Text to Image Diffusion Model
Text-to-image diffusion models generate images from textual descriptions, aiming for high-fidelity and precise alignment. Current research focuses on improving controllability, addressing safety concerns (e.g., preventing generation of inappropriate content), and enhancing personalization capabilities through techniques like continual learning and latent space manipulation. These advancements are significant for various applications, including medical imaging, artistic creation, and data augmentation, while also raising important ethical considerations regarding model safety and bias.
Papers
Gen4Gen: Generative Data Pipeline for Generative Multi-Concept Composition
Chun-Hsiao Yeh, Ta-Ying Cheng, He-Yen Hsieh, Chuan-En Lin, Yi Ma, Andrew Markham, Niki Trigoni, H. T. Kung, Yubei Chen
ProTIP: Probabilistic Robustness Verification on Text-to-Image Diffusion Models against Stochastic Perturbation
Yi Zhang, Yun Tang, Wenjie Ruan, Xiaowei Huang, Siddartha Khastgir, Paul Jennings, Xingyu Zhao
Layout-to-Image Generation with Localized Descriptions using ControlNet with Cross-Attention Control
Denis Lukovnikov, Asja Fischer
RealCompo: Balancing Realism and Compositionality Improves Text-to-Image Diffusion Models
Xinchen Zhang, Ling Yang, Yaqi Cai, Zhaochen Yu, Kai-Ni Wang, Jiake Xie, Ye Tian, Minkai Xu, Yong Tang, Yujiu Yang, Bin Cui