  We describe and demonstrate an optimization-based x-ray image reconstruction
framework called Adorym. Our framework provides a generic forward model,
allowing one code framework to be used for a wide range of imaging methods
ranging from near-field holography to and fly-scan ptychographic tomography. By
using automatic differentiation for optimization, Adorym has the flexibility to
refine experimental parameters including probe positions, multiple hologram
alignment, and object tilts. It is written with strong support for parallel
processing, allowing large datasets to be processed on high-performance
computing systems. We demonstrate its use on several experimental datasets to
show improved image quality through parameter refinement.
