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Like the N-convex algorithm, this algorithm attempts to find a set of candidates whose centroid is close to . The key difference is that instead of taking unique candidates, we allow candidates to populate the set multiple times. The result is that the weight of each candidate is simply given by its frequency in the list, which we can then index by random selection:
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各种信息图和海报设计,现在基本上初稿都可以交给 Nano Banana。之前难倒一众 AI 的「我想洗车。洗车店距离50米。我应该步行还是开车?」热门问题,直接丢给 Nano Banana 2,它会生成一个详细的说明信息图。,更多细节参见服务器推荐
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