T2I Generation Evaluation

Text-to-image generation evaluation asks whether an image follows a prompt and how well it satisfies other needs, such as visual quality, spatial relationships, text rendering, factual accuracy, diversity, and safety. Researchers use controlled benchmarks, human ratings, detector- or program-based checks, and vision-language models that score images with questions or rubrics. Evaluation is expanding from broad alignment and aesthetics scores toward fine-grained tests of composition, cultural representation, specialized knowledge, and real-world use. A central challenge is that automated scores can miss subtle errors or reflect their own biases, while optimizing one measure—such as fidelity or safety—can harm another.