Update app.py

app.py

@@ -4,7 +4,6 @@ import random
 from diffusers import DiffusionPipeline
 import torch
 
-# Set device to GPU if available, else CPU
 device = "cuda" if torch.cuda.is_available() else "cpu"
 
 if torch.cuda.is_available():
@@ -20,27 +19,28 @@ MAX_SEED = np.iinfo(np.int32).max
 MAX_IMAGE_SIZE = 1024
 
 def infer(prompt, negative_prompt, seed, randomize_seed, width, height, guidance_scale, num_inference_steps):
+
     if randomize_seed:
         seed = random.randint(0, MAX_SEED)
         
-    generator = torch.Generator(device).manual_seed(seed)
+    generator = torch.Generator().manual_seed(seed)
     
     image = pipe(
-        prompt=prompt, 
-        negative_prompt=negative_prompt,
-        guidance_scale=guidance_scale, 
-        num_inference_steps=num_inference_steps, 
-        width=width, 
-        height=height,
-        generator=generator
+        prompt = prompt, 
+        negative_prompt = negative_prompt,
+        guidance_scale = guidance_scale, 
+        num_inference_steps = num_inference_steps, 
+        width = width, 
+        height = height,
+        generator = generator
     ).images[0] 
     
     return image
 
 examples = [
-    ["Astronaut in a jungle, cold color palette, muted colors, detailed, 8k"],
-    ["An astronaut riding a green horse"],
-    ["A delicious ceviche cheesecake slice"],
+    "Astronaut in a jungle, cold color palette, muted colors, detailed, 8k",
+    "An astronaut riding a green horse",
+    "A delicious ceviche cheesecake slice",
 ]
 
 css="""
@@ -50,9 +50,13 @@ css="""
 }
 """
 
-power_device = "GPU" if torch.cuda.is_available() else "CPU"
+if torch.cuda.is_available():
+    power_device = "GPU"
+else:
+    power_device = "CPU"
 
 with gr.Blocks(css=css) as demo:
+    
     with gr.Column(elem_id="col-container"):
         gr.Markdown(f"""
         # Text-to-Image Gradio Template
@@ -60,23 +64,26 @@ with gr.Blocks(css=css) as demo:
         """)
         
         with gr.Row():
-            prompt = gr.Textbox(
+            
+            prompt = gr.Text(
                 label="Prompt",
                 show_label=False,
                 max_lines=1,
                 placeholder="Enter your prompt",
                 container=False,
             )
+            
             run_button = gr.Button("Run", scale=0)
         
         result = gr.Image(label="Result", show_label=False)
 
         with gr.Accordion("Advanced Settings", open=False):
-            negative_prompt = gr.Textbox(
+            
+            negative_prompt = gr.Text(
                 label="Negative prompt",
                 max_lines=1,
                 placeholder="Enter a negative prompt",
-                visible=True,
+                visible=False,
             )
             
             seed = gr.Slider(
@@ -90,6 +97,7 @@ with gr.Blocks(css=css) as demo:
             randomize_seed = gr.Checkbox(label="Randomize seed", value=True)
             
             with gr.Row():
+                
                 width = gr.Slider(
                     label="Width",
                     minimum=256,
@@ -107,33 +115,32 @@ with gr.Blocks(css=css) as demo:
                 )
             
             with gr.Row():
+                
                 guidance_scale = gr.Slider(
                     label="Guidance scale",
                     minimum=0.0,
                     maximum=10.0,
                     step=0.1,
-                    value=7.5,
+                    value=0.0,
                 )
                 
                 num_inference_steps = gr.Slider(
                     label="Number of inference steps",
                     minimum=1,
-                    maximum=50,
+                    maximum=12,
                     step=1,
-                    value=25,
+                    value=2,
                 )
         
         gr.Examples(
-            examples=examples,
-            inputs=[prompt],
-            outputs=[result],
-            fn=infer
+            examples = examples,
+            inputs = [prompt]
         )
 
     run_button.click(
-        fn=infer,
-        inputs=[prompt, negative_prompt, seed, randomize_seed, width, height, guidance_scale, num_inference_steps],
-        outputs=[result]
+        fn = infer,
+        inputs = [prompt, negative_prompt, seed, randomize_seed, width, height, guidance_scale, num_inference_steps],
+        outputs = [result]
     )
 
-demo.queue().launch(share=True)
+demo.queue().launch()