Update app.py

app.py

@@ -17,10 +17,46 @@ else:
 pipe = DiffusionPipeline.from_pretrained(model_repo_id, torch_dtype=torch_dtype)
 pipe = pipe.to(device)
 
+def adjust_to_nearest_multiple(value, divisor=8):
+    """
+    Adjusts the input value to the nearest multiple of the divisor.
+    
+    Args:
+    value (int): The value to adjust.
+    divisor (int): The divisor to which the value should be divisible. Default is 8.
+    Returns:
+    int: The nearest multiple of the divisor.
+    """
+    if value % divisor == 0:
+        return value
+    else:
+        # Round to the nearest multiple of divisor
+        return round(value / divisor) * divisor
+
+def adjust_dimensions(height, width):
+    """
+    Adjusts the height and width to be divisible by 8.
+    
+    Args:
+    height (int): The height to adjust.
+    width (int): The width to adjust.
+    Returns:
+    tuple: Adjusted height and width.
+    """
+    new_height = adjust_to_nearest_multiple(height)
+    new_width = adjust_to_nearest_multiple(width)
+    
+    return new_height, new_width
+
+
+# MAX_SEED = np.iinfo(np.int32).max
+# MAX_IMAGE_SIZE = 4100
+
+
 MAX_SEED = np.iinfo(np.int32).max
 MAX_IMAGE_SIZE = 1024
 
-@spaces.GPU(duration=65)
+@spaces.GPU(duration=100)
 def infer(
     prompt,
     negative_prompt="",
@@ -35,6 +71,10 @@ def infer(
     if randomize_seed:
         seed = random.randint(0, MAX_SEED)
 
+    width = min(width, MAX_IMAGE_SIZE // 2)
+    height = min(height, MAX_IMAGE_SIZE // 2)
+    height, width = adjust_dimensions(height, width)
+
     generator = torch.Generator().manual_seed(seed)
 
     image = pipe(