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Paint-Anything:用于图像生成和编辑的统一任意颜色控制

原文标题 · Paint-Anything: Unified Any-Color Control for Image Generation and Editing

作者:谢继、周德伟、黄新宇|专业的设计需要任意颜色控制:能够指定对象的目标颜色与任何24位十六进制值的图像生成和编辑。之前的工作探索了颜色生成、编辑和着色,但通常依赖于专用的颜色表示或专门的推理过程。大型语言模型的进步提供了一个更简单的起点:即使是紧凑的模型

来源:arXiv cs.AI/CL/LG · arxiv.org

正文为英文,可一键机器翻译(仅首次需要等待)

Computer Science > Computer Vision and Pattern Recognition

Title: Paint-Anything: Unified Any-Color Control for Image Generation and Editing

Abstract: Professional design requires any-color control: the ability to specify an object's target color with any 24-bit hex value for image generation and editing. Prior work has explored color generation, editing, and colorization, but often relies on dedicated color representations or specialized inference procedures. Advances in large language models offer a simpler starting point: even compact models can associate hex values with color semantics. We present Paint-Anything, which learns a shared hex-prompt interface for generation and editing through object-level color supervision. We develop a data pipeline that constructs Paint-500K from real images through object grounding, perceptual color labeling, and editing-pair synthesis. Since shadows make real-image labels only approximate colors, we complement this supervision with pure-color anchors whose pixels exactly match their paired hex values. These anchors are used only at high-noise timesteps, leaving low-noise training to natural images. We further introduce Any Color Benchmark (ACBench), comprising ACBench-T2I and ACBench-Edit, to measure object-level hex color fidelity across both tasks. On FLUX.2-4B, Paint-Anything improves ACBench-T2I and ACBench-Edit scores by 85.3% and 28.3%, respectively, relative to the base model, with ablations supporting the training recipe. It also achieves the highest average CompColor score among the compared methods.

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