This LoRA is designed to dramatically emphasize and frame the buttocks of female subjects (18+), specifically focusing on the glutes without revealing any genitalia.

Model accentuates the female silhouette with a dramatic, stylized flair, specifically highlighting petite and compact body types.

This model is an Erotic/Suggestive type LoRA, specialized for posing and framing.

Strength:

• Recommended strength ranges from 0.5 to 2.0, depending on the intensity of the effect you desire.

0.5-0.9: You will notice a subtle enhancement and framing of the cheeks.

1.0-1.5: The effect becomes much more pronounced and stylized.

1.6-2.0: Expect a highly dramatic, hyper-stylized presentation.

Analysis:

Each image underwent detailed analysis and captioning, taking approximately 4 minutes per image across 8 distinct sections.

This ensures the model understands everything from object and body descriptions to environment and camera framing.

Captioning Detail:

Captions frequently exceed 300 words to capture every nuance of the composition.

The Shuffle Tokens option (AI Toolkit) was used to improve generalization and prevent overfitting to specific caption structures.

Trigger Words & Recommendations

While the LoRA responds strongly to natural language prompts describing the pose and focus, it works best with concepts like:

Focus: bent over, ass focus, ass cheeks

Support the Creator

If you enjoy my work and want to help cover the electricity costs required to train and refine these models, please consider supporting me:

Address: https://ko-fi.com/weirddreamer (Donations are always appreciated!)

License & Usage

You are free to use this model in your projects, including mixing it up with other models.

If you include this LoRA in any merge or mix, please give proper credit.

Tags

This model was trained exclusively on natural language captions generated by a vision model (Qwen).

No Danbooru-style tags were used in the training data. For best results, use descriptive full sentences in your prompts rather than comma-separated tag lists.