disable speed up

app.py

@@ -40,25 +40,25 @@ pipeline_t2i = StableGarmentPipeline.from_pretrained(base_model_path, vae=vae, t
 # pipeline = StableDiffusionPipeline.from_pretrained("SG161222/Realistic_Vision_V4.0_noVAE", vae=vae, torch_dtype=torch_dtype).to(device=device)
 pipeline_t2i.scheduler = scheduler
 
-
-# speed up for cpu
-# to channels last
-pipeline_t2i.unet = pipeline_t2i.unet.to(memory_format=torch.channels_last)
-pipeline_t2i.vae = pipeline_t2i.vae.to(memory_format=torch.channels_last)
-pipeline_t2i.text_encoder = pipeline_t2i.text_encoder.to(memory_format=torch.channels_last)
-# pipeline_t2i.safety_checker = pipeline_t2i.safety_checker.to(memory_format=torch.channels_last)
-
-# Create random input to enable JIT compilation
-sample = torch.randn(2,4,64,64).type(torch_dtype)
-timestep = torch.rand(1)*999
-encoder_hidden_status = torch.randn(2,77,768).type(torch_dtype)
-input_example = (sample, timestep, encoder_hidden_status)
-
-# optimize with IPEX
-pipeline_t2i.unet = ipex.optimize(pipeline_t2i.unet.eval(), dtype=torch.bfloat16, inplace=True, sample_input=input_example)
-pipeline_t2i.vae = ipex.optimize(pipeline_t2i.vae.eval(), dtype=torch.bfloat16, inplace=True)
-pipeline_t2i.text_encoder = ipex.optimize(pipeline_t2i.text_encoder.eval(), dtype=torch.bfloat16, inplace=True)
-# pipeline_t2i.safety_checker = ipex.optimize(pipeline_t2i.safety_checker.eval(), dtype=torch.bfloat16, inplace=True)
+if False: #device=="cpu":
+    # speed up for cpu
+    # to channels last
+    pipeline_t2i.unet = pipeline_t2i.unet.to(memory_format=torch.channels_last)
+    pipeline_t2i.vae = pipeline_t2i.vae.to(memory_format=torch.channels_last)
+    pipeline_t2i.text_encoder = pipeline_t2i.text_encoder.to(memory_format=torch.channels_last)
+    # pipeline_t2i.safety_checker = pipeline_t2i.safety_checker.to(memory_format=torch.channels_last)
+
+    # Create random input to enable JIT compilation
+    sample = torch.randn(2,4,64,64).type(torch_dtype)
+    timestep = torch.rand(1)*999
+    encoder_hidden_status = torch.randn(2,77,768).type(torch_dtype)
+    input_example = (sample, timestep, encoder_hidden_status)
+
+    # optimize with IPEX
+    pipeline_t2i.unet = ipex.optimize(pipeline_t2i.unet.eval(), dtype=torch.bfloat16, inplace=True, sample_input=input_example)
+    pipeline_t2i.vae = ipex.optimize(pipeline_t2i.vae.eval(), dtype=torch.bfloat16, inplace=True)
+    pipeline_t2i.text_encoder = ipex.optimize(pipeline_t2i.text_encoder.eval(), dtype=torch.bfloat16, inplace=True)
+    # pipeline_t2i.safety_checker = ipex.optimize(pipeline_t2i.safety_checker.eval(), dtype=torch.bfloat16, inplace=True)
 
 
 pipeline_tryon = None