feat: add nsfw image censoring via config and checkbox (#958)

* add nsfw image censoring

activatable via config, uses CompVis/stable-diffusion-safety-checker

* fix progressbar call for nsfw output

* use config to set cache dir for safety checker

* add checkbox black_out_nsfw

makes both enabling via config and checkbox possible, where config overrides the checkbox value

* fix: add missing diffusers package

* feat: extract safety checker, remove dependency to diffusers

* feat: make code compatible again after merge with main

* feat: move censor to extras, optimize safety checker file handling

* refactor: rename folder safety_checker_models to safety_checker

extras/censor.py

@@ -0,0 +1,56 @@
+# modified version of https://github.com/AUTOMATIC1111/stable-diffusion-webui-nsfw-censor/blob/master/scripts/censor.py
+import numpy as np
+import os
+
+from extras.safety_checker.models.safety_checker import StableDiffusionSafetyChecker
+from transformers import CLIPFeatureExtractor, CLIPConfig
+from PIL import Image
+import modules.config
+
+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")
+
+safety_feature_extractor = None
+safety_checker = None
+
+
+def numpy_to_pil(image):
+    image = (image * 255).round().astype("uint8")
+    pil_image = Image.fromarray(image)
+
+    return pil_image
+
+
+# check and replace nsfw content
+def check_safety(x_image):
+    global safety_feature_extractor, safety_checker
+
+    if safety_feature_extractor is None or safety_checker is None:
+        safety_checker_model = modules.config.downloading_safety_checker_model()
+        safety_feature_extractor = CLIPFeatureExtractor.from_json_file(preprocessor_config_path)
+        clip_config = CLIPConfig.from_json_file(config_path)
+        safety_checker = StableDiffusionSafetyChecker.from_pretrained(safety_checker_model, config=clip_config)
+
+    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_single(x):
+    x_checked_image, has_nsfw_concept = check_safety(x)
+
+    # replace image with black pixels, keep dimensions
+    # workaround due to different numpy / pytorch image matrix format
+    if has_nsfw_concept[0]:
+        imageshape = x_checked_image.shape
+        x_checked_image = np.zeros((imageshape[0], imageshape[1], 3), dtype = np.uint8)
+
+    return x_checked_image
+
+
+def censor_batch(images):
+    images = [censor_single(image) for image in images]
+
+    return images