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Diversifying Long Prompt Image Generation through Structured Prompt Embedding Space Sampling
Modern text-to-image models produce impressive visual results from richly specified prompts, yet their behavior under long prompts remains insufficiently understood. In this paper, we study a practical failure mode in which accumulated semantic constraints progressively suppress output variation, ca
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