Example Concept: Fine-tuning a diffusion model involves retraining the model on a curated dataset that contains high-quality examples of the textures you want to generate. This process adjusts the model's weights to better capture the intricate details and nuances of the textures, enhancing the realism of the generated images. Key steps include selecting a pre-trained model, preparing a dataset with diverse texture samples, and using transfer learning techniques to update the model's parameters.