Core Viewpoint - The article discusses the challenges of hallucination samples generated by diffusion probability models (DPMs) in image generation tasks, particularly focusing on a specific type of hallucination called "counting hallucination" [1][2]. Group 1: Research Background - Despite the prevalence of hallucination issues in DPMs, there has been a lack of systematic methods to quantify these factual errors, hindering the development of high-reliability generative models [2]. - A research team from the University of Adelaide, Meituan, and Shanghai Jiao Tong University has conducted a systematic study on counting hallucinations in diffusion models [2][3]. Group 2: Key Questions and Dataset - The research team posed several key questions regarding the quantification of counting hallucinations and the effectiveness of common optimization techniques [3][7]. - They constructed the CountHalluSet dataset suite, which includes three datasets with increasing complexity of countable objects: ToyShape, SimObject, and RealHand [10]. Group 3: Findings and Experiments - The study revealed that increasing sampling steps can reduce counting hallucination rates in synthetic datasets but may increase them in real datasets due to overfitting [19]. - The research found that higher-order ODE solvers can lower overall failure rates but may increase counting hallucination rates, indicating a trade-off in model sensitivity to counting constraints [20][21]. - The study identified that the complexity of object shapes correlates with the severity of counting hallucinations, with more complex structures leading to higher rates of errors [26]. Group 4: Correlation Analysis - The correlation between counting hallucination rates and FID scores varies depending on the dataset and solver type, suggesting that FID may not reliably reflect factual consistency [30][32]. - Non-counting failure rates showed a stable and significant correlation with FID across conditions, indicating that FID is more effective in assessing overall visual consistency rather than specific factual features [32]. Group 5: Proposed Solution - The research team proposed a Joint-Diffusion Model (JDM) that incorporates structural constraints during the diffusion process to guide the model in generating the correct number of objects [33][35]. - This approach enhances the semantic consistency and visual credibility of generated results, effectively mitigating counting hallucination issues [35]. Group 6: Future Directions - The work opens avenues for exploring higher-order factual consistency in generative models, extending the analysis to more complex hallucination types and integrating abstract knowledge into the diffusion process [37]. - The ultimate goal is to transform generative models from mere creative tools into reliable world models applicable in critical fields requiring high accuracy [37].