Beyond textual constraints : Learning novel diffusion conditions with fewer examples

• Main Authors: YU, Yuyang, LIU, Bangzhen, ZHENG, Chenxi, XU, Xuemiao, ZHANG, Huaidong, HE, Shengfeng
• Format: text
• Language: English
• Published: Institutional Knowledge at Singapore Management University 2024
• Subjects: Prompt-free conditional learning; Conditional negative rectification; Training; Computer vision; Adaptation models; Codes; Text to image; Diffusion processes; Diffusion model; Image synthesis; Controllable image generation; Artificial Intelligence and Robotics; Graphics and Human Computer Interfaces
• Online Access: https://ink.library.smu.edu.sg/sis_research/9774; https://ink.library.smu.edu.sg/context/sis_research/article/10774/viewcontent/Yu_Beyond_CVPR_2024_paper.pdf
• Institution: Singapore Management University

In this paper, we delve into a novel aspect of learning novel diffusion conditions with datasets an order of magnitude smaller. The rationale behind our approach is the elimination of textual constraints during the few-shot learning process. To that end, we implement two optimization strategies. The...