PAIR Diffusion: A Comprehensive Multimodal Object-Level Image Editor

Vidit Goel, Elia Peruzzo, Yifan Jiang, Dejia Xu, Xingqian Xu, Nicu Sebe, Trevor Darrell, Zhangyang Wang, Humphrey Shi; Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR), 2024, pp. 8609-8618

Generative image editing has recently witnessed extremely fast-paced growth. Some works use high-level conditioning such as text while others use low-level conditioning. Nevertheless most of them lack fine-grained control over the properties of the different objects present in the image i.e. object-level image editing. In this work we tackle the task by perceiving the images as an amalgamation of various objects and aim to control the properties of each object in a fine-grained manner. Out of these properties we identify structure and appearance as the most intuitive to understand and useful for editing purposes. We propose PAIR Diffusion a generic framework that enables a diffusion model to control the structure and appearance properties of each object in the image. We show that having control over the properties of each object in an image leads to comprehensive editing capabilities. Our framework allows for various object-level editing operations on real images such as reference image-based appearance editing free-form shape editing adding objects and variations. Thanks to our design we do not require any inversion step. Additionally we propose multimodal classifier-free guidance which enables editing images using both reference images and text when using our approach with foundational diffusion models. We validate the above claims by extensively evaluating our framework on both unconditional and foundational diffusion models.