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.

构建准确全面地代表真实世界的世界模型是有条件的图像生成模型的最高追求，本文使用最新的文本到图像和图像到图像的模型及其问题来绘制一种提供严谨保守性，多样性和真实性多目标的一体化视图的一致性 - 多样性 - 真实性 Pareto 前沿，我们的实验表明，现实性和一致性可以同时提高，但现实性 / 一致性和多样性之间存在明显的权衡，通过计算地理多样性数据集上的 Pareto 前沿，我们发现在所有评估方面，潜在扩散模型的第一版本往往比最新模型表现更好，并且地理区域之间存在明显的一致性 - 多样性 - 真实性差异，总体而言，我们的分析清楚地显示了没有最佳模型的事实，并且模型的选择应由下游应用确定，通过这项分析，我们邀请研究界考虑 Pareto 前沿作为衡量世界模型进展的分析工具。