Why image models give you 8 identical witches: the alignment-diversity tradeoff, explained
simi_97k · x · 2026-10-28
The T-DIG authors explain the root cause: image generation models are optimized for high single-image prompt alignment, which comes at the cost of diversity when generating multiple images.
Prior work improves diversity by pushing images apart in embedding space, but that doesn't necessarily correlate with what the user actually wants to vary. T-DIG instead makes diversity follow the user's text condition — same prompt, but you choose whether the variation axis is art style, background, or clothing.
Related event: CMU Open-Sources T-DIG: Text-Conditioned Diversity for Image Generation(7 posts)→
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