Consistency-diversity-realism Pareto fronts of conditional image generative models (2406.10429v1)

Abstract: Building world models that accurately and comprehensively represent the real world is the utmost aspiration for conditional image generative models as it would enable their use as world simulators. For these models to be successful world models, they should not only excel at image quality and prompt-image consistency but also ensure high representation diversity. However, current research in generative models mostly focuses on creative applications that are predominantly concerned with human preferences of image quality and aesthetics. We note that generative models have inference time mechanisms - or knobs - that allow the control of generation consistency, quality, and diversity. In this paper, we use state-of-the-art text-to-image and image-and-text-to-image models and their knobs to draw consistency-diversity-realism Pareto fronts that provide a holistic view on consistency-diversity-realism multi-objective. Our experiments suggest that realism and consistency can both be improved simultaneously; however there exists a clear tradeoff between realism/consistency and diversity. By looking at Pareto optimal points, we note that earlier models are better at representation diversity and worse in consistency/realism, and more recent models excel in consistency/realism while decreasing significantly the representation diversity. By computing Pareto fronts on a geodiverse dataset, we find that the first version of latent diffusion models tends to perform better than more recent models in all axes of evaluation, and there exist pronounced consistency-diversity-realism disparities between geographical regions. Overall, our analysis clearly shows that there is no best model and the choice of model should be determined by the downstream application. With this analysis, we invite the research community to consider Pareto fronts as an analytical tool to measure progress towards world models.

• The paper introduces a novel benchmark framework that maps trade-offs among consistency, diversity, and realism in conditional image generative models using Pareto fronts.
• It details a methodology leveraging inference-time 'knobs' and metrics like DSG, cosine similarity, and precision to assess model performance.
• Experimental results reveal that improvements in one objective often degrade another, guiding model design choices for specific applications.

Consistency-Diversity-Realism Pareto Fronts of Conditional Image Generative Models

The paper "Consistency-Diversity-Realism Pareto Fronts of Conditional Image Generative Models" by Pietro Astolfi et al., presents an analysis of state-of-the-art conditional image generative models, with a focus on evaluating their potential as comprehensive world models. These models are not only expected to generate high-quality and consistent images but also to represent the diversity of the real world. The authors illustrate that optimizing for conventional human preferences often overlooks the crucial aspect of representation diversity, which is essential for accurate world models.

Key Contributions

The paper introduces a novel framework for benchmarking conditional image generative models by mapping them onto consistency-diversity-realism Pareto fronts. These fronts provide a holistic view of the attainable trade-offs between these three critical objectives. Moreover, the paper leverages inference-time mechanisms, referred to as 'knobs', which allow the control of generation consistency, quality, and diversity.

Methodology

The authors describe their approach in detail, utilizing the following metrics to assess the performance of models along the three dimensions:

1. Consistency: Measured using Davidsonian Scene Graph (DSG) scores, which utilize Visual Question Answering (VQA) approaches to evaluate prompt-generation consistency.
2. Diversity: Evaluated using inter-sample similarity and recall metrics. Cosine similarity and DreamSim feature extractors are employed for conditional diversity, while recall metrics assess marginal diversity.
3. Realism: Assessed through image reconstruction quality and precision metrics.

The analysis encompasses several state-of-the-art models, including versions of Latent Diffusion Models (LDM) and Retrieval-Augmented Diffusion Models (RDM), as well as a neural image compression model, PerCo. The models are evaluated using benchmarks such as MSCOCO and the GeoDE dataset, reflecting global geographical diversity.

Experimental Findings

Text-to-Image (T2I) Models

The paper reveals several insights from the evaluation of T2I models:

1. Consistency-Diversity Trade-off: The Pareto fronts indicate that there is a notable trade-off where improvements in consistency often result in decreased diversity. Models such as XL achieve high consistency but do so by sacrificing diversity.
2. Realism-Diversity Relationship: Similarly, a higher realism is often achieved at the expense of diversity. XL-Turbo, which uses an adversarial objective, attains the highest realism, but its diversity significantly diminishes.
3. Consistency-Realism Correlation: Improvements in realism are generally correlated with improvements in consistency.

Image-Content-Text-to-Image (I-T2I) Models

For I-T2I models:

1. Marginal and Conditional Metrics Divergence: The paper observes that while PerCo achieves high marginal diversity and realism, its conditional diversity lags, highlighting how compression models excel in reconstructing image manifold but fail to produce diverse outputs for the same prompt.
2. Trade-offs Confirmation: The consistency-diversity and realism-diversity trade-offs persist, with RDM providing the most conditionally diverse samples and PerCo delivering the highest realism.

Geographical Representation

The analysis extends to geographic disparities using the GeoDE dataset:

1. Regional Performance Variance: There are clear disparities in model performance across different regions, with Europe and the Americas exhibiting better performance metrics than Africa and West Asia.
2. Model Evolution Impacts: Older models like LDM 1.5 outperform recent ones in terms of diversity across all regions, whereas XL effectively reduces regional disparities in consistency and diversity but at the cost of realism.

Impact of Knobs

The authors explore the influence of various knobs on the consistency-diversity-realism trade-offs:

1. Guidance Scale: Increasing guidance scale generally enhances consistency and realism but reduces diversity.
2. Post-hoc Filtering: Improves consistency and realism while marginally affecting diversity.
3. Retrieval Augmentation: The incorporation of additional neighbors has a minor impact on the objectives.
4. Compression Rate: Lower bitrates in compression models improve diversity but decrease realism.

Conclusions and Future Directions

The paper concludes that no single model excels across all three objectives and suggests that model choice should be determined based on the specific requirements of downstream applications. The use of Pareto fronts as a benchmarking tool provides a nuanced understanding of the trade-offs inherent in current generative models.

The findings of this paper have significant implications for the development of future conditional image generative models. The insights into the trade-offs between consistency, diversity, and realism suggest areas for focused improvements. Future work may explore understanding whether these trade-offs are intrinsic to the models or can be mitigated through advanced techniques. The authors also call for extending the evaluation framework to include closed models and additional types of conditioning, further enriching the analysis.

The paper invites the AI research community to adopt Pareto fronts as a standard evaluation tool, facilitating informed discussions and comparisons that drive progress in the field of generative modeling.