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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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- ai-toolkit |
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- character-style |
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- person |
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- z-image-turbo |
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datasets: |
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- simone-v2 |
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--- |
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# Model Card: Simone-ZIT-v2.2 (Z-Image De-Turbo) |
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This is a specialized character LoRA optimized for **Z-Image De-Turbo (De-Distilled)**. Like the previous versions, it is trained as a **Style**, meaning the character is embedded into the model's weights without a specific trigger word. |
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## Training Philosophy |
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The model focuses on high-fidelity character consistency across various environments while maintaining the speed and quality benefits of the Z-Image Turbo architecture. |
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**Caption Strategy:** |
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* **No Trigger Word:** The character's name and defining features (hair/eye color) were omitted from captions to bake them directly into the subject weight. |
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* **Full Environment Decoupling:** Meticulous tagging of clothing, backgrounds, and lighting ensures the character remains a constant subject while the scene remains flexible. |
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* **Flexible Generation:** The character manifests automatically when describing a "woman" or "subject." |
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## Usage & Prompting |
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Describe a woman and her surroundings. The model will automatically apply the Simone character style. |
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* **Example Prompt:** `A woman wearing a silk blouse and tailored trousers, sitting in a modern sunlit cafe.` |
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* **LoRA Strength:** Recommended 0.7 - 1.0 for character accuracy. |
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* **Compatibility:** Best used with Z-Image Turbo/De-Turbo workflows. |
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## Technical Specifications |
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| Parameter | Value | |
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|---|---| |
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| **Trigger Word** | None (Trained as Style) | |
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| **Model Architecture** | Z-Image De-Turbo (De-Distilled) | |
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| **Rank (Dimension)** | 128 | |
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| **Batch Size** | 4 (Constant throughout training) | |
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| **Precision** | float8 | |
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| **Total Steps** | 4000 | |
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| **Save Frequency** | Every 200 steps | |
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| **Training Hardware** | NVIDIA H200 Tensor Core GPU | |
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| **Training Toolkit** | Ostris - AI Toolkit | |
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--- |
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*Model card generated for the Simone-ZIT series.* |
