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v1.0__noobai-v-pred-1/README.md

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+---
+language:
+- en
+tags:
+- art
+---
+
+# Flat Color - Style
+
+**Creator**: [motimalu](https://civitai.com/user/motimalu)
+**Type**: LORA
+**Base Model**: NoobAI
+**Version**: v1.0 [noobai-v-pred-1]
+**Trigger Words**: `flat color, no lineart`
+
+**Civitai Model ID**: 1132089
+**Civitai Version ID**: 1272715
+
+**Stats (at time of fetch)**:
+*   Downloads: 660
+*   Rating: 0 (0 ratings)
+*   Favorites: N/A
+
+---
+
+## 📄 Description (Parent Model)
+
+Flat Color
+-
+Style
+Trained on images without visible lineart, flat colors, and
+little to no indication of depth.
+ℹ️ LoRA work best when applied to the base models on which they are trained.
+Please read the
+About This Version
+on the appropriate base models
+and workflow/training information.
+This is a small style LoRA I thought would be interesting to try with a v-pred model (noobai v-pred), for the reduced color bleeding and strong blacks in particular.
+The effect is quite nice and easy to evaluate in training, so I've extended the dataset with videos in following versions for text-to-video models like Wan and Hunyuan, and it is what I am generally using to test LoRA training on new models now.
+Recommended prompt structure:
+Positive prompt:
+flat color, no lineart, blending, negative space,
+{{tags}}
+masterpiece, best quality, very aesthetic, newest
+
+## Version Notes (v1.0 [noobai-v-pred-1])
+
+Trained on
+NoobAI-XL (NAI-XL) V-Pred 1.0-Version
+With the kohya_ss dev branch,
+v_paratemization
+,
+zero_terminal_ssr
+enabled, and noise offet set to 0.
+
+---
+
+## Civitai Links
+
+*   **[🔗 View This Version on Civitai →](https://civitai.com/models/1132089?modelVersionId=1272715)**
+*   [View Full Model Page →](https://civitai.com/models/1132089)
+*   [View Creator Profile →](https://civitai.com/user/motimalu)
+
+---
+
+## Preview Images
+
+![Preview Image](./previews/1272715_51114519.jpeg)
+
+---
+
+## File Information
+
+*   **Filename**: `noobai_vpred_1_flat_color_v1.safetensors`
+*   **Size**: 217.89 MB
+*   **Hash (AutoV2)**: `4ADED5080D`
+*   **Hash (SHA256)**: `4ADED5080D1B202A1A033DE9995EFA264E490BAA1AC11170DF689E419CC26986`
+