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text-driven parametric image editing that matches the latents encoded in the posterior of a diffusion model’s forward process.
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site name
author
updated
2026-03-03 06:43:54
raw text
Posterior Distillation Sampling Posterior Distillation Sampling CVPR 2024 Juil Koo , Chanho Park , Minhyuk Sung KAIST PDF arXiv Code Bibtex Abstract 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 ...
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