Diffusion Models Struggle with Multi-Object Generation, Study Finds
A recent study published on arXiv (2605.00273) examines the challenges text-to-image diffusion models face when producing multiple objects. The authors present Mosaic, a structured approach for generating datasets aimed at clarifying the impacts of data. Their findings reveal that the complexity of scenes, rather than an imbalance in concepts, is the primary reason for these shortcomings. Additionally, they highlight that learning to count is particularly challenging in scenarios with limited data.
Key facts
- Diffusion models are unreliable in multi-object generation.
- The study introduces Mosaic (Multi-Object Spatial relations, Attribution, Counting).
- Scene complexity plays a dominant role over concept imbalance.
- Counting is uniquely difficult to learn in low-data regimes.
- The paper is from arXiv:2605.00273.
Entities
Institutions
- arXiv