Consistency-diversity-realism Pareto fronts of conditional image generative models

🌈 Abstract

The article discusses the use of conditional image generative models as "world models" that can accurately and comprehensively represent the real world. It notes that current research in generative models mostly focuses on creative applications concerned with image quality and aesthetics, rather than representation diversity. The article proposes using "consistency-diversity-realism Pareto fronts" as a framework to evaluate the potential of conditional image generative models as world models, by analyzing the tradeoffs between these three objectives.

🙋 Q&A

[01] Consistency-diversity-realism multi-objective for text-to-image models

1. What are the key findings regarding the consistency-diversity tradeoff for text-to-image (T2I) models?

• The Pareto front is composed of three T2I models: LDM1.5, LDM2.1, and LDM.
• Improving diversity, both marginal and conditional, comes at the expense of consistency. LDM2.1 and LDM1.5 achieve the best marginal and conditional diversities respectively, while LDM reaches the best consistency.
• Newer models like LDM sacrifice diversity to improve consistency and realism.

2. What are the key findings regarding the realism-diversity tradeoff for T2I models?

• There is a tradeoff between realism and diversity, with higher diversity coinciding with lower realism for LDM1.5 and LDM2.1.
• LDM achieves the highest realism, but at the cost of a steep decrease in sample diversity compared to LDM2.1.
• When considering conditional metrics, LDM achieves the best conditional realism, but considerably lower conditional diversity compared to LDM1.5 and LDM2.1.

3. What are the key findings regarding the consistency-realism tradeoff for T2I models?

• Realism and consistency show a relatively strong positive correlation, with the correlation being stronger for conditional metrics than marginal metrics.
• The Pareto front is dominated by LDM and LDM, highlighting how the advancement of T2I generative models has favored consistency-realism over diversity.

[02] Pareto fronts of image&text-to-image models

1. What are the key findings regarding the consistency-diversity tradeoff for image&text-to-image (I-T2I) models?

• For marginal metrics, the Pareto front is composed only of PerCo neural compression models, which achieve both high consistency and diversity.
• For conditional metrics, the Pareto front is composed of three models: RDM, LDM, and PerCo, with RDM reaching the best conditional diversity and PerCo achieving the highest conditional consistency.

2. What are the key findings regarding the realism-diversity tradeoff for I-T2I models?

• For marginal metrics, PerCo is the only model producing Pareto optimal points, with high realism and diversity.
• For conditional metrics, the Pareto front contains all three models, with RDM producing the most conditionally diverse samples and PerCo producing the samples with the highest conditional realism.

3. What are the key findings regarding the consistency-realism tradeoff for I-T2I models?

• PerCo achieves the best results in terms of both realism and consistency, and is the only model producing Pareto optimal points.
• LDM achieves much higher consistency and realism than RDM, which is attributed to the different dataset scale and model capacities.

[03] Pareto fronts for geographic disparities in T2I models

1. What are the key findings regarding the consistency-diversity tradeoff across different geographic regions?

• Europe, the Americas, and Southeast Asia exhibit the best Pareto fronts, with higher diversity and consistency than Africa and West Asia.
• LDM1.5 appears in the Pareto fronts of all regions, while LDM2.1 appears less frequently and not at all in Europe.
• LDM bridges the consistency and diversity performance gap between Africa and Europe/Americas, but these improvements disappear when distilling to LDM.

2. What are the key findings regarding the realism-diversity tradeoff across different geographic regions?

• The Pareto fronts of West Asia and Africa are visibly worse than other regions in terms of realism and diversity.
• LDM1.5 generally dominates the Pareto fronts of all regions.
• As realism is maximized, stereotypes and geographic disparities increase, with higher variance across region-wise Pareto fronts.

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