ColorPeel: Color Prompt Learning with Diffusion Models via Color and Shape Disentanglement

Text-to-Image (T2I) generation has made significant advancements with the advent of diffusion models. These models exhibit remarkable ability to produce images based on textual prompts. Current T2I models allow users to specify object colors using linguistic color names. However, these labels encompass broad color ranges, making it difficult to achieve precise color matching. To tackle this challenging task, named as color prompt learning, we propose to learn specific color prompts tailored to user-selected colors. These prompts are finally employed to generate objects with the exact desired colors. Observing the existing T2I adaptation approaches cannot achieve satisfactory performance, we propose to generate basic geometric objects in the target color. Leveraging color and shape disentanglement, our method, denoted as ColorPeel, successfully assists the T2I models to peel off the novel color prompts from these colored shapes. In the experiments, we demonstrate the efficacy of ColorPeel in achieving precise color generation with T2I models and generalize ColorPeel to effectively learn abstract attribute concepts, including textures, materials, etc. Our findings provide a valuable step towards improving precision and versatility of T2I models, offering new opportunities for creative applications and design tasks.