  Reverse sampling and score-distillation have emerged as main workhorses in
recent years for image manipulation using latent diffusion models (LDMs). While
reverse diffusion sampling often requires adjustments of LDM architecture or
feature engineering, score distillation offers a simple yet powerful
model-agnostic approach, but it is often prone to mode-collapsing. To address
these limitations and leverage the strengths of both approaches, here we
introduce a novel framework called {\em DreamSampler}, which seamlessly
integrates these two distinct approaches through the lens of regularized latent
optimization. Similar to score-distillation, DreamSampler is a model-agnostic
approach applicable to any LDM architecture, but it allows both distillation
and reverse sampling with additional guidance for image editing and
reconstruction. Through experiments involving image editing, SVG reconstruction
and etc, we demonstrate the competitive performance of DreamSampler compared to
existing approaches, while providing new applications.
