Color Me Correctly: Bridging Perceptual Color Spaces and Text Embeddings for Improved Diffusion Generation

2026-07-27 · Source: cs.CV updates on arXiv.org · Field: Technology & Digital — Artificial Intelligence & Machine Learning · Depth: Expert, extended

Summary

The proposed method, "Color Me Correctly," uses a Large Language Model (LLM) to enhance color alignment in text-to-image (T2I) diffusion models, specifically addressing ambiguous and compound color terms like "Tiffany blue" or "jungle green." Current models often misinterpret these, leading to inaccurate image generation. This training-free framework employs GPT-4o to disambiguate color terms in prompts, then refines text embeddings by leveraging spatial relationships in the CIELab color space. It utilizes a perceptually weighted interpolation mechanism based on the ΔE₀₀ metric, avoiding the need for additional training or external reference images. The authors also introduce TintBench, a new benchmark dataset comprising 1,000 prompts (500 single-color, 500 multi-color) with five categories of compound color names. Experimental results on Stable Diffusion 1.4 and SDXL demonstrate superior performance in user studies across prompt alignment, color fidelity, and ambiguity resolution.

Key takeaway

For Machine Learning Engineers developing text-to-image models, if you are struggling with accurate color rendering from nuanced prompts, consider integrating LLM-based semantic disambiguation and CIELab-grounded embedding refinement. This training-free approach significantly improves color fidelity and resolves ambiguities, enhancing user satisfaction in applications like product visualization or interior design. You should explore TintBench for rigorous evaluation of your model's color understanding.

Key insights

Bridging LLM semantic understanding with CIELab perceptual color space improves T2I color fidelity for ambiguous terms.

Principles

• LLMs can effectively disambiguate complex color terms in prompts.
• CIELab space exhibits high correlation with text embedding color relationships.
• Perceptually weighted color blending enhances embedding accuracy for nuanced hues.

Method

An LLM (GPT-4o) disambiguates color prompts. Text embeddings are then refined via perceptually weighted interpolation in CIELab space using the ΔE₀₀ metric, guiding the diffusion model without retraining.

In practice

• Use LLMs to pre-process ambiguous color prompts for T2I models.
• Employ CIELab for perceptually uniform color space operations in embedding refinement.
• Apply Gaussian-based softmax for blending weights between basic color embeddings.

Topics

• Text-to-Image Generation
• Diffusion Models
• Color Disambiguation
• Large Language Models
• CIELab Color Space
• Text Embeddings
• TintBench Dataset

Best for: AI Engineer, Research Scientist, AI Scientist, Machine Learning Engineer, Computer Vision Engineer

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Editorial summary, takeaway, and curation by AIssential. Original article published by cs.CV updates on arXiv.org.