Update app.py

app.py

@@ -1,7 +1,7 @@
 import gradio as gr
-#import torch
+import torch
 #from torch import autocast
-#from diffusers import StableDiffusionPipeline
+from diffusers import StableDiffusionPipeline
 from datasets import load_dataset
 from PIL import Image  
 #from io import BytesIO
@@ -17,9 +17,9 @@ model_id = "CompVis/stable-diffusion-v1-4"
 device = "cuda"
 
 #If you are running this code locally, you need to either do a 'huggingface-cli login` or paste your User Access Token from here https://huggingface.co/settings/tokens into the use_auth_token field below. 
-#pipe = StableDiffusionPipeline.from_pretrained(model_id, use_auth_token=True, revision="fp16", torch_dtype=torch.float16)
-#pipe = pipe.to(device)
-#torch.backends.cudnn.benchmark = True
+pipe = StableDiffusionPipeline.from_pretrained(model_id, use_auth_token=True, revision="fp16", torch_dtype=torch.float16)
+pipe = pipe.to(device)
+torch.backends.cudnn.benchmark = True
 
 ##When running locally, you won`t have access to this, so you can remove this part
 #word_list_dataset = load_dataset("stabilityai/word-list", data_files="list.txt", use_auth_token=True)
@@ -31,10 +31,10 @@ def infer(prompt):
     samples = 4
     steps = 50
     scale = 7.5
-    #When running locally you can also remove this filter
-    for filter in word_list:
-        if re.search(rf"\b{filter}\b", prompt):
-            raise gr.Error("Unsafe content found. Please try again with different prompts.")
+    ##When running locally you can also remove this filter
+    #for filter in word_list:
+     #   if re.search(rf"\b{filter}\b", prompt):
+      #      raise gr.Error("Unsafe content found. Please try again with different prompts.")
         
     #generator = torch.Generator(device=device).manual_seed(seed)
     #print("Is GPU busy? ", is_gpu_busy)