后验蒸馏采样
Posterior Distillation Sampling
AI总结：
本文提出后验蒸馏采样PDS，将扩散模型的二维图像编辑能力扩展到参数空间，通过匹配源与目标的随机潜变量，在保持源内容身份的同时采样符合目标属性的参数化图像，并在NeRF和SVG上验证了有效性。
摘要：
我们提出了后验蒸馏采样（Posterior Distillation Sampling，PDS），这是一种基于扩散模型的参数化图像编辑的新型优化方法。现有的基于优化的方法利用扩散模型强大的二维先验来处理各种参数化图像，但主要集中于生成任务。与生成不同，编辑需要在符合目标属性与保留源内容身份之间取得平衡。最近的二维图像编辑方法通过利用扩散模型生成过程中编码的随机潜变量实现了这种平衡。为了将扩散模型在像素空间中展现的编辑能力扩展到参数空间，我们将二维图像编辑方法重新表述为一种名为PDS的优化形式。PDS匹配源与目标的随机潜变量，从而能够在多样的参数空间中采样符合期望属性同时保持源身份的目标。我们证明这种优化类似于以目标属性运行生成过程，但使该过程与源生成过程的轨迹对齐。在神经辐射场（Neural Radiance Fields）和可缩放矢量图形（Scalable Vector Graphics）表示上的大量编辑结果表明，PDS能够在各种参数空间中采样目标，以满足上述平衡。
英文摘要：
We introduce Posterior Distillation Sampling (PDS), a novel optimization method for parametric image editing based on diffusion models. Existing optimization-based methods, which leverage the powerful 2D prior of diffusion models to handle various parametric images, have mainly focused on generation. Unlike generation, editing requires a balance between conforming to the target attribute and preserving the identity of the source content. Recent 2D image editing methods have achieved this balance by leveraging the stochastic latent encoded in the generative process of diffusion models. To extend the editing capabilities of diffusion models shown in pixel space to parameter space, we reformulate the 2D image editing method into an optimization form named PDS. PDS matches the stochastic latents of the source and the target, enabling the sampling of targets in diverse parameter spaces that align with a desired attribute while maintaining the source's identity. We demonstrate that this optimization resembles running a generative process with the target attribute, but aligning this process with the trajectory of the source's generative process. Extensive editing results in Neural Radiance Fields and Scalable Vector Graphics representations demonstrate that PDS is capable of sampling targets to fulfill the aforementioned balance across various parameter spaces.
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