Generative models have, over the past decade, become one of the most important areas of artificial intelligence and computer vision. Among the most successful approaches, generative adversarial networks and diffusion models stand out. In this thesis, we presented the fundamental principles of both methods, analyzed their advantages and disadvantages, and practically compared them with each other. The goal is to provide a comprehensive overview of their functioning and to assess which method is more suitable for specific tasks.
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