Example Concept: Fine-tuning a diffusion model involves retraining the model on a dataset that is representative of the target artistic style. This can be achieved by using transfer learning techniques, where the model is initialized with pre-trained weights and then updated with the new style-specific data. The process requires careful selection of hyperparameters and may involve adjusting the learning rate, batch size, and number of training epochs to ensure the model learns the nuances of the desired style without overfitting.