| 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 | |