Example Concept: To fine-tune a diffusion model for a specific art style, you typically start with a pre-trained model and further train it using a dataset that contains images representative of the target style. This involves adjusting hyperparameters such as learning rate and batch size to ensure the model learns the nuances of the style without overfitting. Additionally, techniques like style transfer or incorporating style-specific loss functions can help in achieving more accurate stylistic outputs.