title: RealCompo: Dynamic Equilibrium between Realism and Compositionality
    Improves Text-to-Image Diffusion Models
summary: Diffusion models have achieved remarkable advancements in text-to-image
  generation. However, existing models still have many difficulties when faced
  with multiple-object compositional generation. In this paper, we propose a new
  training-free and transferred-friendly text-to-image generation framework,
  namely RealCompo, which aims to leverage the advantages of text-to-image and
  layout-to-image models to enhance both realism and compositionality of the
  generated images. An intuitive and novel balancer is proposed to dynamically
  balance the strengths of the two models in denoising process, allowing
  plug-and-play use of any model without extra training. Extensive experiments
  show that our RealCompo consistently outperforms state-of-the-art text-to-image
  models and layout-to-image models in multiple-object compositional generation
  while keeping satisfactory realism and compositionality of the generated
  images. Code is available at https://github.com/YangLing0818/RealCompo
paper_authors._id: 65d56827543d28ba7d595852
paper_authors.name: Xinchen Zhang
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paper_authors.fullname: Xinchen Zhang
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paper_authors._id: 65d56827543d28ba7d595853
paper_authors.name: Ling Yang
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paper_authors.fullname: Ling Yang
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paper_authors.user: Lingaaaaaaa
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paper_authors.name: Yaqi Cai
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paper_authors.fullname: YaQi Cai
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paper_authors.name: Zhaochen Yu
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paper_authors.name: Jiake Xie
paper_authors._id: 65d56827543d28ba7d595857
paper_authors.name: Ye Tian
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paper_authors.fullname: tyfeld
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paper_authors._id: 65d56827543d28ba7d595858
paper_authors.name: Minkai Xu
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paper_authors._id: 64c0e950aa57599de1c75dad
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paper_authors.fullname: Minkai Xu
paper_authors.type: user
paper_authors.user: mkxu
paper_authors._id: 65d56827543d28ba7d595859
paper_authors.name: Yong Tang
paper_authors._id: 65d56827543d28ba7d59585a
paper_authors.name: Yujiu Yang
paper_authors.status: admin_assigned
paper_authors.statusLastChangedAt: 2024-02-21T12:20:56.658Z
paper_authors._id: 64ca1fe838837b12d5e529b7
paper_authors.avatarUrl: /avatars/44a3ad9e59318784ac531993b5f69f6b.svg
paper_authors.fullname: Yujiu Yang
paper_authors.type: user
paper_authors.user: Thu-redrobot
paper_authors._id: 65d56827543d28ba7d59585b
paper_authors.name: Bin Cui
paper_title: RealCompo: Dynamic Equilibrium between Realism and Compositionality
    Improves Text-to-Image Diffusion Models
paper_summary: Diffusion models have achieved remarkable advancements in text-to-image
  generation. However, existing models still have many difficulties when faced
  with multiple-object compositional generation. In this paper, we propose a new
  training-free and transferred-friendly text-to-image generation framework,
  namely RealCompo, which aims to leverage the advantages of text-to-image and
  layout-to-image models to enhance both realism and compositionality of the
  generated images. An intuitive and novel balancer is proposed to dynamically
  balance the strengths of the two models in denoising process, allowing
  plug-and-play use of any model without extra training. Extensive experiments
  show that our RealCompo consistently outperforms state-of-the-art text-to-image
  models and layout-to-image models in multiple-object compositional generation
  while keeping satisfactory realism and compositionality of the generated
  images. Code is available at https://github.com/YangLing0818/RealCompo
paper_ai_summary: RealCompo is a training-free text-to-image generation framework that enhances realism and compositionality by dynamically balancing text-to-image and layout-to-image models.
paper_ai_keywords: diffusion models text-to-image generation layout-to-image models multiple-object compositional generation balancer denoising process