Implementation of Diffusion Model in Realistic Face Generation

This research focuses on adapting and fine-tuning diffusion models specifically to realistic face generation which has emerged as a compelling research area. By following a novel architecture that combines the diffusion process with a latent space model, which is popularly known as Stable Diffusion, enabling it to focus on facial attributes such as age, gender, facial features, and so on, without requiring massive computational units. Furthermore, we are using a dataset having diverse facial images to train and evaluate the performance of our model. This study looks into numerous sectors where the applications of this realistic face-generation technique can make the overall process more efficient including the consideration of related practical challenges. Our custom fine-tuned model has been put up for comparison with state-of-the-art diffusion models in the sector of face generation based on prompts of their facial descriptions.