Discover how various benchmarks, including MS-COCO and DrawBench, are used to evaluate AI image generation models.

Explore how human annotators assess AI-generated images based on alignment, quality, aesthetics, and originality.

Discover the HEIM benchmark—a new tool designed to holistically evaluate text-to-image models across 12 crucial aspects like quality, bias, and more.

Meet the authors whose contributions formed a holistic evaluation of text-to-image models.

Explore the limits of current AI model evaluations.

Explore an holistic evaluation of image generation models sets new benchmarks for quality, ethics, aesthetics, and societal impact.

Discover HEIM, the groundbreaking benchmark assessing text-to-image models across 12 key aspects like quality, fairness, originality, and more.

Discover how 26 cutting-edge text-to-image models, from diffusion to GANs, stack up in performance.

An analysis of 26 text-to-image models across 12 aspects using 62 scenarios and 25 metrics.

Discover HEIM’s enhanced metrics, blending human and automated evaluations to assess AI models on fairness, robustness, multilinguality, and more.

xplore 62 diverse scenarios designed to assess text-to-image AI models across 12 key aspects like alignment, originality, and bias.

Discover how HEIM evaluates text-to-image models across 12 crucial aspects, from aesthetics and originality to bias and multilinguality.

HEIM evaluates AI image models using a 4-part framework: aspects, scenarios, adaptations, and metrics.

The HEIM benchmark evaluates 26 text-to-image models across 12 critical aspects, revealing diverse strengths and weaknesses.

This article outlines the human evaluation process for AI-generated images using Amazon Mechanical Turk and compliance with Human Subjects.

Explore the latest advancements in text-to-image models like Stable Diffusion, DALL-E, and Dreamlike Diffusion.