LLM-guided Instance-level Image Manipulation with Diffusion U-Net Cross-Attention Maps

Palaev, Andrey; Khan, Adil; Kazmi, Ahsan

Abstract

The advancement of text-to-image synthesis has introduced powerful generative models capable of creating realistic images from textual prompts. However, precise control over image attributes remains challenging, especially at the instance level. While existing methods offer some control through fine-tuning or auxiliary information, they often face limitations in flexibility and accuracy. To address these challenges, we propose a pipeline leveraging Large Language Models (LLMs), open-vocabulary detectors and cross-attention maps and intermediate activations of diffusion U-Net for instance-level image manipulation. Our method detects objects mentioned in the prompt and present in the generated image, enabling precise manipulation without extensive training or input masks. By incorporating cross-attention maps, our approach ensures coherence in manipulated images while controlling object positions. Our approach enables precise manipulations at the instance level without fine-tuning or auxiliary information such as masks or bounding boxes.

Citation

Palaev, A., Khan, A., & Kazmi, A. (2024, November). LLM-guided Instance-level Image Manipulation with Diffusion U-Net Cross-Attention Maps. Paper presented at The 35th British Machine Vision Conference, Glasgow

• Presentation Conference Type: Conference Paper (unpublished)
• Conference Name: The 35th British Machine Vision Conference
• Start Date: Nov 25, 2024
• End Date: Nov 28, 2024
• Acceptance Date: Jul 20, 2024
• Deposit Date: Aug 26, 2024
• Publicly Available Date: Dec 17, 2024
• Peer Reviewed: Peer Reviewed
• Public URL: https://hull-repository.worktribe.com/output/4791256
• Publisher URL: https://bmvc2024.org/proceedings/457/