fix

Author: suzukimain

src/diffusers/pipelines/pipeline_utils.py

@@ -1986,3 +1986,19 @@ def unfuse_qkv_projections(self, unet: bool = True, vae: bool = True):
             else:
                 self.vae.unfuse_qkv_projections()
                 self.fusing_vae = False
+    
+    def safety_checker_Level(self, Level):
+        """
+        Adjust the filter intensity.
+        
+        Args:
+            Level (`int` or `float` or one of the following [`WEAK`], [`MEDIUM`], [`NOMAL`], [`STRONG`], [`MAX`])
+        """
+        _safety_checker = getattr(self, "safety_checker", None)
+        if _safety_checker is not None:
+            if hasattr(_safety_checker, "update_safety_checker_Level"):
+                self.safety_checker.update_safety_checker_Level(Level)
+            else:
+                logger.warning("`safety_checker_Level` is ignored because `update_safety_checker_Level` is not in `safety_checker`.")
+        else:
+            logger.warning("Since there is no `safety_checker`, `safety_checker_Level` is ignored.")

src/diffusers/pipelines/stable_diffusion/pipeline_stable_diffusion.py

@@ -563,12 +563,6 @@ def run_safety_checker(self, image, device, dtype):
                 images=image, clip_input=safety_checker_input.pixel_values.to(dtype)
             )
         return image, has_nsfw_concept
-    
-    def safety_checker_Level(self, Level):
-        if self.safety_checker is not None:
-            self.safety_checker.update_safety_checker_Level(Level)
-        else:
-            logger.warning("`safety_checker_Level` is ignored because `safety_checker=None` is passed.")
 
     def decode_latents(self, latents):
         deprecation_message = "The decode_latents method is deprecated and will be removed in 1.0.0. Please use VaeImageProcessor.postprocess(...) instead"

src/diffusers/pipelines/stable_diffusion/safety_checker.py

@@ -34,6 +34,7 @@ class StableDiffusionSafetyChecker(PreTrainedModel):
     main_input_name = "clip_input"
 
     _no_split_modules = ["CLIPEncoderLayer"]
+    
 
     def __init__(self, config: CLIPConfig):
         super().__init__(config)
@@ -46,8 +47,14 @@ def __init__(self, config: CLIPConfig):
 
         self.concept_embeds_weights = nn.Parameter(torch.ones(17), requires_grad=False)
         self.special_care_embeds_weights = nn.Parameter(torch.ones(3), requires_grad=False)
+        
+        self.adjustment = 0.0
 
     def update_safety_checker_Level(self, Level):
+        """
+        Args:
+            Level (`int` or `float` or one of the following [`WEAK`], [`MEDIUM`], [`NOMAL`], [`STRONG`], [`MAX`])
+        """
         Level_dict = {
             "WEAK": -1.0,
             "MEDIUM": -0.5,
@@ -56,11 +63,21 @@ def update_safety_checker_Level(self, Level):
             "MAX": 1.0,
             }
         if Level in Level_dict:
-            Level = Level_dict[Level] 
+            Level = Level_dict[Level]     
         if isinstance(Level, (float, int)): 
             setattr(self,"adjustment",Level)
         else:
             raise ValueError("`int` or `float` or one of the following ['WEAK'], ['MEDIUM'], ['NOMAL'], ['STRONG'], ['MAX']")
+        
+        if self.adjustment<0:
+            logger.warning(
+                f"You have disabled the safety checker for {self.__class__} by passing `safety_checker=None`. Ensure"
+                " that you abide to the conditions of the Stable Diffusion license and do not expose unfiltered"
+                " results in services or applications open to the public. Both the diffusers team and Hugging Face"
+                " strongly recommend to keep the safety filter enabled in all public facing circumstances, disabling"
+                " it only for use-cases that involve analyzing network behavior or auditing its results. For more"
+                " information, please have a look at https://github.com/huggingface/diffusers/pull/254 ."
+                )
 
     @torch.no_grad()
     def forward(self, clip_input, images):
@@ -78,20 +95,20 @@ def forward(self, clip_input, images):
 
             # increase this value to create a stronger `nfsw` filter
             # at the cost of increasing the possibility of filtering benign images
-            adjustment = 0.0
+            #adjustment = 0.0
 
             for concept_idx in range(len(special_cos_dist[0])):
                 concept_cos = special_cos_dist[i][concept_idx]
                 concept_threshold = self.special_care_embeds_weights[concept_idx].item()
-                result_img["special_scores"][concept_idx] = round(concept_cos - concept_threshold + adjustment, 3)
+                result_img["special_scores"][concept_idx] = round(concept_cos - concept_threshold + self.adjustment, 3)
                 if result_img["special_scores"][concept_idx] > 0:
                     result_img["special_care"].append({concept_idx, result_img["special_scores"][concept_idx]})
-                    adjustment = 0.01
+                    self.adjustment = 0.01
 
             for concept_idx in range(len(cos_dist[0])):
                 concept_cos = cos_dist[i][concept_idx]
                 concept_threshold = self.concept_embeds_weights[concept_idx].item()
-                result_img["concept_scores"][concept_idx] = round(concept_cos - concept_threshold + adjustment, 3)
+                result_img["concept_scores"][concept_idx] = round(concept_cos - concept_threshold + self.adjustment, 3)
                 if result_img["concept_scores"][concept_idx] > 0:
                     result_img["bad_concepts"].append(concept_idx)
 
@@ -124,9 +141,9 @@ def forward_onnx(self, clip_input: torch.Tensor, images: torch.Tensor):
 
         # increase this value to create a stronger `nsfw` filter
         # at the cost of increasing the possibility of filtering benign images
-        adjustment = 0.0
+        #adjustment = 0.0
 
-        special_scores = special_cos_dist - self.special_care_embeds_weights + adjustment
+        special_scores = special_cos_dist - self.special_care_embeds_weights + self.adjustment
         # special_scores = special_scores.round(decimals=3)
         special_care = torch.any(special_scores > 0, dim=1)
         special_adjustment = special_care * 0.01