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Diffusion models struggle with multi-object generation due to scene complexity

A new research paper investigates the limitations of diffusion models in generating multiple objects within images. The study introduces a controlled dataset generation framework called 'mosaic' to analyze concept generalization and compositional generalization. Findings indicate that scene complexity, rather than data imbalance, is the primary factor affecting multi-object generation, with counting tasks proving particularly difficult in low-data scenarios. AI

Summary written by gemini-2.5-flash-lite from 2 sources. How we write summaries →

IMPACT Highlights fundamental limitations in diffusion models for multi-object generation, suggesting a need for improved inductive biases and data design.

RANK_REASON Academic paper on diffusion model limitations.

Read on arXiv cs.CV →

COVERAGE [2]

  1. arXiv cs.CV TIER_1 · Yujin Jeong, Arnas Uselis, Iro Laina, Seong Joon Oh, Anna Rohrbach ·

    When Do Diffusion Models learn to Generate Multiple Objects?

    arXiv:2605.00273v1 Announce Type: new Abstract: Text-to-image diffusion models achieve impressive visual fidelity, yet they remain unreliable in multi-object generation. Despite extensive empirical evidence of these failures, the underlying causes remain unclear. We begin by aski…

  2. arXiv cs.CV TIER_1 · Anna Rohrbach ·

    When Do Diffusion Models learn to Generate Multiple Objects?

    Text-to-image diffusion models achieve impressive visual fidelity, yet they remain unreliable in multi-object generation. Despite extensive empirical evidence of these failures, the underlying causes remain unclear. We begin by asking how much of this limitation arises from the d…