InstructDiffusion: A Generalist Modeling Interface for Vision Tasks
Zigang Geng Binxin Yang Tiankai Hang Chen Li Shuyang Gu Ting Zhang Jianmin Bao Zheng Zhang Han Hu Dong Chen Baining Guo
We present InstructDiffusion, a unifying and generic framework for aligning
computer vision tasks with human instructions. Unlike existing approaches that
integrate prior knowledge and pre-define the output space (e.g., categories and
coordinates) for each vision task, we cast diverse vision tasks into a
human-intuitive image-manipulating process whose output space is a flexible and
interactive pixel space. Concretely, the model is built upon the diffusion
process and is trained to predict pixels according to user instructions, such
as encircling the man's left shoulder in red or applying a blue mask to the
left car. InstructDiffusion could handle a variety of vision tasks, including
understanding tasks (such as segmentation and keypoint detection) and
generative tasks (such as editing and enhancement). It even exhibits the
ability to handle unseen tasks and outperforms prior methods on novel datasets.
This represents a significant step towards a generalist modeling interface for
vision tasks, advancing artificial general intelligence in the field of
computer vision.