File size: 2,147 Bytes
2065c15 | 1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24 25 26 27 28 29 30 31 32 33 34 35 36 37 38 39 40 41 42 43 44 45 46 47 48 49 50 51 52 53 54 55 56 | from transformers import pipeline
from PIL import Image
import io
import logging
import os
import comfy.model_management as model_management
from reactor_utils import download
from scripts.reactor_logger import logger
MODEL_EXISTS = False
def ensure_nsfw_model(nsfwdet_model_path):
"""Download NSFW detection model if it doesn't exist"""
global MODEL_EXISTS
downloaded = 0
nd_urls = [
"https://huggingface.co/AdamCodd/vit-base-nsfw-detector/resolve/main/config.json",
"https://huggingface.co/AdamCodd/vit-base-nsfw-detector/resolve/main/model.safetensors",
"https://huggingface.co/AdamCodd/vit-base-nsfw-detector/resolve/main/preprocessor_config.json",
]
for model_url in nd_urls:
model_name = os.path.basename(model_url)
model_path = os.path.join(nsfwdet_model_path, model_name)
if not os.path.exists(model_path):
if not os.path.exists(nsfwdet_model_path):
os.makedirs(nsfwdet_model_path)
download(model_url, model_path, model_name)
if os.path.exists(model_path):
downloaded += 1
MODEL_EXISTS = True if downloaded == 3 else False
return MODEL_EXISTS
SCORE = 0.96
logging.getLogger("transformers").setLevel(logging.ERROR)
def nsfw_image(img_data, model_path: str):
if not MODEL_EXISTS:
logger.status("Ensuring NSFW detection model exists...")
if not ensure_nsfw_model(model_path):
return True
device = model_management.get_torch_device()
with Image.open(io.BytesIO(img_data)) as img:
if "cpu" in str(device):
predict = pipeline("image-classification", model=model_path)
else:
device_id = 0
if "cuda" in str(device):
device_id = int(str(device).split(":")[1])
predict = pipeline("image-classification", model=model_path, device=device_id)
result = predict(img)
if result[0]["label"] == "nsfw" and result[0]["score"] > SCORE:
logger.status(f'NSFW content detected with score={result[0]["score"]}, skipping...')
return True
return False
|