Leveraging Semantic Attribute Binding for Free-Lunch Color Control in Diffusion Models
This addresses the problem of limited color control in diffusion models for users needing precise image generation, representing a novel method for a known bottleneck.
The paper tackles the challenge of precise color specification in text-to-image diffusion models by introducing ColorWave, a training-free method that achieves exact RGB-level color control without fine-tuning, outperforming prior approaches in accuracy and applicability across diverse object categories.
Recent advances in text-to-image (T2I) diffusion models have enabled remarkable control over various attributes, yet precise color specification remains a fundamental challenge. Existing approaches, such as ColorPeel, rely on model personalization, requiring additional optimization and limiting flexibility in specifying arbitrary colors. In this work, we introduce ColorWave, a novel training-free approach that achieves exact RGB-level color control in diffusion models without fine-tuning. By systematically analyzing the cross-attention mechanisms within IP-Adapter, we uncover an implicit binding between textual color descriptors and reference image features. Leveraging this insight, our method rewires these bindings to enforce precise color attribution while preserving the generative capabilities of pretrained models. Our approach maintains generation quality and diversity, outperforming prior methods in accuracy and applicability across diverse object categories. Through extensive evaluations, we demonstrate that ColorWave establishes a new paradigm for structured, color-consistent diffusion-based image synthesis.