mgie

We have hosted the application mgie in order to run this application in our online workstations with Wine or directly.

Quick description about mgie:

MGIE�Guiding Instruction-based Image Editing�demonstrates how a multimodal LLM can parse natural-language editing instructions and then drive image transformations accordingly. The project focuses on making edits explainable and controllable: the model interprets text guidance, reasons over image content, and outputs edits aligned with user intent. It�s positioned as an ICLR 2024 Spotlight work, with code and references that show how to connect language planning to concrete image operations. This bridges a gap between free-form prompts and precise edits by letting users describe �what� and �where� in everyday language. The repo includes instructions, examples, and links that situate MGIE within Apple�s broader line of multimodal research. For practitioners, MGIE provides a blueprint for text-to-edit systems that are more semantically grounded than naive prompt-only pipelines.

Features:

• Natural-language instruction parsing for image editing
• Multimodal reasoning that ties text plans to visual changes
• Examples and demos aligned with the research paper
• Fine-grained, region-aware editing behavior
• Open code for reproducibility and adaptation
• Basis for controllable, explainable image-editing agents

Programming Language: Python.
Categories: