Z‑Image Turbo is a bit different from “classic” Stable Diffusion, so a lot of old prompting habits don’t quite apply. I’ll walk through how to prompt it deeply and safely, with special focus on controlling content (nudity, stereotypes, unwanted artifacts) even though the model does not support traditional negative prompts at all. ([Hugging Face][1])
Key facts that matter for prompting:
| #!/usr/bin/env python3 | |
| """ | |
| Expose Ollama models to LM Studio by symlinking its model files. | |
| NOTE: On Windows, you need to run this script with administrator privileges. | |
| """ | |
| import json | |
| import os | |
| from pathlib import Path |
| import requests | |
| import csv | |
| import time | |
| # Base URL without the page parameter | |
| base_url = 'https://danbooru.donmai.us/tags.json?limit=1000&search[hide_empty]=yes&search[is_deprecated]=no&search[order]=count' | |
| # Specify the filename for the CSV | |
| csv_filename = 'danbooru_tags_post_count.csv' |