diff --git a/modules/processing.py b/modules/processing.py
index 92bf66f..e777a96 100644
--- a/modules/processing.py
+++ b/modules/processing.py
@@ -19,6 +19,14 @@ import modules.face_restoration
 import modules.images as images
 import modules.styles
 
+from diffusers.pipelines.stable_diffusion.safety_checker import StableDiffusionSafetyChecker
+from transformers import AutoFeatureExtractor
+
+# load safety model
+safety_model_id = "CompVis/stable-diffusion-safety-checker"
+safety_feature_extractor = None
+safety_checker = None
+
 # some of those options should not be changed at all because they would break the model, so I removed them from options.
 opt_C = 4
 opt_f = 8
@@ -146,6 +154,28 @@ def fix_seed(p):
     p.subseed = int(random.randrange(4294967294)) if p.subseed is None or p.subseed == -1 else p.subseed
 
 
+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 process_images(p: StableDiffusionProcessing) -> Processed:
     """this is the main loop that both txt2img and img2img use; it calls func_init once inside all the scopes and func_sample once per batch"""
 
@@ -248,6 +278,11 @@ def process_images(p: StableDiffusionProcessing) -> Processed:
             x_samples_ddim = p.sd_model.decode_first_stage(samples_ddim)
             x_samples_ddim = torch.clamp((x_samples_ddim + 1.0) / 2.0, min=0.0, max=1.0)
 
+            if opts.filter_nsfw:
+                x_samples_ddim_numpy = x_samples_ddim.cpu().permute(0, 2, 3, 1).numpy()
+                x_checked_image, has_nsfw_concept = check_safety(x_samples_ddim_numpy)
+                x_samples_ddim = torch.from_numpy(x_checked_image).permute(0, 3, 1, 2)
+
             for i, x_sample in enumerate(x_samples_ddim):
                 x_sample = 255. * np.moveaxis(x_sample.cpu().numpy(), 0, 2)
                 x_sample = x_sample.astype(np.uint8)
diff --git a/modules/shared.py b/modules/shared.py
index 891d7fb..37c333f 100644
--- a/modules/shared.py
+++ b/modules/shared.py
@@ -111,6 +111,7 @@ class Options:
         "outdir_save": OptionInfo("log/images", "Directory for saving images using the Save button", component_args=hide_dirs),
         "samples_save": OptionInfo(True, "Save indiviual samples"),
         "samples_format": OptionInfo('png', 'File format for individual samples'),
+        "filter_nsfw": OptionInfo(False, "Filter NSFW content"),
         "grid_save": OptionInfo(True, "Save image grids"),
         "return_grid": OptionInfo(True, "Show grid in results for web"),
         "grid_format": OptionInfo('png', 'File format for grids'),