Update app.py

app.py

@@ -18,7 +18,7 @@ pipe = DiffusionPipeline.from_pretrained(model_repo_id, torch_dtype=torch_dtype)
 pipe = pipe.to(device)
 
 MAX_SEED = np.iinfo(np.int32).max
-MAX_IMAGE_SIZE = 1024
+MAX_IMAGE_SIZE = 520
 
 from datasets import load_dataset, Dataset
 
@@ -79,9 +79,7 @@ def infer(
 
 
 examples = [
-    "Astronaut in a jungle, cold color palette, muted colors, detailed, 8k",
-    "An astronaut riding a green horse",
-    "A delicious ceviche cheesecake slice", 
+    " A Woman using Leather Pants 
 ]
 
 css = """
@@ -132,7 +130,7 @@ with gr.Blocks(css=css) as demo:
                     minimum=256,
                     maximum=MAX_IMAGE_SIZE,
                     step=32,
-                    value=1024,  # Replace with defaults that work for your model
+                    value=520,  # Replace with defaults that work for your model
                 )
 
                 height = gr.Slider(
@@ -140,7 +138,7 @@ with gr.Blocks(css=css) as demo:
                     minimum=256,
                     maximum=MAX_IMAGE_SIZE,
                     step=32,
-                    value=1024,  # Replace with defaults that work for your model
+                    value=520,  # Replace with defaults that work for your model
                 )
 
             with gr.Row():
@@ -155,7 +153,7 @@ with gr.Blocks(css=css) as demo:
                 num_inference_steps = gr.Slider(
                     label="Number of inference steps",
                     minimum=1,
-                    maximum=50,
+                    maximum=10,
                     step=1,
                     value=2,  # Replace with defaults that work for your model
                 )