Element-level visual manipulation is essential in digital content creation, but current diffusion-based methods lack the precision and flexibility of traditional tools. In this work, we introduce BlobCtrl, a framework that unifies element-level generation and editing using a probabilistic blob-based representation. By employing blobs as visual primitives, our approach effectively decouples and represents spatial location, semantic content, and identity information, enabling precise element-level manipulation. Our key contributions include: 1) a dual-branch diffusion architecture with hierarchical feature fusion for seamless foreground-background integration; 2) a self-supervised training paradigm with tailored data augmentation and score functions; and 3) controllable dropout strategies to balance fidelity and diversity. To support further research, we introduce BlobData for large-scale training and BlobBench for systematic evaluation. Experiments show that BlobCtrl excels in various element-level manipulation tasks while maintaining computational efficiency, offering a practical solution for precise and flexible visual content creation. Project page:this https URL
View on arXiv@article{li2025_2503.13434,
title={ BlobCtrl: A Unified and Flexible Framework for Element-level Image Generation and Editing },
author={ Yaowei Li and Lingen Li and Zhaoyang Zhang and Xiaoyu Li and Guangzhi Wang and Hongxiang Li and Xiaodong Cun and Ying Shan and Yuexian Zou },
journal={arXiv preprint arXiv:2503.13434},
year={ 2025 }
}