  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.
