用配色板增强扩散模型的色彩控制，让图像整体配色更精准。
Exploring Palette based Color Guidance in Diffusion Models
- 将配色板作为独立引导机制，与文本提示并行使用。
- 在自建数据集上验证，配色板显著提升整体色彩一致性。
- 适合需要精细调色的设计师和内容创作者。
随着扩散模型的发展，文本到图像（T2I）生成取得了显著进展。现有T2I模型允许用户通过语言颜色名称指定物体颜色，部分方法通过提示学习实现颜色与物体的个性化关联。然而，现有模型难以全面控制图像整体的配色方案，尤其对背景元素及未在提示中明确提及的次要物体。本文提出一种新方法，在扩散图像着色框架中引入配色板作为独立引导机制，探索多种配色表示方式的有效性。为此，我们构建了专用的配色板-文本-图像数据集，并进行了广泛的定量与定性分析。结果表明，引入配色板引导能显著提升模型生成预期配色方案的能力，实现更受控、更精细的着色过程。
原文摘要 · Abstract (English)
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.
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