One-Prompt-One-Story: Free-Lunch Consistent Text-to-Image Generation
  Using a Single Prompt
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.
6793900eddc6cc37fdc74928
Tao Liu
claimed_verified
2025-01-24T13:30:20.097Z
65a909fe8581aad8c97a67d3
/avatars/96570e47117e957543d9f0fe5e1d9d57.svg
liutao
user
byliutao
6793900eddc6cc37fdc74929
Kai Wang
6793900eddc6cc37fdc7492a
Senmao Li
6793900eddc6cc37fdc7492b
Joost van de Weijer
6793900eddc6cc37fdc7492c
Fahad Shahbaz Khan
6793900eddc6cc37fdc7492d
Shiqi Yang
6793900eddc6cc37fdc7492e
Yaxing Wang
6793900eddc6cc37fdc7492f
Jian Yang
6793900eddc6cc37fdc74930
Ming-Ming Cheng
One-Prompt-One-Story: Free-Lunch Consistent Text-to-Image Generation
  Using a Single Prompt
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.
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.
text-to-image T2I diffusion models context consistency Singular-Value Reweighting Identity-Preserving Cross-Attention