Apple Free ML-MGIE Online🔥

Guiding Instruction-based Image Editing via Multimodal Large Language Models

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

Apple ML-MGIE Operating Principles and Characteristics

What is Apple ML-MGIE and How Does It Work?

Definition and Overview

Apple ML-MGIE, or Multimodal Large Language Models Guided Instruction-based Image Editing, is an advanced technology developed by Apple. It leverages the power of multimodal large language models (MLLMs) combined with diffusion models for instruction-based image editing. This technology aims to edit and generate images by understanding textual instructions from users, showcasing the potential of AI in creativity and image processing.

How It Works

ML-MGIE operates by integrating two core technologies: Multimodal Large Language Models (MLLMs) for cross-modal understanding and response generation, and diffusion models for high-quality image generation. This integration bridges MLLMs and diffusion models in image editing, providing superior performance compared to existing technologies like InstructPix2Pix. ML-MGIE uses an algorithm called LLaVA to derive expressive instructions for enhanced instruction-based image editing, enabling it to understand and execute complex image editing tasks from concise human instructions.

Comparison with Existing Technologies

Compared to existing instruction-based image editing technologies, ML-MGIE demonstrates significant advantages. By combining MLLMs with diffusion models, it not only understands more complex instructions but also generates higher quality images. The use of the LLaVA algorithm further enhances its comprehension and execution abilities, surpassing existing technologies in performing complex image editing tasks.

Key Features of Apple ML-MGIE

Visual Perception Response Generation

ML-MGIE can generate responses to visual content through language models, meaning it can understand image content and generate relevant textual descriptions or answer questions related to the image. This capability is particularly useful in providing image descriptions, augmented reality applications, and visual data analysis.

Cross-modal Understanding

ML-MGIE exhibits strong capabilities in cross-modal understanding, linking information across different modalities (e.g., text and image) for comprehensive understanding. For example, it can enhance scene understanding by analyzing image content alongside relevant textual descriptions. This cross-modal comprehension is vital for improving human-computer interaction, enhancing search engine results, and creating more intelligent educational tools.

Guidance for Image Editing

A significant application of ML-MGIE is guiding instruction-based image editing. It can edit images according to user instructions, such as changing the color, shape, or size of objects within an image. This is achieved by integrating multimodal large language models with diffusion models, where ML-MGIE shows superior performance compared to technologies like InstructPix2Pix. This capability can be applied to automated image editing tools, improving the efficiency and accuracy of image editing.


How to Use Apple ML-MGIE?

Currently, the specific usage instructions for Apple ML-MGIE have not been made public. However, based on the available information, users will be able to guide ML-MGIE in image editing by providing natural language instructions. The detailed code and usage methods will be published after the internal review is completed.

Pricing Model of Apple ML-MGIE

Apple ML-MGIE is currently available for a free trial. You can click "try it for free" at the top of this website to access the free trial.

Use Case Examples of Apple ML-MGIE

The primary application scenario for Apple ML-MGIE is image editing. Users can guide ML-MGIE in editing images by providing natural language instructions, such as "change the background color to blue" or "add a smiling face icon at the top right corner of the image."

Advantages and Disadvantages of Apple ML-MGIE

The advantages of Apple ML-MGIE mainly lie in its strong cross-modal understanding, visual perception response generation capabilities, and superior performance in image editing. However, there is currently no clear information available regarding the disadvantages of ML-MGIE.

Alternatives to Apple ML-MGIE

Apple's ML-MGIE is a multimodal large language model for guided image editing that uses LLaVA (Language for Visual Arts) to generate expressive instructions for enhanced instruction-based image editing. It is the first work to combine multimodal large language models with diffusion models for image editing, showcasing superior performance compared to InstructPix2Pix.

For other image models, several are worth recommending and comparing:

  • DALL-E 2: Developed by OpenAI, a text-to-image model that generates high-quality images from textual descriptions. DALL-E 2 excels in creativity and diversity but may not be as direct as ML-MGIE in specific image editing tasks.
  • Stable Diffusion: An open-source AI model that draws images based on textual descriptions. It has advantages in community support and accessibility but may not be as strong in precision and guided editing as models specifically designed for image editing like ML-MGIE.
  • Imagen: A text-to-image model developed by Google, similar to DALL-E 2, focused on generating images from text prompts. Although Imagen excels in image quality, its usability is limited, and it may not be as flexible as ML-MGIE in specific image editing tasks.
  • EfficientNet: An efficient image classification network optimized through NAS (neural architecture search) and model scaling. While EfficientNet excels in image recognition tasks, it is primarily used for classification rather than image editing.