Interest in spatial omics is on the rise, but generation of highly multiplexed images used in many spatial analyses remains challenging, due to cost, expertise, methodical constraints, and/or access to technology. To remove these barriers, and thereby increase access to highly multiplexed images, we have developed the Virtual Alignment of pathoLogy Image Series (VALIS) software, which enables one to rapidly and easily generate highly multiplexed images by aligning (registering) any number of whole slide images (WSI) that were serially sliced and/or cyclically stained using immunohistochemistry (IHC) and/or immunofluorescence (IF). VALIS can save the registered WSI in the opensource ome.tiff format, facilitating downstream spatial analyses. Herein, we present two such examples, one based on a 32 marker IF image created by registering and merging 11 rounds of cyclic IF (CyCIF), and a second based on an 18 marker image created by registering, processing, and merging 18 brightfield images cyclically stained using IHC. In addition to being easy to use, VALIS is robust, having been tested with 273 IHC samples and 340 IF samples, each of which contained between 2-69 images per sample. The registered WSI tend to have low error and are completed within a matter of minutes. VALIS can also use the registration parameters to warp point data, such as cell centroids previously determined via cell segmentation. VALIS is written in Python and requires only few lines of code for execution. VALIS therefore provides a free, opensource, flexible, and easy to use pipeline for rigid and non-rigid registration of IF and/or IHC WSI, facilitating spatial analyses of prospective and existing datasets, breathing new life into the countless collections of brightfield and immunofluorescence images.