  With upcoming wide field surveys from the ground and space the number of
known dwarf galaxies at $\lesssim 25$ Mpc is expected to dramatically increase.
Insight into their nature and analyses of these systems' intrinsic properties
will rely on reliable distance estimates. Traditional techniques such as tip of
the red giant branch (TRGB) or surface brightness fluctuations (SBF) are
limited in their widespread applicability, especially in the semi-resolved
regime. In this work we turn to the rapidly growing field of simulation based
inference to infer distances, and other physical properties, of dwarf galaxies
directly from multi-band images. We introduce Silkscreen: a code leveraging
neural posterior estimation to infer the posterior distribution of parameters
while simultaneously training a convolutional neural network to extract summary
statistics from the images. Utilizing this combination of machine learning and
Bayesian inference, we demonstrate the method's ability to recover accurate
distances from ground-based survey images for a set of nearby galaxies ($2 < D
({\rm Mpc)} < 12$) with measured SBF or TRGB distances. We discuss caveats of
the current implementation along with future prospects, focusing on the goal of
amortized inference. For surveys like LSST, Silkscreen, once amortized, will
facilitate inference for new dwarf galaxies in a matter of seconds using only
broadband cutouts. While we focus here on dwarf galaxies, we note that this
method can be generalized to more luminous systems as well.
