Update app.py

app.py

@@ -9,24 +9,26 @@ dtype = torch.float16
 device = torch.device("cuda:0")
 
 pipeline = AutoPipelineForText2Image.from_pretrained("stabilityai/stable-diffusion-xl-base-1.0", variant="fp16", torch_dtype=dtype)
-pipeline.vae.register_to_config("force_upcast"=False)
+pipeline.vae.register_to_config(force_upcast=False)
 pipeline.to(device)
 
 
 if USE_TORCH_COMPILE:
     pipeline.unet = torch.compile(pipeline.unet, mode="reduce-overhead", fullgraph=True)
     
-def generate(num_images_per_prompt: int = 1):
+def generate(num_images_per_prompt: int = 1):\
+    print("Start...")
     print("Version", torch.__version__)
-    prompt = 77 * "a"
-    num_inference_steps = 40
-    start_time = time.time()
-    pipeline(prompt, num_images_per_prompt=num_images_per_prompt, num_inference_steps=num_inference_steps).images
-    end_time = time.time()
 
-
-    print(f"For {num_inference_steps} steps", end_time - start_time)
-    print("Avg per step", (end_time - start_time) / num_inference_steps)
+    for _ in range(3):
+        prompt = 77 * "a"
+        num_inference_steps = 40
+        start_time = time.time()
+        pipeline(prompt, num_images_per_prompt=num_images_per_prompt, num_inference_steps=num_inference_steps).images
+        end_time = time.time()
+    
+        print(f"For {num_inference_steps} steps", end_time - start_time)
+        print("Avg per step", (end_time - start_time) / num_inference_steps)
 
 
 with gr.Blocks(css="style.css") as demo: