Guiding Instruction-based Image Editing via Multimodal Large Language Models

Instruction-based image editing improves the controllability and flexibility of image manipulation via natural commands without elaborate descriptions or regional masks. However, human instructions are sometimes too brief for current methods to capture and follow. Multimodal large language models (MLLMs) show promising capabilities in cross-modal understanding and visual-aware response generation via LMs. We investigate how MLLMs facilitate edit instructions and present MLLM-Guided Image Editing (MGIE). MGIE learns to derive expressive instructions and provides explicit guidance. The editing model jointly captures this …

capabilities cs.cv current editing flexibility human image language language models large language large language models lms manipulation masks mllms modal multimodal natural regional show understanding via visual