项目地址：https://magicfusion.github.io
Jing Zhao, Heliang Zheng, Chaoyue Wang, Long Lan, Wenjing Yang
开源人工智能社区的出现产生了强大的文本引导扩散模型的宝库，这些模型是在各种数据集上训练的。虽然很少有探索将这些模型组合起来，以结合它们的优势。在这项工作中，我们提出了一种简单而有效的方法，称为 Saliency-aware Noise Blending (SNB)，该方法可以增强融合的文本引导扩散模型，以实现更可控的生成。
具体来说，我们通过实验发现，无分类器指导的响应与生成的图像的显著性高度相关。因此，我们建议通过以显著性感知的方式混合两个扩散模型的预测噪声，来信任其专业领域的不同模型。SNB无需培训，可以在DDIM采样过程中完成。此外，它可以自动对齐两个噪声空间的语义，而不需要额外的注释，如掩码。广泛的实验表明，SNB在各种应用中的有效性令人印象深刻。
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