Holistic Evaluation of Text-To-Image Models (2311.04287v1)

Abstract: The stunning qualitative improvement of recent text-to-image models has led to their widespread attention and adoption. However, we lack a comprehensive quantitative understanding of their capabilities and risks. To fill this gap, we introduce a new benchmark, Holistic Evaluation of Text-to-Image Models (HEIM). Whereas previous evaluations focus mostly on text-image alignment and image quality, we identify 12 aspects, including text-image alignment, image quality, aesthetics, originality, reasoning, knowledge, bias, toxicity, fairness, robustness, multilinguality, and efficiency. We curate 62 scenarios encompassing these aspects and evaluate 26 state-of-the-art text-to-image models on this benchmark. Our results reveal that no single model excels in all aspects, with different models demonstrating different strengths. We release the generated images and human evaluation results for full transparency at https://crfm.stanford.edu/heim/v1.1.0 and the code at https://github.com/stanford-crfm/helm, which is integrated with the HELM codebase.

Holistic Evaluation of Text-to-Image Models: A Comprehensive Benchmark

The paper presents the Holistic Evaluation of Text-to-Image Models (HEIM), a novel benchmark designed to systematically evaluate text-to-image models across 12 critical aspects: alignment, quality, aesthetics, originality, reasoning, knowledge, bias, toxicity, fairness, robustness, multilinguality, and efficiency. Recognizing the limitations of previous benchmarks that focused primarily on text-image alignment and image quality, this work aims to fill the evaluative gaps by introducing a more comprehensive framework.

Evaluation Framework

HEIM evaluates models using a blend of human and automated metrics across 62 scenarios. These scenarios are curated to reflect diverse use cases and assess various capabilities and potential risks associated with text-to-image models. Particular attention is given to ethical and societal implications, such as bias and toxicity, highlighting their importance in real-world applications.

The evaluation leverages datasets like MS-COCO, alongside newly created scenarios, to test models in multiple contexts, including reasoning tasks and aesthetic evaluations, which have been underexplored in previous research.

Key Findings

The paper evaluates 26 state-of-the-art models, uncovering several significant insights:

1. Diverse Strengths: Different models excel in different areas. For example, DALL-E 2 performs well in text-image alignment, while Openjourney shows strengths in aesthetics.
2. Inadequate Automated Metrics: The weak correlation between automated metrics (e.g., CLIPScore and FID) and human evaluations underscores the necessity of human ratings, especially for aspects like aesthetics and originality.
3. Areas for Improvement: Models generally underperform in reasoning and multilingual capabilities, emphasizing the need for further advancements in these areas.
4. Ethical Considerations: Despite some efforts in bias and toxicity mitigation, current models still face challenges, which could have legal and ethical implications.
5. The Efficacy of Prompt Engineering: Techniques like Promptist exhibit potential in enhancing the visual appeal of generated images, without substantially compromising alignment.

Implications and Future Directions

HEIM provides a valuable tool for researchers and developers to comprehensively assess and compare text-to-image models, facilitating informed decision-making for model deployment. The findings suggest that a single model that excels across all aspects remains elusive, pointing to potential pathways for future research, including the integration of multiple models or techniques.

Beyond immediate application, HEIM sets a precedent for multifaceted evaluation in AI, encouraging the community to prioritize both technological capabilities and societal impacts. Future research may expand HEIM by introducing additional scenarios and metrics, reflecting evolving needs and new challenges.

In conclusion, HEIM represents a significant step toward a holistic understanding of text-to-image generation models, offering a robust framework to assess their capabilities and moral implications comprehensively. It encourages the AI community to strive for balanced advancements across diverse aspects, ensuring they align with ethical standards and societal expectations.