Color Me Correctly: Bridging Perceptual Color Spaces and Text Embeddings
  for Improved Diffusion Generation
Authors: name: Sung-Lin Tsai, name: Bo-Lun Huang, name: Yu Ting Shen, name: Cheng Yu Yeo, name: Chiang Tseng, name: Bo-Kai Ruan, name: Wen-Sheng Lien, name: Hong-Han Shuai

Accurate color alignment in text-to-image (T2I) generation is critical for
applications such as fashion, product visualization, and interior design, yet
current diffusion models struggle with nuanced and compound color terms (e.g.,
Tiffany blue, lime green, hot pink), often producing images that are misaligned
with human intent. Existing approaches rely on cross-attention manipulation,
reference images, or fine-tuning but fail to systematically resolve ambiguous
color descriptions. To precisely render colors under prompt ambiguity, we
propose a training-free framework that enhances color fidelity by leveraging a
large language model (LLM) to disambiguate color-related prompts and guiding
color blending operations directly in the text embedding space. Our method
first employs a large language model (LLM) to resolve ambiguous color terms in
the text prompt, and then refines the text embeddings based on the spatial
relationships of the resulting color terms in the CIELAB color space. Unlike
prior methods, our approach improves color accuracy without requiring
additional training or external reference images. Experimental results
demonstrate that our framework improves color alignment without compromising
image quality, bridging the gap between text semantics and visual generation.