Efficient bias mitigation in T2I diffusion models using Concept Graphs

📰 ArXiv cs.AI

Learn to mitigate bias in text-to-image diffusion models using Concept Graphs and CO-ALIGN, a novel approach for efficient bias reduction

advanced Published 7 Jul 2026

Action Steps

Build a concept graph to represent relationships between concepts and objects
Apply CO-ALIGN to align the concept graph with the text encoder
Use the aligned concept graph to guide the diffusion model and reduce bias
Test the model on a diverse set of inputs to evaluate bias mitigation
Compare the results with existing bias mitigation techniques to assess effectiveness

Who Needs to Know This

AI researchers and engineers working on text-to-image models can benefit from this technique to reduce harmful bias in their models, improving overall model fairness and robustness

Key Insight

💡 Concept Graphs can be used to efficiently mitigate bias in text-to-image diffusion models by aligning concepts and objects

Key Takeaways

Learn to mitigate bias in text-to-image diffusion models using Concept Graphs and CO-ALIGN, a novel approach for efficient bias reduction

Full Article

Title: Efficient bias mitigation in T2I diffusion models using Concept Graphs

Abstract:
arXiv:2607.03397v1 Announce Type: new Abstract: Text-to-Image diffusion models often propagate harmful bias inherited from the training data. Existing bias mitigation techniques typically intervene only at the text encoder or provide inference-time guidance, often leading to generations that collapse into semantically incoherent outputs. To address these limitations, we introduce CO-ALIGN (Concept Ontology Alignment), a novel bias mitigation approach based on concept-graph alignment that operate

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