How can I fine-tune a Stable Diffusion model for more realistic portrait generation?

Example Concept: Fine-tuning a Stable Diffusion model involves using a pre-trained model as a starting point and further training it with a curated dataset of high-quality portraits. This process adjusts the model's weights to better capture the nuances of realistic human features, lighting, and textures. The training should be done with careful parameter tuning, such as learning rate and batch size, to avoid overfitting and ensure the model generalizes well to new inputs.