Existing attribute editing methods treat semantic attributes as binary, resulting in a single edit per attribute. However, attributes such as eyeglasses, smiles, or hairstyles exhibit a vast range of diversity. In this work, we formulate the task of \textit{diverse attribute editing} by modeling the multidimensional nature of attribute edits. This enables users to generate multiple plausible edits per attribute. We capitalize on disentangled latent spaces of pretrained GANs and train a Denoising Diffusion Probabilistic Model (DDPM) to learn the latent distribution for diverse edits. Specifically, we train DDPM over a dataset of edit latent directions obtained by embedding image pairs with a single attribute change. This leads to latent subspaces that enable diverse attribute editing. Applying diffusion in the highly compressed latent space allows us to model rich distributions of edits within limited computational resources. Through extensive qualitative and quantitative experiments conducted across a range of datasets, we demonstrate the effectiveness of our approach for diverse attribute editing. We also showcase the results of our method applied for 3D editing of various face attributes.

现有的属性编辑方法将语义属性视为二进制，导致每个属性只能进行一次编辑。然而，如眼镜、微笑或发型等属性展示了广泛的多样性。在本研究中，我们通过建模属性编辑的多维性质来提出“多样属性编辑”的任务，从而使用户能够为每个属性生成多个合理的编辑。我们利用预训练 GAN 的分离潜空间和训练一个去噪扩散概率模型（DDPM）来学习用于多样编辑的潜分布。具体而言，我们通过嵌入具有单个属性变化的图像对来训练 DDPM，从而得到能够实现多样属性编辑的潜子空间。在高度压缩的潜空间中应用扩散使我们能够在有限的计算资源内模拟丰富的编辑分布。通过广泛的定性和定量实验，我们展示了我们的方法在多样属性编辑方面的有效性。我们还展示了我们的方法在各种面部属性的三维编辑方面的结果。

基于扩散模型的风格生成对抗网络中的属性变化探索