curated dataset of high-quality images (1024x1024) using the [Ostris - AI Toolkit](https://github.com/ostris/ai-toolkit).
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**Architecture:** Specifically optimized for **Z-Image De-Turbo (De-Distilled)**.
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## Training Settings
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| Parameter | Value |
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|---|---|
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| **Trigger Word** | `cowl neck` |
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| **Model Architecture** | Z-Image De-Turbo (De-Distilled) |
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| **Batch Size** | 2 |
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| **Rank (Dimension)** | 48 |
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| **Precision** | float8 (Transformer & Text Encoder) |
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| **Save Frequency** | Every 200 steps |
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| **Total Training Steps** | 4000 |
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## Prompting Strategy & "Mix & Match"
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This LoRA was trained using **granular, tag-based descriptive captions**. This approach "de-couples" specific attributes, allowing you to freely combine features from across the dataset.
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* **Modular Control:** You are not limited to the training images. You can mix attributes—for example, taking a `silk` texture from one style, a `deep drape` from another, and setting the color to `emerald green`.
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* **Tag-Based Precision:** Since every detail was tagged, the model performs best when you describe the specific materials (satin, jersey, wool), finishes, and colors you want to see.
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**Example Prompt:**
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> `cowl neck, emerald green color, satin material, deep draped folds, elegant aesthetic, studio lighting`
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## Usage
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- **Inference:** Use settings compatible with Z-Image Turbo/De-Turbo.
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- **LoRA Strength:** 0.6 - 1.0 (Start at 0.8).
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- **Precision:** Trained in **float8**. Ensure your environment (ComfyUI, Diffusers, etc.) supports this for optimal results.
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## Training Infrastructure
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- **Toolkit:** Ostris AI Toolkit
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- **Dataset Size:** [Indsæt antal] images (1024x1024)
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- **Training Method:** LoRA
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---
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*Model card generated for the cowl series.*
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