Update app.py

app.py

@@ -4,9 +4,11 @@ import random
 from diffusers import DiffusionPipeline
 import torch
 
+# Check for GPU availability and set the device accordingly
+device = "cuda" if torch.cuda.is_available() else "cpu"
 
-if torch.cuda.is_available():
-    torch.cuda.max_memory_allocated(device=device)
+# Load the diffusion pipeline based on the availability of GPU
+if device == "cuda":
     pipe = DiffusionPipeline.from_pretrained("stabilityai/sdxl-turbo", torch_dtype=torch.float16, variant="fp16", use_safetensors=True)
     pipe.enable_xformers_memory_efficient_attention()
     pipe = pipe.to(device)
@@ -14,34 +16,37 @@ else:
     pipe = DiffusionPipeline.from_pretrained("stabilityai/sdxl-turbo", use_safetensors=True)
     pipe = pipe.to(device)
 
+# Constants
 MAX_SEED = np.iinfo(np.int32).max
 MAX_IMAGE_SIZE = 1024
 
+# Inference function
 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().manual_seed(seed)
+    generator = torch.Generator(device=device).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 for the Gradio UI
 examples = [
     "Astronaut in a jungle, cold color palette, muted colors, detailed, 8k",
     "An astronaut riding a green horse",
     "A delicious ceviche cheesecake slice",
 ]
 
+# CSS styling for the Gradio UI
 css="""
 #col-container {
     margin: 0 auto;
@@ -49,9 +54,11 @@ css="""
 }
 """
 
+# Determine the power device (GPU or CPU) for display purposes
+power_device = "GPU" if device == "cuda" else "CPU"
 
+# Gradio UI setup
 with gr.Blocks(css=css) as demo:
-    
     with gr.Column(elem_id="col-container"):
         gr.Markdown(f"""
         # Text-to-Image Gradio Template
@@ -59,7 +66,6 @@ with gr.Blocks(css=css) as demo:
         """)
         
         with gr.Row():
-            
             prompt = gr.Text(
                 label="Prompt",
                 show_label=False,
@@ -73,12 +79,11 @@ with gr.Blocks(css=css) as demo:
         result = gr.Image(label="Result", show_label=False)
 
         with gr.Accordion("Advanced Settings", open=False):
-            
             negative_prompt = gr.Text(
                 label="Negative prompt",
                 max_lines=1,
                 placeholder="Enter a negative prompt",
-                visible=False,
+                visible=True,
             )
             
             seed = gr.Slider(
@@ -92,7 +97,6 @@ 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,
@@ -110,32 +114,31 @@ 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=0.0,
+                    value=7.5,
                 )
                 
                 num_inference_steps = gr.Slider(
                     label="Number of inference steps",
                     minimum=1,
-                    maximum=12,
+                    maximum=50,
                     step=1,
-                    value=2,
+                    value=25,
                 )
         
         gr.Examples(
-            examples = examples,
-            inputs = [prompt]
+            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.launch(share=True)
+demo.queue().launch()