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We present PyGFit, a program designed to measure PSF-matched photometry fromimages with disparate pixel scales and PSF sizes. While PyGFit has a number ofuses, its primary purpose is to extract robust spectral energy distributions(SEDs) from crowded images. It does this by fitting blended sources in crowded,low resolution images with models generated from a higher resolution image.This approach minimizes the impact of crowding and also yields consistentlymeasured fluxes in different filters, minimizing systematic uncertainty in thefinal SEDs. We present an example of applying PyGFit to real data and performsimulations to test its fidelity. The uncertainty in the best-fit flux risessharply as a function of nearest-neighbor distance for objects with a neighborwithin 60% of the PSF size. Similarly, the uncertainty increases quickly forobjects blended with a neighbor more than four times brighter. For all otherobjects the fidelity of PyGFit's results depends only on flux, and theuncertainty is primarily limited by sky noise.

PyGFit: A Tool for Extracting PSF Matched Photometry