Update app.py

app.py

@@ -19,14 +19,50 @@ repo = "SG161222/RealVisXL_V4.0"
 pipeline = AutoPipelineForText2Image.from_pretrained(repo, torch_dtype=torch.float16).to('cuda')        
 
 
+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 = 1344
+MAX_IMAGE_SIZE = 4100
 
 @spaces.GPU(duration=60)
 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)
+
+    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 = pipeline(prompt = prompt,