One-Prompt-One-Story: Free-Lunch Consistent Text-to-Image Generation
  Using a Single Prompt
Authors: name: Tao Liu, name: Kai Wang, name: Senmao Li, name: Joost van de Weijer, name: Fahad Shahbaz Khan, name: Shiqi Yang, name: Yaxing Wang, name: Jian Yang, name: Ming-Ming Cheng

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.