一提示一故事：单提示自由午餐一致的文本到图像生成

摘要

文本到图像生成模型可以从输入提示中创建高质量的图像。然而，它们在支持故事叙述中保持一致生成保留身份的要求方面存在困难。解决这一问题的现有方法通常需要在大型数据集上进行广泛训练或对原始模型架构进行额外修改。这限制了它们在不同领域和不同扩散模型配置中的适用性。在本文中，我们首先观察到语言模型的固有能力，即上下文一致性，通过单个提示理解身份。受固有上下文一致性的启发，我们提出了一种新颖的无需训练的一致文本到图像（T2I）生成方法，称为“一提示一故事”（1Prompt1Story）。我们的方法1Prompt1Story将所有提示连接成单个输入，供T2I扩散模型使用，最初保留角色身份。然后，我们使用两种新技术：奇异值重新加权和保持身份的交叉注意力来优化生成过程，确保与每帧的输入描述更好地对齐。在实验中，我们将我们的方法与各种现有的一致T2I生成方法进行比较，通过定量指标和定性评估展示其有效性。代码可在https://github.com/byliutao/1Prompt1Story找到。

English

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.

一提示一故事：单提示自由午餐一致的文本到图像生成

One-Prompt-One-Story: Free-Lunch Consistent Text-to-Image Generation Using a Single Prompt

摘要

Summary

Support