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arXiv · 2312.14216

DreamDistribution: Learning Prompt Distribution for Diverse In-distribution Generation

Brian Nlong Zhao·Yuhang Xiao·Jiashu Xu·Xinyang Jiang·Yifan Yang·Dongsheng Li·Laurent Itti·Vibhav Vineet·Yunhao Ge
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Abstract

The popularization of Text-to-Image (T2I) diffusion models enables the generation of high-quality images from text descriptions. However, generating diverse customized images with reference visual attributes remains challenging. This work focuses on personalizing T2I diffusion models at a more abstract concept or category level, adapting commonalities from a set of reference images while creating new instances with sufficient variations. We introduce a solution that allows a pretrained T2I diffusion model to learn a set of soft prompts, enabling the generation of novel images by sampling prompts from the learned distribution. These prompts offer text-guided editing capabilities and additional flexibility in controlling variation and mixing between multiple distributions. We also show the adaptability of the learned prompt distribution to other tasks, such as text-to-3D. Finally we demonstrate effectiveness of our approach through quantitative analysis including automatic evaluation and human assessment. Project website: https://briannlongzhao.github.io/DreamDistribution

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Brian Nlong Zhao, Yuhang Xiao, Jiashu Xu, Xinyang Jiang, Yifan Yang, Dongsheng Li, Laurent Itti, Vibhav Vineet, Yunhao Ge. 2025-04-21. DreamDistribution: Learning Prompt Distribution for Diverse In-distribution Generation. https://arxiv.org/abs/2312.14216

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