Update app.py

app.py

@@ -42,21 +42,22 @@ def save_generated_image(image, prompt):
     
     return filepath
 
+# Fixed inference function - properly handle Progress parameter
 @spaces.GPU(duration=120)
 def inference(
-    prompt: str,
-    seed: int,
-    randomize_seed: bool,
-    width: int,
-    height: int,
-    guidance_scale: float,
-    num_inference_steps: int,
-    lora_scale: float,
-    progress: gr.Progress = gr.Progress(track_tqdm=True),
+    prompt,
+    seed=42,
+    randomize_seed=True,
+    width=1024,
+    height=768,
+    guidance_scale=3.5,
+    num_inference_steps=30,
+    lora_scale=1.0,
+    progress=None,
 ):
     if randomize_seed:
         seed = random.randint(0, MAX_SEED)
-    generator = torch.Generator(device=device).manual_seed(seed)
+    generator = torch.Generator(device=device).manual_seed(int(seed))
     
     image = pipeline(
         prompt=prompt,
@@ -74,6 +75,19 @@ def inference(
     # Return just the image and seed
     return image, seed
 
+# Create version for examples
+def example_inference(prompt):
+    return inference(
+        prompt=prompt,
+        seed=42,
+        randomize_seed=True,
+        width=1024,
+        height=768,
+        guidance_scale=3.5,
+        num_inference_steps=30,
+        lora_scale=1.0
+    )
+
 # Updated examples with 1880s clothing style
 examples = [
     "Cézanne's painting of a lively outdoor gathering in the 1880s, with men in formal top hats, frock coats, and women in bustled dresses with elaborate hats, enjoying a summer afternoon. The scene captures the Belle Époque atmosphere with dappled sunlight filtering through trees, highlighting the fashionable attire of the period. [trigger]",
@@ -86,15 +100,6 @@ examples = [
 
 # First example for preloading
 default_prompt = examples[0]
-default_settings = {
-    "seed": 42,
-    "randomize_seed": True,
-    "width": 1024,
-    "height": 768,
-    "guidance_scale": 3.5,
-    "num_inference_steps": 30,
-    "lora_scale": 1.0
-}
 
 # Improved custom CSS with better visuals
 custom_css = """
@@ -185,19 +190,9 @@ button:hover {
 }
 """
 
-# Add preloading function for the first example
+# Fixed preload function
 def preload_example():
-    # Generate the default example image
-    image, seed_value = inference(
-        prompt=default_prompt,
-        seed=default_settings["seed"],
-        randomize_seed=default_settings["randomize_seed"],
-        width=default_settings["width"],
-        height=default_settings["height"],
-        guidance_scale=default_settings["guidance_scale"],
-        num_inference_steps=default_settings["num_inference_steps"],
-        lora_scale=default_settings["lora_scale"],
-    )
+    image, seed_value = inference(prompt=default_prompt)
     return default_prompt, image, seed_value
 
 with gr.Blocks(css=custom_css, analytics_enabled=False) as demo:
@@ -229,9 +224,9 @@ with gr.Blocks(css=custom_css, analytics_enabled=False) as demo:
                 minimum=0,
                 maximum=MAX_SEED,
                 step=1,
-                value=default_settings["seed"],
+                value=42,
             )
-            randomize_seed = gr.Checkbox(label="Randomize seed", value=default_settings["randomize_seed"])
+            randomize_seed = gr.Checkbox(label="Randomize seed", value=True)
 
             with gr.Row():
                 width = gr.Slider(
@@ -239,14 +234,14 @@ with gr.Blocks(css=custom_css, analytics_enabled=False) as demo:
                     minimum=256,
                     maximum=MAX_IMAGE_SIZE,
                     step=32,
-                    value=default_settings["width"],
+                    value=1024,
                 )
                 height = gr.Slider(
                     label="Height",
                     minimum=256,
                     maximum=MAX_IMAGE_SIZE,
                     step=32,
-                    value=default_settings["height"],
+                    value=768,
                 )
 
             with gr.Row():
@@ -255,30 +250,30 @@ with gr.Blocks(css=custom_css, analytics_enabled=False) as demo:
                     minimum=0.0,
                     maximum=10.0,
                     step=0.1,
-                    value=default_settings["guidance_scale"],
+                    value=3.5,
                 )
                 num_inference_steps = gr.Slider(
                     label="Number of inference steps",
                     minimum=1,
                     maximum=50,
                     step=1,
-                    value=default_settings["num_inference_steps"],
+                    value=30,
                 )
                 lora_scale = gr.Slider(
                     label="LoRA scale",
                     minimum=0.0,
                     maximum=1.0,
                     step=0.1,
-                    value=default_settings["lora_scale"],
+                    value=1.0,
                 )
 
         with gr.Group(elem_classes="example-region"):
             gr.Markdown("### Examples")
             gr.Examples(
                 examples=examples,
-                inputs=[prompt],
+                inputs=prompt,
                 outputs=[result, seed_output],
-                fn=inference,
+                fn=example_inference,  # Use the simplified example function
                 cache_examples=True,
             )
 
@@ -299,7 +294,7 @@ with gr.Blocks(css=custom_css, analytics_enabled=False) as demo:
         outputs=[result, seed_output],
     )
     
-    # Preload the first example when the app starts - FIXED
+    # Preload the first example when the app starts
     demo.load(
         fn=preload_example,
         inputs=None,