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Text-to-Image Models: Assessing Diversity And Generalisation With A New Framework.
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Text-to-Image Models: Assessing Diversity And Generalisation With A New Framework.
Recent research introduces DIM-CIM, a reference-free framework evaluating both the inherent diversity and generalisation capability of text-to-image models, revealing a trade-off between these qualities as model scale increases and demonstrating a strong correlation between training data diversity and the diversity of generated images.
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