The Historic Centre of Arequipa, a UNESCO World Heritage Site, contains buildings constructed mainly of sillar, a white volcanic ignimbrite, whose ornamented façades are time-consuming to document. This study evaluates whether image-to-image translation models can convert façade photographs into two-dimensional façade representations. A dataset of 578 heritage façades was collected through field photography and Google Street View, preprocessed through obstacle removal, homography-based rectification, and stitching, and paired with reference representations produced with generative tools and corrected by architects. Three models (Pix2Pix, Pix2PixHD, and Contrastive Unpaired Translation, CUT) were trained and evaluated on an independent test set of 66 façades using the mean absolute error (MAE), structural similarity index measure (SSIM), and peak signal-to-noise ratio (PSNR). Pix2Pix obtained the best scores (MAE = 7.24, SSIM = 0.768, PSNR = 22.90 dB), followed closely by Pix2PixHD, whereas CUT performed worse (MAE = 20.80, SSIM = 0.418, PSNR = 16.04 dB). An exploratory assessment of three buildings showed that 50–58% of the architectural components were successfully generated. The models reproduced the overall façade layout but not complex ornamentation. However, the limited availability of heritage-building data restricts the evaluation of model generalization.