Update app.py

app.py

@@ -11,10 +11,10 @@ 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 = StableDiffusionPipeline.from_pretrained(model_id, use_auth_token="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", revision="fp16", torch_dtype=torch.float16)
 pipe = pipe.to(device)
 #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)
+word_list_dataset = load_dataset("stabilityai/word-list", data_files="list.txt", use_auth_token="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")
 word_list = word_list_dataset["train"]['text']
 
 def infer(prompt, samples, steps, scale, seed):
@@ -26,15 +26,15 @@ 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,
-        )
+    #with autocast("cuda"):
+        #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)