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

modules/processing.py

@@ -19,20 +19,11 @@ 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
 
 
-
 class StableDiffusionProcessing:
     def __init__(self, sd_model=None, outpath_samples=None, outpath_grids=None, prompt="", prompt_style="None", seed=-1, subseed=-1, subseed_strength=0, seed_resize_from_h=-1, seed_resize_from_w=-1, sampler_index=0, batch_size=1, n_iter=1, steps=50, cfg_scale=7.0, width=512, height=512, restore_faces=False, tiling=False, do_not_save_samples=False, do_not_save_grid=False, extra_generation_params=None, overlay_images=None, negative_prompt=None):
         self.sd_model = sd_model
@@ -154,28 +145,6 @@ 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"""
 
@@ -279,9 +248,8 @@ def process_images(p: StableDiffusionProcessing) -> Processed:
             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)
+                import modules.safety as safety
+                x_samples_ddim = modules.safety.censor_batch(x_samples_ddim)
 
             for i, x_sample in enumerate(x_samples_ddim):
                 x_sample = 255. * np.moveaxis(x_sample.cpu().numpy(), 0, 2)

modules/safety.py

+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