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README.md

@@ -168,14 +168,29 @@ from qai_hub_models.models.controlnet_quantized import Model
 
 # Load the model
 model = Model.from_pretrained()
+controlnet_model = model.controlnet
 text_encoder_model = model.text_encoder
 unet_model = model.unet
 vae_decoder_model = model.vae_decoder
-controlnet_model = model.controlnet
 
 # Device
 device = hub.Device("Samsung Galaxy S23")
 
+# Trace model
+controlnet_input_shape = controlnet_model.get_input_spec()
+controlnet_sample_inputs = controlnet_model.sample_inputs()
+
+traced_controlnet_model = torch.jit.trace(controlnet_model, [torch.tensor(data[0]) for _, data in controlnet_sample_inputs.items()])
+
+# Compile model on a specific device
+controlnet_compile_job = hub.submit_compile_job(
+    model=traced_controlnet_model ,
+    device=device,
+    input_specs=controlnet_model.get_input_spec(),
+)
+
+# Get target model to run on-device
+controlnet_target_model = controlnet_compile_job.get_target_model()
 # Trace model
 text_encoder_input_shape = text_encoder_model.get_input_spec()
 text_encoder_sample_inputs = text_encoder_model.sample_inputs()
@@ -221,21 +236,6 @@ vae_decoder_compile_job = hub.submit_compile_job(
 
 # Get target model to run on-device
 vae_decoder_target_model = vae_decoder_compile_job.get_target_model()
-# Trace model
-controlnet_input_shape = controlnet_model.get_input_spec()
-controlnet_sample_inputs = controlnet_model.sample_inputs()
-
-traced_controlnet_model = torch.jit.trace(controlnet_model, [torch.tensor(data[0]) for _, data in controlnet_sample_inputs.items()])
-
-# Compile model on a specific device
-controlnet_compile_job = hub.submit_compile_job(
-    model=traced_controlnet_model ,
-    device=device,
-    input_specs=controlnet_model.get_input_spec(),
-)
-
-# Get target model to run on-device
-controlnet_target_model = controlnet_compile_job.get_target_model()
 
 ```
 
@@ -250,6 +250,10 @@ provided job URL to view a variety of on-device performance metrics.
 
 # Device
 device = hub.Device("Samsung Galaxy S23")
+profile_job_controlnet_quantized = hub.submit_profile_job(
+    model=model_controlnet_quantized,
+    device=device,
+)
 profile_job_textencoder_quantized = hub.submit_profile_job(
     model=model_textencoder_quantized,
     device=device,
@@ -262,10 +266,6 @@ profile_job_vaedecoder_quantized = hub.submit_profile_job(
     model=model_vaedecoder_quantized,
     device=device,
 )
-profile_job_controlnet_quantized = hub.submit_profile_job(
-    model=model_controlnet_quantized,
-    device=device,
-)
 
 ```
 
@@ -275,6 +275,14 @@ To verify the accuracy of the model on-device, you can run on-device inference
 on sample input data on the same cloud hosted device.
 ```python
 
+input_data_controlnet_quantized = model.controlnet.sample_inputs()
+inference_job_controlnet_quantized = hub.submit_inference_job(
+    model=model_controlnet_quantized,
+    device=device,
+    inputs=input_data_controlnet_quantized,
+)
+on_device_output_controlnet_quantized = inference_job_controlnet_quantized.download_output_data()
+
 input_data_textencoder_quantized = model.text_encoder.sample_inputs()
 inference_job_textencoder_quantized = hub.submit_inference_job(
     model=model_textencoder_quantized,
@@ -299,14 +307,6 @@ inference_job_vaedecoder_quantized = hub.submit_inference_job(
 )
 on_device_output_vaedecoder_quantized = inference_job_vaedecoder_quantized.download_output_data()
 
-input_data_controlnet_quantized = model.controlnet.sample_inputs()
-inference_job_controlnet_quantized = hub.submit_inference_job(
-    model=model_controlnet_quantized,
-    device=device,
-    inputs=input_data_controlnet_quantized,
-)
-on_device_output_controlnet_quantized = inference_job_controlnet_quantized.download_output_data()
-
 ```
 With the output of the model, you can compute like PSNR, relative errors or
 spot check the output with expected output.