title: One-Prompt-One-Story: Free-Lunch Consistent Text-to-Image Generation
    Using a Single Prompt
summary: Text-to-image generation models can create high-quality images from input
  prompts. However, they struggle to support the consistent generation of
  identity-preserving requirements for storytelling. Existing approaches to this
  problem typically require extensive training in large datasets or additional
  modifications to the original model architectures. This limits their
  applicability across different domains and diverse diffusion model
  configurations. In this paper, we first observe the inherent capability of
  language models, coined context consistency, to comprehend identity through
  context with a single prompt. Drawing inspiration from the inherent context
  consistency, we propose a novel training-free method for consistent
  text-to-image (T2I) generation, termed "One-Prompt-One-Story" (1Prompt1Story).
  Our approach 1Prompt1Story concatenates all prompts into a single input for T2I
  diffusion models, initially preserving character identities. We then refine the
  generation process using two novel techniques: Singular-Value Reweighting and
  Identity-Preserving Cross-Attention, ensuring better alignment with the input
  description for each frame. In our experiments, we compare our method against
  various existing consistent T2I generation approaches to demonstrate its
  effectiveness through quantitative metrics and qualitative assessments. Code is
  available at https://github.com/byliutao/1Prompt1Story.
paper_authors._id: 6793900eddc6cc37fdc74928
paper_authors.name: Tao Liu
paper_authors.status: claimed_verified
paper_authors.statusLastChangedAt: 2025-01-24T13:30:20.097Z
paper_authors._id: 65a909fe8581aad8c97a67d3
paper_authors.avatarUrl: /avatars/96570e47117e957543d9f0fe5e1d9d57.svg
paper_authors.fullname: liutao
paper_authors.type: user
paper_authors.user: byliutao
paper_authors._id: 6793900eddc6cc37fdc74929
paper_authors.name: Kai Wang
paper_authors._id: 6793900eddc6cc37fdc7492a
paper_authors.name: Senmao Li
paper_authors._id: 6793900eddc6cc37fdc7492b
paper_authors.name: Joost van de Weijer
paper_authors._id: 6793900eddc6cc37fdc7492c
paper_authors.name: Fahad Shahbaz Khan
paper_authors._id: 6793900eddc6cc37fdc7492d
paper_authors.name: Shiqi Yang
paper_authors._id: 6793900eddc6cc37fdc7492e
paper_authors.name: Yaxing Wang
paper_authors._id: 6793900eddc6cc37fdc7492f
paper_authors.name: Jian Yang
paper_authors._id: 6793900eddc6cc37fdc74930
paper_authors.name: Ming-Ming Cheng
paper_title: One-Prompt-One-Story: Free-Lunch Consistent Text-to-Image Generation
    Using a Single Prompt
paper_summary: Text-to-image generation models can create high-quality images from input
  prompts. However, they struggle to support the consistent generation of
  identity-preserving requirements for storytelling. Existing approaches to this
  problem typically require extensive training in large datasets or additional
  modifications to the original model architectures. This limits their
  applicability across different domains and diverse diffusion model
  configurations. In this paper, we first observe the inherent capability of
  language models, coined context consistency, to comprehend identity through
  context with a single prompt. Drawing inspiration from the inherent context
  consistency, we propose a novel training-free method for consistent
  text-to-image (T2I) generation, termed "One-Prompt-One-Story" (1Prompt1Story).
  Our approach 1Prompt1Story concatenates all prompts into a single input for T2I
  diffusion models, initially preserving character identities. We then refine the
  generation process using two novel techniques: Singular-Value Reweighting and
  Identity-Preserving Cross-Attention, ensuring better alignment with the input
  description for each frame. In our experiments, we compare our method against
  various existing consistent T2I generation approaches to demonstrate its
  effectiveness through quantitative metrics and qualitative assessments. Code is
  available at https://github.com/byliutao/1Prompt1Story.
paper_ai_summary: A novel training-free method, "One-Prompt-One-Story," enhances consistent identity-preserving text-to-image generation using Singular-Value Reweighting and Identity-Preserving Cross-Attention.
paper_ai_keywords: text-to-image T2I diffusion models context consistency Singular-Value Reweighting Identity-Preserving Cross-Attention