给AI指定精确颜色，像给设计师发色号
以前让AI给物体上色，你只能说“红色”“深蓝”，它自己猜个大概；这篇让AI能听懂精确到十六进制色号(比如#FF5733)的指令，生成和编辑图片时都能按这个色号上色。做法是给模型喂了50万张带精确颜色标注的真实图片，再用纯色图片做锚点校准，因为真实照片里的颜色会被阴影干扰。在FLUX.2-4B上，按色号生成和编辑的准确率分别提升了85.3%和28.3%。它不是你明天就能用的工具，但这是AI从“听懂颜色”到“听懂精确颜色”的一步，做设计工具的人值得盯一下。
📄 原文摘要(英文)
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.