One-Prompt-One-Story: Free-Lunch Consistent Text-to-Image Generation Using a Single Prompt
Liu, Tao Wang, Kai Li, Senmao van de Weijer, Joost Khan, Fahad Yang, Shiqi Wang, Yaxing Yang, Jian Cheng, Ming-Ming
Liu, Tao
Wang, Kai
Li, Senmao
van de Weijer, Joost
Khan, Fahad
Yang, Shiqi
Wang, Yaxing
Yang, Jian
Cheng, Ming-Ming
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Department
Computer Vision
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Conference proceeding
Date
2025
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Language
English
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Abstract
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. © 2025 13th International Conference on Learning Representations, ICLR 2025. All rights reserved.
Citation
T. Liu et al., “One-Prompt-One-Story: Free-Lunch Consistent Text-to-Image Generation Using a Single Prompt,” International Conference on Representation Learning, vol. 2025, pp. 24470–24497, May 2025
Source
13th International Conference on Learning Representations, ICLR 2025
Conference
13th International Conference on Learning Representations, ICLR 2025
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Source
13th International Conference on Learning Representations, ICLR 2025
Publisher
International Conference on Learning Representations, ICLR