Contextualizing museum objects into their historical scene is important, both for understanding how those items are genuinely used and for testing the capabilities of Stable Difussion (SD) methods to generate historical scenes with unusual, ancient items. For these aims, we propose two approaches based on SD: one that relies on inpainting and outpainting and one that uses LoRA model training and generation again with SD. We make a comparison between the two approaches. We evaluate how these methods excel and fail, respectively, with the help of two cultural heritage experts. They evaluate the produced images based on six criteria that take into account information like historical context alignment, coherence and aesthetic quality, object integration, proportionality, clothing accuracy and atmospheric appropriateness, respectively.