The state-of-the-art methods for story visualization demonstrate a significant demand for training data and storage, as well as limited flexibility in story presentation, thereby rendering them impractical for real-world applications. We introduce CogCartoon, a practical story visualization method based on pre-trained diffusion models. To alleviate dependence on data and storage, we propose an innovative strategy of character-plugin generation that can represent a specific character as a compact 316 KB plugin by using a few training samples. To facilitate enhanced flexibility, we employ a strategy of plugin-guided and layout-guided inference, enabling users to seamlessly incorporate new characters and custom layouts into the generated image results at their convenience. We have conducted comprehensive qualitative and quantitative studies, providing compelling evidence for the superiority of CogCartoon over existing methodologies. Moreover, CogCartoon demonstrates its power in tackling challenging tasks, including long story visualization and realistic style story visualization.

最先进的故事可视化方法表明，对训练数据和存储有很大的需求，并且故事呈现的灵活性有限，因此它们对于实际应用程序不切实际。我们介绍了 CogCartoon，这是一种基于预先训练的扩散模型的实用故事可视化方法。为了减轻对数据和存储的依赖，我们提出了一种创新的字符插件生成策略，该策略可以使用一些训练样本将特定字符表示为紧凑的 316 KB 插件。为了提高灵活性，我们采用了插件引导和布局引导的推理策略，使用户能够在方便时无缝地将新字符和自定义布局合并到生成的图像结果中。我们进行了全面的定性和定量研究，为 CogCartoon 优于现有方法提供了令人信服的证据。此外，CogCartoon 展示了它在处理具有挑战性的任务方面的能力，包括长篇故事可视化和现实风格的故事可视化。