Update app.py

app.py

@@ -4,28 +4,20 @@ from torch import autocast
 from diffusers import StableDiffusionPipeline
 
 model_id = "CompVis/stable-diffusion-v1-4"
-pipe = StableDiffusionPipeline.from_pretrained(model_id, use_auth_token='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', torch_dtype=torch.float32, low_cpu_mem_usage=True)
-has_cuda = torch.cuda.is_available()
-device = torch.device('cpu' if not has_cuda else 'cuda')    
+pipe = StableDiffusionPipeline.from_pretrained(model_id, use_auth_token='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', low_cpu_mem_usage=True)
+device = 'cpu'  
 pipe = pipe.to(device)
 
 def convert(prompt):
     samples = 4
-    generator = torch.Generator(device=device)
-    torch.cuda.empty_cache()
-    with autocast("cuda"):
-        images_list = pipe(
-            [prompt] * samples,
-            height=256, width=384,
-            num_inference_steps=50,
-        )
-        images = []
-        for i, image in enumerate(images_list["sample"]):
-            images.append(image)
+    images_list = pipe([prompt] * samples, height=256, width=384, num_inference_steps=50)
+    images = []
+    for i, image in enumerate(images_list["sample"]):
+        images.append(image)
     return images
 
 
 gr.Interface(convert,
              inputs = [gr.inputs.Textbox(label="Enter text")],    
-             outputs = [gr.outputs.Image(label="Generated Image")], 
+             outputs = [gr.outputs.Gallery(label="Images").style(grid=4)], 
              title="Text to Image Generation").launch()