<p>The generation of synthetic images can be an important element in supporting the augmentation and analysis of multimedia data. It has applications in many scientific fields. Also, in geological and mining sciences. This study presents generative artificial intelligence approaches, particularly on Generative Adversarial Networks (GANs) and diffusion models (Stable Diffusion), as widely used techniques for generating new data based on existing training datasets. The performance of these algorithms and the results obtained both with and without transfer learning, using local resources as well as commercial solutions offering high resolution of the generated images, are presented. Results are presented from text to image, image(s) to image, and text/image(s) to image scenarios. Local model training, transfer learning based on a predefined model, and transfer learning using commercial tools were used. The results indicate that the choice of architecture and model significantly influences the quality of generated images, ranging from visuals that differ from real-world data to high-resolution representations that are nearly indistinguishable from original samples. As a result of this work, the possibilities of generating synthetic data as a tool to support geological and mining research were presented, considering the technological and practical aspects of implementing these solutions.</p>