Instructions to use metrosir/sd with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use metrosir/sd with Transformers:
# Load model directly from transformers import AutoModel model = AutoModel.from_pretrained("metrosir/sd", device_map="auto") - Notebooks
- Google Colab
- Kaggle
| import requests | |
| import random | |
| import time | |
| import base64 | |
| import hashlib | |
| import json | |
| def lightning(): | |
| start = int(time.time()) | |
| url = "https://wsoqr-01gwy9mc1gzh3b4ce9b708vp31.litng-ai-03.litng.ai/predict" | |
| form = { | |
| "prompt": "extremely detailed CG unity 8k wallpaper, masterpiece, best quality, ultra-detailed, best illustration, best shadow, photorealistic:1.4, 1 gorgeous girls,oversize pink_hoodie,under eiffel tower,grey_hair:1.1, collarbone,puffy breasts:1.5,full body shot,shiny eyes,enjoyable expression,evil smile,slim legs,narrow waist,detailed face, looking at viewer,looking back,gorgeous skin,short curly hair,kneeling,puffy ass up,climbing,lying,rosy pussy,nsfw,insert left_hand into pussy", | |
| } | |
| resp = requests.post(url, json=form) | |
| resp_data = json.loads(resp.content) | |
| print(resp.status_code, '\n', resp_data) | |
| print("time cost(ms): ", int(time.time())*1e3-start*1e3) | |
| lightning() |