Combitech AB was interested in how to automate the process of annotating aluminum scrap when it was adjacent to other metals. This was to ultimately create an annotated dataset that could be utilized for training a segmentation model. The idea was to make use of generative models to generate samples of general scrap metals. Then, with this model, introduce a small dataset of only aluminum, to try to change the features into a domain suitable for aluminum. Since the contents of the samples were generated separately, the system would know where the aluminum was and could then annotate it.  This master's thesis aimed to investigate whether it was possible to construct generative models to generate these samples and see if they had realistic characteristics. It was also investigated if it was possible to get a meaningful model based on a relatively small dataset (aluminum in this case). The data used were two datasets, one with general scrap metal (excluding aluminum) and the other containing only aluminum scrap. Unconditional diffusion models were utilized as generative models. The scrap model achieved satisfactory results, making it possible to generate samples that carried similar properties as the real scrap dataset. When it came to aluminum, which had a much smaller dataset than the scrap dataset, it was possible to get promising results when utilizing transfer learning. However, the same good quality as the scrap model gave was not achieved. This master's thesis has shown that it is possible to get a model to generate realistic-looking images of scrap metal. Furthermore, this scrap model served as a good base when training other generative models to generate images of metals, even if the provided datasets were small. In this way, a foundation was laid for an investigation of an automatic annotation system.