探索扩散模型中基于调色板的颜色引导
Exploring Palette based Color Guidance in Diffusion Models
AI总结：
本文提出将调色板作为独立引导机制集成至扩散模型，以解决现有文本到图像模型对整体配色方案控制不足的问题，显著提升了图像着色过程的可控性与精细度。
摘要：
随着扩散模型的出现，文本到图像（T2I）生成取得了显著进展。当前的T2I模型允许用户使用语言颜色名称指定对象颜色，一些方法旨在通过提示学习个性化颜色与对象的关联。然而，现有模型难以对整个图像的配色方案提供全面控制，尤其是对于提示中未明确提及的背景元素和次要对象。本文提出一种新方法，通过将调色板作为与提示指令并行的独立引导机制集成，来增强配色方案控制。我们在基于扩散的图像着色框架中，通过探索各种调色板表示方法，研究了调色板引导的有效性。为促进此探索，我们构建了专门的调色板-文本-图像数据集，并进行了广泛的定量和定性分析。结果表明，引入调色板引导显著提升了模型生成所需配色方案图像的能力，实现了更可控、更精细的着色过程。
英文摘要：
With the advent of diffusion models, Text-to-Image (T2I) generation has seen substantial advancements. Current T2I models allow users to specify object colors using linguistic color names, and some methods aim to personalize color-object association through prompt learning. However, existing models struggle to provide comprehensive control over the color schemes of an entire image, especially for background elements and less prominent objects not explicitly mentioned in prompts. This paper proposes a novel approach to enhance color scheme control by integrating color palettes as a separate guidance mechanism alongside prompt instructions. We investigate the effectiveness of palette guidance by exploring various palette representation methods within a diffusion-based image colorization framework. To facilitate this exploration, we construct specialized palette-text-image datasets and conduct extensive quantitative and qualitative analyses. Our results demonstrate that incorporating palette guidance significantly improves the model's ability to generate images with desired color schemes, enabling a more controlled and refined colorization process.
机构、总结、中文摘要均由AI分析而来，可能存在错误，请以论文正文为准。
