remove nsfw checker

app.py

@@ -186,15 +186,15 @@ def load_models(model_path, enable_compile=False, attention_backend="native"):
 
     pipe.to("cuda", torch.bfloat16)
 
-    from diffusers.pipelines.stable_diffusion import StableDiffusionSafetyChecker
-    from transformers import CLIPImageProcessor
+    # from diffusers.pipelines.stable_diffusion import StableDiffusionSafetyChecker
+    # from transformers import CLIPImageProcessor
 
-    safety_model_id = "CompVis/stable-diffusion-safety-checker"
-    safety_feature_extractor = CLIPImageProcessor.from_pretrained(safety_model_id)
-    safety_checker = StableDiffusionSafetyChecker.from_pretrained(safety_model_id, torch_dtype=torch.float16).to("cuda")
+    # safety_model_id = "CompVis/stable-diffusion-safety-checker"
+    # safety_feature_extractor = CLIPImageProcessor.from_pretrained(safety_model_id)
+    # safety_checker = StableDiffusionSafetyChecker.from_pretrained(safety_model_id, torch_dtype=torch.float16).to("cuda")
 
-    pipe.safety_feature_extractor = safety_feature_extractor
-    pipe.safety_checker = safety_checker
+    # pipe.safety_feature_extractor = safety_feature_extractor
+    # pipe.safety_checker = safety_checker
     return pipe
 
 
@@ -439,15 +439,15 @@ def generate(
         if pipe is None:
             raise gr.Error("Model not loaded.")
 
-        has_unsafe_concept = is_unsafe_prompt(
-            pipe.text_encoder,
-            pipe.tokenizer,
-            system_prompt=UNSAFE_PROMPT_CHECK,
-            user_prompt=prompt,
-            max_new_token=UNSAFE_MAX_NEW_TOKEN,
-        )
-        if has_unsafe_concept:
-            raise UnsafeContentError("Input unsafe")
+        # has_unsafe_concept = is_unsafe_prompt(
+        #     pipe.text_encoder,
+        #     pipe.tokenizer,
+        #     system_prompt=UNSAFE_PROMPT_CHECK,
+        #     user_prompt=prompt,
+        #     max_new_token=UNSAFE_MAX_NEW_TOKEN,
+        # )
+        # if has_unsafe_concept:
+        #     raise UnsafeContentError("Input unsafe")
 
         final_prompt = prompt
 
@@ -470,10 +470,10 @@ def generate(
             shift=shift,
         )
 
-        safety_checker_input = pipe.safety_feature_extractor([image], return_tensors="pt").pixel_values.cuda()
-        _, has_nsfw_concept = pipe.safety_checker(images=[torch.zeros(1)], clip_input=safety_checker_input)
-        has_nsfw_concept = has_nsfw_concept[0]
-        if has_nsfw_concept:
+        # safety_checker_input = pipe.safety_feature_extractor([image], return_tensors="pt").pixel_values.cuda()
+        # _, has_nsfw_concept = pipe.safety_checker(images=[torch.zeros(1)], clip_input=safety_checker_input)
+        # has_nsfw_concept = has_nsfw_concept[0]
+        # if has_nsfw_concept:
             raise UnsafeContentError("input unsafe")
 
     except UnsafeContentError: