Spaces:
Runtime error
Runtime error
File size: 2,535 Bytes
66d92ae |
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 57 58 59 60 61 |
import os
import numpy as np
import torch
from transformers import CLIPConfig, CLIPImageProcessor
import ldm_patched.modules.model_management as model_management
import modules.config
from extras.safety_checker.models.safety_checker import StableDiffusionSafetyChecker
from ldm_patched.modules.model_patcher import ModelPatcher
safety_checker_repo_root = os.path.join(os.path.dirname(__file__), 'safety_checker')
config_path = os.path.join(safety_checker_repo_root, "configs", "config.json")
preprocessor_config_path = os.path.join(safety_checker_repo_root, "configs", "preprocessor_config.json")
class Censor:
def __init__(self):
self.safety_checker_model: ModelPatcher | None = None
self.clip_image_processor: CLIPImageProcessor | None = None
self.load_device = torch.device('cpu')
self.offload_device = torch.device('cpu')
def init(self):
if self.safety_checker_model is None and self.clip_image_processor is None:
safety_checker_model = modules.config.downloading_safety_checker_model()
self.clip_image_processor = CLIPImageProcessor.from_json_file(preprocessor_config_path)
clip_config = CLIPConfig.from_json_file(config_path)
model = StableDiffusionSafetyChecker.from_pretrained(safety_checker_model, config=clip_config)
model.eval()
self.load_device = model_management.text_encoder_device()
self.offload_device = model_management.text_encoder_offload_device()
model.to(self.offload_device)
self.safety_checker_model = ModelPatcher(model, load_device=self.load_device, offload_device=self.offload_device)
def censor(self, images: list | np.ndarray) -> list | np.ndarray:
self.init()
model_management.load_model_gpu(self.safety_checker_model)
single = False
if not isinstance(images, (list, np.ndarray)):
images = [images]
single = True
safety_checker_input = self.clip_image_processor(images, return_tensors="pt")
safety_checker_input.to(device=self.load_device)
checked_images, has_nsfw_concept = self.safety_checker_model.model(images=images,
clip_input=safety_checker_input.pixel_values)
checked_images = [image.astype(np.uint8) for image in checked_images]
if single:
checked_images = checked_images[0]
return checked_images
default_censor = Censor().censor
|