Color Me Correctly: Bridging Perceptual Color Spaces and Text Embeddings
  for Improved Diffusion Generation
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.
68c76cd9ee0eed1697d6b670
Sung-Lin Tsai
claimed_verified
2025-09-17T13:06:31.858Z
67cdd29bb9fcf876f04069c3
/avatars/3405f293630d26a282abae9676ee19e5.svg
Sung-Lin, Tsai
user
cauliflowerpastry
68c76cd9ee0eed1697d6b671
Bo-Lun Huang
68c76cd9ee0eed1697d6b672
Yu Ting Shen
68c76cd9ee0eed1697d6b673
Cheng Yu Yeo
68c76cd9ee0eed1697d6b674
Chiang Tseng
68c76cd9ee0eed1697d6b675
Bo-Kai Ruan
68c76cd9ee0eed1697d6b676
Wen-Sheng Lien
68c76cd9ee0eed1697d6b677
Hong-Han Shuai
Color Me Correctly: Bridging Perceptual Color Spaces and Text Embeddings
  for Improved Diffusion Generation
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.
A training-free framework uses a large language model to disambiguate color terms and refine text embeddings for improved color accuracy in text-to-image generation.
diffusion models text-to-image (T2I) generation color fidelity large language model (LLM) color-related prompts text embeddings CIELAB color space color accuracy image quality text semantics visual generation