InstructX: Towards Unified Visual Editing with MLLM Guidance
Authors: name: Chong Mou, name: Qichao Sun, name: Yanze Wu, name: Pengze Zhang, name: Xinghui Li, name: Fulong Ye, name: Songtao Zhao, name: Qian He

With recent advances in Multimodal Large Language Models (MLLMs) showing
strong visual understanding and reasoning, interest is growing in using them to
improve the editing performance of diffusion models. Despite rapid progress,
most studies lack an in-depth analysis of MLLM design choices. Moreover, the
integration of MLLMs and diffusion models remains an open challenge in some
difficult tasks, such as video editing. In this paper, we present InstructX, a
unified framework for image and video editing. Specifically, we conduct a
comprehensive study on integrating MLLMs and diffusion models for
instruction-driven editing across diverse tasks. Building on this study, we
analyze the cooperation and distinction between images and videos in unified
modeling. (1) We show that training on image data can lead to emergent video
editing capabilities without explicit supervision, thereby alleviating the
constraints imposed by scarce video training data. (2) By incorporating
modality-specific MLLM features, our approach effectively unifies image and
video editing tasks within a single model. Extensive experiments demonstrate
that our method can handle a broad range of image and video editing tasks and
achieves state-of-the-art performance.