Paint Anything: Toward Any-Color Controllable Image Generation and Editing

Any-color control---the ability to specify objects by arbitrary 24-bit hex values rather than coarse color words---is a practical yet challenging requirement for image generation and editing, where users often need exact object colors, brand colors, or carefully colored compositions. Prior work has explored color generation, color editing, and image colorization, but most methods rely on task-specific modules or training-free inference-time techniques. This fragmentation makes any-color control difficult to use as a unified text-prompt capability. In this work, we present Paint-Anything, a unified model for any-color controllable generation and editing that directly embeds 24-bit hex colors into text prompts and supports diverse color-control tasks within one framework. Our key insight is that modern text-to-image models already contain partial color understanding, but this capability remains unstable unless numeric color tokens are aligned with localized pixel colors. To this end, we construct Paint-500K, a 500K color-control dataset, and combine it with a high-noise gate for lightweight RGB alignment. We further introduce \textbf{PCBench}, a diagnostic benchmark suite for object-level hex color fidelity, with two parts: \textbf{PCBench-T2I} for generation and \textbf{PCBench-Edit} for editing. Experiments and ablations show that \ours{} substantially improves any-color generation and editing over the base model, suggesting that arbitrary hex-color control can be learned as a native prompt-following behavior of modern image models.