• Proposes a plug-and-play LoRA module for few-shot fine-tuning, enabling exemplar-driven inpainting with high fidelity and customization.
• Introduces GPT-4V prompting and prior noise initialization to further enhance the fidelity of inpainting outputs.
• Achieves state-of-the-art performance both qualitatively and quantitatively compared to existing methods (Textual Inversion and Paint by Example).
• Provides a practical solution for object insertion from a single exemplar image without requiring large-scale dataset training.