  Text-to-Image (T2I) generation has made significant advancements with the
advent of diffusion models. These models exhibit remarkable abilities 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 color prompt learning, we
propose to learn specific color prompts tailored to user-selected colors.
Existing T2I personalization methods tend to result in color-shape
entanglement. To overcome this, we generate several basic geometric objects in
the target color, allowing for color and shape disentanglement during the color
prompt learning. 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. Furthermore, we generalize ColorPeel to
effectively learn abstract attribute concepts, including textures, materials,
etc. Our findings represent a significant step towards improving precision and
versatility of T2I models, offering new opportunities for creative applications
and design tasks. Our project is available at
https://moatifbutt.github.io/colorpeel/.
