“Hallucination” is a term used in the AI community to describe the plausible falsehoods produced by deep generative neural networks. It is often considered a negative, especially in relation with large language models or medical image reconstruction. Yet, in many computational photography applications, we rely on such hallucinations to create pleasing images. It often does not matter if all (or any) information was present in the real world if the produced falsehoods are visually plausible. Starting from that premise, I will present our recent work on hallucinations in image reconstruction, semantic image editing, and novel image synthesis, using different generative models such as generative adversarial networks, diffusion models, and neural cellular automata.