Update app.py

app.py

@@ -3,50 +3,57 @@ import spaces
 import numpy as np
 import random
 import torch
-from diffusers import StableDiffusion3Pipeline, SD3Transformer2DModel, FlowMatchEulerDiscreteScheduler
+from diffusers import StableDiffusion3Pipeline
 
+# Set device and data type
 device = "cuda" if torch.cuda.is_available() else "cpu"
 dtype = torch.float16
 
+# Load the Stable Diffusion model
 repo = "stabilityai/stable-diffusion-3-medium-diffusers"
-pipe = StableDiffusion3Pipeline.from_pretrained(repo, torch_dtype=torch.float16).to(device)
+pipe = StableDiffusion3Pipeline.from_pretrained(repo, torch_dtype=dtype).to(device)
 
+# Constants
 MAX_SEED = np.iinfo(np.int32).max
 MAX_IMAGE_SIZE = 1344
 
 @spaces.GPU
 def infer(prompt, negative_prompt, seed, randomize_seed, width, height, guidance_scale, num_inference_steps, progress=gr.Progress(track_tqdm=True)):
-
     if randomize_seed:
         seed = random.randint(0, MAX_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
-    ).images[0] 
-    
-    return image, seed
+    try:
+        image = pipe(
+            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, seed
+    except Exception as e:
+        return str(e), seed  # Return error message if any
 
+# Example prompts
 examples = [
     "Astronaut in a jungle, cold color palette, muted colors, detailed, 8k",
     "An astronaut riding a green horse",
     "A delicious ceviche cheesecake slice",
 ]
 
-css="""
+css = """
 #col-container {
     margin: 0 auto;
     max-width: 580px;
 }
 """
 
+# Create Gradio interface
 with gr.Blocks(css=css) as demo:
     
     with gr.Column(elem_id="col-container"):
@@ -56,7 +63,6 @@ with gr.Blocks(css=css) as demo:
         """)
         
         with gr.Row():
-            
             prompt = gr.Text(
                 label="Prompt",
                 show_label=False,
@@ -70,7 +76,6 @@ 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,
@@ -88,7 +93,6 @@ 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,
@@ -106,7 +110,6 @@ with gr.Blocks(css=css) as demo:
                 )
             
             with gr.Row():
-                
                 guidance_scale = gr.Slider(
                     label="Guidance scale",
                     minimum=0.0,
@@ -124,14 +127,16 @@ with gr.Blocks(css=css) as demo:
                 )
         
         gr.Examples(
-            examples = examples,
-            inputs = [prompt]
+            examples=examples,
+            inputs=[prompt]
         )
+    
     gr.on(
         triggers=[run_button.click, prompt.submit, negative_prompt.submit],
-        fn = infer,
-        inputs = [prompt, negative_prompt, seed, randomize_seed, width, height, guidance_scale, num_inference_steps],
-        outputs = [result, seed]
+        fn=infer,
+        inputs=[prompt, negative_prompt, seed, randomize_seed, width, height, guidance_scale, num_inference_steps],
+        outputs=[result, seed]
     )
 
-demo.launch()
+# Launch the app
+demo.launch(timeout=10)  # Increase timeout if needed