Introducing ImagenWorld: A Real World Benchmark for Image Generation and Editing

ImagenWorld is a comprehensive benchmark designed to expose and explain the errors of image generation models, offering a detailed evaluation framework across six major tasks that include text-to-image generation and multiple-reference image editing. Covering six diverse visual domains, ImagenWorld provides a realistic evaluation of model performance by employing human annotators to rate images on criteria such as prompt relevance, aesthetic quality, content coherence, and artifact presence, along with tagging specific errors using segmentation maps. The benchmark evaluated 14 state-of-the-art models using a unified protocol to ensure reproducible comparisons, revealing that editing remains a challenging frontier, text-heavy domains often result in distorted outputs, and careful data curation can enhance performance beyond mere model scaling. With over 20,000 annotated examples, ImagenWorld not only highlights where models succeed or fail but also encourages the development of more robust and trustworthy generative systems by offering tools for visual quality assessment, human reasoning, and error attribution.

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