apple/ml-mgie

Health v2 · maintenance only

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3872 stars · 250 forks observed · 2026-10-07

What it is AI-extracted, prompt v1, taxonomy v1, 2026-08-29, confidence not recorded

MGIE (MLLM-Guided Image Editing) is Apple's research implementation of instruction-based image editing guided by multimodal large language models, from an ICLR 2024 Spotlight paper. It uses an MLLM (LLaVA-based) to derive expressive editing instructions from brief natural-language commands and performs image manipulation end-to-end with diffusion models.

Use cases

• edit images with natural language instructions
• research multimodal LLM guided image manipulation
• reproduce MGIE paper results
• build instruction-based photo editing demos
• explore MLLM visual-aware response generation

When to choose

• you need research-grade instruction-based image editing with MLLM guidance
• you want to build on or study the MGIE ICLR'24 method
• you have GPU resources and want a LLaVA + diffusion editing pipeline

When to avoid

• you need a production-ready or actively maintained image editor
• you lack a CUDA GPU or want lightweight CPU inference
• you need a permissively licensed model for commercial use

Facets

library · maturity experimental

image-processing machine-learning deep-learning llm-inference computer-vision image-processing large-language-models artificial-intelligence deep-learning python instruction-based-image-editing multimodal-llm diffusion iclr-2024 research-code apple gpu linux

Member repositories

For agents

markdown · JSON · MCP: product_card(name="apple-aiml-research/ml-mgie")