Update app.py

app.py

@@ -26,26 +26,26 @@ scheduler = EulerAncestralDiscreteScheduler(
 pipe = StableDiffusionXLPipeline.from_pretrained(model_id, torch_dtype=torch.float16,scheduler=scheduler).to("cuda")
 pipe.force_zeros_for_empty_prompt = False
 
-print("Optimizing BRIA-2.2 - this could take a while")
-t=time.time()
-pipe.unet = torch.compile(
-    pipe.unet, mode="reduce-overhead", fullgraph=True # 600 secs compilation
-)
-with torch.no_grad():
-    outputs = pipe(
-        prompt="an apple",
-        num_inference_steps=30,
-    )
+# print("Optimizing BRIA-2.2 - this could take a while")
+# t=time.time()
+# pipe.unet = torch.compile(
+#     pipe.unet, mode="reduce-overhead", fullgraph=True # 600 secs compilation
+# )
+# with torch.no_grad():
+#     outputs = pipe(
+#         prompt="an apple",
+#         num_inference_steps=30,
+#     )
 
-    # This will avoid future compilations on different shapes
-    unet_compiled = torch._dynamo.run(pipe.unet)
-    unet_compiled.config=pipe.unet.config
-    unet_compiled.add_embedding = Dummy()
-    unet_compiled.add_embedding.linear_1 = Dummy()
-    unet_compiled.add_embedding.linear_1.in_features = pipe.unet.add_embedding.linear_1.in_features
-    pipe.unet = unet_compiled
+#     # This will avoid future compilations on different shapes
+#     unet_compiled = torch._dynamo.run(pipe.unet)
+#     unet_compiled.config=pipe.unet.config
+#     unet_compiled.add_embedding = Dummy()
+#     unet_compiled.add_embedding.linear_1 = Dummy()
+#     unet_compiled.add_embedding.linear_1.in_features = pipe.unet.add_embedding.linear_1.in_features
+#     pipe.unet = unet_compiled
 
-print(f"Optimizing finished successfully after {time.time()-t} secs")
+# print(f"Optimizing finished successfully after {time.time()-t} secs")
 
 @spaces.GPU(enable_queue=True)
 def infer(prompt,negative_prompt,seed,resolution):