Personalized text-to-image (P-T2I) generation aims to create new, text-guided images featuring the personalized subject with a few reference images. However, balancing the trade-off relationship between prompt fidelity and identity preservation remains a critical challenge. To address the issue, we propose a novel P-T2I method called Layout-and-Retouch, consisting of two stages: 1) layout generation and 2) retouch. In the first stage, our step-blended inference utilizes the inherent sample diversity of vanilla T2I models to produce diversified layout images, while also enhancing prompt fidelity. In the second stage, multi-source attention swapping integrates the context image from the first stage with the reference image, leveraging the structure from the context image and extracting visual features from the reference image. This achieves high prompt fidelity while preserving identity characteristics. Through our extensive experiments, we demonstrate that our method generates a wide variety of images with diverse layouts while maintaining the unique identity features of the personalized objects, even with challenging text prompts. This versatility highlights the potential of our framework to handle complex conditions, significantly enhancing the diversity and applicability of personalized image synthesis.
Create Anything Anywhere: Layout-Controllable Personalized Diffusion Model for Multiple Subjects
MoE Key Laboratory of Brain-inspired Intelligent Perception and Cognition, University of Science and Technology of China（脑启发智能感知与认知大学科学与技术大学实验室）；Institute of Artificial Intelligence, Hefei Comprehensive National Science Center（人工智能研究所）
Yo'City: Personalized and Boundless 3D Realistic City Scene Generation via Self-Critic Expansion
School of Computer Science, Peking University（北京大学计算机科学学院）；Beihang University（北航）；Southeast University（东南大学）；ByteDance Seed（字节跳动种子）；Guangdong Laboratory of Artificial Intelligence and Digital Economy (SZ)（广东省人工智能与数字经济实验室）；Beijing Normal University（北京师范大学）；Theta Labs（Theta实验室）
OMG: Occlusion-friendly Personalized Multi-concept Generation in Diffusion Models
Shenzhen Campus of Sun Yat-sen University（中山大学深圳校区）；Tencent AI Lab（腾讯AI实验室）；International Digital Economy Academy（国际数字经济学院）；Nanjing University（南京大学）；Harbin Institute of Technology, Shenzhen（哈尔滨工业大学（深圳））；Shenzhen University（深圳大学）；The Hong Kong University of Science and Technology（香港科技大学）
One Sentence, One Drama: Personalized Short-Form Drama Generation via Multi-Agent Systems
MedVisAI Lab, Lee Kong Chian School of Medicine, Nanyang Technological University（MedVisAI实验室，李光前医学院，南洋理工大学）；National University of Singapore（新加坡国立大学）；Beijing Institute of Technology（北京理工大学）；Tsinghua University（清华大学）；University of Electronic Science and Technology of China（电子科技大学）；Guangming Laboratory（光明实验室）
ProSpect: Prompt Spectrum for Attribute-Aware Personalization of Diffusion Models
Institute of Automation, CAS（中国科学院自动化研究所）；School of Artificial Intelligence, UCAS（中国科学院大学人工智能学院）；Institute of Computing Technology, CAS（中国科学院计算技术研究所）；Kuaishou Technology（快手科技）；National Cheng-Kung University（国立成功大学）；University of Konstanz（康斯坦茨大学）
AI-T2I: Aggregating-and-Isolating Cross-Attention to Diffusion Models for Text-to-Image Synthesis
Institute of Advanced Medicine and Frontier Technology（先进医学与前沿技术研究院）；Hefei University of Technology（合肥工业大学）；Key Laboratory of Knowledge Engineering With Big Data, Ministry of Education（大数据知识工程重点实验室）；Department of Automation, Tsinghua University（清华大学自动化系）
