2.1

app.py

@@ -31,18 +31,7 @@ def initialize_pipeline():
 
 @spaces.GPU
 def generate_image(prompt, seed, num_inference_steps):
-    """
-    Generate an image using Stable Diffusion.
-    This function runs on Zero GPU when deployed on Hugging Face Spaces.
-
-    Args:
-        prompt: Text description of the image to generate
-        seed: Random seed for reproducibility
-        num_inference_steps: Number of denoising steps
-
-    Returns:
-        PIL Image
-    """
+    
     # Initialize pipeline
     pipeline = initialize_pipeline()
     device = pipeline.device
@@ -93,15 +82,15 @@ def create_interface():
             gr.Slider(
                 minimum=0,
                 maximum=2147483647,
-                value=42,
+                randomize=True,
                 step=1,
                 label="Seed",
                 info="Random seed for reproducibility",
             ),
             gr.Slider(
                 minimum=1,
-                maximum=150,
-                value=50,
+                maximum=50,
+                value=15,
                 step=1,
                 label="Diffusion Steps",
                 info="Number of denoising steps (more steps = higher quality but slower)",
@@ -115,8 +104,8 @@ def create_interface():
         description="Generate images from text using Stable Diffusion. Enter a prompt, set the seed for reproducibility, and adjust the number of diffusion steps. Watch the diffusion process as a video.",
         examples=[
             ["A beautiful sunset over mountains", 42, 50],
-            ["A cat wearing a space suit, digital art", 123, 50],
-            ["Cyberpunk city at night, neon lights", 456, 75],
+            ["A cat wearing a space suit, digital art", 123, 20],
+            ["Cyberpunk city at night, neon lights", 456, 15],
         ],
         cache_examples=False,
     )