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However, in multi-concept generation, one concept token often dominates the output while others are suppressed-a phenomenon we term the Dominant-vs-Dominated (DvD) imbalance. To systematically study this failure mode, we introduce DominanceBench and examine its underlying causes from both data and internal-mechanistic perspectives. Our controlled fine-tuning study, which mimics concept learning during diffusion-model training, shows that concepts learned from visually homogeneous (low-variation) concept-specific training images exhibit stronger dominance when composed with others. Cross-attention analysis indicates that dominant tokens concentrate attention in early denoising steps, followed by reduced representation of competing concepts. Head-ablation analysis further shows that this dominance is di","title":"Dominant vs. Dominated: Concept-Level Generative Collapse in Diffusion Models","url":"https://arxiv.org/abs/2512.20666","vendor":"arxiv_cs_ai"},"summary":"arXiv:2512.20666v2 Announce Type: replace-cross \nAbstract: Text-to-image diffusion models have attracted significant attention for their ability to generate diverse, high-fidelity images. However, in multi-concept generation, one concept token often dominates the output while others are suppressed-a phenomenon we term the Dominant-vs-Dominated (DvD) imbalance. To systematically study this failure mode, we introduce DominanceBench and examine its underlying causes from both data and internal-mechanistic perspectives. 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