Recent advancements in text-to-image diffusion models have demonstrated their
remarkable capability to generate high-quality images from textual prompts.
However, increasing research indicates that these models memorize and replicate
images from their training data, raising tremendous concerns about potential
copyright infringement and privacy risks. In our study, we provide a novel
perspective to understand this memorization phenomenon by examining its
relationship with cross-attention mechanisms. We reveal that during
memorization, the cross-attention tends to focus disproportionately on the
embeddings of specific tokens. The diffusion model is overfitted to these token
embeddings, memorizing corresponding training images. To elucidate this
phenomenon, we further identify and discuss various intrinsic findings of
cross-attention that contribute to memorization. Building on these insights, we
introduce an innovative approach to detect and mitigate memorization in
diffusion models. The advantage of our proposed method is that it will not
compromise the speed of either the training or the inference processes in these
models while preserving the quality of generated images. Our code is available
at this https URL .

研究论文概述：最近的文本到图像扩散模型在生成高质量图像方面展示了显著的能力，但是越来越多的研究表明这些模型从训练数据中记忆并复制图像，引发了对潜在版权侵权和隐私风险的巨大担忧。在这项研究中，我们通过检查交叉注意力机制与记忆现象的关系，提供了一种新的视角来理解这一现象。我们揭示了在记忆过程中，交叉注意力倾向于不成比例地关注特定标记的嵌入。扩散模型对这些标记嵌入进行了过度拟合，记住了相应的训练图像。为了阐明这一现象，我们进一步确定并讨论了与记忆相关的十分内在发现的交叉注意力。基于这些见解，我们提出了一种创新方法来检测和减轻扩散模型中的记忆现象。我们提出的方法的优势是在保留生成图像质量的同时不会影响这些模型的训练或推理过程的速度。