数据集的痛点：很多专注身份的方法都附带了庞大规模的高质量数据集，但是这些数据集基本都没有提供精确的布局与身份标注对，而且奇缺实例较多的复杂场景，限制了模型的训练效果。少数包含布局标注的数据集，除了缺乏复杂场景外这一“通病”外，其参考图像与真值图像之间也缺少足够的变化，限制了数据集的多样性。

The development of large language models (LLMs) is entering a pivotal phase with the emergence of diffusion-based architectures. These models, spearheaded by Inception Labs through its new Mercury ...

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Diffusion models gradually refine and produce a requested output, sometimes starting from random noise—values generated by the model itself—and sometimes working from user-provided data. Think of ...