Update app.py

app.py

@@ -16,13 +16,11 @@ DESCRIPTION = """## Qwen Image Hpc/."""
 
 # Helper functions
 def save_image(img):
-    """Saves a PIL image to a file with a unique name."""
     unique_name = str(uuid.uuid4()) + ".png"
     img.save(unique_name)
     return unique_name
 
 def randomize_seed_fn(seed: int, randomize_seed: bool) -> int:
-    """Generates a random seed if the randomize option is enabled."""
     if randomize_seed:
         seed = random.randint(0, MAX_SEED)
     return seed
@@ -35,27 +33,15 @@ dtype = torch.bfloat16
 device = torch.device("cuda" if torch.cuda.is_available() else "cpu")
 
 # --- Model Loading ---
-# Load the pipeline from pretrained weights
-pipe_qwen = DiffusionPipeline.from_pretrained(
-    "Qwen/Qwen-Image",
-    torch_dtype=dtype
-).to(device)
-
-# --- Regional Compilation ---
-# Apply regional compilation to speed up cold-starts while retaining benefits.
-# This compiles the repeated transformer blocks for ~2x faster initialization.
-print("Applying regional compilation...")
-pipe_qwen.transformer.compile(fullgraph=True, mode="reduce-overhead")
-print("Compilation complete.")
-
+pipe_qwen = DiffusionPipeline.from_pretrained("Qwen/Qwen-Image", torch_dtype=dtype).to(device)
 
 # Aspect ratios
 aspect_ratios = {
-    "1:1": (1024, 1024),
-    "16:9": (1344, 768),
-    "9:16": (768, 1344),
-    "4:3": (1152, 896),
-    "3:4": (896, 1152)
+    "1:1": (1328, 1328),
+    "16:9": (1664, 928),
+    "9:16": (928, 1664),
+    "4:3": (1472, 1140),
+    "3:4": (1140, 1472)
 }
 
 # Generation function for Qwen/Qwen-Image
@@ -292,4 +278,4 @@ with gr.Blocks(css=css, theme="bethecloud/storj_theme") as demo:
     )
 
 if __name__ == "__main__":
-    demo.queue(max_size=50).launch(share=False)
+    demo.queue(max_size=50).launch(share=False, mcp_server=True, ssr_mode=False, show_error=True)