LongText-Bench is a small benchmark dataset for evaluating models’ ability to render long textual content in generated images. Released by the X-Omni team, it provides English and Chinese tracks and is intended for text-to-image evaluation focused on longer textual content (paragraphs, multi-word strings) and different content categories. The dataset on Hugging Face contains a single split (train) with 320 examples and fields such as category (8 classes), length (short/long), prompt (text prompt), text (the target textual content to render), text_length, and prompt_id. Metadata on the HF page lists the dataset language as English and Chinese, license Apache-2.0, and tags it with task_categories:text-to-image.
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