About InstructX

InstructX is a unified framework designed for instruction-guided image and video editing. Developed by the Intelligent Creation Lab at Bytedance, it combines the capabilities of multimodal large language models (MLLMs) with diffusion models to enable precise and flexible editing operations across different types of content.

What is InstructX?

InstructX addresses the challenge of making visual edits through natural language instructions. Rather than requiring manual operations with traditional editing tools, users can describe their desired changes in plain text. The framework interprets these instructions and applies the requested edits accurately to both images and videos.

The framework is built on the insight that MLLMs can significantly improve editing tasks when properly integrated with diffusion models. Research conducted during the development of InstructX demonstrated that MLLM-guided approaches outperform diffusion-only methods across all editing categories tested.

Core Technology

At the heart of InstructX is the combination of multimodal large language models and diffusion models. The MLLM component interprets editing instructions and analyzes content to understand context. It then provides guidance to the diffusion model, which performs the actual editing operations.

The architecture uses metaqueries, LoRA fine-tuning, and a small connector to enable efficient communication between components. This design allows the MLLM to actively participate in the editing process rather than simply providing features. The research team tested multiple architectural approaches and found this combination to be most effective for editing tasks.

Key Capabilities

• Instruction-Guided Editing: Accepts natural language instructions for editing operations
• Unified Framework: Handles both images and videos within the same system
• Multiple Editing Operations: Supports object swap, color change, add/remove, style transfer, and reference-based editing
• Zero-Shot Video Editing: Can edit videos by learning from image editing data
• MLLM Guidance: Provides intelligent interpretation and direction throughout the editing process
• VIE-Bench: Includes a comprehensive benchmark with 140 instances across eight editing categories

Research Foundations

The development of InstructX was guided by systematic research exploring fundamental questions about combining MLLMs with diffusion models. The research team investigated whether MLLMs actually improve editing results, which architectural approaches work best, and whether image editing data can enable video editing capabilities.

Their findings showed clear benefits from MLLM integration, identified optimal architectural components, and demonstrated successful capability transfer from image to video editing. These insights inform both the design of InstructX and provide guidance for future research in instruction-guided editing.

VIE-Bench Benchmark

InstructX introduces VIE-Bench (Video Instruction-Based Editing Benchmark), which provides a standardized way to evaluate instruction-based video editing approaches. The benchmark consists of 140 high-quality instances across eight categories: local edits (object swap, color change, add, remove), global edits (style change, tone/weather change), hybrid edits, and reference-based edits.

This benchmark enables objective comparison of different editing frameworks and helps track progress in the field. Each instance in VIE-Bench is designed to test specific editing capabilities and provides a consistent evaluation standard for the research community.

Editing Task Support

InstructX supports a diverse range of editing tasks. Object swap operations replace elements while maintaining scene consistency. Color modifications change the appearance of specific objects or regions. Add and remove operations insert or delete elements naturally. Style transfers transform overall appearance. Tone and weather adjustments modify atmospheric conditions. Hybrid operations combine multiple edits in coordinated ways. Reference-based editing uses example images to guide the process.

Development Team

InstructX was developed by the Intelligent Creation Lab at Bytedance. The research team includes Chong Mou, Qichao Sun, Yanze Wu (corresponding author and project lead), Pengze Zhang, Xinghui Li, Fulong Ye, Songtao Zhao (project lead), and Qian He. Their work combines expertise in computer vision, natural language processing, and machine learning to advance the field of instruction-guided editing.

Applications

InstructX has applications across various domains including content creation, film and video production, advertising and marketing, e-commerce, education, and research. The framework enables efficient editing workflows where natural language instructions can produce high-quality results without extensive manual operations.