Efficient bias mitigation in T2I diffusion models using Concept Graphs
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 operates on the model's internal concept ontology. By aligning concepts within the text encoder and denoiser, CO-ALIGN achieves substantial bias reduction while preserving generative integrity. We demonstrate the effectiveness of concept-graph alignmen...
| Authors | Mansi, Avinash Kori, Francesco Leofante |
|---|---|
| Primary category | cs.AI |
| Published | 3 July 2026 |
| Last updated at source | 3 July 2026 |
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