  Score distillation sampling (SDS) has proven to be an important tool,
enabling the use of large-scale diffusion priors for tasks operating in
data-poor domains. Unfortunately, SDS has a number of characteristic artifacts
that limit its usefulness in general-purpose applications. In this paper, we
make progress toward understanding the behavior of SDS and its variants by
viewing them as solving an optimal-cost transport path from a source
distribution to a target distribution. Under this new interpretation, these
methods seek to transport corrupted images (source) to the natural image
distribution (target). We argue that current methods' characteristic artifacts
are caused by (1) linear approximation of the optimal path and (2) poor
estimates of the source distribution. We show that calibrating the text
conditioning of the source distribution can produce high-quality generation and
translation results with little extra overhead. Our method can be easily
applied across many domains, matching or beating the performance of specialized
methods. We demonstrate its utility in text-to-2D, text-based NeRF
optimization, translating paintings to real images, optical illusion
generation, and 3D sketch-to-real. We compare our method to existing approaches
for score distillation sampling and show that it can produce high-frequency
details with realistic colors.
