title: Generating Multi-Image Synthetic Data for Text-to-Image Customization
summary: Customization of text-to-image models enables users to insert custom concepts
  and generate the concepts in unseen settings. Existing methods either rely on
  costly test-time optimization or train encoders on single-image training
  datasets without multi-image supervision, leading to worse image quality. We
  propose a simple approach that addresses both limitations. We first leverage
  existing text-to-image models and 3D datasets to create a high-quality
  Synthetic Customization Dataset (SynCD) consisting of multiple images of the
  same object in different lighting, backgrounds, and poses. We then propose a
  new encoder architecture based on shared attention mechanisms that better
  incorporate fine-grained visual details from input images. Finally, we propose
  a new inference technique that mitigates overexposure issues during inference
  by normalizing the text and image guidance vectors. Through extensive
  experiments, we show that our model, trained on the synthetic dataset with the
  proposed encoder and inference algorithm, outperforms existing tuning-free
  methods on standard customization benchmarks.
paper_authors._id: 67a2fddb4044bf1c86f765a3
paper_authors.name: Nupur Kumari
paper_authors.status: admin_assigned
paper_authors.statusLastChangedAt: 2025-02-05T13:37:30.825Z
paper_authors._id: 62f6a894c3372328414c7021
paper_authors.avatarUrl: /avatars/e8b10912355712f38f10805c31bea962.svg
paper_authors.fullname: Nupur Kumari
paper_authors.type: user
paper_authors.user: nupurkmr9
paper_authors._id: 67a2fddb4044bf1c86f765a4
paper_authors.name: Xi Yin
paper_authors._id: 67a2fddb4044bf1c86f765a5
paper_authors.name: Jun-Yan Zhu
paper_authors._id: 67a2fddb4044bf1c86f765a6
paper_authors.name: Ishan Misra
paper_authors._id: 67a2fddb4044bf1c86f765a7
paper_authors.name: Samaneh Azadi
paper_title: Generating Multi-Image Synthetic Data for Text-to-Image Customization
paper_summary: Customization of text-to-image models enables users to insert custom concepts
  and generate the concepts in unseen settings. Existing methods either rely on
  costly test-time optimization or train encoders on single-image training
  datasets without multi-image supervision, leading to worse image quality. We
  propose a simple approach that addresses both limitations. We first leverage
  existing text-to-image models and 3D datasets to create a high-quality
  Synthetic Customization Dataset (SynCD) consisting of multiple images of the
  same object in different lighting, backgrounds, and poses. We then propose a
  new encoder architecture based on shared attention mechanisms that better
  incorporate fine-grained visual details from input images. Finally, we propose
  a new inference technique that mitigates overexposure issues during inference
  by normalizing the text and image guidance vectors. Through extensive
  experiments, we show that our model, trained on the synthetic dataset with the
  proposed encoder and inference algorithm, outperforms existing tuning-free
  methods on standard customization benchmarks.
paper_ai_keywords: Contrast custom concepts text-to-image models encoders multi-image supervision image quality high-quality Synthetics Performization Dataset (SynCD) shared attention mechanisms fine-grained visual details inference technique overexposure normalization text and image guidance vectors LLaRS