Tuesday, February 27, 2024
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Apple releases ‘MGIE’, an AI model for instruction-based image editing

Apple has released "MGIE," an AI model for instruction-based image editing.

Apple has released a new open-source AI model, called “MGIE,” that can edit images based on natural language instructions. MGIE, which stands for MLLM-Guided Image Editing, leverages multimodal large language models (MLLMs) to interpret user commands and perform pixel-level manipulations, according to VentureBeat.

The model can handle various editing aspects, such as Photoshop-style modification, global photo optimization, and local editing. VentureBeat notes that MGIE is the result of a collaboration between Apple and researchers from the University of California, Santa Barbara.

 The model was presented in a paper accepted at the International Conference on Learning Representations (ICLR) 2024. It demonstrates the effectiveness of MGIE in improving automatic metrics and human evaluation, all while maintaining competitive inference efficiency.

Here’s some info from the paper: “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.”

Dennis Sellers
the authorDennis Sellers
Dennis Sellers is the editor/publisher of Apple World Today. He’s been an “Apple journalist” since 1995 (starting with the first big Apple news site, MacCentral). He loves to read, run, play sports, and watch movies.