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• The proposed ColorFLUX framework outperforms existing state-of-the-art colorization methods, including closed-source commercial models, on synthetic and real datasets.result 95%
• The structure-color decoupling strategy separates structure preservation from color restoration, enabling accurate colorization while maintaining structural consistency.method 90%
• The progressive Direct Preference Optimization (Pro-DPO) strategy allows the model to learn subtle color preferences through coarse-to-fine transitions in color augmentation.method 90%
• Visual semantic prompts, which extract fine-grained semantic information directly from old photos, are introduced to address the limitations of text-based prompts and help eliminate color bias.technical 90%
• Existing restoration models often struggle with accurate colorization of old photos due to a substantial domain gap caused by unique degradations like faded brightness and altered color hues.limitation 85%
• The framework's training consists of three distinct stages: structure consistency training, basic color learning, and fine-color adjustment.method 95%
• The ColorFLUX inference process includes a preprocessor to remove degradations in old photos before colorization.technical 85%
• Learning directly from a wide range of color augmentations may cause the model to overfit to simple preference patterns and neglect subtle fine-color adjustments.technical 80%

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