Introducing Apple ML-MGIE

Guiding Instruction-based Image Editing via Multimodal Large Language Models

Introducing Apple ML-MGIE


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 visual imagination and performs manipulation through end-to-end training. We evaluate various aspects of Photoshop-style modification, global photo optimization, and local editing. Extensive experimental results demonstrate that expressive instructions are crucial to instruction-based image editing, and our MGIE can lead to a notable improvement in automatic metrics and human evaluation while maintaining competitive inference efficiency.

Classic Examples of Apple ML-MGIE

Input Instruction InsPix2Pix LGIE MGIE GroundTruth
turn the day into night
make the forest path into a beach
make the frame red
as if the shop was a library
make it the vatican
turn the sunset into a firestorm