This study aims to provide a comprehensive understanding of GANs and diffusion models in the context of deepfake generation. By exploring their architectures, functionalities, and comparative strengths and weaknesses, we seek to shed light on the intricacies of these technologies and their implications for the development and detection of deepfakes.