June 11, 2024, 4:51 a.m. | Xiaojie Li, Chenghao Gu, Shuzhao Xie, Yunpeng Bai, Weixiang Zhang, Zhi Wang

cs.CV updates on arXiv.org arxiv.org

arXiv:2406.06258v1 Announce Type: new
Abstract: Fine-Tuning Diffusion Models enable a wide range of personalized generation and editing applications on diverse visual modalities. While Low-Rank Adaptation (LoRA) accelerates the fine-tuning process, it still requires multiple reference images and time-consuming training, which constrains its scalability for large-scale and real-time applications. In this paper, we propose \textit{View Iterative Self-Attention Control (VisCtrl)} to tackle this challenge. Specifically, VisCtrl is a training-free method that injects the appearance and structure of a user-specified subject into another …

abstract applications arxiv attention control cs.cv customization diffusion diffusion models diverse editing fine-tuning free images iterative lora low low-rank adaptation multiple paper personalized process real-time real-time applications reference scalability scale self-attention training type via view visual while

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