Suggestion: Enhance Model's Textual Understanding of Precise Colors via the meodai/color-names Dataset
This is a suggestion regarding model training that I believe could significantly improve the model's ability to precisely control color via text—a core pain point for many artists and designers today.
The Problem: Conceptual Color vs. Precise Color Current models (like CLIP) excel at understanding broad concepts like "red" or "blue" but struggle to respond to text prompts requesting specific, precise color values (e.g., they cannot generate "goldenrod" from a prompt like RGB(218, 165, 32)).
The Solution: Fine-Tuning with the meodai/color-names Dataset meodai/color-names (https://github.com/meodai/color-names) is an excellent open-source project that maps tens of thousands of creative, human-readable color names (like "Angry Sky Blue") to their precise HEX/RGB values.
Implementation Suggestion: I recommend your team consider integrating this dataset into the data preprocessing pipeline for the model's next training or fine-tuning cycle:
When processing training images, extract their dominant colors (e.g., via K-Means clustering).
Use meodai_colornames.csv (e.g., via a KDTree for fast lookups) to reverse-match these RGB values to their most fitting creative color names.
Dynamically inject these rich color names into the original training prompts.
Expected Benefit: By training this way, the model's text encoder will learn to directly associate precise RGB values with expressive, human-centric vocabulary (like "Dragon's Blood").
This would grant users an unprecedented level of nuanced color control via pure text, dramatically increasing the model's professional usability and artistic expressiveness. This would undoubtedly be a significant competitive advantage.
Thank you for taking the time to read my suggestion. I look forward to seeing your team's future breakthroughs!
You can see that the current model's attention to color is still very limited and cannot accurately express...