put safety checker into a separate file because it's already crowded in processing

modules/safety.py

@@ -0,0 +1,42 @@
+import torch
+from diffusers.pipelines.stable_diffusion.safety_checker import StableDiffusionSafetyChecker
+from transformers import AutoFeatureExtractor
+from PIL import Image
+
+import modules.shared as shared
+
+safety_model_id = "CompVis/stable-diffusion-safety-checker"
+safety_feature_extractor = None
+safety_checker = None
+
+def numpy_to_pil(images):
+    """
+    Convert a numpy image or a batch of images to a PIL image.
+    """
+    if images.ndim == 3:
+        images = images[None, ...]
+    images = (images * 255).round().astype("uint8")
+    pil_images = [Image.fromarray(image) for image in images]
+
+    return pil_images
+
+# check and replace nsfw content
+def check_safety(x_image):
+    global safety_feature_extractor, safety_checker
+
+    if safety_feature_extractor is None:
+        safety_feature_extractor = AutoFeatureExtractor.from_pretrained(safety_model_id)
+        safety_checker = StableDiffusionSafetyChecker.from_pretrained(safety_model_id)
+
+    safety_checker_input = safety_feature_extractor(numpy_to_pil(x_image), return_tensors="pt")
+    x_checked_image, has_nsfw_concept = safety_checker(images=x_image, clip_input=safety_checker_input.pixel_values)
+
+    return x_checked_image, has_nsfw_concept
+
+
+def censor_batch(x):
+    x_samples_ddim_numpy = x.cpu().permute(0, 2, 3, 1).numpy()
+    x_checked_image, has_nsfw_concept = check_safety(x_samples_ddim_numpy)
+    x = torch.from_numpy(x_checked_image).permute(0, 3, 1, 2)
+
+    return x