Update app.py

app.py

@@ -11,10 +11,12 @@ 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=False, revision="fp16", torch_dtype=torch.float16)
+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="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")
+word_list_dataset = load_dataset("stabilityai/word-list", data_files="list.txt", use_auth_token=True)
 word_list = word_list_dataset["train"]['text']
 
 def infer(prompt, samples, steps, scale, seed):
@@ -25,16 +27,14 @@ def infer(prompt, samples, steps, scale, seed):
         
     generator = torch.Generator(device=device).manual_seed(seed)
     
-    #If you are running locally with CPU, you can remove the `with autocast("cuda")`
-    #with autocast("cuda"):
-        #images_list = pipe(
-            #[prompt] * samples,
-            #num_inference_steps=steps,
-           #guidance_scale=scale,
-            #generator=generator,
-        #)
+    images_list = pipe(
+        [prompt] * samples,
+        num_inference_steps=steps,
+        guidance_scale=scale,
+        generator=generator,
+    )
     images = []
-    #safe_image = Image.open(r"unsafe.png")
+    safe_image = Image.open(r"unsafe.png")
     for i, image in enumerate(images_list["sample"]):
         if(images_list["nsfw_content_detected"][i]):
             images.append(safe_image)